DPP Ph. D. Research

“Digital Servitization” Scopus – Bibliometrix

Main Information

Overall Summary

This Scopus collection represents a relatively recent and rapidly growing body of literature (2017-2025 timespan, 61.81% annual growth rate) comprising 367 documents from 146 sources. The collection demonstrates a considerable level of collaboration, both nationally and internationally, and articles are the dominant document type. While the average document age is young (2.44 years), they have a decent citation impact (35.5 citations per document).

Detailed Interpretation:

Critical Discussion Points & Further Investigation:

By considering these points, researchers can move beyond simply reporting statistics and begin to develop a deeper understanding of the research landscape, its key actors, and its future directions. This comprehensive interpretation provides a solid foundation for further investigation and the development of meaningful research questions.

Annual Scientific Production

Average Citations Per Year

Three-Field Plot

Overall Structure and Interpretation

Analysis and Key Observations

1. Core Authors and Their Influence:
* Parida V and Sjödin D: These authors appear to be highly influential in the field. The thickness of the lines connecting them to the keywords “digital servitization” and “servitization” indicates their strong association with these concepts. Their cited references also have strong connections, suggesting that Parida V and Sjödin D’s work is also building on the foundational work of these authors
2. Keyword Clusters:
* Digital Servitization vs. Servitization: The plot highlights two main keywords: “digital servitization” and “servitization”. Digital servitization is generally accepted as the next evolution in Servitization. Given the prominence of each keyword, it suggests that the research in the dataset covers both the general concept of servitization and its more modern, digitally-enabled variant.
3. Cited Reference Patterns: The left side of the plot reveals some key influential papers in the field. Analyzing the content of these frequently cited articles could reveal the theoretical foundations and key debates that underpin the research on servitization and digital servitization. The plot also reveals that the work of Sjödin, Parida, and Kohtamaki are heavily influenced by publications that have the same composition. It suggests that these authors have long-standing working relationships.
4. Emerging Themes (Less Obvious): The plot provides hints of other relevant areas. The connections between authors and keywords such as “Business models in ecosyst” and “Industry 4.0” suggest that the study of servitization and digital servitization is heavily coupled with new technologies.

Critically Discussing the Results

1. Database Coverage: The analysis is based on Scopus data. Scopus is a comprehensive database, but it doesn’t cover every publication. Consider whether the results might be different if another database (e.g., Web of Science) were used.
2. Keyword Merging: The use of “KW_Merged” suggests that some keyword standardization was done. Understand how the merging process works. If not done rigorously, it could introduce bias or obscure important nuances.
3. Citation Bias: Citation analysis can be influenced by citation bias. Highly cited works aren’t necessarily the *best* works, but may be the most visible or well-known.
4. Temporal Trends: The plot represents a snapshot of the data. If the dataset covers a long time period, consider whether the relationships between authors, keywords, and cited references have changed over time. A time-based analysis could reveal the evolution of the field.
5. Missing Context: The plot provides a high-level overview. To truly interpret the results, you need to delve into the actual publications and understand the specific research questions, methodologies, and findings.

Further Research Directions

By considering these points, you can use the Three-Field Plot as a starting point for a more in-depth and critical discussion of the research landscape in servitization and digital servitization.

Most Relevant Sources

Core Sources by Bradford’s Law

Sources’ Local Impact

Sources’ Production over Time

Most Relevant Authors

Authors’ Production over Time

Overall Trends and Observations

Individual Author Analysis

Here’s a breakdown of each author, combining information from the plot and the provided article list:

Points for Critical Discussion

Recommendations for Further Research

By combining the bibliometric data with a deep understanding of the research domain, you can draw meaningful conclusions about the evolution, key contributors, and future directions of “Digital Servitization” research.

Authors’ Local Impact

Most Relevant Affiliations

Affiliations’ Production over Time

Corresponding Author’s Countries
Overall Interpretation

The plot illustrates the scientific productivity of different countries, measured by the number of publications where researchers from that country are listed as the corresponding author. It further differentiates between research conducted solely within that country (SCP) and collaborative research with international partners (MCP). The MCP Ratio provides a clear indicator of the intensity of international collaboration for each country.

