Main Information
Overall Summary: This bibliometric collection represents a focused body of research on a particular topic (you haven’t specified the topic, but the analysis suggests it’s a relatively active and growing field). The data indicates increasing research output, a moderate level of collaboration, and a good citation impact.
Detailed Interpretation:
- Timespan (2010-2025): This 15-16 year period gives a reasonable temporal window to observe trends and impact. It’s recent enough to be relevant to current research, yet long enough to capture publication patterns. The fact that the current year is 2025 indicates that the collection is up-to-date, capturing recent contributions in the field.
- Sources (Journals, Books, etc.): 349: The collection draws from 349 different sources (journals, books, conference proceedings). This number provides a measure of the breadth of the literature included. A higher number suggests a broader, more interdisciplinary field, while a lower number might indicate a more specialized and concentrated area of study. It would be beneficial to analyze which specific sources are contributing the most documents.
- Documents: 718: This is the core number, representing the total number of publications included in the analysis. 718 publications are a decent amount, sufficient for a statistically meaningful analysis, assuming these publications are relatively homogenous in scope and quality. It is worth noting that the number of included documents is affected by the query used to collect the data.
- Annual Growth Rate %: 23.71: A high annual growth rate of 23.71% is significant. This suggests a rapidly expanding field with increasing research activity. This could be driven by new discoveries, emerging technologies, or increasing societal interest in the topic. This high growth rate suggests that the field is dynamic and attracting more researchers over time. *However*, it is crucial to investigate *why* the growth rate is so high. Is this a genuine expansion of the field or the result of changes in indexing practices in SCOPUS, or variations in the data collected across the years (e.g. a greater focus on a sub-area in later years)?
- Document Average Age: 3.89 years: The average age of documents being 3.89 years is quite low, indicating that the literature is relatively recent. This is consistent with the high annual growth rate, suggesting that the field is undergoing rapid development and change. This implies that more recent publications are likely more relevant and important for understanding the current state of the research.
- Average Citations per Doc: 49.06: This is a crucial metric for assessing the impact of the publications in your collection. An average of 49.06 citations per document is quite high. This suggests that the publications within this collection are, on average, well-cited and influential within the field. This indicates that the research is being actively used and built upon by other researchers. However, it’s important to consider the distribution of citations. A few highly cited papers could skew the average. It is recommended that you examine the distribution of citations, perhaps through a histogram or by calculating the median, to understand the typical citation rate of documents in your collection. *Be also aware that recently published papers generally have fewer citations because it takes time for research to accumulate citations. Thus, a relatively high value here could be because of highly cited papers and the relatively recent timespan.*
- References: 42866: This indicates the total number of references cited in all documents within the collection. This number shows the depth of the collected papers, and gives you an insight into how researchers are aware of other research related to their papers.
- Keywords Plus (ID): 1861; Author’s Keywords (DE): 1818: These numbers represent the total number of unique keywords used to describe the documents. “Keywords Plus” are terms automatically generated by SCOPUS based on the cited references, while “Author’s Keywords” are provided by the authors themselves. The similarity of these numbers indicates a good overlap between the terms assigned by the authors and the terms extracted by SCOPUS. Analyzing these keywords can help identify the main research themes and topics within the collection. Comparing the frequency and co-occurrence of these keywords can reveal the key research areas and their relationships.
- Authors: 1811: The number of authors (1811) represents the total number of unique researchers who have contributed to the publications in this collection. It provides an idea of how many individuals are actively involved in this research area.
- Authors of single-authored docs: 77: This gives you the number of researchers (77) that have written a single-authored document.
- Single-authored docs: 88: This is the number of documents (88) written by a single author. This number and the one above are relatively low compared to the total number of documents (718) and authors (1811). This indicates a trend towards collaborative research within this field.
- Co-Authors per Doc: 3.05: This number, 3.05, is the average number of authors per document. It’s a measure of the degree of collaboration within the field. A value of 3.05 suggests that collaboration is common, which is typical in many scientific disciplines.
- International co-authorships %: 28.41: Nearly 30% international co-authorships is a strong indicator of global collaboration in this field. It suggests that researchers are actively collaborating across national borders. This is important because international collaboration often leads to higher impact research due to the diversity of perspectives and resources.
- Document Types (article, book, etc.): The breakdown of document types provides insights into the types of research being conducted. The dominance of “article” (494) indicates a field heavily driven by journal publications. The presence of “book” (16) and “book chapter” (70) suggests that there are also more comprehensive and synthesized works available. “Conference paper” (85) and “conference review” (8) signal the importance of conference proceedings as a venue for disseminating research findings, especially early-stage work. The presence of document types such as editorial, erratum, note, retracted is normal and will generally not impact the analysis. The number of reviews (38) signals that the collection comprises overview papers of the field.
Further Analysis and Considerations:
- Citation Analysis: While the average citations per document is useful, it’s crucial to delve deeper into citation patterns. Identify the most highly cited papers and analyze their content. Are there specific papers that have had a disproportionate impact on the field? Perform a co-citation analysis to understand the relationships between different papers and identify key research clusters.
- Author and Source Analysis: Identify the most prolific and influential authors and sources (journals, conferences) in the collection. Which researchers and publications are driving the field forward?
- Keyword Analysis: Analyze the frequency and co-occurrence of keywords to identify the main research themes and topics within the collection. This can help you understand the key areas of focus and their relationships.
- Evolution Over Time: Examine how the research landscape has changed over the 2010-2025 period. Are there any emerging trends or shifts in focus? Track the growth of different research areas by analyzing the frequency of keywords over time.
- Database Bias: Be aware that SCOPUS has its own coverage biases. Results might differ using Web of Science or other databases. The analysis will be influenced by the strengths and weaknesses of SCOPUS in indexing different publications and fields.
In conclusion, your bibliometric collection reflects a rapidly growing and impactful research area. The high citation rates, increasing publication output, and strong international collaboration suggest that this field is dynamic, relevant, and attracting significant attention. By further analyzing the data, you can gain deeper insights into the specific research themes, key players, and emerging trends within this field.
Average Citations Per Year
Three-Field Plot
Overall Structure:
- The plot visualizes a network of relationships. The “rivers” flowing between the fields represent the connections between specific items in each field. The thicker the river, the stronger the connection (i.e., more documents link those specific authors, references, and keywords).
Interpretation by Field:
- AU (Authors – Central Field): This column lists the authors who published the papers in your Scopus dataset. The height of each bar likely indicates the number of publications by that author in your dataset. From the snippet, we see authors like “bocken nmp”, “evans s”, and “bocken n” are prominent.
- CR (Cited References – Left Field): This column shows the references cited by the articles in your dataset. The height of each bar probably indicates how often that specific reference is cited across your collection. We see specific publications like “osterwalder a. pigneur y. business model generation” and papers by “amit r. zott c.” are frequently cited. The citations are represented with a shortened version including author(s), the title, and publication details.
- KW\_Merged (Keywords – Right Field): This column shows the keywords associated with the publications in your dataset. Keywords are a way to understand the key topics. The bar height likely represents how often each keyword appears. “Business model innovation,” “sustainable development,” and “sustainability” seem to be dominant keywords.
Connections and Relationships:
The “rivers” connecting the fields show how these elements relate:
- Author – Cited Reference: An author on the AU list is connected to a cited reference if that author’s work in your dataset cites that specific reference. For example, “bocken nmp” has connections to “osterwalder a. pigneur y. business model generation”. This means that the author Bocken has cited the popular business model work, so we can assume Bocken’s research is related to the business model framework.
- Author – Keyword: An author is connected to a keyword if their publications in your dataset are associated with that keyword. For example, “evans s” is linked to “sustainable development,” suggesting that Evans’s publications in this dataset frequently deal with this topic.
- Cited Reference – Keyword: There is an indirect relationship here: by looking at the connections, we can infer that the reference “osterwalder a. pigneur y. business model generation” is also related to the keyword “business model innovation” and “sustainable development,” because of their relationship with the same author.
Data-Driven Interpretation and Potential Insights:
- Key Influences: You can identify which cited references are most influential in the research of specific authors. If a particular author has strong connections to a specific set of highly cited references, those references have significantly influenced the author’s work in the context of your dataset.
