Main Information
Overall Scope and Productivity:
- Timespan (2019-2025): Your collection covers a relatively recent period. This suggests that the research within this dataset is likely focused on contemporary issues and advancements.
- Documents (25): The collection contains 25 documents. This is a small dataset, and interpretations should be made cautiously, acknowledging its limited size. It might represent a highly focused area or a preliminary exploration.
- Sources (Journals, Books, etc.): 25: This is interesting. The number of sources is equal to the number of documents, meaning that each document is from a unique source. This could indicate a broad literature review across diverse sources, or it could be an artifact of the search strategy.
- Annual Growth Rate % (20.09): A 20.09% annual growth rate suggests a rapidly evolving field, at least as represented by the publications included in your dataset. This indicates a growing interest and activity in this research area. However, with a small dataset like this, a few additional publications each year can significantly inflate the percentage.
- References (1497): The high number of references for a relatively small number of documents shows that the research builds upon a substantial body of existing knowledge. It demonstrates a thorough integration of prior research.
Impact and Influence:
- Average Citations per Doc (85.64): This is a very high average citation rate for such a recent timespan (2019-2025). This suggests that the documents in your collection have, on average, had a significant impact on the research community. It could indicate the research addresses a central problem or gap, or that the research is of particularly high quality. However, it’s crucial to investigate *why* the citation rate is so high. Are there a few highly cited “star” papers skewing the average? A citation analysis showing the distribution would be helpful.
- Document Average Age (2.04): The average document age is very low. With the timespan ranging from 2019 to 2025, an average document age of approximately 2 years means the majority of the documents were published relatively recently.
- Keywords Plus (ID: 118) & Author’s Keywords (DE: 93): This represents the diversity of topics and concepts explored within the collection. The difference between “Keywords Plus” (automatically generated by Scopus, indicating broader themes) and “Author’s Keywords” (author-assigned, indicating the authors’ focus) can provide insights into the intellectual structure of the field. A comparative analysis of these keywords (e.g., frequency analysis, co-occurrence analysis) could reveal the core themes and emerging trends.
Authors and Collaboration:
- Authors (61): A significant number of authors contributed to the 25 documents. This suggests the presence of collaborative efforts and possibly a relatively broad community working within this area.
- Authors of single-authored docs (6): The presence of 6 single-authored documents suggests individual contributions are still a factor. However, the overall ratio points towards a field leaning towards collaborative research.
- Single-authored docs (6): This reinforces the point above. These documents may represent foundational work, reviews, or perspectives from established researchers.
- Co-Authors per Doc (2.44): This reinforces the collaborative nature of the research. An average of over two authors per document implies that collaborative research is the norm.
- International co-authorships % (32): A 32% international co-authorship rate indicates a considerable level of global collaboration. This suggests the research area attracts international attention and fosters cooperation across borders.
Document Types:
- article (19): The majority of the documents are articles, which is typical of academic research.
- book chapter (1): The inclusion of a book chapter suggests that the research also contributes to broader, more synthesized overviews of the field.
- conference paper (4): The inclusion of conference papers indicates that this research area has active meetings and conferences for disseminating findings. This suggests that there are opportunities for networking.
- erratum (1): While just one, the presence of an erratum indicates attention to detail and commitment to accuracy within the field.
Database Context (Scopus):
- Knowing the data comes from Scopus is important. Scopus is a large, multidisciplinary database known for its coverage of peer-reviewed literature. This adds credibility to the collected data.
Critical Considerations and Next Steps:
1. Small Sample Size: The most important caveat is the small dataset size (25 documents). Any conclusions drawn should be considered preliminary and require further validation with a larger sample.
2. Citation Analysis: Investigate the citation distribution. Are a few highly cited papers driving the high average? Identify these “star” papers and analyze their content to understand their influence.
3. Keyword Analysis: Perform a more detailed analysis of the keywords (both author-assigned and Scopus-generated) to identify the core themes and trends within the dataset. Network analysis of keyword co-occurrence would be beneficial.
4. Content Analysis: Conduct a more in-depth content analysis of the documents to identify the major research questions, methodologies, and findings.
5. Comparison with Other Datasets: Compare these statistics with similar analyses from larger datasets covering the same or related fields. This would provide context and allow you to assess the relative impact and trends within your specific collection.
6. Search Strategy: Review the search query used to create the dataset. Is it possible that the query is biasing the results in some way? A slight adjustment could change the entire analysis.
By considering these interpretations and conducting further analyses, you can gain a more comprehensive understanding of the research landscape represented by your bibliometric data. Remember that bibliometrics provides a *map* to navigate the literature; it doesn’t replace the need to read and critically evaluate the individual papers themselves.
Annual Scientific Production
Average Citations Per Year
Three-Field Plot
Overall Structure and Interpretation
The three-field plot visualizes connections between the three chosen fields (AU, CR, and KW_Merged). The thickness of the lines connecting elements indicates the strength or frequency of the association. In essence, it shows which authors frequently cite specific references and the keywords associated with those authors and cited works.