Key Observations & Discussion Points

1. Most Productive Countries:
* Italy leads in the total number of articles (53). However, it’s important to note that a significant portion of Italian research is domestic (SCP = 32), with an MCP percentage of 39.6%. This suggests a strong national research base, but room for increased international engagement.
* China follows with 38 articles, but its MCP percentage is relatively low at 26.3% (MCP = 10). This indicates a greater emphasis on domestic research within the analyzed dataset. This might reflect specific national research priorities or funding policies.
* Sweden ranks third in overall productivity (36 articles) but stands out with a significantly high MCP percentage of 72.2% (MCP = 26). This points to a strong culture of international collaboration within Swedish research.

2. International Collaboration Leaders:
* Several countries exhibit very high MCP percentages, indicating a strong reliance on international collaboration: Belgium (100%), Hong Kong (83.3%), France (75%), USA (75%), Norway (71.4%), and Serbia (69.2%). It’s important to note that Belgium has a relatively small number of publications overall (3), so the 100% MCP could be due to the nature of the research within the specific dataset. The other countries, despite not being the highest in total publication count, clearly prioritize international research partnerships.
* The high MCP percentages for smaller countries (e.g., Norway, Serbia) could indicate that international collaboration is essential for accessing resources, expertise, or research opportunities that might not be readily available domestically.

3. Balance between Domestic and Global Research:
* Countries like Italy and China have a larger proportion of SCPs compared to MCPs. This could suggest a strong domestic research infrastructure, well-defined national research agendas, or potentially a preference for funding domestic projects.
* Sweden, Finland, France, USA, and other countries with high MCP ratios are actively participating in the global research landscape. This can lead to increased impact, knowledge exchange, and access to diverse perspectives and resources.

4. SCOPUS Database Considerations:
* Remember that this analysis is based on data from SCOPUS. The results might differ if a different database (e.g., Web of Science, Dimensions) were used. Each database has different coverage and indexing policies, which can influence the number of publications attributed to each country.
* SCOPUS tends to have a stronger representation of certain disciplines and regions. Therefore, interpretations should be made cautiously, considering the potential for biases inherent in the database.

5. Corresponding Author Bias:
* The analysis focuses on the country of the *corresponding author*. While this provides a useful indicator, it’s important to acknowledge that collaborations often involve researchers from multiple countries, and the corresponding author’s country may not fully represent the entire collaborative network.
* The choice of corresponding author can be influenced by various factors (e.g., project leadership, institutional affiliation), which might not always reflect the overall contribution of researchers from different countries.

Further Investigation & Critical Questions

In summary, this “Corresponding Author’s Country Collaboration Plot” offers a valuable overview of the global research landscape within the specific SCOPUS dataset. It highlights the varying levels of productivity and international engagement among different countries. However, it is crucial to interpret the results critically, considering the limitations of the data source, the corresponding author bias, and the broader context of research policies and funding environments. Further investigation into the specific factors driving these trends will provide a more comprehensive understanding of international research collaboration.

Countries’ Scientific Production

Countries’ Production over Time

Most Cited Countries

Most Global Cited Documents

Most Local Cited Documents

Overall Observations:

Interpreting Specific Articles:

Here’s a breakdown of how to interpret the data for individual articles, along with examples from your list:

* High Local and Global Impact (Normalized): Articles with high NLC and NGC values represent research that is both highly relevant to your specific dataset *and* broadly influential in the wider academic community.
* GEBAUER H, 2021, IND MARK MANAGE: This article has particularly high normalized citation counts both locally (NLC=13.79) and globally (NGC=5.11) suggesting a very high impact within this collection and beyond. The NLC is the highest in the list, marking its relevance to the scope of the analysis.
* FAVORETTO C, 2022, IND MARK MANAGE: This article also shows a similar pattern to Gebauer with high normalized citations (NLC=13.32 and NGC=4.06). Considering the publication year, this is a strong sign of both relevance to the field and potential overall impact.
* High Local Impact, Moderate Global Impact: Articles with high NLC but relatively lower NGC are highly relevant to the themes within your dataset but might not be as widely cited in other research areas. This could indicate a niche topic or a contribution primarily valuable within a specific research community.
* CHEN Y, 2021, INT J OPER PROD MANAGE: This article in INT J OPER PROD MANAGE has an NLC of 10.61 but an NGC of only 3.79. This suggests it’s highly relevant to your specific dataset’s themes but perhaps less broadly influential in the wider academic literature.
* KOLAGAR M, 2022, J BUS RES: This article has an NLC of 8.81 while an NGC of 3.41. This is a similar dynamic where the article is quite relevant to the collection in study but shows less global influence.
* High Global Impact, Moderate Local Impact: Articles with high NGC but lower NLC have significant influence in the broader academic landscape but may be less directly related to the core themes of your specific dataset.
* VENDRELL-HERRERO F, 2017, IND MARK MANAGE: This article stands out because its NLC = 1 and NGC = 1. While the raw citation counts show 125 LC and 533 GC, the normalization places both metrics at 1. This article may be of interest to the area but is not more relevant than average article, when controlling for year. This means it had less impact than other papers published in the same year.