- Thematic Clusters: Notice how authors, cited references, and keywords form clusters. This can reveal dominant themes or sub-fields within your dataset. For example, the prevalence of “business model innovation,” “sustainable development,” and citations to Osterwalder and Pigneur suggests a strong focus on sustainable business model innovation within your collection.
- Author Expertise: You can infer the areas of expertise of different authors based on the keywords associated with their publications.
- Evolution of Research: You can track the evolution of research themes over time. By further filtering the dataset, you may see different keywords and different clusters, revealing how a theme has evolved or changed over the years.
Critical Discussion Points:
- Database Bias: Remember this analysis is based on Scopus data. Scopus has its own coverage biases, which could affect the representation of authors, journals, and keywords.
- Keyword Limitations: “KW_Merged” suggests keywords might have been combined or standardized. Consider the implications of this merging – were some nuances lost?
- Context of the Collection: The interpretation depends heavily on the specific search query you used to create the Scopus collection. What were you searching for? Was the query comprehensive, or did it focus on some specific area?
- Normalization: Was the raw data normalized? For example, did you account for author name variations, or standardize keywords? Normalization steps can influence the structure of the network.
Next Steps & Questions to Consider:
- Focus on Specific Authors/Keywords: Use the plot to investigate the connections associated with a specific author or keyword of interest. This can provide deeper insights into their work or the evolution of that topic.
- Investigate Disconnections: Are there any authors who seem disconnected from certain keywords, even though you might expect a relationship? Why might that be?
- Expand the Analysis: Consider adding time-based filters to the analysis to observe changes in these relationships over time.
- Compare Across Datasets: If possible, compare this three-field plot with one generated from another database (e.g., Web of Science) to assess the robustness of your findings.
By carefully examining these connections and considering the potential biases and limitations, you can extract valuable insights from this three-field plot. Good luck!
Most Relevant Sources
Core Sources by Bradford’s Law
Sources’ Local Impact
Sources’ Production over Time
Most Relevant Authors
Authors’ Production over Time
Overall Observations:
- Research Focus: The plot indicates a strong emphasis on sustainable business models, circular economy, and business model innovation within the selected authors’ publications. The presence of these topics across multiple authors suggests they are central themes in this research area.
- Temporal Trends: Most of the represented authors have been consistently publishing in this field from around 2012-2014 onwards, indicating a relatively established research area with sustained interest.
- Citation Impact: The color intensity allows for identifying high-impact years. Some authors show very clear peaks in citation counts, likely attributable to specific influential publications.
Individual Author Analysis:
* BOCKEN NMP: This author shows the most substantial publication record in terms of both number of articles and citation impact.
* The peak in citations likely originates from the 2014 publication “A LITERATURE AND PRACTICE REVIEW TO DEVELOP SUSTAINABLE BUSINESS MODEL ARCHETYPES, JOURNAL OF CLEANER PRODUCTION”. This article has a very high TCpY of 231.2, suggesting it is a foundational paper in the field. Other notable papers include the 2020 “BARRIERS AND DRIVERS TO SUSTAINABLE BUSINESS MODEL INNOVATION: ORGANIZATION DESIGN AND DYNAMIC CAPABILITIES, LONG RANGE PLANNING” and 2018 “EXPERIMENTING WITH A CIRCULAR BUSINESS MODEL: LESSONS FROM EIGHT CASES, ENVIRONMENTAL INNOVATION AND SOCIETAL TRANSITIONS”
* The sustained activity and high citation counts demonstrate their significant influence and ongoing contribution to the field.
* EVANS S: Similar to Bocken NMP, Evans S’s high citation counts are in 2014 with “A LITERATURE AND PRACTICE REVIEW TO DEVELOP SUSTAINABLE BUSINESS MODEL ARCHETYPES, JOURNAL OF CLEANER PRODUCTION” (TCpY 231.2), likely co-authored with Bocken NMP. Subsequent high-impact years include 2018, potentially driven by “SUSTAINABLE BUSINESS MODEL INNOVATION: A REVIEW, JOURNAL OF CLEANER PRODUCTION” and “BUSINESS MODELS AND SUPPLY CHAINS FOR THE CIRCULAR ECONOMY, JOURNAL OF CLEANER PRODUCTION”. This author has a sustained and impactful presence.
* PARIDA V: This author shows a strong publication burst in 2019 and onwards, with high citation counts in 2022.
* The 2022 publication “LINKING CIRCULAR ECONOMY AND DIGITALISATION TECHNOLOGIES: A SYSTEMATIC LITERATURE REVIEW OF PAST ACHIEVEMENTS AND FUTURE PROMISES, TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE” (TCpY 110.8) appears to be their most influential, linking circular economy concepts with digitalization. Other key papers are in 2019: “DIGITAL SERVITIZATION BUSINESS MODELS IN ECOSYSTEMS: A THEORY OF THE FIRM, JOURNAL OF BUSINESS RESEARCH”, and “REVIEWING LITERATURE ON DIGITALIZATION, BUSINESS MODEL INNOVATION, AND SUSTAINABLE INDUSTRY: PAST ACHIEVEMENTS AND FUTURE PROMISES, SUSTAINABILITY (SWITZERLAND)”
* GEISSDOERFER M: Appears to have a strong year in 2018, possibly related to co-authorship with Evans S on “SUSTAINABLE BUSINESS MODEL INNOVATION: A REVIEW, JOURNAL OF CLEANER PRODUCTION” (TCpY 126.1) and “BUSINESS MODELS AND SUPPLY CHAINS FOR THE CIRCULAR ECONOMY, JOURNAL OF CLEANER PRODUCTION”. The 2016 publication focusing on design thinking is also notable.
* AAGAARD A: Appears to be a newer entrant or someone whose work has gained more prominence recently, with higher citation counts appearing later in the timeline, particularly in 2022 with “EXPLORING BUSINESS MODEL INNOVATION IN SMES IN A DIGITAL CONTEXT: ORGANIZING SEARCH BEHAVIOURS, EXPERIMENTATION AND DECISION-MAKING, CREATIVITY AND INNOVATION MANAGEMENT”. The focus seems to be on business model innovation within SMEs in a digital context.
Possible Interpretations and Discussion Points:
- Collaboration: The co-authorship of highly cited articles (e.g., Bocken NMP and Evans S) indicates potentially influential collaborations within the field.
- Emerging Trends: The shift in Parida V’s research towards digitalization and circular economy suggests a growing trend in integrating these two domains. Aagaard’s focus on SMEs in a digital context may reflect a similar, more specific trend.
- Long-Term Impact vs. Recent Interest: Some authors (e.g., Bocken NMP, Evans S) have foundational, highly cited work from earlier years, while others (e.g., Aagaard, Parida) have more recent peaks, indicating a potential evolution of research interests and impact over time.
- Journal Influence: The frequent appearance of the “Journal of Cleaner Production” suggests it is a key publication venue for this research area.
- Methodological Approaches: The titles of the highly cited articles suggest a mix of literature reviews (e.g., Bocken NMP, Evans S, Parida V), case studies, and the development of frameworks and tools.
Critical Considerations:
- Database Bias: This analysis is based on Scopus data. The results may differ if a different database (e.g., Web of Science) were used.
- Citation Lag: More recent publications haven’t had as much time to accumulate citations. The impact of recent work might be underestimated.
- TCpY Limitations: TCpY (Total Citations per Year) can be sensitive to the age of a publication. Older publications have more time to accumulate citations.
- Scope of Analysis: The interpretation is limited to the selected authors. The broader field may include other influential researchers not represented here.
This analysis provides a starting point for understanding the key authors, their contributions, and the evolving trends within this field. Further investigation, considering the limitations above, would be needed to draw more definitive conclusions. Remember to critically evaluate the results in the context of the broader literature and research landscape.
Author Productivity through Lotka’s Law
Authors’ Local Impact
Most Relevant Affiliations
Affiliations’ Production over Time
Corresponding Author’s Countries
Overall Productivity and Collaboration:
- Italy leads in total publications (58), suggesting a strong domestic research base in this field. However, its MCP ratio (22.4%) is relatively low compared to other leading countries.