Field-Specific Analysis
- AU (Authors – Central Field): This column lists the authors in your dataset. The position of an author vertically doesn’t necessarily have an inherent meaning; they’re primarily organized to avoid overlapping connections.
- CR (Cited References – Left Field): This column presents the most frequently cited references within your collection. We can see references like “Penrose, E. The theory of the growth of the firm (1959)” and several works by Teece are prominent. The color of each node in this column might represent clusters, time, or some other metadata property.
- KW_Merged (Keywords – Right Field): This column displays the most frequent keywords from the articles in your dataset. Notice keywords such as “Artificial Intelligence,” “Dynamic Capabilities,” “Innovation,” “Business,” “Industrial Technology,” “Digital transformation,” and others. The fact that you’ve used KW_Merged suggests these keywords might have been consolidated or cleaned up from different keyword fields in the Scopus data.
Key Relationships and Insights
Here are some potential interpretations based on the observed connections:
1. Dynamic Capabilities and Foundational Authors: The frequent appearance of Teece’s work on dynamic capabilities is notable. Several authors in the ‘AU’ field are connected to Teece’s publications, indicating a strong research stream focusing on dynamic capabilities within your dataset.
2. Artificial Intelligence Theme: The keyword “Artificial Intelligence” is prominently featured and connects to several authors. The cited references linked to this keyword include “fosso wamba s.” and others, indicating a research area that intersects with AI.
3. Innovation and Related Concepts: The presence of keywords like “Innovation,” “Disruptive Innovation,” “Business Model Innovation,” and the connection to Rogers’ “Diffusion of Innovations” suggests another important research theme within the collection.
4. Digital Transformation and Related Themes: The presence of “Digital Transformation,” “Digital Servitization”, “Digital Technologies” and “Ecosystems” shows that these themes are also popular.
5. Business and Industrial Context: The keywords “Business,” “Industrial Technology,” and “Industrial Management” suggests a focus on applying technology and innovation within industrial or business contexts.
How to Use This Information
- Identify Key Influences: The plot highlights the most influential works (cited references) shaping the research in your collection. These are the foundational papers that researchers in this area build upon.
- Map Intellectual Structures: The connections reveal relationships between authors, cited works, and keywords. You can use this to map the intellectual structure of the research field represented by your dataset. For example, you could trace how specific keywords relate back to seminal papers and the authors who cite them.
- Spot Emerging Trends: By analyzing the more recent publications and their keyword associations, you can potentially identify emerging research trends.
- Contextualize Author Contributions: The plot helps contextualize the work of individual authors in your dataset. You can see which concepts and influential works are most closely associated with their publications.
Critical Considerations:
- Data Cleaning and Preprocessing: The quality of the analysis depends heavily on the quality of your data. Ensure the author names, cited references, and keywords are consistently formatted and disambiguated. The use of “KW_Merged” suggests some cleaning, but double-check.
- Database Coverage: The results are limited to the scope of the Scopus database and the specific search query used to retrieve your dataset.
- Citation Bias: Be aware of potential citation biases. Highly cited works may not always be the most groundbreaking, but may be more visible due to other factors (e.g., journal prestige, author reputation).
- Parameter Sensitivity: The specific parameters used to generate the plot (e.g., minimum number of connections) can influence the results. Experiment with different parameters to see how the network structure changes.
- Interpretation is Key: This plot provides a visual representation of relationships. It’s up to you to interpret those relationships in the context of your research question and existing knowledge of the field.
In conclusion, this three-field plot provides a valuable overview of the intellectual landscape within your Scopus dataset. By carefully analyzing the connections between authors, cited references, and keywords, you can gain deeper insights into the key themes, influential works, and potential research directions within this field. Remember to consider the limitations of the data and the analysis when drawing your conclusions. Good luck!
Most Relevant Sources
Sources’ Production over Time
Most Relevant Authors
Authors’ Production over Time
Authors’ Local Impact
Most Relevant Affiliations
Affiliations’ Production over Time
Countries’ Scientific Production
Countries’ Production over Time
Most Cited Countries
Most Global Cited Documents
Most Local Cited Documents
Most Local Cited References
Reference Spectroscopy
Overall Trends and Observations:
- Increasing Citation Frequency Over Time: The black line, representing the total number of cited references per publication year, generally trends upward. This is a common pattern in many fields, reflecting the increasing volume of published research and the accumulation of knowledge. The field appears to have experienced a substantial increase in activity in the more recent years of the dataset.
- Significant Peaks: The red line, highlighting the deviation from the 5-year median, reveals specific years where publications from that year were cited *more frequently* than would be expected based on recent trends. These years represent pivotal moments in the development of the field.