Critical Discussion Points & Further Investigation:

Based on this data, consider these questions and avenues for further investigation:

In your research report, be sure to:

By combining this quantitative data with qualitative analysis and critical thinking, you can draw meaningful conclusions and contribute valuable insights to your field. Good luck!

Most Local Cited References

Reference Spectroscopy

Overall Trends and Key Observations

Interpretation of Specific Peak Years and Associated References

The list of cited references provided for the top 10 peak years reinforces the themes emerging from the plot. Let’s analyze them chronologically:

* 1985: Dominated by foundational works across strategy, organizational theory, and qualitative research.
* *Lincoln & Guba (1985)*: “Naturalistic Inquiry” points to the importance of qualitative research methods in the field.
* *Granovetter (1985)*: “Economic Action and Social Structure: The Problem of Embeddedness” highlights the incorporation of sociological perspectives into understanding economic behavior within organizations.
* *Mintzberg (1985)*: “Of Strategies, Deliberate and Emergent” explores strategic management.
* *Porter (1985)*: “The Competitive Advantage” is a cornerstone text in strategic management.
* *Williamson (1985)*: “The Economic Institutions of Capitalism” focuses on transaction cost economics.
* Interpretation: In 1985 the field was dominated by topics related to organizational management, strategy and qualitative research.

* 1988: “Servitization” emerges as a key theme.
* *Vandermerwe & Rada (1988)*: “Servitization of Business” introduces the concept of servitization.

* 1999: Focus on Process research and servitization emerges as a key theme.
* *Wise & Baumgartner (1999)*: “Go Downstream” continues the theme of servitization, emphasizing the downstream profit imperative in manufacturing.
* *Langley (1999)*: “Strategies for Theorizing from Process Data” emphasizes the use of process data in organizational and management research.

* 2003: Interest in servitization grows as well as management research.
* *Oliva & Kallenberg (2003)*: “Managing the Transition from Products to Services” emphasizes the transition to product-service systems.
* *Tranfield et al. (2003)*: “Towards a Methodology for Developing Evidence-Informed Management Knowledge…” This indicates a growing emphasis on rigorous methodologies in management research, particularly systematic reviews.

* 2005: Paradoxes of service emerges and a strategy for smart services arise
* *Gebauer et al. (2005)*: “Overcoming the Service Paradox in Manufacturing Companies” tackles the challenges faced by manufacturing companies when integrating services.
* *Allmendinger & Lombreglia (2005)*: “Four Strategies for the Age of Smart Services” emphasis on the age of smart services.

* 2007: Dynamic capabilities and customer solutions emerge.
* *Eisenhardt & Graebner (2007)*: “Theory Building from Cases: Opportunities and Challenges” highlights the importance of case study research and its challenges.
* *Teece (2007)*: “Explicating Dynamic Capabilities” introduces the concept of dynamic capabilities for sustaining competitive advantage.
* *Tuli et al. (2007)*: “Rethinking Customer Solutions” focuses on relational processes of customer solutions.

* 2010: Focus on business models and service research.
* *Teece (2010)*: “Business Models, Business Strategy and Innovation” explores the link between business models and innovation.
* *Gebauer et al. (2010)*: “Match or Mismatch” shows the configuration in the service business of manufacturing.
* *Matthyssens & Vandenbempt (2010)*: “Service Addition as Business Market Strategy” shows identification of transition trajectories.

* 2014: Growth in literature on Servitization.
* *Porter & Heppelmann (2014)*: “How Smart, Connected Products are Transforming Competition” emphasizes the transformation of competition through smart, connected products.
* *Baines & Lightfoot (2014)*: “Servitization of the Manufacturing Firm” continues the theme of servitization.
* *Gawer & Cusumano (2014)*: “Industry Platforms and Ecosystem Innovation” Focuses on industry platforms and their relation to innovation.
* *Grubic (2014)*: “Servitization and Remote Monitoring Technology” emphasis on servitization and remote monitoring technology.
* *Kindström & Kowalkowski (2014)*: “Service Innovation in Product-Centric Firms” emphasizes servitization in product-centric firms.