- Sweden (55 articles) and Germany (48 articles) follow Italy in overall productivity. Sweden exhibits a moderately higher MCP ratio (34.5%) than Germany (20.8%), indicating a greater emphasis on international collaboration.
- The United Kingdom (47 articles) displays a significantly higher MCP ratio (44.7%) compared to Italy and Germany, showing that international collaboration is a substantial component of its research output in this field.
- China (40 articles) shows a 40% MCP ratio. This suggests a good balance between domestic and international research efforts. China’s significant research output coupled with a relatively high MCP ratio shows its growing global presence and connectivity within the research community.
- The Netherlands (35 articles) stands out with the highest MCP ratio (51.4%), implying a strong reliance on international partnerships. This could reflect the Netherlands’ strategic approach to research, leveraging global expertise and resources.
Balancing Domestic and International Research Engagement:
- Countries like Austria (50%), France (41.7%), Belgium (42.9%) and the United Kingdom (44.7%) demonstrate a notable inclination towards international collaboration, as evidenced by their high MCP ratios. This might be due to factors such as participation in international research programs, strong research networks, or smaller domestic research capacity requiring external partnerships.
- Countries like South Africa (10%) and Malaysia (14.3%) show the lowest MCP ratios, suggesting a stronger focus on domestic research or potentially facing challenges in forming international collaborations. This could stem from factors such as funding priorities, geographical isolation, or limited international research networks.
Key Observations and Potential Discussion Points:
1. Collaboration as a Strategy: The data suggests that some countries (e.g., Netherlands, UK, Austria) view international collaboration as a core strategy for research advancement. Discuss why this might be the case. Consider funding structures, research priorities, and national policies that promote or hinder collaboration.
2. Domestic Research Strength: Italy and Germany, while highly productive overall, have a lower MCP ratio. Is this a reflection of strong domestic research capabilities, or could they benefit from increased international collaboration?
3. Global Research Landscape: The plot offers a snapshot of the global research landscape within your specific field of study. Consider how this landscape is evolving, especially with the rise of research output from countries like China and India.
4. Limitations of the Data: This plot only considers the *corresponding author’s* country. It’s important to acknowledge that collaborations may exist where the corresponding author is not from a specific country.
How to Use This Interpretation:
- In your report: Summarize the key findings from this analysis. For example: “Our analysis reveals that Italy is the most productive country in terms of total publications, but the Netherlands has the highest proportion of international collaborations. The United Kingdom shows a good balance of domestic and international efforts. We then need to investigate the reasons for countries with lower collaboration rates.”
- For discussion: Use these points to stimulate discussions within your research team. Ask questions like: “Why do you think the Netherlands has such a high collaboration rate? What can we learn from this? Are there countries we should be targeting for future collaborations?”
- For further investigation: This analysis can lead to more in-depth investigations. For example, you could examine the specific institutions or research groups driving international collaborations in the Netherlands. You could also analyze the topics or keywords associated with international collaborations to understand the focus of collaborative research.
Remember to contextualize these findings within your specific research area and the goals of your bibliometric analysis. Good luck!
Countries’ Scientific Production
| ITALY | 224 |
| UK | 186 |
| GERMANY | 177 |
| SWEDEN | 171 |
| CHINA | 141 |
| NETHERLANDS | 114 |
| FINLAND | 87 |
| USA | 85 |
| SPAIN | 84 |
| BRAZIL | 82 |
| INDIA | 80 |
| DENMARK | 60 |
| FRANCE | 54 |
| INDONESIA | 46 |
| AUSTRIA | 45 |
| NORWAY | 40 |
| SOUTH AFRICA | 35 |
| AUSTRALIA | 31 |
| SOUTH KOREA | 28 |
| PORTUGAL | 23 |
| BELGIUM | 21 |
| MALAYSIA | 21 |
| HUNGARY | 19 |
| GREECE | 18 |
| THAILAND | 18 |
| IRAN | 17 |
| SWITZERLAND | 15 |
| ROMANIA | 14 |
| CHILE | 13 |
| NIGERIA | 13 |
| CANADA | 12 |
| JAPAN | 12 |
| POLAND | 11 |
| CROATIA | 10 |
| LITHUANIA | 10 |
| SAUDI ARABIA | 9 |
| COLOMBIA | 8 |
| TURKEY | 8 |
| CYPRUS | 7 |
| PAKISTAN | 7 |
| SINGAPORE | 7 |
| SLOVENIA | 7 |
| BANGLADESH | 6 |
| LATVIA | 6 |
| NEW ZEALAND | 6 |
| ESTONIA | 5 |
| BAHRAIN | 4 |
| ARGENTINA | 3 |
| CZECH REPUBLIC | 3 |
| ICELAND | 3 |
| KENYA | 3 |
| SERBIA | 3 |
| UNITED ARAB EMIRATES | 3 |
| IRAQ | 2 |
| IRELAND | 2 |
| JORDAN | 2 |
| MALTA | 2 |
| MAURITIUS | 2 |
| MEXICO | 2 |
Countries’ Production over Time
Most Cited Countries
Most Global Cited Documents
Most Local Cited Documents
Key Observations and Interpretation:
1. Dominance of *Journal of Cleaner Production (J CLEAN PROD)* and *Environmental Innovation and Societal Transitions*: A significant portion of the top locally cited articles are published in these journals. This suggests that these journals are central to the specific research field defined by your dataset. This indicates that your research aligns well with these journals’ scope and focus.
2. Prominent Authors: Bocken, Geissdoerfer and Schaltegger: These names appear repeatedly. This strongly indicates that these authors are key figures in this research area. Investigating their research further can provide a deeper understanding of the field’s core concepts and debates.
3. High Local AND Global Impact:
* BOCKEN NMP, 2014, J CLEAN PROD: This article stands out with the highest local citations (LC=162) and a strong global citation count (GC=2774). Both its normalized local citation (NLC=11.08) and normalized global citation (NGC=10.63) are high, indicating sustained influence both within your field and in the broader academic landscape. This suggests it’s a seminal paper for your research area.
* GEISSDOERFER M, 2018, J CLEAN PROD: The second most cited article locally (LC=124), this also has a significant number of global citations (GC=1009). High NLC suggests that in the context of this field, this article has a strong relevance.
* EVANS S, 2017, BUS STRATEGY ENVIRON: With LC=102 and GC=924, it stands out as having substantial local and global influence, again reinforced by high NLC and NGC values.
4. High Local Relevance, Moderate Global Impact:
* SCHALTEGGER S, 2012, INT J INNOV SUSTAINABLE DEVELOP: LC=95 and GC=874 suggests a substantial impact, with Normalized values being 5.94 and 5.14 respectively.
* JOYCE A, 2016, J CLEAN PROD: LC=78 and GC=837 points toward an article that is relevant within your research field.
* These articles might be focused on a niche area within the broader field, making them highly relevant to researchers working directly on that specific topic.
5. Articles with Relatively Lower Global Impact:
* Some articles have respectable local citations but lower global citations (e.g., GIROTRA K, 2013, MANUF SERV OPER MANAGE). This doesn’t necessarily mean they are unimportant. It could indicate that they address a very specific problem, use a methodology that is only relevant in certain contexts, or were published in journals with a more specialized audience. The research may be highly specialized and therefore of less interest to a general audience but extremely relevant within your specific research community.
* It’s important to consider the journal in which these articles were published. A specialized journal might naturally lead to lower global citations but high local relevance if that journal is a key outlet for research in your area.
Recommendations for Further Analysis and Discussion:
- Content Analysis: Read the abstracts (and ideally the full text) of the most highly cited articles (especially those with high LC and GC) to identify the key themes, methodologies, and findings that are driving their influence. Look for connections between these articles.
- Citation Network Analysis: Use Biblioshiny’s network analysis tools to visualize the citation relationships between these top articles. This can reveal clusters of research and identify the “connecting” papers that bridge different sub-topics.
- Keyword Analysis: Examine the keywords associated with these articles to identify the core concepts and research areas that are most prominent in your dataset.