- Predominance of Business/Management Literature: The prominent peak years all contain references that fall within the business and management literature. This indicates a strong foundation of, and potentially an origin point in, business and management.
Analysis of Peak Years:
* 1989: This year exhibits a very strong positive deviation. The list of highly cited publications reveals a focus on core concepts in strategy and organizational research. The most cited articles cover topics such as:
* Case study research methodologies (Eisenhardt)
* Systems thinking (Ackoff)
* Strategic management in different environments (Covin & Slevin)
* Technology acceptance (Davis)
* Competitive advantage (Dierickx & Cool)
* Global Strategy (Kogut)
* Strategic orientation (Venkatraman)
* The concept of “fit” in strategic research (Venkatraman)
These publications suggest that the field experienced a period of consolidation and methodological development during the late 1980s.
* 1997: This year is dominated by the work of Teece, Pisano, and Shuen on *Dynamic Capabilities*. The frequency of this article indicates its profound influence on subsequent research. Other publications from this year suggest a broader interest in socio-technical systems, the attention-based view of the firm, and strategic alignment.
* 2000: This year shows a persistent interest in *Dynamic Capabilities*, as well as Knowledge Sharing Networks and Cyberenterprises. Given the multiple citations of the same article, it can be said with confidence that “Dynamic Capabilities” are an intellectual cornerstone.
* 2003: This year exhibits a continued interest in Dynamic Capabilities, as well as the business model concept. The presence of “Diffusion of Innovations” by Rogers suggests that the study is influenced by that research.
* 2007: This year continues the trend of publications focused on *Dynamic Capabilities*. The presence of “Fighting against Windmills” on SIS and Organizational Deep Structures suggests a focus on the implications of structural features of organizations.
* 2010: This year sees a strong interest in *Business Model Innovation* and *Business Model Design*. Publications focused on Strategic Agility show that these are persistent and influential issues in the field.
* 2013: The significant publications identified for this year focused on:
* Qualitative research rigor (Gioia *et al.*)
* Digital Business Strategy (Bharadwaj *et al.*)
* Business Models and Technological Innovation (Baden-Fuller & Haefliger)
* Dynamic capabilities (Peteraf *et al.*)
* 2018: This year focuses on dynamic capabilities and business models. Li *et al.* focused on Digital transformation, which suggests that this is another field of growing interest.
* 2021: Publications this year covered topics such as the Role of Institutional Pressures and Resources in the Adoption of Big Data Analytics, as well as Artificial Intelligence Capability.
* 2024: Publications this year show a strong interest in Artificial Intelligence.
Key Implications for Researchers:
- Core Literature Identification: The RPYS plot helps you identify the most influential publications and authors in your field. This is crucial for building a strong theoretical foundation for your research.
- Trend Analysis: The peaks and valleys in the red line reveal the ebb and flow of attention to specific research areas. This allows you to contextualize your work within the broader evolution of the field.
- Gap Identification: By understanding the historical development of the field, you can identify potential research gaps and areas that require further investigation.
- Methodological Awareness: The presence of publications on research methodologies (e.g., Eisenhardt on case studies, Gioia et al. on qualitative rigor) highlights the importance of methodological rigor in the field.
Critical Considerations:
- Database Bias: The analysis is based on data from SCOPUS. Results may differ if using other databases (Web of Science, Google Scholar, etc.) due to different coverage.
- Citation Counts as a Proxy: Citation counts are a useful, but imperfect, measure of influence. Highly cited articles are not necessarily “better” than less cited ones. Consider the *context* of citations. Are the articles being cited positively, negatively, or neutrally?
- Field Specificity: The interpretation is specific to the field as defined by the articles included in your analysis. Broader or narrower searches could yield different results.
- Recent Publications: Be aware that the citation frequency of very recent publications may not yet fully reflect their long-term impact.
By carefully considering these points, you can use the RPYS plot as a valuable tool for understanding the intellectual history of your field and guiding your future research endeavors. Please let me know if you’d like to explore any of these aspects in more detail.
Most Frequent Words
WordCloud
TreeMap
Words’ Frequency over Time
Trend Topics
Overall Observations:
- Recent Focus: The plot shows keyword trends between 2020 and 2024. The terms with the largest bubbles (highest median frequency) appear towards the end of the period, suggesting a recent surge in research related to these areas.
- Emerging vs. Established Topics: We can see some terms appearing consistently throughout the period, while others seem to emerge later. This provides insight into the evolution of research interests.
- Dominant Themes: The keywords listed suggest a strong focus on topics related to business, technology, and innovation.
Specific Keyword Interpretations:
- “Artificial Intelligence”: This term peaks in 2024, indicating a significant increase in research publications related to AI. Given that AI is used in a great variety of field, further investigation is needed to understand the specific areas of AI research that have driven this increased prominence.