* 2017: Servitization gets a boost from digitization.
* *Coreynen et al. (2017)*: “Boosting Servitization through Digitization”
* *Vendrell-Herrero et al. (2017)*: “Servitization, Digitization and Supply Chain Interdependency”
* *Kowalkowski et al. (2017)*: “Servitization and Deservitization”

* 2020: Digital Servitization and agile creation.
* *Paschou et al. (2020)*: “Digital Servitization in Manufacturing”
* *Tronvoll et al. (2020)*: “Transformational Shifts Through Digital Servitization”
* *Sjödin et al. (2020)*: “An Agile Co-Creation Process for Digital Servitization”

Potential Discussion Points

In summary, this RPYS plot provides a valuable overview of the intellectual history of the field, revealing key themes, influential publications, and methodological trends. By analyzing the peaks and the associated references, you can gain a deeper understanding of the field’s development and identify potential areas for further research. Remember to always contextualize the findings within the specific scope and limitations of your dataset (in this case, research indexed within SCOPUS).

Most Frequent Words

WordCloud

Words’ Frequency over Time

Trend Topics

Overall Observations:

Specific Trend Interpretations:

Critical Discussion Points and Further Investigation:

Recommendations for Further Analysis:

By considering these points and conducting further analysis, you can develop a more nuanced and comprehensive understanding of the research landscape. Let me know if you want me to elaborate on any of these points or suggest further avenues of investigation!

Clustering by Coupling

Co-occurrence Network

Overall Structure:

The network shows the co-occurrence of keywords within your dataset. The size of the nodes (circles) indicates the frequency of the keyword, and the lines (edges) represent how often those keywords appear together in the same publications. The `association` normalization ensures that the strength of the connections reflects the degree of co-occurrence relative to the individual frequencies of the keywords. A higher association score suggests a stronger relationship between the terms. The network is displaying two clusters of keywords, one in blue and the other in red.

Community Detection (Walktrap Algorithm):

The `walktrap` algorithm has been used for community detection, attempting to identify clusters of keywords that are more densely connected to each other than to keywords in other clusters. You’ve identified two main communities (colored blue and red). This suggests that the literature in your collection can be broadly divided into two main thematic areas.

Key Terms and Relevance:

* Other highly connected terms (Blue Community):
* “Manufacturing”: Highlights the connection to the manufacturing sector.
* “Innovation”: Servitization and digital servitization are often seen as forms of innovation.
* “Digital Platforms,” “Internet of Things,” “Digitalization”: Underscores the role of digital technologies in enabling servitization.

* Other highly connected terms (Red Community):
* “Value Co-creation”: This is a central concept in service research and highlights the collaborative aspect of value creation in PSS.
* “Product-Service Systems (PSS)”: Defines the specific type of offering under investigation.
* “Circular Economy”: Highlights the sustainability aspect.

Interpretation and Discussion Points for your Research:

1. Two Distinct but Related Themes: The network highlights two complementary areas within your literature collection: the trend towards service-based models in manufacturing and the design/sustainability aspects of product-service systems. Explore the intersection of these two communities. Are digital technologies being used to facilitate circular economy principles in product-service systems?

2. Dominance of “Servitization”: The centrality of “servitization” suggests it’s a major driver or lens through which much of the research in your collection is focused. Discuss the evolution of servitization research and its implications for different industries.

3. Digital Transformation: The emphasis on “digital servitization” and related digital technologies indicates that the digital aspect is a critical component of contemporary servitization research. Investigate the specific digital technologies being studied (e.g., IoT, AI, cloud computing) and how they are changing traditional service models.

4. Sustainability Considerations: The emergence of a “circular economy” theme suggests a growing awareness of sustainability in PSS design. Discuss the role of PSS in promoting sustainable consumption and production patterns.

5. Limitations: Remember that this network is based on *keyword* co-occurrence. It provides a high-level overview of the topics covered in the literature. A more in-depth content analysis of the articles themselves would be needed to fully understand the relationships between these themes.

6. Network Parameters: Note that the parameters you used (normalization, clustering algorithm, etc.) can influence the network structure. Experimenting with different parameters can reveal different aspects of the data. For instance, changing the `cluster` algorithm to Louvain can change slightly the nodes included in each cluster.

By focusing on these points, you can provide a richer and more nuanced interpretation of your bibliometric analysis. Remember to relate these findings back to your specific research questions and objectives. Good luck!