- Thematic Evolution: Consider the publication years. Are there trends over time? Is there a shift in focus or methodology evident from the publications in earlier vs. later years?
- Compare Journal Impact Factors: While not definitive, compare the impact factors of the journals represented. This can provide some context for the global citation counts.
Example Discussion Points for your Research:
- “Our analysis reveals the central role of the *Journal of Cleaner Production* and *Environmental Innovation and Societal Transitions* in [your research area], highlighting their importance as platforms for disseminating research in this field.”
- “The high local and global citation counts of Bocken et al. (2014) suggest its foundational importance in defining the key concepts and research agenda for [your research area].”
- “While some articles have lower global citation counts, their high local relevance indicates a focus on specific challenges or contexts within [your research area], suggesting opportunities for further investigation in these niche areas.”
- “The prominence of authors such as Bocken, Geissdoerfer and Schaltegger points to an established expertise in this field.”
By combining these quantitative insights with a qualitative understanding of the article content, you can develop a richer and more nuanced interpretation of your bibliometric results. Remember to contextualize your findings within the broader literature and explain the implications for future research.
Most Local Cited References
Reference Spectroscopy
Overall Interpretation:
This RPYS plot visualizes the historical roots of the research area represented by your SCOPUS dataset. The black line shows the general citation landscape, indicating the increasing volume of cited references over time. The red line, representing the deviation from the 5-year median, pinpoints the most historically influential years. The higher the peak on the red line, the more significant that year’s publications were in shaping the research field’s trajectory. The specific publications associated with these peak years provide a content-based interpretation of the field’s evolution.
Detailed Analysis of Peak Years and Corresponding References:
Let’s analyze the prominent peak years you identified:
* 1989: A clear focus on qualitative research methods with repeated citations of Eisenhardt’s work on “Building Theories from Case Study Research.” This suggests that case study methodology was foundational in the area being studied.
* Discussion Point: Why was case study research so important at this point? Was it a shift from other methodologies? What theoretical advancements did it enable?
* 1994: Continues the emphasis on qualitative methods (“Qualitative Data Analysis” by Miles & Huberman, and “Case Study Research” by Yin). Also introduces “Towards the Sustainable Corporation” by Elkington, signaling an early interest in sustainability, but one could also interpret the presence of this reference as an interdisciplinary nature of the field by borrowing insights from sustainability discourse.
* Discussion Point: Is the field primarily qualitative or mixed-methods? Was the interest in sustainability an early trend, or has it become more central recently?
* 1997: Shifts toward strategic management and innovation, with Teece et al.’s “Dynamic Capabilities,” Elkington’s “Cannibals with Forks” (further solidifying sustainability), and Christensen’s “The Innovator’s Dilemma.”
* Discussion Point: How did the dynamic capabilities framework influence the field? Is innovation a core theme? How has the field addressed the challenges outlined in “The Innovator’s Dilemma”?
* 2000: Deepens the focus on dynamic capabilities (Eisenhardt & Martin), leadership (Hamel), cognitive aspects (Gavetti & Levinthal), and the emergence of business models as a topic (Linder & Cantrell, Roy).
* Discussion Point: How did the understanding of dynamic capabilities evolve after the initial publications? How has the field integrated cognitive perspectives on strategy?
* 2002: Business models become even more prominent (Magretta, Chesbrough & Rosenbloom). McDonough & Braungart’s “Cradle to Cradle” reinforces the sustainability angle.
* Discussion Point: What specific aspects of business models were being explored in the early 2000s? How has the field embraced (or not) the “Cradle to Cradle” philosophy?
* 2005: Refinement of the business model concept (Shafer et al., Morris et al., Osterwalder et al.).
* Discussion Point: How did these publications contribute to the convergence (or divergence) of business model definitions?
* 2008: Frameworks for business models (Richardson), and sustainable business models (Stubbs & Cocklin), with a nod to the fit between strategy and business models (Zott & Amit).
* Discussion Point: How did the concept of “sustainable business models” evolve? What are the key frameworks used in the field?
* 2010: Dominance of Osterwalder & Pigneur’s “Business Model Generation,” along with further exploration of business models, strategy, and innovation by Teece.
* Discussion Point: What impact did “Business Model Generation” have on the field? Has it become the dominant framework, or are there alternative approaches?
* 2013: Focus on business models for sustainable innovation (Boons and colleagues).
* Discussion Point: This seems like a more dedicated focus on sustainability, building on earlier interest.
* 2017: Assessment of business model innovation research (Foss & Saebi, Massa et al.), and a unified perspective on sustainable business models (Evans et al.).
* Discussion Point: What are the key challenges and future directions identified in the assessments of business model innovation research?
Overall Discussion Points:
- Evolution of Themes: Trace the evolution of key themes, such as qualitative research methods, dynamic capabilities, business models, and sustainability. How have these themes interacted and influenced each other?
- Interdisciplinary Influences: Identify influences from other fields, such as strategic management, innovation studies, environmental studies, and cognitive science.
- Methodological Shifts: Has the field shifted from qualitative to quantitative methods, or vice versa?
- Key Authors and Institutions: Identify the most influential authors and institutions. Are there specific research groups or schools of thought that have shaped the field?
- Limitations: Acknowledge the limitations of the analysis. The dataset is from SCOPUS, so the analysis may not capture the entire landscape of the field.
Critical Considerations:
- Citation Bias: Be aware of potential citation biases. Highly cited works may not necessarily be the *best* works, but rather the most visible or influential.
- Contextual Factors: Consider the broader context in which these publications appeared. What were the major trends and events that may have influenced the research?
- Alternative Interpretations: Be open to alternative interpretations of the data. The RPYS plot provides a historical perspective, but it is important to consider other factors that may have shaped the field.
By combining the quantitative data from the RPYS plot with a qualitative understanding of the key publications, you can develop a rich and nuanced interpretation of the research area’s history and evolution. Remember to critically evaluate the data and consider alternative perspectives to create a comprehensive and insightful analysis.
Most Frequent Words
WordCloud
TreeMap
Words’ Frequency over Time
Trend Topics
Overall Trends:
- Shift Towards Sustainability and Innovation: A clear trend shows a progression from general business and economic terms towards those related to sustainability, innovation, and digital transformation. Early years focus on more foundational concepts like “developing countries”, “organizational change”, “renewable energy”, and “electric utilities”. As time progresses, the focus shifts to “corporate sustainability,” “value creation”, “sustainable business models”, and ultimately dominant themes such as “digital transformation”, “Industry 4.0”, and “sustainable development goals.”
- Business Model Innovation and Sustainability Integration: A substantial cluster of keywords revolve around “business models,” “business model innovation,” “sustainable development,” and variations thereof. This suggests growing research on how businesses are adapting their core models to incorporate sustainability principles.
- Emergence of Digital Transformation and Industry 4.0: The most recent years (towards 2024) show a strong emphasis on “Digital Transformation” and “Industry 4.0” becoming central research themes. This indicates the increasing importance of these concepts in the field covered by the Scopus dataset you used.
Detailed Breakdown by Theme:
- Early Stage (2014-2018): Foundations and Emerging Concepts This period sees the rise of concepts such as “developing countries”, “organizational change”, “renewable energy” and then “electric utilities”, “manufacture”, “sustainable consumption”, “social entrepreneurship”, “technological innovation” and “profitability”. This suggests an initial focus on fundamental economic and social themes.
- Mid-Stage (2018-2022): Sustainability and Business Model Adaptation This is where we see a rise of “corporate sustainability”, “value creation”, “sustainable business models”, “business model”, and “sustainable development”. This suggests the integration of sustainability issues with business model innovation and value creation.
- Late Stage (2022-2024): Digitalization and Advanced Sustainability The most recent period features key themes like “Digital Transformation”, “Industry 4.0” and “sustainable development goals”. These themes suggest that the research emphasis has shifted to the digital transformation of companies and sustainable business practices in recent years.
Interpreting the Visual Elements:
- Bubble Size: The larger bubbles indicate higher frequency. This emphasizes that “digital transformation”, “Industry 4.0” and “sustainable development goals” are not just recent trends, but topics with significant attention in the literature.