- “Business Models”: The trend line suggests this term has a fairly steady presence between 2020 and 2024. It would be interesting to explore what specific aspects of business models are being studied. For instance, are there connections to digital transformation, sustainability, or specific industries?
- “Business Model Innovation”: This term has a steady presence since its first appearence in 2023.
- “Innovation”: The plot indicates a relative importance throughout the period. This broad term would benefit from further refinement. Analyze the co-occurrence of “innovation” with other keywords to understand the specific contexts in which it is being studied.
- “Dynamic Capabilities”: This term appears in 2020 and peaks during the same year. This refers to the firm’s ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments. Explore the specific contexts in which “dynamic capabilities” is being studied. Is it related to digital transformation, innovation, or resilience?
- “Digital Transformation”: This term appears in 2020 and peaks during the same year. This term refers to the integration of digital technology into all areas of a business, fundamentally changing how it operates and delivers value. Similar to the others, it needs to be related to the other terms.
- “Enterprise Resource Management”: This term appears in 2020 and peaks during the same year. This software system integrates various functions within an organization, such as planning, manufacturing, sales, and marketing. The relevance of this term should be investigated.
Suggestions for Further Analysis:
- Co-occurrence Analysis: Explore which keywords frequently appear together. This will reveal the relationships between different research themes. For example, is “artificial intelligence” often discussed in the context of “business models” or “digital transformation”?
- Document Examination: Sample a subset of the publications associated with these keywords to understand the research questions, methodologies, and findings.
- Author and Institution Analysis: Identify the leading authors and institutions publishing on these topics. This will provide insights into the key players in these research areas.
- Journal Analysis: Determine which journals are publishing the most articles on these topics. This will indicate the primary outlets for disseminating this research.
- Compare with other Databases: Analyze data from other databases like Web of Science or Dimensions to see if similar trends are observed. Differences could highlight database-specific biases or coverage.
- Expand Keyword List: Increase the number of keywords per year to capture a more comprehensive picture of the research landscape. However, be mindful of potential noise from less relevant terms.
By combining these interpretations with further analyses, you can gain a deeper understanding of the research trends in your field and identify potential areas for future investigation.
Clustering by Coupling
Co-occurrence Network
Overall Structure:
The network visualizes the co-occurrence of keywords extracted from the titles and abstracts of documents in your SCOPUS collection. The nodes represent individual keywords, and the edges connecting them indicate how frequently those keywords appear together in the same documents. The thicker the edge, the stronger the association between the keywords. The size of the node reflects its centrality or importance within the network (e.g., based on degree centrality – number of connections). The Louvain algorithm, via Walktrap clustering, has been used to detect communities within the network, represented by different colors.
Key Parameters and Their Impact:
- Normalization (Association): Normalizing using the “association” strength ensures that the co-occurrence counts are adjusted for the individual frequencies of the terms. This is crucial because high-frequency terms are more likely to co-occur with other terms simply by chance. Association strength helps to identify more meaningful co-occurrences.
- Clustering (Walktrap): The Walktrap algorithm is a random walk-based community detection method. It identifies groups of nodes (keywords) that are more densely connected to each other than to nodes in other groups. This helps identify distinct themes or topics within the research area. The use of the Walktrap algorithm suggests an exploration into the community structure within your dataset, highlighting dominant themes. The community.repulsion setting is set to 0.05, which may impact the separation between the clusters.
- Edges.min = 2: This threshold means that only co-occurrences appearing in at least 2 documents are considered, filtering out potentially spurious or less relevant associations.
- Remove Isolates: This parameter removes keywords that do not co-occur with any other keywords in the network, helping to focus on the interconnected themes.
Community Analysis (Colored Clusters):
- Red Cluster: This cluster appears to be centered around “artificial intelligence,” “enterprise resource management”, “business model innovation” and related terms like “digital servitization” and “artificial intelligence technologies”. This suggests a theme focused on the *application of AI within business contexts, particularly for ERP and digital transformation*.
- Blue Cluster: This cluster is organized around “dynamic capabilities” and related terms such as “business digital transformation”, “competitive advantage digital technologies”, and “industrial management” suggest a *focus on strategic management and the role of technology in enhancing competitive advantage through dynamic capabilities*.
- Green Cluster: This smaller cluster focuses on “sustainability”, “circular economy”, and “manufacturing”. This indicates a theme related to *sustainable manufacturing practices and the circular economy.*
- Purple Cluster: This lone node, “industrial technology,” might represent a more general or overarching theme connected to the other clusters or could be an under-explored area in your dataset.
Central Keywords and Their Relevance:
- “Artificial Intelligence”: Appears to be a central and dominant theme, given its large node size. This suggests that a significant portion of the research in your collection relates to AI.
- “Dynamic Capabilities”: Another important keyword, indicating a strong focus on the ability of organizations to adapt and change in response to dynamic environments.
- “Enterprise Resource Management”: Highlighted for its co-occurrence with AI and transformation, suggesting that digital transformation and innovation in ERP systems are of particular interest.
