Cross-Category Innovation Strategy and Evolution of Digital Platform Ecosystems: A Technology-Driven Perspective

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The paper focuses on cross-category innovation strategies and the evolution of digital platform ecosystems, using ByteDance as a case study to explore how digital technology drives ecosystem development.  This research makes significant contributions to the field of cross-category innovation in digital platform ecosystems by revealing the interaction mechanism between technology-driven strategic actions and ecosystem evolution through a longitudinal case study.

Although the article uses simple language and is well organized, the authors are recommended to carefully study the relevant content and make any necessary adjustments to the specified sections:

1.Introduction: It is suggested to briefly introduce the technical background and highlight the core issues of the research.

2.Research Methodology: This paper employs a single-case study method, which may have external validity problems, that is, whether the research results are applicable to other situations. It is suggested to explain this to improve the universality of the conclusion.

3.Data Analysis: In the data analysis process, a more detailed description of how to combine multiple data sources and methods for triangulation should be provided to improve the reliability and validity of the study.

4.Conclusions and Findings: It is suggested to conduct a more in-depth analysis and discussion on the specific application methods, influencing factors and mechanisms of sustainable innovation ability.

Author Response

Response to Reviewers’ Comments

Reviewer 1’s comments:

Reviewer #1: The paper focuses on cross-category innovation strategies and the evolution of digital platform ecosystems, using ByteDance as a case study to explore how digital technology drives ecosystem development. This research makes significant contributions to the field of cross-category innovation…

 

Response to Reviewer 1’s Comments

 

Dear Reviewer 1,

 

Thank you for your detailed reading and helpful suggestions to revise our manuscript. We hope that you will find the revision to be an improvement.

 

The changes in the revised manuscript are highlighted in “red”. Below we outline the changes we made, in response to your comments item by item. 

 

Comments 1: Introduction: It is suggested to briefly introduce the technical background and highlight the core issues of the research.

 

Response 1:

We appreciate you raising this point of definition. We totally shared this point with you. In the original text, the definition was indeed unclear, and we acknowledge that this could have led to confusion. In response, we have updated the technical background in the Introduction to explicitly highlight the role of digital technology, as per your suggestion. We believe this revision makes our definition more precise and aligned with the existing literature. We have carefully revised the definition to explicitly clarify that The core research issue aims to address how to construct a digital platform ecosystem that breaks through path dependency and provide valuable insights into sustainable development.

 

Comments 2: Research Methodology: This paper employs a single-case study method, which may have external validity problems, that is, whether the research results are applicable to other situations. It is suggested to explain this to improve the universality of the conclusion.

 

Response 2:

We sincerely thank the reviewer for the valuable comment. We acknowledge that single-case studies require careful justification of their suitability for addressing "how" and "what" research questions. To address this, we have enhanced the methodological rationale in the manuscript and expanded the generalizability discussion in Section 5 (Research Conclusions and Prospects).

 

Comments 3: Data Analysis: In the data analysis process, a more detailed description of how to combine multiple data sources and methods for triangulation should be provided to improve the reliability and validity of the study.

 

Response 3:

Thank you very much for your insightful suggestion. We fully recognize the importance of detailing how multiple data sources and triangulation methods are combined to enhance reliability and validity. In response to your feedback, we have expanded the description in Section 3.3 Data Collection. This addition clarifies our strategy for integrating diverse data types to ensure robust, multi-dimensional validation.

 

Comments 4: Conclusions and Findings: It is suggested to conduct a more in-depth analysis and discussion on the specific application methods, influencing factors and mechanisms of sustainable innovation ability.

 

Response 4:

Thank you for this insightful suggestion. We acknowledge the need to deepen the discussion on sustainable innovation capabilities, which is critical for enhancing the practical relevance and theoretical depth of the study. To address this, we have expanded 5.1 (2): Strategies that balance legitimacy and distinctiveness can avoid category rigidity or excessive innovation, thereby enhancing the sustainable development of digital platform ecosystems…

 

Once again, we thank you for your encouragement, valuable feedback and detailed suggestions. We hope that you find this updated version of the paper has addressed your suggestions/concerns well.