Thematic Map
Understanding Strategic Diagrams

Strategic diagrams (or strategic maps) are visual representations of the relationships between research themes based on two key dimensions:

The map is divided into four quadrants, each with its own characteristics:

Overall Analysis Based on Your Data

From the strategic map and the list of the most central papers in each cluster, here’s a combined interpretation:

* The papers for ‘sustainability’ are SJÖDIN D, 2023, TECHNOL FORECAST SOC CHANGE; SJÖDIN D, 2024, TECHNOL FORECAST SOC CHANGE and LEE M-J, 2024, TECHNOL FORECAST SOC CHANGE. The journal where these papers are published, *TECHNOL FORECAST SOC CHANGE* is a top journal in the field of technology management and innovation. It is worth to investigate these papers to understand the current trends of this topic.

* The papers for ‘digital platforms’ are MADANAGULI A, 2023, TECHNOL FORECAST SOC CHANGE; AYALA NF, 2025, INT J PROD ECON and KOLDEWEY C, 2024, PROC ANNU HAWAII INT CONF SYST SCI. The journal *TECHNOL FORECAST SOC CHANGE* is again present within the most central articles, but there are also two new journals: *INT J PROD ECON* and *PROC ANNU HAWAII INT CONF SYST SCI*.
* The papers for ‘digital transformation’ are PEZZOTTA G, 2022, IFIP ADVANCES IN INFORMATION AND COMMUNICATION TECHNOLOGY; PEZZOTTA G, 2023, IFIP ADVANCES IN INFORMATION AND COMMUNICATION TECHNOLOGY and BEDUCCI E, 2025, IFIP ADVANCES IN INFORMATION AND COMMUNICATION TECHNOLOGY. All the papers are published in *IFIP ADVANCES IN INFORMATION AND COMMUNICATION TECHNOLOGY*, an important venue.
* The papers for ‘digital servitization’ are LINDE L, 2023, IEEE TRANS ENG MANAGE; PEZZOTTA G, 2021, IFIP ADVANCES IN INFORMATION AND COMMUNICATION TECHNOLOGY and CHEN K-L, 2023, EUR J INF SYST. The journals in which these papers are published are *IEEE TRANS ENG MANAGE, IFIP ADVANCES IN INFORMATION AND COMMUNICATION TECHNOLOGY* and *EUR J INF SYST*.
* Niche Themes: The strategic map quadrant identified as the ‘Niche Theme’ contains the following clusters: ‘asset management’, ‘autonomous solutions’ and ‘paradox theory’.

* The papers for ‘asset management’ are GALIMBERTI M, 2024, IFIP ADVANCES IN INFORMATION AND COMMUNICATION TECHNOLOGY; STOLL O, 2023, IFIP ADVANCES IN INFORMATION AND COMMUNICATION TECHNOLOGY and GHEDINI L, 2024, IFAC-PAPERSONLINE. It could be interesting to investigate the *IFAC-PAPERSONLINE* paper by GHEDINI L et al., 2024, since it comes from a different context than the first two papers.

* The papers for ‘autonomous solutions’ are LEMINEN S, 2022, TECHNOL FORECAST SOC CHANGE; THOMSON L, 2022, INT ENTREP MANAGE J and SANDVIK HO, 2022, SERV BUS.
* Basic Themes:The strategic map quadrant identified as the ‘Basic Theme’ contains the following clusters: ‘product-service systems’ and ‘smart products’.

* The papers for ‘product-service systems’ are SHOLIHAH M, 2024, IEEE INT CONF IND ENG ENG MANAGE; PASCHOU T, 2018, PROCEDIA CIRP and BRESSANELLI G, 2024, J CLEAN PROD.
* The papers for ‘smart products’ are SMANIA GS, 2022, J CLEAN PROD; DALENOGARE LS, 2023, TECHNOVATION and SMANIA GS, 2024, TECHNOVATION.
* Emerging or Declining Themes: The strategic map quadrant identified as the ‘Emerging or Declining Theme’ contains the following clusters: ‘new service development (nsd)’.

* The papers for ‘new service development (nsd)’ are SKLYAR A, 2019, J BUS RES; MORGAN T, 2024, J BUS IND MARK and TOUFAILY E, 2023, J BUS BUS MARK.

Key Considerations and Further Investigation

I hope this detailed analysis is helpful. Let me know if you have any other questions!