Interpretation and Discussion Points:
1. AI-Driven Business Transformation: The network highlights a significant intersection between AI and business functions like enterprise resource management and business model innovation. Discuss how AI is being explored as a driver of digital transformation in these areas.
2. Dynamic Capabilities and Competitive Advantage: The co-occurrence of “dynamic capabilities” with terms like “competitive advantage” and “digital technologies” suggests that organizations are leveraging digital technologies to build and enhance their dynamic capabilities to achieve a competitive edge.
3. Sustainability in Manufacturing: The smaller cluster around “sustainability” and “circular economy” suggests an emerging area of research focused on integrating sustainable practices into manufacturing processes. Discuss the potential drivers and barriers to adopting circular economy models in manufacturing.
4. Bridge between Concepts: Examine the edges connecting the different clusters. For example, how does the connection between the “AI” cluster and the “Dynamic Capabilities” cluster reflect the use of AI to support organizational agility and adaptation?
5. Scope and Limitations: Remember that this network is based on keyword co-occurrence, which provides a simplified view of the research landscape. While valuable, it does not capture the nuances of individual studies or the complex relationships between concepts. The choice of keywords and database (SCOPUS) limits the scope of the analysis.
Critical Appraisal and Further Research:
- Keyword Selection: Discuss whether the keywords used in your analysis adequately represent the research area. Are there any important keywords missing from the network?
- Database Coverage: Acknowledge the limitations of using only SCOPUS data. Consider whether including data from other databases would provide a more comprehensive picture of the research landscape.
- Temporal Trends: Consider performing a temporal analysis to examine how the relationships between keywords have evolved over time. This could reveal emerging trends and shifts in research focus.
- Content Analysis: To gain a deeper understanding of the relationships between the keywords, conduct a content analysis of the documents in your collection. This can help you identify the specific ways in which the concepts are being discussed and applied.
By considering these points, you can move beyond a simple description of the network and develop a more insightful and critical interpretation of your bibliometric results. Remember to contextualize your findings within the broader literature and highlight the implications for future research.
Thematic Map
Overall Interpretation of the Strategic Map
The strategic map visualizes the intellectual structure of your research area (based on your Scopus KW_Merged data with the specified parameters). It plots research themes based on their *centrality* (relevance) and *density* (development). The map is divided into four quadrants, each representing a different strategic role for the themes:
- Motor Themes (Upper Right): High centrality and high density. These themes are well-developed and important drivers in the field.
- Niche Themes (Upper Left): Low centrality and high density. These are specialized or highly developed areas, but they don’t have broad impact on the whole field.
- Emerging or Declining Themes (Lower Left): Low centrality and low density. These themes are either new and still developing or are fading in importance.
- Basic Themes (Lower Right): High centrality and low density. These are fundamental, important themes but may not be highly developed.
Cluster-Specific Analysis
Let’s analyze the specific clusters you’ve identified, keeping in mind that these clusters were derived using the walktrap community detection algorithm in Biblioshiny.
1. Artificial Intelligence (Motor Theme):
* Location on Map: Upper Right – a “Motor Theme.”
* Interpretation: “Artificial intelligence,” “dynamic capabilities,” and “digital transformation” are *central* to the field *and* are well-developed research areas. This suggests that AI and its applications within a digital context are core drivers of innovation and change within the domain you’re investigating.
* Top Articles:
* SJÖDIN D, 2023, TECHNOL FORECAST SOC CHANGE (Pagerank: 0.208): This article appears to be a key publication in AI, likely addressing future trends and societal changes related to the technology.
* GALLEGO-GOMEZ C, 2020, INT J ENTERP INF SYST (Pagerank: 0.154): This suggests the importance of AI in enterprise information systems.
* BLACK S, 2024, INT J INF MANAGE (Pagerank: 0.148): This article focuses on information management, indicating a relevance of AI in processing and managing information.
* Insights: The high pageranks of the articles belonging to this cluster highlights the importance and impact of AI in the field.
2. Innovation (Motor Theme):
* Location on Map: Upper Right – a “Motor Theme.”
* Interpretation: “Innovation,” “circular economy,” and “disruptive innovation” are also central themes. This cluster underscores the importance of creating innovative solutions, embracing sustainable practices, and challenging existing paradigms.
* Top Articles:
* AL HALBUSI H, 2025, TECHNOL SOC (Pagerank: 0.139): This publication suggests that technology is closely linked to societal progress.
* ELMEHDI E, 2025, IBIMA BUS REV (Pagerank: 0.04): This shows a relationship between innovation and business aspects.
3. Industry 4.0 (Niche Theme):
* Location on Map: Upper Left – a “Niche Theme.”
* Interpretation: This cluster, representing “industry 4.0”, “supply chain management”, and “supply chains,” indicates a highly developed, more specialized area. While important, these themes are not as central to the overall field represented by your data.