- Interquartile Range (Light Blue Line): The length of the light blue lines reveals how consistent the frequency of each term is across the documents within a given year. Longer lines imply greater variability, possibly indicating diverse interpretations or applications of that term. Shorter lines signify greater consensus or a more focused use of the term.
Critical Discussion Points & Further Investigation:
1. Database Bias: Remember that this analysis is based on SCOPUS data. Results might differ if you use another database (e.g., Web of Science). Scopus has a specific subject coverage, so be mindful of potential biases in the topic representation.
2. Keyword Selection: The analysis relies on the “KW_Merged” field. How were these keywords generated? (Author-provided, database-indexed, or algorithmically extracted?) This impacts the accuracy and representativeness of the trend analysis.
3. Granularity and Context: The analysis is limited to the top 3 keywords per year. This may obscure other relevant trends. Consider exploring with a higher “N. of words per Year” parameter to capture a more comprehensive picture. Also, consider the context in which these keywords are used. Are they discussed in similar ways, or do they represent distinct research streams?
4. Causation vs. Correlation: The plot shows trends, but doesn’t imply causation. External factors (e.g., policy changes, technological breakthroughs) could influence these research topics.
5. Future Research Directions: The strong focus on “Digital Transformation,” “Industry 4.0,” and “Sustainable Development Goals” suggests that research could explore the intersection of these themes. How are digital technologies being used to achieve sustainable development goals in the context of Industry 4.0? What are the business model innovations that facilitate this integration?
In summary, this trend topics plot reveals a dynamic research landscape, shifting from fundamental business concepts to a strong focus on sustainability-driven innovation, culminating in the integration of digital transformation and Industry 4.0 principles. By critically considering the data sources and parameters, you can use this visualization to identify relevant research gaps and inform your own research agenda.
Clustering by Coupling
Co-occurrence Network
Overall Structure:
The network clearly shows two distinct clusters or communities, visualized in blue and red. This separation suggests two dominant, but somewhat distinct, lines of research within your dataset. The larger nodes indicate terms that appear more frequently and have a higher degree of co-occurrence with other terms within the dataset. The edges (lines) represent the co-occurrence relationship between the terms. The thicker the edge, the more frequent the co-occurrence.
Community Analysis:
* Blue Cluster: This community centers around “Sustainable Development” and “Business Models.” Other prominent terms include “Business Models,” “Sustainable Innovation”, “Sustainable Business” and related concepts such as “Ecosystems”, “Value Creation”, and “Sustainable Development Goals”. This cluster likely represents research focusing on the theoretical aspects of sustainability implementation within organizations and across development sectors.
* *Interpretation:* This cluster suggests a body of literature interested in conceptual frameworks, strategies, and assessments related to sustainable development and business model adaptations for sustainability.
- Red Cluster: The core of this community is “Business Model Innovation,” linking to terms like “Circular Economy,” “Circular Business Model,” “Digital Transformation”, “Business Development” and “Strategic Approach”. Furthermore, it incorporates terms like “Industry 4.0,” “Stakeholders,” “Supply Chain Management,” and “Entrepreneurship.”
* *Interpretation:* This community suggests a research focus on *how* businesses are innovating their models to achieve sustainability, with a strong emphasis on practical applications, digitalization, and stakeholder engagement. The presence of terms like “Circular Economy” indicates an interest in resource efficiency and closed-loop systems. The appearance of “Industry 4.0” and “Digital Transformation” may mean that there are attempts to connect technological transformations to business model innovation within a sustainability framework.
Most Connected Terms (Centrality):
The prominence of “Sustainable Development” and “Business Model Innovation” is evident by their larger node sizes. This indicates these terms are central to the research within your dataset and have the highest degree of connection to other keywords.
- “Sustainable Development” (Blue): Its centrality highlights the importance of this overarching concept in the context of the other terms in its cluster. This is a broad concept, indicating research focused on defining, measuring, and achieving sustainable development goals.
- “Business Model Innovation” (Red): This signifies that a significant portion of the research focuses on the process of changing business models, specifically with innovation as a driving force. This likely includes studies on various innovation strategies, barriers to innovation, and the impact of innovation on business performance and sustainability outcomes.
Connections Between Clusters:
The connections (edges) between the blue and red clusters indicate a bridge between the more conceptual/theoretical aspects of “Sustainable Development” and “Business Models” and the more practical/applied field of “Business Model Innovation.” This suggests that research in this area seeks to translate sustainable development principles into concrete business practices through innovative business model design. The terms “Digital Transformation”, “Sustainable Business Model Innovation”, “Sustainability Transition Commerce” and “Business Model” form the most relevant bridges between these two clusters.
Possible Research Questions/Directions:
Based on this analysis, you might consider exploring the following research questions:
- How are companies innovating their business models to align with sustainable development goals?
- What are the key drivers and barriers to business model innovation for sustainability?
- How does the adoption of Industry 4.0 technologies impact business model innovation and sustainability performance?
- What role do stakeholders play in driving sustainable business model innovation?
- What is the role of digital transformation on sustainable transition in commerce?
Critical Considerations:
- Database Bias: The analysis is based on SCOPUS data. Results might differ if you used other databases (Web of Science, etc.) due to variations in indexing and coverage.
- Search Query: The composition of your initial search query to collect the data will heavily influence the network’s structure.
- Normalization: Using “association” for normalization emphasizes the strength of relationships between terms, highlighting the most prominent co-occurrences. Other normalization methods might reveal different patterns.
- Clustering Algorithm: The “walktrap” algorithm identifies communities based on random walks within the network. Other algorithms might produce slightly different community structures.
- Network Parameters: The parameters you chose (e.g., edge filtering, label size) influence the visual clarity and emphasis of certain aspects of the network.
- Temporal Dynamics: This is a static snapshot. Examining network changes over time would reveal trends in research focus.
In summary, the network suggests a strong research interest in how business model innovation can drive sustainable development, with a notable focus on circular economy principles and the potential of Industry 4.0 technologies. The two distinct clusters represent, on one hand, the theorization of sustainable development linked to business models, and, on the other hand, the practical implementation of business model innovation for sustainability.
Thematic Map
Overall Structure
The strategic diagram is structured as a 2×2 matrix.
- X-axis (Relevance Degree/Centrality): This axis represents the *centrality* of a cluster, indicating its importance and connection to other clusters within the network. Higher centrality suggests the cluster represents fundamental or cross-cutting themes in the field.
- Y-axis (Development Degree/Density): This axis represents the *density* of a cluster, indicating the strength of the internal connections within the cluster. Higher density suggests the cluster is well-developed and focused.
Therefore, the four quadrants represent:
- Upper Right (Motor Themes): Clusters high in both centrality and density. These are the driving forces of the field, representing well-developed and important research areas. From the information given, this quadrant is empty, but if there were clusters here, they would be well developed and very central to the research area.
- Upper Left (Niche Themes): Clusters high in density but low in centrality. These are specialized areas of research with strong internal connections but weaker links to the broader field. The “sustainability” cluster falls into this quadrant.
- Lower Right (Basic Themes): Clusters high in centrality but low in density. These are fundamental, cross-cutting themes that are important to the field but may not be as well-developed as “motor themes.” “business model innovation” falls into this quadrant.
- Lower Left (Emerging or Declining Themes): Clusters low in both centrality and density. These are either nascent research areas or topics that are losing relevance. This quadrant is empty in your case.
Cluster Descriptions and Interpretation
You have two clusters on this map: “business model innovation” and “sustainability”. Let’s analyze them:
* Business Model Innovation (Bottom Right – Basic Theme):
* *Position:* Located in the “Basic Theme” quadrant, indicating high centrality but relatively low density.
* *Interpretation:* This suggests that “business model innovation” is a fundamentally important theme in the field, acting as a connector to other research areas. However, the lower density implies that research within this cluster might be fragmented or still developing. There is a core set of articles, but there may be a need to explore more nuanced connections and develop a more consolidated body of knowledge within this area.