Factorial Analysis
Overall Structure and Dimensions

Cluster Identification and Interpretation

Based on the visual arrangement of the terms, we can discern a few potential clusters:

1. “Traditional” Manufacturing and Service Cluster (Upper Right Quadrant):
* Terms: “industrial management,” “service industry,” “manufacture,” “metadata,” “manufacturing companies”, “digital technologiesal research.”
* Interpretation: This cluster appears to represent more established research areas within manufacturing and service, potentially focusing on management and optimization. The presence of “metadata” might suggest work related to data handling in these contexts.

2. Digital Transformation and Business Model Innovation Cluster (Around the Origin and Lower-Right Quadrant):
* Terms: “business models,” “internet of things,” “servitization,” “digital transformation,” “value creation,” “business model innovation”, “systematic literature review”, “dynamic capabilities”, “product-service systems (pss)”.
* Interpretation: This cluster seems to capture the shift towards digitalization and new business models, particularly those involving service-oriented approaches. “Internet of Things” and “Digital Transformation” are central, indicating the importance of technology in driving these changes. The presence of “dynamic capabilities” and “systematic literature review” suggests research focused on understanding and adapting to this evolving landscape.

3. Product-Service System Design Cluster (Left Quadrant):
* Terms: “product-service systems”, “life cycle”, “decision making”, “circular economy”, “product design”, “smart products,” “smart product-service system,” “value co-creation”.
* Interpretation: This cluster focuses on the design and management of product-service systems. Terms like “life cycle,” “decision making,” and “circular economy” suggest a focus on sustainability and lifecycle considerations. “Smart Products” and “Smart product-service system” emphasize the integration of technology within these systems.

Interpretation of Dimensions

Given the cluster locations, we can tentatively interpret the dimensions as follows:

Key Contributing Terms and Their Relevance

Further Considerations and Critical Discussion Points

Next Steps for the Researcher

1. Validate Cluster Interpretation: Examine the documents associated with each cluster to confirm the thematic coherence and refine your interpretations.
2. Explore Sub-Clusters: Within the broad clusters, there might be finer-grained relationships worth investigating.
3. Consider Additional Analyses: Experiment with different parameter settings (e.g., different `minDegree` values, `ngrams`, and stemming options) to see how the results change and gain further insights.
4. Compare to Existing Literature: Relate the findings to existing reviews and meta-analyses in the field to see how your analysis supports or challenges current understandings.
5. Address Limitations: Acknowledge the limitations of the analysis, such as database biases and parameter choices, in your research report.

By carefully considering these points, you can move beyond a descriptive interpretation of the factorial map to a more critical and insightful discussion of the research landscape.

Co-citation Network

Overall Structure:

The network appears to have a modular structure, with distinct clusters or communities. The most prominent feature is a large, dense cluster on the right side, connected via fainter links to smaller groups on the left, top and center. This suggests that the research field represented in this network has sub-disciplines or specializations, with some degree of cross-referencing between them.

Community Detection (Walktrap Algorithm):

The Walktrap algorithm has identified (at least) four distinct communities (indicated by node colors).

Most Connected Terms (High Citation Counts):

The prominence of the “kohtamaki m.” articles across several years (2019-1, 2019-2, 2020-2, 2021), and “sjodin d. 2020-1, 2020-2” suggests that these authors and their work are highly influential within this research area. *Kohtamaki* seem to be central figures. To understand their relevance, you would need to investigate the topics these authors cover. Given the co-citation context, their work likely defines the dominant themes in the larger, blue cluster.

Specific Observations and Interpretation:

Suggestions for Further Analysis and Interpretation:

1. Content Analysis: The next step is to investigate the *content* of the most cited articles (especially those by Kohtamaki and Sjodin). Read the abstracts and, if necessary, the full texts to understand the key themes, methodologies, and findings. This will give you a clear picture of what this network represents.

2. Cluster Themes: Repeat a similar content analysis for articles within the smaller clusters (red, green, purple) to determine what specific topics or perspectives they represent.

3. Inter-Cluster Relationships: Examine the works “parida v.” and “teece d.” This will help understanding the linking elements in the research.

4. Database Considerations: Remember this analysis is based on data from SCOPUS. There might be different patterns or insights if you were to use Web of Science or another bibliographic database.

5. Limitations: Co-citation networks reflect *cited* relationships, which may not perfectly align with intellectual influence. Some highly influential works might be criticized or challenged, rather than cited positively. Be aware of this limitation.

By combining this network analysis with a deeper understanding of the cited literature, you can generate meaningful insights into the structure, dynamics, and intellectual landscape of your research field. Remember to consult original research papers to validate and refine the interpretation derived from this network.