* Top Articles:
* NUERK J, 2025, SYST ENG (Pagerank: 0.081): This likely deals with Industry 4.0 and systems engineering challenges.
* NÜRK J, 2019, EUR J BUS SCI TECHNOL (Pagerank: 0.04): This article deals with European bussiness science and technology aspects of Industry 4.0.
* Insights: The lower centrality suggests that, while “Industry 4.0” is a significant research area, its impact on other areas in the field may be more specific or focused.
4. Artificial Intelligence Technologies (Basic Theme):
* Location on Map: Lower Right – a “Basic Theme.”
* Interpretation: “Artificial intelligence technologies” is a fundamental component of the research area, but may not be a well developed area.
5. Digital Technologies (Emerging or Declining Theme):
* Location on Map: Lower Left – an “Emerging or Declining Theme.”
* Top Articles:
* MASENYA TM, 2023, BUS MODELS AND INNOV TECHNOL FOR SMES (Pagerank: 0.08): Focuses on business models, innovative technologies, and SMEs.
* Interpretation: The combination of “digital technologies” with “case studies” suggests that this area is on the periphery of the core themes.
* This could mean that these are emerging trends not yet fully integrated or areas that are declining in importance.
6. Strategic Agility (Emerging or Declining Theme):
* Location on Map: Lower Left – an “Emerging or Declining Theme.”
* Top Articles:
* WANG N, 2025, BUS PROCESS MANAGE J (Pagerank: 0.008): Deals with business process management.
* Interpretation: The combination of “strategic agility” suggests that this area is on the periphery of the core themes.
* This could mean that these are emerging trends not yet fully integrated or areas that are declining in importance.
Critical Discussion Points & Further Investigation
* Parameter Choices: Consider the influence of your parameter choices. For example:
* *KW_Merged:* Using merged keywords can provide a broader view, but might obscure more specific sub-themes.
* *minfreq = 2:* Raising the minimum frequency threshold could filter out potentially emerging niche areas.
* *n = 250:* The top 250 terms represent the most frequent. Increasing this number could make the analysis more comprehensive.
* Temporal Trends: This map represents a snapshot in time. Consider running the analysis on different time slices to observe how themes evolve (e.g., is Industry 4.0 becoming more central?).
* Database Coverage: The analysis is based on Scopus data. Compare with results from other databases (Web of Science, etc.) to assess potential biases.
* Walktrap Algorithm: The walktrap algorithm is sensitive to the parameters used. Try using other community detection algorithms and verify the stability of the clusters.
By carefully considering these aspects, you can refine your interpretation of the strategic map and gain deeper insights into the intellectual landscape of your research area. Remember that this is a tool for exploration and hypothesis generation, not a definitive answer.
Factorial Analysis
Overall Structure and Dimensional Interpretation:
- Dimensions: The map is plotted on two dimensions: Dim 1 explains 19.38% of the variance and Dim 2 explains 14.84%. This means Dim 1 is the primary differentiating factor among the keywords, followed by Dim 2. It’s important to note that MCA often results in relatively low percentage of variance explained by the first few dimensions. This is normal and reflects the complexity of relationships in textual data.
- Interpretation of Dimensions: Without further context about your research question, interpreting the *meaning* of each dimension is speculative. However, based on the keyword positions:
* Dim 1 (Horizontal): Might represent a spectrum from *location-specific contexts* (far left, “Baghdad [Iraq]”) to *more general business and technology concepts* (towards the right, “Adaptive Systems”, “Supply Chains”). It could also indicate a distinction between *traditional/regional* and *modern/global* business approaches.
* Dim 2 (Vertical): Seems to differentiate between keywords related to *specific operational technologies/solutions* (lower part, “Industrial Technology”, “Strategic Agility”, “Business Development”) and *high level-strategic themes* (upper part, “Adaptive Systems”).
Clustering and Keyword Relationships:
- Central Cluster: There is a significant cluster of terms located around the origin (0,0). This cluster seems to focus on: “Business Models,” “Digital Servitization”, “Digital Intelligence” and “Competitive Advantage”. This suggests a strong interconnection of these concepts within your dataset. The central clustering around “digital transformation” and “business models” likely points to the core themes dominating the literature you analyzed.
- “Baghdad [Iraq]” Outlier: The keyword “Baghdad [Iraq]” is a clear outlier on the far left. This indicates that papers using this keyword are distinct from the rest of the collection. It likely represents a specific regional focus that doesn’t strongly overlap with the broader business and technology themes in the other clusters.
- “Circular Economy” Cluster: A cluster appears on the left side of the map with “Circular Economy” and “Managerial Skills”. This suggests a focus on environmental sustainability.
- “Industrial Technology” Cluster: A cluster appears on the lower part of the map with “Industrial Technology” and “Strategic Agility”. This suggests a focus on a specific sector.