* *Key Articles:*
* OTTERBACH N, 2024, RESOUR CONSERV RECYCL (pagerank 0.342)
* BOCKEN NMP, 2020, LONG RANGE PLANN (pagerank 0.293)
* SNIHUR Y, 2022, LONG RANGE PLANN (pagerank 0.284)
* *Implications:* This points to a need for more focused research within business model innovation to strengthen its internal coherence and potentially move it towards becoming a “motor theme.” Further research could focus on consolidating existing knowledge, exploring new methodologies, or applying the concept to different contexts.
* Sustainability (Top Left – Niche Theme):
* *Position:* Located in the “Niche Theme” quadrant, indicating high density but relatively low centrality.
* *Interpretation:* “Sustainability” is a well-developed research area with strong internal connections. However, its lower centrality suggests it may be somewhat isolated from other themes in your dataset. The research within this cluster is tightly knit, with a strong focus and shared understanding, but it might not be as deeply integrated into the broader field represented by your keyword analysis.
* *Key Articles:*
* BOCKEN N, 2022, TECHNOL FORECAST SOC CHANGE (pagerank 0.365)
* REINHARDT R, 2019, J ENVIRON MANAGE (pagerank 0.337)
* PIZZICHINI L, 2025, TECHNOL SOC (pagerank 0.317)
* *Implications:* While this cluster is strong, it may be beneficial to explore ways to connect it more explicitly to other areas, particularly “business model innovation.” Research could focus on bridging these themes, examining how sustainable practices can be integrated into innovative business models, or vice versa.
Methodological Considerations
* Data Source (SCOPUS): Keep in mind that the analysis is based on data from SCOPUS. The results might differ if you used a different database (e.g., Web of Science).
* Keyword Selection (KW\_Merged): The use of merged keywords (KW\_Merged) is crucial. Understand *how* the keywords were merged. Was it a simple concatenation, or were synonyms and related terms considered? This impacts the accuracy of the cluster representation.
* Parameters:
* `n: 250`: The top 250 keywords were used.
* `minfreq: 3`: Keywords appearing less than 3 times were excluded. This helps filter out less relevant terms.
* `ngrams: 1`: Only single-word keywords were used. Exploring n-grams (e.g., “sustainable business model”) might reveal more nuanced relationships.
* `stemming: FALSE`: Stemming can group words with the same root (e.g., “innovate,” “innovation,” “innovative”). Disabling stemming might lead to some fragmentation.
* `size: 0.3`, `n.labels: 3`, `community.repulsion: 0`, `repel: FALSE`: These parameters affect the visual layout of the graph.
* `cluster: walktrap`: The Walktrap algorithm was used for community detection. Be aware of the strengths and limitations of this specific algorithm.
* Pagerank: Pagerank is used to identify the most central documents within each cluster.
Further Research Directions
Based on this strategic map, you could consider the following research directions:
1. Bridging the Gap: Investigate the intersection of “sustainability” and “business model innovation.” How can sustainable practices be effectively integrated into innovative business models? What are the barriers and enablers?
2. Developing “Business Model Innovation”: Conduct a systematic review of the “business model innovation” literature to identify key themes, gaps, and future research directions.
3. Exploring Emerging Trends: While the lower-left quadrant is currently empty, consider if there are any *very* recent trends or keywords that are not yet captured due to the `minfreq` parameter. Lowering this threshold might reveal emerging areas.
4. Sensitivity Analysis: Experiment with different clustering algorithms, keyword merging strategies, and parameter settings to assess the robustness of your findings. How does the map change if you use a different `cluster` algorithm or change the `minfreq`?
5. Qualitative Analysis: Supplement the bibliometric analysis with a qualitative review of the key articles in each cluster to gain a deeper understanding of the research themes and their interrelationships.
By carefully considering the strategic map, the cluster characteristics, and the underlying methodological choices, you can derive meaningful insights into the structure and evolution of your research field. Remember to critically evaluate your findings and consider alternative interpretations. Good luck!
Factorial Analysis
Overall Structure and Variance:
- Axes: The map is based on two dimensions (Dim 1 and Dim 2). Dim 1 explains 39.51% of the variance in the data, while Dim 2 explains 21.01%. This means that the horizontal axis (Dim 1) is the primary driver of differentiation between keywords. The total explained variance by the first two dimensions is around 60%, implying that the map captures a substantial portion of the key themes.
- Method: The use of Multiple Correspondence Analysis (MCA) is appropriate for analyzing categorical data like keywords. MCA helps to visualize the relationships between keywords by projecting them onto a lower-dimensional space.
Clustering and Keyword Themes:
* Left Side (Negative Dim 1 values): This area seems to be associated with broader sustainability concepts, supply chain management, and planning. We can observe terms like:
* “Life cycle”
* “Supply chains”
* “Sustainable business”
* “Planning”
* “Business modeling”
* “Value proposition”
* “Environments”
* “Sustainable development”
* “Value creation”
* Right Side (Positive Dim 1 values): This area emphasizes innovation, business development, digitalization, and strategy. The cluster includes terms such as:
* “Business”
* “Strategic approach”
* “Business Development”
* “Model”
* “Conceptual framework”
* “Environmental economics”
* “Innovation”
* “Sustainability transitions”
* “Digital technologies”
* “Digital Transformation”
* “Digitalization”
* “Strategy”
* Upper Area (Positive Dim 2 values): Keywords located in this area appear to focus on theoretical business model innovation, and sustainable development:
* “Circular business model”
* “Environmental Impact”
* “Circular business models”
* “Systematic literature review”
* “Sustainable business model”
* “Supply chain management”
* “Sustainable innovation”
* Bottom Area (Negative Dim 2 values): Keywords located in this area appear to focus on digitalization strategies and goals for sustainable development:
* “Sustainable development goals”
* “Business model”
* “Digital transformation”
* “Digitalization”
* “Strategy”
Interpretation and Discussion Points:
1. Key Themes: Based on the clustering, the main themes represented in the collection are:
* Sustainable Supply Chains and Planning: Focused on the operational and strategic aspects of sustainability.
* Innovation and Business Development: Highlighting how innovation drives new business models and strategies in the context of sustainability.
* Digitalization and Strategy: Illustrating the role of digital technologies in achieving sustainability goals.
2. Relationships: The map indicates the relationships between these themes. For instance:
* The proximity of “innovation” and “sustainable business model” suggests that innovation is a key driver for developing new sustainable business models.
* The relative distance between “planning” and “strategy” might suggest these topics are often explored separately.
* The location of “digitalization” somewhat separated from the core sustainability themes on the left suggests digitalization may be viewed more as an enabler or separate field impacting sustainability, rather than an inherent part of it.
3. Gap Analysis: Consider areas where keywords are *sparse*. This could indicate under-researched areas. Are there obvious keyword combinations missing? For example, is there a strong connection between “circular economy” and “digitalization”? If not, it may represent a gap.
4. Limitations:
* The analysis is based on *KW\_Merged* (merged keywords), it is important to consider how these keywords were merged and what the merging process may have omitted or overemphasized.
* *MinDegree: 14* implies that only keywords appearing at least 14 times were included. This threshold can exclude less frequent but potentially important emerging topics.
* The explained variance (around 60%) indicates that there are other factors not captured in this two-dimensional representation.
Recommendations for Further Analysis:
- Explore Higher Dimensions: Analyze the loadings of the keywords on dimensions 3 and higher to see if other relevant themes emerge.
- Sensitivity Analysis: Experiment with different `minDegree` values to see how the map changes. A lower `minDegree` may reveal emerging trends but could also introduce noise.
- Compare Subsets: If possible, divide the dataset into subsets (e.g., by year of publication) and compare the factorial maps to see how the research landscape has evolved over time.
- Qualitative Analysis: Use the map to guide a more in-depth qualitative review of the papers associated with each cluster.
By carefully considering the structure of the map, the clustering of keywords, and the limitations of the analysis, you can gain valuable insights into the research landscape represented by your Scopus collection. Remember to use this bibliometric analysis as a starting point for a more comprehensive understanding of the field.