Historiograph

Overall Observations:

Detailed Analysis by Temporal Stages and Potential Clusters:

1. Foundation (2017-2019):

* Vendrell-Herrero (2017): *”The Impact Of Digital Technologies On Services Characteristics: Towards Digital Servitization”* – This paper likely sets the stage by exploring how digital technologies are changing the nature of services, paving the way for the concept of digital servitization. Its high position suggests it’s highly influential to the field.
* Bustinza (2018): *”Servitization: A Contemporary Thematic Review Of Four Major Research Streams”* – This appears to be a review paper, consolidating knowledge about servitization. It’s a reference point for the field.
* Sklyar (2019): *”Creating Isolating Mechanisms Through Digital Servitization: The Case Of Covirán”* – This paper likely investigates how digital servitization can be used to create competitive advantages for businesses, possibly through case study analysis.

*Interpretation:* This initial phase focuses on defining the concept of digital servitization, understanding its impact on service characteristics, and exploring its potential for creating competitive advantages.

2. Expansion and Diversification (2020):

* Tronvoll (2020): *”How Ai Capabilities Enable Business Model Innovation: Scaling Ai Through Co-Evolutionary Processes And Feedback Loops”* – This paper delves into the role of AI in enabling new business models within the context of digital servitization, suggesting an increasing focus on the technological enablers.
* Paiola (2020): *”Towards Service 4.0: A New Framework And Research Priorities”* – The “Service 4.0” concept suggests a connection to the broader Industry 4.0 movement. This paper likely proposes a framework for understanding and researching digital servitization in this context.
* Kamalaldin (2020): *”Digital Servitization In Manufacturing As A New Stream Of Research: A Review And A Further Research Agenda”* – This review paper likely identifies key research gaps and suggests future directions for the field.
* Coreynen (2020): *”Ecosystem Of Outcome-Based Contracts: A Complex Of Economic Outcomes, Availability And Performance”* – This paper focuses on the importance of designing outcome-based contracts in digital servitization ecosystems.
* Naik (2020): *”Firm Boundaries In Servitization: Interplay And Repositioning Practices”* – Addresses strategic shifts in how companies define their boundaries.
* Huikkola (2020): *”Digitizing Service Level Agreements In Service-Oriented Enterprise Architecture: Relevance Of The Multi-Perspective Approach”* – Focuses on the practical implementation and management of digital services through service level agreements (SLAs).
* Grandinetti (2020): *”Evaluation Of Digital Business Model Opportunities: A Framework For Avoiding Digitalization Traps”* – Focuses on avoiding digitalization traps in Business Models.
* Paschou (2020): *”Servitization, Digitization And Supply Chain Interdependency”* – Addresses the implications of servitization and digitization in the supply chain.

*Interpretation:* This phase sees an explosion of research, exploring different facets of digital servitization, including its relationship with AI, its implications for manufacturing, and its impact on business models.

3. Refinement and Application (2021-2022):

* Gebauer (2021): *”Exploring Technology-Driven Service Innovation In Manufacturing Firms Through The Lens Of Service Dominant Logic”* – This paper investigates service innovation in manufacturing through a service-dominant logic perspective, suggesting a theoretical grounding for the field.
* Chen (2021): *”The Competences For Digital Servitization: A Survey On Italian Based Firms”* – This empirical study focuses on identifying the specific skills and knowledge required for digital servitization, adding a practical dimension to the research.
* Struyf (2021): *”An Organizational Change Framework For Digital Servitization: Evidence From The Veneto Region”* – This paper likely focuses on the organizational changes required to implement digital servitization successfully, providing insights for managers.
* Hsuan (2021): *”Behind The Scenes Of Digital Servitization: Actualising Iot-Enabled Affordances”* – This paper likely explores the role of IoT in digital servitization.
* Favoretto (2022): *”Uncovering Productivity Gains Of Digital And Green Servitization: Implications From The Automotive Industry”* – This paper focuses on the practical outcomes of digital and green servitization, particularly in the automotive industry.
* Kolagar (2022): *“Competences in Digital Servitization: A New Framework”* – This work probably proposes a framework for competences in digital servitization.
* Raddats (2022): *”Transformational Shifts Through Digital Servitization”*

*Interpretation:* This phase focuses on refining the understanding of digital servitization, identifying the necessary competencies, and exploring its impact on organizational structures and performance.