Relevance of Contributing Terms:
* Most Extreme Terms: Keywords furthest from the origin have the strongest influence in defining the dimensions. For example:
* “Baghdad [Iraq]”: strongly influences the negative end of Dim 1.
* “Business Development”: strongly influences the negative end of Dim 2.
* “Adaptive Systems”: strongly influences the positive end of Dim 2.
Critical Discussion Points & Further Exploration:
1. Variance Explained: A combined variance of ~34% for the first two dimensions is moderate. Consider exploring higher dimensions in MCA to see if additional meaningful separations emerge. The first two dimensions are often considered to be noisy.
2. Keyword Preprocessing: Revisit your keyword merging and cleaning. Are there synonyms that could be combined? Are there overly general keywords that could be removed to improve clarity?
3. Research Question Alignment: Does this map align with your initial research questions? If you were expecting to see different relationships or clusters, consider adjusting your search query or keyword field.
4. Sensitivity Analysis: Experiment with different `minDegree` values. Increasing it might filter out less relevant keywords and sharpen the clusters.
5. Qualitative Analysis: This bibliometric analysis provides a broad overview. Supplement it with a qualitative reading of a subset of the most influential papers (those associated with the most extreme keywords on the map) to gain deeper insights into the specific research being conducted in these areas.
6. Stemming: If similar terms are not grouped together, enabling stemming may improve the graph.
In summary: This MCA map provides a valuable starting point for understanding the relationships between keywords in your Scopus dataset. Focus on interpreting the dimensions, analyzing the clusters, and critically evaluating the influence of individual keywords to guide your research and refine your analysis. Remember to always ground your interpretation in the context of your specific research question.
Co-citation Network
Overall Structure:
The network appears to be relatively sparse, with several distinct clusters rather than a single, densely interconnected component. This suggests that the papers in your collection draw upon several distinct intellectual traditions or research streams, rather than a unified body of knowledge. The separation of the clusters likely reflects specializations, methodological preferences, or even different schools of thought within the broader field represented by your data.
Community Detection and Interpretation (Walktrap Algorithm):
The Walktrap algorithm has identified four communities (indicated by different colours: blue, red, green, and purple). Let’s interpret each one:
- Blue Cluster: This cluster is clearly dominated by the work of *Teece D.J.* It seems to center around his influential work from 1997 onwards, perhaps his work on *dynamic capabilities*. The presence of *Rogers E.M. 2003* potentially indicates that this cluster also deals with aspects of innovation diffusion and adoption, given Rogers’ seminal work on the topic. *Arndt F. 2018* may signal a more recent development or application of these theories.
- Red Cluster: This cluster also has *Teece D.* as one of the central references, but also features references such as *Eisenhardt K.* and *Penrose E.T.* The presence of Eisenhardt suggests possible themes on strategy, organization, and potentially methods for conducting research such as case study research. Penrose might indicates a theoretical influence related to the resource-based view of the firm. This cluster may represent a stream of research integrating dynamic capabilities with strategy and resource based theories.
- Green Cluster: Consists of two papers, *Bag S. 2021* and *Jorzik P. 2024*. Given the recent publication years, this cluster potentially represents emerging research, new applications, or recent literature reviews. Without more context, it’s hard to pinpoint the specific theme.
- Purple Cluster: Includes *Silva I. 2007* and *Rifkin J. 2008*. Again, more context would be needed, but these papers might be related to a specific subfield or a particular research project, given the tight co-citation.
Relevance of Most Connected Terms:
The most connected terms are the nodes with the larger labels, and it is clear that *Teece D.J.* is a central figure. This means that the research in your collection heavily relies on and builds upon Teece’s work. The specific year (1997) is crucial because it likely references his pivotal publication on dynamic capabilities, which has significantly shaped strategy, innovation, and organizational research. Given the presence of other references, *Rogers, Eisenhardt, Penrose* we can hypothesize that the main themes of your collection are strategy, innovation and dynamic capabilities.
Interpretation and Critical Discussion Guidance:
1. Dominance of Teece’s Work: The strong centrality of Teece’s work needs to be acknowledged and discussed. You should delve into the nature of this influence. Is it primarily theoretical? Methodological? Is his work being extended, challenged, or simply applied in new contexts within the papers in your collection? Consider whether this dominance might indicate a potential bias or limitation in the scope of the included literature.
2. Community-Specific Analysis: Examine the specific themes and research questions addressed within each community. What are the key debates and perspectives within each? How do they relate to each other? Are there any bridging papers or concepts that connect these communities?
3. Temporal Trends: The presence of both older (e.g., Penrose) and newer (e.g., Bag, Jorzik) publications suggests an evolution of the field. Analyze how the field has changed over time. Are the newer publications simply building upon older theories, or are they introducing new perspectives or challenging established ideas?