Co-citation Network
Overall Network Structure
The network appears to be composed of several distinct communities or clusters, visually represented by different colors. This indicates that the literature base is not monolithic but rather consists of different sub-fields, schools of thought, or research themes. The size of the nodes (circles) likely represents the citation count of that specific publication within your dataset. Larger nodes indicate higher citation impact within your collection.
Community Analysis
Here’s a breakdown of what we can infer from the communities:
- Green Cluster: This cluster appears dense, indicating a strong, well-defined body of literature. The presence of “geissdoerfer m. 2018-1”, “boons f. 2013-1”, “Zott c. 2011-1” and “Eisenhardt k.m. 2007” may suggest a focus on business models, sustainable innovation, or related themes. “Our common future (1987)” suggests that the community may be related to environmental studies.
- Purple Cluster: This cluster contains a few nodes, among which “Osterwalder a. 2010-1” stands out as the largest. The citation ‘Freeman r.e. 1984’ is probably related to Strategic Management.
- Red Cluster: Nodes such as “Zott c. 2011-2”, “Boons f. 2013-4”, “Chesbrough h. 2010-2” and “Teece d.j. 2010-2” appear. This community seems to be interested in the Business Model, Open Innovation and the general topic of strategic management.
- Orange Cluster: This cluster only has one node, “Osterwalder a. 2010-2”, which may be a specific publication.
- Blue Cluster: The nodes present in this community, such as “Geissdoerfer m. 2018-2”, “Bocken n.m.p. 2014-2” seem to be linked to business models, sustainable innovation, and the circular economy.
Most Connected Terms and Their Relevance
The publications with the largest nodes (the most cited references within your dataset) are key to understanding the field’s core concepts.
- Osterwalder a. 2010-1: Given its central position and large size, this likely represents a cornerstone publication, potentially on business model innovation or design. Andreas Osterwalder is well known for his work on the Business Model Canvas, so this reference likely points to that work.
- Teece d.j.: The presence of Teece indicates relevance of Dynamic Capabilities, knowledge management and the Resource Based View of the Firm.
Data-Driven Interpretation & Critical Discussion
1. Synthesis of Key Themes: Based on the most connected references and the community structures, synthesize the core themes represented in your research field. How do these themes connect or diverge?
2. Emerging Trends: Analyze the publications with more recent dates. Are they connected to the central clusters, or do they form new, emerging communities?
3. Cross-Disciplinary Influences: Identify publications that bridge different clusters. These papers represent cross-disciplinary influences and potential areas for innovation.
4. Theoretical Foundations: Examine the presence of classic works. These publications represent the theoretical foundations upon which the field is built. The relevance of “Our common future (1987)” and ‘Freeman r.e. 1984’ could give an interesting background.
5. Limitations: A co-citation analysis is limited by the scope of your initial search query and the database (SCOPUS in this case). Different search terms or databases might yield different results. Also, co-citation only reflects intellectual connections *as perceived by authors citing these works*. It doesn’t necessarily reflect the *actual* influence or quality of the cited publications.
Further Steps
- Keyword Analysis: Conduct a keyword analysis of the publications within each cluster to refine your understanding of the specific topics covered.
- Author Analysis: Analyze the authors who frequently publish within and across clusters. This can reveal influential researchers and potential collaborators.
By combining this interpretation of the co-citation network with your own knowledge of the field, you can develop a more nuanced and insightful understanding of the research landscape. Let me know if you would like me to elaborate on any of these points or perform further analyses!
Historiograph
Overall Observations:
- Core Theme: The network clearly revolves around the central theme of sustainable business model innovation (BMI). The article titles confirm this focus, with frequent mentions of “business model innovation,” “sustainability,” and related concepts like “social entrepreneurship,” “value mapping,” and “responsible innovation.”
- Temporal Range: The data spans from 2012 to 2019, providing a snapshot of knowledge development over roughly an 8-year period.
- Author Influence: The names “Bocken” and “Geissdoerfer” appear frequently, suggesting their prominent role and consistent contributions to the field. These authors could be considered leading figures in the area of sustainable BMI.
Cluster Analysis and Temporal Evolution
Based on the network structure and article titles, we can infer the following temporal trends and thematic clusters:
1. Early Foundations (2012-2014):
- Key Papers: schaltegger s, 2012: Sustainability Innovators And Anchor Draggers: A Global Expert Study On Sustainable Fashion, girotra k, 2013: Sustainable Business: Integrating Csr In Business And Functions, bocken n, 2013: Value Uncaptured Perspective For Sustainable Business Model Innovation, bocken nmp, 2014: Business Model Innovation Of Social Entrepreneurship Firm: A Case Study Of Terracycle
- Focus: This period seems to lay the groundwork for the field. It covers general ideas about integrating sustainability, exploring the uncaptured value in sustainable business models, and expert perspectives on sustainable fashion. Bocken’s 2014 paper introduces the link between BMI and social entrepreneurship.
- Interpretation: This is the initial phase, where broad concepts are established, and the potential of BMI for sustainability is being explored.
2. Deepening the Understanding of Barriers, Open Innovation, and Frameworks (2015-2016):
- Key Papers: bocken nmp, 2015: Open Innovation: Reaching Out To The Grass Roots Through Smes – Exploring Issues Of Opportunities And Challenges To Reach Economic Sustainability, bocken nmp, 2016: Analysing Barriers To Sustainable Business Model Innovations: Innovation Systems Approach, joyce a, 2016: Keep The Door Open: Innovating Toward A More Sustainable Future, schaltegger s, 2016: Towards A Framework For Business Model Innovation In Health Care Delivery In Developing Countries, geissdoerfer m, 2016: Incentivizing Biodiversity Conservation In Artisanal Fishing Communities Through Territorial User Rights And Business Model Innovation
- Focus: The research begins to delve deeper, focusing on practical applications and challenges. It addresses “barriers” to sustainable BMI, exploring the role of open innovation, frameworks for specific sectors like healthcare, and incentivizing conservation through BMI.
- Interpretation: The field is evolving from theoretical concepts to actionable insights. Understanding the limitations and developing frameworks becomes a priority.
3. Exploring Specific Models, Tensions, and Implementation (2017-2018):
- Key Papers: evans s, 2017: A Model For Improving The Adoption Of Sustainability In The Context Of Globalization And Innovation, inigo ea, 2017: The Hazards Of Exponential Growth For The Solar Industry – And How Innovating Stronger Business Models Is Key To Survival, geissdoerfer m, 2017: Pay-Per-Use Business Models As A Driver For Sustainable Consumption: Evidence From The Case Of Homie, yang m, 2017: Managing Tensions In Sustainable Business Models: Exploring Instrumental And Integrative Strategies, geissdoerfer m, 2018: Value Mapping For Sustainable Business Thinking, geissdoerfer m, 2018: Business Model Innovation For Sustainability, sarasini s, 2018: Responsible Innovation Toward Sustainable Development In Small And Medium-Sized Enterprises: A Resource Perspective, bocken nmp, 2018: The Cambridge Business Model Innovation Process
- Focus: This phase dives into specific business models like pay-per-use, addresses tensions within sustainable BMI, and analyzes the role of responsible innovation in SMEs.
- Interpretation: The research becomes more nuanced, acknowledging the complexities of implementing sustainable BMIs. Managing trade-offs and adapting models to specific contexts become crucial.
4. Sufficiency and Future Directions (2019):
- Key Papers: pieroni mpp, 2019: Towards A Sufficiency-Driven Business Model: Experiences And Opportunities, bocken n, 2019: Want To Change The World? Think Differently: An Interview With Paul Polman, Ceo Of Unilever, Part 2
- Focus: There is a shift to sufficiency-driven models, which move beyond efficiency and aim to reduce overall consumption.
- Interpretation: The field is beginning to consider more radical approaches to sustainability, challenging traditional growth-oriented business models.
Pivotal Works:
Identifying *truly* pivotal works without citation counts is tricky, but based on the network structure and titles, the following likely hold significant weight:
- bocken nmp, 2014: Business Model Innovation Of Social Entrepreneurship Firm: A Case Study Of Terracycle: This paper seems important for linking BMI with social entrepreneurship.