 

Sincerely,

The Authors

Shuo Sun, Bing Gu, and Fangcheng Tang

 

 

Reviewer 2 Report

Comments and Suggestions for Authors

Here are some suggestions for revisions or additional explanations for your consideration:

1. There’s a lack of comparisons between cases from different industries or regions. The evolution of ecosystems on different platforms, like e-commerce, social media, and tools, can vary significantly. Conclusions from a single case might not accurately reflect the characteristics of other types of platforms.

2. The research mainly relies on secondary data, such as annual reports, media coverage, and academic literature, but it lacks support from primary data like interviews with company executives, user surveys, or internal documents. This secondary data might be limited by the incomplete nature of publicly available information.

3. It would be helpful to explain how you’re using triangulation to ensure the credibility of the data. Also, there hasn’t been a systematic analysis of how technology interacts with other driving factors, such as changes in user demand, policy regulations, and market competition.

4. When it comes to practical guidance, the research lacks specific operational steps, like how to implement technology modularization or the principles for designing ecosystem governance. This makes the findings less directly useful for companies.

5. While ByteDance's algorithmic recommendation technology raises concerns about issues like information bubbles and data privacy, the research doesn’t discuss how to balance innovation driven by technology with commercial goals and social responsibility. This overlooks the importance of ethical considerations in a sustainable ecosystem.

6. Lastly, it would be great to know what the main areas of innovation in this paper are. Could you elaborate on any new approaches in research methods or subjects?

Author Response

Response to Reviewers’ Comments

Reviewer 2’s comments:

Reviewer #2: Here are some suggestions for revisions or additional explanations for your consideration:

 

Response to Reviewer 2’s Comments

 

Dear Reviewer 2,

 

Thank you for your detailed reading and helpful suggestions to revise our manuscript. We hope that you will find the revision to be an improvement.

 

The changes in the revised manuscript are highlighted in “red”. Below we outline the changes we made, in response to your comments item by item. 

 

Comments 1: There’s a lack of comparisons between cases from different industries or regions. The evolution of ecosystems on different platforms, like e-commerce, social media, and tools, can vary significantly. Conclusions from a single case might not accurately reflect the characteristics of other types of platforms.

 

Response 1:

Thank you for highlighting this important perspective. We acknowledge that  multi-case approach could enrich the study, but we chose the inductive single-case approach for its strengths in addressing our research questions: "How" and "Why" digital platform ecosystems achieve cross-category innovation. This method aligns with Eisenhardt (1989) and Yin (2018), who emphasize its efficacy in uncovering underlying mechanisms and theoretical logic in complex, context-dependent phenomena. To address this, we have enhanced the methodological rationale in the manuscript and expanded the generalizability discussion in Section 5 (Research Conclusions and Prospects).

 

Comments 2: The research mainly relies on secondary data, such as annual reports, media coverage, and academic literature, but it lacks support from primary data like interviews with company executives, user surveys, or internal documents. This secondary data might be limited by the incomplete nature of publicly available information.

 

Response 2:

Thank you very much for your insightful suggestion. We fully recognize the importance of detailing how multiple data sources and triangulation methods are combined to enhance reliability and validity. In response to your feedback, we have expanded the description in Section 3.3 Data Collection. This addition clarifies our strategy for integrating diverse data types to ensure robust, multi-dimensional validation.

 

Comments 3: It would be helpful to explain how you’re using triangulation to ensure the credibility of the data. Also, there hasn’t been a systematic analysis of how technology interacts with other driving factors, such as changes in user demand, policy regulations, and market competition.

 

Response 3:

We are grateful for your guidance, which has strengthened the study’s theoretical robustness and practical relevance. We acknowledge the need to systematically explore technology’s interplay with user demand, policy, and market competition. These revisions systematically address your suggestions by:

Providing granular details on data triangulation in Section 3.3.