Key Takeaways and Potential Discussion Points:

* Interdisciplinary Nature: The topics covered (AI, IoT, business models, organizational change, service-dominant logic, supply chains) highlight the interdisciplinary nature of digital servitization research.
* Practical Focus: The increasing number of studies focusing on implementation, required competencies, and performance outcomes suggests a growing interest in the practical application of digital servitization.
* Future Research Directions: Based on this analysis, future research could focus on:
* Developing standardized frameworks for measuring the impact of digital servitization.
* Investigating the ethical and societal implications of digital servitization.
* Exploring the role of emerging technologies (e.g., blockchain, metaverse) in enabling new forms of digital servitization.
* Limitations: This analysis is based solely on the title of the documents and the temporal citation network. A full understanding would require reading the actual articles.

This analysis provides a good starting point for understanding the evolution of digital servitization research. Remember to validate these interpretations with a thorough review of the actual papers. Good luck!

Collaboration Network
Overall Structure

The network appears relatively fragmented, consisting of several distinct clusters or communities of authors. This suggests that research in this field, at least as represented in your dataset, might be happening in somewhat isolated pockets rather than a highly interconnected global network. Several nodes appear isolated, this suggests some authors in the dataset do not collaborate with other authors.

Community Detection (Walktrap Algorithm)

The Walktrap algorithm has identified distinct communities, visualized by different colors. This algorithm identifies communities by simulating random walks on the graph. Nodes that are easily reachable from each other via short random walks are likely to be in the same community. Key observations:

Key Authors (Most Connected Nodes)

The size of the nodes indicates their degree centrality (number of connections). Larger nodes represent authors with more collaborations within this dataset.

Interpretation and Discussion Points

1. Interdisciplinary Nature: The presence of distinct communities might suggest interdisciplinary aspects to the research. Are these communities working on different facets of a broader problem, or are they completely separate research streams? If so, you can explore potential for cross-pollination of ideas and methodologies.
2. Impact of Key Authors: The prominent authors (“parida v”, “rapaccini m”, “adrodegari f”) likely have significant influence in the field. Examine their publications to understand the core themes and research directions they are driving. Are they bridging different communities?
3. Collaboration Patterns: The ‘association’ normalization implies that the edge weights reflect the strength of the collaboration relative to the authors’ overall publication activity. Strong links suggest frequent and sustained collaboration.
4. Potential for Increased Collaboration: The fragmented nature of the network suggests potential for increased collaboration between different communities. Identify authors or research groups that could benefit from closer interaction. You can also consider the research landscape based on these communities.
5. Database Bias: It’s important to acknowledge that SCOPUS, like any database, has its biases in terms of journal coverage and regional representation. This network reflects collaborations as indexed in SCOPUS.

Critical Evaluation and Further Exploration

By considering these points, you can move beyond a descriptive overview of the collaboration network to a more insightful interpretation of the research landscape and the dynamics of collaboration within your chosen field. Remember that this is a *representation* of collaboration based on publication data, not necessarily a complete picture of all research interactions.

Countries’ Collaboration World Map
Key Observations and Interpretation:

1. Major Hubs of Scientific Production: The map clearly shows that the United States and China are the most prominent hubs of scientific production, as indicated by the darkest shades of blue. This suggests a high volume of research output originating from these countries, reflecting their significant investment in research and development. Other countries that seem to be having a good production are the european countries like Italy, Sweden, Germany, France and the UK.

2. Key International Partnerships: The connecting lines highlight the significant collaborative relationships between countries. Several notable partnerships emerge:

* US-Europe: The lines connecting the US to various European countries are prominent, indicating strong transatlantic scientific collaborations. The number of collaborations is really high.
* US-China: A notable connection exist between China and the US.
* China-Europe: Collaboration between China and European nations are also prominent, although might be less relevant.
* European Intra-Collaboration: There’s strong collaboration within Europe itself, linking countries like Germany, the UK, France, Italy, and Scandinavian nations.

3. Global Patterns of Collaboration:

* Western Dominance: The map largely shows collaborations concentrated among Western countries (North America and Europe) and China. This suggests a potential bias or focus within the SCOPUS database or research field this data represents towards these regions.
* Limited Collaboration with Developing Nations: The collaborations with countries in Africa and South America appear less intense compared to those involving the US, Europe, and China. This is important because it could highlight inequalities in access to research resources and opportunities for collaboration.

Critical Discussion Points & Considerations:

Recommendations for Further Analysis:

By considering these points, you can move beyond a descriptive overview of the collaboration map and develop a more nuanced and critical understanding of the dynamics of international scientific collaboration within your chosen field.

I hope this helps! Let me know if you have more questions or want to explore any of these points further.

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