4. Limitations: Acknowledge that co-citation analysis has limitations. It only reflects citation patterns and doesn’t necessarily indicate intellectual influence or agreement. Two papers might be co-cited because they both critique the same work.
5. Database and Search Strategy: Since the data comes from SCOPUS, it’s important to acknowledge the specific coverage of this database. The results might be different if a different database (e.g., Web of Science) or a different search strategy had been used.
By considering these points, you can move beyond a descriptive account of the network and offer a more insightful and critical interpretation of the intellectual landscape represented by your data. Good luck!
Collaboration Network
Overall Structure:
- Sparse Network: The network appears quite sparse, meaning there aren’t a lot of connections between authors in your dataset. This suggests a relatively fragmented research landscape, with many authors working in smaller, isolated groups, at least based on the data you’ve analyzed. This might also be a characteristic of the field itself if the analyzed data covers multiple sub-disciplines or the sample size is limited.
- Community Structure: The `walktrap` algorithm has identified distinct communities, visually represented by differently colored clusters of authors. This indicates that while there isn’t widespread collaboration, there are identifiable groups of authors who frequently collaborate with each other. The communities range in size, suggesting differences in the cohesiveness of research groups or the prevalence of collaboration within different sub-areas of the topic.
- Normalization (Association): The `normalize = “association”` parameter is crucial. This means that the strength of the links between authors represents the *degree to which their co-authorship is more frequent than expected by chance*. This normalization accounts for the overall publication rate of authors. Stronger links indicate a more significant association beyond what you’d predict based on their individual publication activity.
- Edges and Size: Edges sizes and node size represent the strenght of collaboration. The number of collaboration of two authors.
Community Analysis:
- Community Identification: Each colored cluster represents a community of collaborating authors. To gain meaningful insight, you would need to identify the research themes or keywords associated with each community. This could be done by looking at the publications of the authors within each cluster.
- Community Size: The size of each cluster reflects the number of authors within that collaborative group. Larger clusters might indicate more established or well-funded research areas.
Most Connected Terms (Authors):
The network visualization attempts to label the 50 most connected authors (`label.n = 50`). The size of the label is set to `labelsize = 2` and `label.cex = TRUE`. Pay attention to the central players within each cluster.
- Key Players: Within each community, identify the authors with the largest labels and those that appear to have many connections within the cluster. These are likely the leading researchers or key collaborators in that specific area. For instance: “Popa S”, “Al Hartosakosta P”, “Antonio IL”, “Kraus S”, “Holgersson M”, “Bjorkdahl J”.
- Inter-Community Connections: Are there any visible links between different communities? If so, which authors are bridging these communities? These “bridge” authors play a crucial role in knowledge transfer and cross-disciplinary collaboration. There seem to be a limited number of inter-community connections, indicating a relative separation of research areas.
Interpreting with SCOPUS Context:
Because the data comes from SCOPUS, you can delve deeper into the profiles of these authors and their publications:
- Research Topics: Examine the keywords, abstracts, and subject areas of the publications by the most connected authors to understand the themes of each community.
- Funding Sources: Investigate if the authors or groups have received funding for their research. This can provide insights into the resources available to each community.
- Journal Publications: Determine the journals in which the authors frequently publish. This can reveal the key outlets for research in this area.
Critical Discussion Points:
- Missing Links: Why is the network so sparse? Is this a true reflection of the research field, or is it a limitation of the data (e.g., search terms used to extract data from SCOPUS)? Are there collaborations happening that aren’t captured in SCOPUS (e.g., collaborations outside of formal publications)?
- Community Boundaries: Are the boundaries between communities rigid, or is there evidence of knowledge flow between them? Could fostering more connections between communities lead to new research directions or breakthroughs?
- Impact of Key Players: To what extent are the most connected authors shaping the research agenda in their respective communities? Are they leading the field in terms of innovation and impact?
- Geographic Distribution: Consider the affiliations of the authors. Does the network reveal any geographic clusters or international collaborations?
- Temporal trends: Since you are using Scopus data, try breaking down the collaboration network in smaller periods. Is the number of nodes and the density of the graph increasing over time?
Suggestions for Further Analysis:
- Keyword Analysis: Run a keyword co-occurrence analysis on the publications in your SCOPUS dataset. This can help you identify the main research themes and how they relate to the different author communities.
- Citation Analysis: Analyze the citation patterns within and between communities. Are authors primarily citing work within their own community, or is there significant cross-citation?
- Temporal Analysis: Examine how the collaboration network has evolved over time. Are new communities emerging? Are existing communities becoming more or less connected?
- Expand the Search: Try broadening your SCOPUS search terms or including other databases to see if this changes the structure of the collaboration network.
By combining the visual representation of the collaboration network with deeper analysis of the underlying SCOPUS data, you can gain a more comprehensive understanding of the research landscape in your chosen area. Remember to critically evaluate the limitations of your data and analysis methods and to consider alternative interpretations of your results.