- bocken nmp, 2016: Analysing Barriers To Sustainable Business Model Innovations: Innovation Systems Approach: Understanding barriers is crucial for the advancement of the field.
- geissdoerfer m, 2018: Business Model Innovation For Sustainability: This sounds like a key synthesis paper.
Important Considerations for the Researcher:
- Citation Counts: This analysis is based solely on the network structure and titles. Examining actual citation counts for each paper would provide a more accurate understanding of their impact.
- Database Coverage: The analysis is based on SCOPUS data. Expanding the data source to include Web of Science, Google Scholar, and other relevant databases would provide a more comprehensive view.
- Qualitative Analysis: A deeper dive into the *content* of these papers, beyond just the titles, is essential for a richer interpretation of the field’s development.
- External Context: Consider how these research trends align with broader societal and economic developments related to sustainability.
I hope this interpretation is helpful! Let me know if you have any other questions.
Collaboration Network
Overall Structure:
- Fragmented Network: The most striking feature is the highly fragmented nature of the network. Instead of a single, large connected component, we see several distinct clusters of authors. This suggests a field where collaboration is perhaps localized within specific sub-disciplines, research groups, or geographic regions. There isn’t a strong, overarching network tying everyone together.
- Community Structure: The `walktrap` clustering algorithm has identified distinct communities within the network, indicated by the different colors. This reinforces the idea of sub-disciplines or research groups operating somewhat independently.
- Limited Inter-Community Connections: Visually, there appear to be only a few weak links between the communities. The dotted lines representing edges seem to denote weaker association (due to the normalization by association). This suggests that while there might be some cross-pollination of ideas, the majority of collaborative work stays within these defined groups.
Key Authors and Their Influence:
- Central Figures: The size of the nodes represents the degree of connectedness (number of collaborations). “Bocken NMP”, “Evans S”, and “Short SW” seem to be the most central figures within this network. These authors act as “brokers” or “hubs,” connecting different authors or research groups.
- Community Leaders: Within each colored community, there are likely key authors who act as leaders or central nodes within their respective areas. For example, within the green community, “Bocken N” seems to be important. The red community features “Barth,” “Hoveskog” and “Hivenblad P” as central figures. The pink community appears to be lead by “Breuer-Hüdeke-Freund F”.
- Peripheral Authors: Many authors appear only connected to one or two others (e.g., “Brunner M”). These authors likely work in very specialized areas or are just beginning to establish their collaborative networks.
Interpretation and Discussion Points:
1. Specialization and Siloing: The fragmented network and distinct communities suggest that the research area covered by this Scopus collection might be highly specialized. Researchers are likely working on niche topics within the broader field, leading to strong collaborations within these niches but limited interaction across them.
* *Discussion point:* Is this level of specialization beneficial or detrimental to the advancement of the field? Are there opportunities to foster more interdisciplinary collaboration?
2. Influence of Key Authors: The centrality of “Bocken NMP”, “Evans S”, and “Short SW” indicate their significant role in shaping the research landscape. They may be:
* Leading experts in the field.
* Heads of prominent research groups.
* Organizers of conferences or special journal issues that facilitate collaboration.
* *Discussion point:* Are these authors truly influential in terms of the impact of their research (citations, etc.), or are they just prolific collaborators? A citation analysis could complement this collaboration network analysis.
3. Potential for Growth: The limited connections between communities suggest that there’s untapped potential for new collaborations and cross-disciplinary research.
* *Discussion point:* What are the barriers preventing more inter-community collaboration? Are there specific initiatives that could be implemented to bridge these gaps? Perhaps exploring keywords associated with each community could reveal areas of overlap and potential synergy.
4. Data Source Bias: Remember that this analysis is based on data from Scopus. Collaborations might exist that are not captured in this database (e.g., collaborations that resulted in publications in journals not indexed by Scopus).
* *Discussion point:* How might the choice of database (Scopus vs. Web of Science, etc.) influence the observed network structure?
5. Methodological Considerations: The parameters used to generate the graph have a direct impact on the visualization.
* *Discussion point:* How would different normalization methods (e.g., Jaccard index) affect the network structure? How sensitive are the community detection results to the choice of algorithm (walktrap vs. other methods)? The `community.repulsion` parameter is also influencing the aspect of the graph.
Recommendations for Further Analysis:
- Keyword Analysis: Analyze the keywords associated with publications by authors in each community. This can help to identify the specific research topics being pursued within each group and pinpoint potential areas of overlap.
- Citation Analysis: Examine the citation patterns within and between communities. Are authors primarily citing work within their own community, or is there significant cross-citation?
- Temporal Analysis: Analyze how the collaboration network has evolved over time. Are new communities emerging? Are existing communities merging or splitting?
- Geographic Analysis: Map the locations of the authors in the network. This can reveal geographic clusters of collaboration.
By considering these points, you can develop a more nuanced and insightful interpretation of the author collaboration network and its implications for the field. Remember to always critically evaluate your findings and acknowledge the limitations of the data and methods used.
Countries’ Collaboration World Map
Key Observations:
- Major Hubs of Scientific Production: The countries with the darkest blue shading are the largest producers of research, according to this dataset. These clearly include:
* United States: Appears to be a major hub, as expected given its research infrastructure and funding.
* China: Another significant hub, reflecting the rapid growth of Chinese research output.
* United Kingdom: Appears to be a notable research producer.
* Germany: Seems to be a major player in European research.
* Italy: Appears as a European hot-spot.
* Australia: The highest research output for Oceania.
- Key International Partnerships: The lines connecting countries represent co-authorship relationships. Thicker lines indicate more frequent collaboration. Some important collaborations appear to be:
* US-Europe: Strong ties, particularly with the UK and Germany.
* US-China: Notable, likely indicating increasing collaboration between these two research powerhouses.
* Europe-China: Also significant, suggesting a strong China-EU research axis.
* Intra-European: Many connections, highlighting strong regional collaboration within Europe.
* US-Australia: Appears to be another important partnership.
- Global Patterns of Collaboration:
* North America and Europe are highly interconnected. This suggests a mature research ecosystem with established collaboration networks.
* China’s collaborations are expanding. The connections to multiple regions show China’s increasing integration into the global research landscape.
* Some regions appear less connected. South America and Africa have lighter shading and fewer connections, potentially indicating lower overall research output or less integration into international networks within the analyzed dataset.
* Collaborations follow geographical proximity. This is evident within Europe, but also with countries such as Australia and New Zealand.
Interpretation and Discussion Points:
1. Data-Driven Perspective: The map provides a data-driven overview of which countries are contributing the most to scientific research (as indexed in SCOPUS) and how they are collaborating. It allows for quick identification of key players and partnerships.
2. SCOPUS Database Specifics: It’s critical to remember that this visualization is based on SCOPUS data. The patterns observed may differ if a different database (e.g., Web of Science) were used. Coverage of different regions and fields can vary between databases.
3. Co-authorship as a Proxy for Collaboration: The analysis uses co-authorship as a measure of collaboration. While co-authorship is a common metric, it’s important to acknowledge its limitations. It doesn’t necessarily reflect the depth or quality of collaboration.
4. Impact of Funding and Policies: The observed patterns likely reflect national research funding policies, international collaboration programs, and historical scientific relationships.
5. Geopolitical Considerations: International collaboration can be influenced by geopolitical factors. Analyzing changes in collaboration patterns over time might reveal shifts related to political events or strategic research priorities.
6. Limitations: This map shows a general overview. A more in-depth analysis would benefit from considering:
* Normalization: Normalizing the number of publications by population size or research expenditure could provide a more nuanced picture.
* Field-Specific Analysis: Analyzing collaboration patterns within specific research fields could reveal distinct dynamics.
* Temporal Trends: Examining how collaboration patterns have changed over time.
7. Further investigation: Based on these initial findings, consider digging deeper into the nature of the collaborations (e.g., research topics, types of institutions involved) and the reasons behind the observed patterns (e.g., funding schemes, historical relationships).
In summary, this collaboration map provides a useful starting point for understanding the global landscape of scientific collaboration. By considering the limitations of the data and methodology, and by asking further questions, researchers can gain valuable insights from this type of analysis.