Adding digital technology’s structural characteristics and interaction mechanisms with ecosystem dynamics in Section 4.1: Digital technology reshapes innovation logic through a layered modular architecture with functional decoupling, endowing technical components with high programmability and reconfigurability. This enables digital objects to transcend physical limitations, fostering an open, distributed innovation ecosystem.

Adding in Section 4.2: The homogeneous migration and cross-domain aggregation capabilities of digital technology not only support platform cross-category innovation but also spawn digital platform ecosystems centered on data network effects...

These additions systematically enhancing the study’s theoretical depth. Thank you very much for your suggestion.

 

Comments 4: When it comes to practical guidance, the research lacks specific operational steps, like how to implement technology modularization or the principles for designing ecosystem governance. This makes the findings less directly useful for companies.

 

Response 4:

Thank you for your insightful feedback on the practical guidance aspect of our research. We fully recognize the importance of translating theoretical insights into actionable steps for enterprises, and we appreciate your suggestion to enhance this dimension.

While specific operational details remain unelaborated, the study illustrates practical principles through ByteDance’s use of microservices and API-driven architecture to decompose technical systems into reusable components for modularization. Due to space constraints, the discussion was not extended further, and we look forward to exploring this in greater depth in future research.

While the cross-category innovation strategy in this study focuses primarily on market expansion, ecosystem governance is acknowledged as a critical research direction. Due to the strategic breadth of the current scope, governance mechanisms were not fully integrated into the analysis. To address this, we will strengthen the manuscript by adding more ecosystem governance in 5.3. Research Limitations and Prospects.

 

Comments 5: When it comes to practical guidance, the research lacks specific operational steps, like how to implement technology modularization or the principles for designing ecosystem governance. This makes the findings less directly useful for companies.

 

Response 5:

Thank you for your thoughtful feedback. Balancing technological innovation with social responsibility is very important. We acknowledge that ethical considerations like information bubbles and data privacy, critical to sustainable ecosystems, were not explicitly addressed in this study. While we recognize the significance of ethical challenges posed by technologies like algorithmic recommendations, integrating them would have extended the scope beyond the study’s core objectives. We view your feedback as a crucial reminder of the need for balanced innovation. Future studies could explore “How governance frameworks interact with cross-category strategies. 

 

Comments 6: When it comes to practical guidance, the research lacks specific operational steps, like how to implement technology modularization or the principles for designing ecosystem governance. This makes the findings less directly useful for companies.

 

Response 6:

Thank you for your thoughtful question, which allows us to highlight the study’s key innovations. The research uniquely focuses on digital technology-driven cross-category innovation strategies and their evolutionary processes within platform ecosystems, addressing a critical gap in understanding how platforms navigate category boundaries in the digital age. It analyzes strategic balances in collaborative interactions among ecosystem actors, and systematically synthesize a cross-category innovation strategy model that reveals its inherent logic and evolutionary patterns. Additionally, the study explores transformations in the structural and value systems of digital platform ecosystems during category evolution. These contributions collectively enhance the research’s innovativeness and frontiers by integrating theoretical lenses from categorization theory and dynamic capabilities, coupled with empirical insights from a longitudinal case analysis.

 

Once again, we thank you for your encouragement, valuable feedback and detailed suggestions. We hope that you find this updated version of the paper has addressed your suggestions/concerns well.

 

Sincerely,

The Authors

Shuo Sun, Bing Gu, and Fangcheng Tang

Reviewer 3 Report

Comments and Suggestions for Authors

It is a great honor to have the opportunity to review this manuscript. The manuscript employs ByteDance as a case study to explore cross-category innovation strategies and the evolutionary mechanisms of digital platform ecosystems, a topic of significant theoretical and practical importance. The research framework is clear, integrating categorization theory with a technology-driven perspective to propose an analytical model of "technology-driven–strategic action–ecosystem evolution," offering a novel lens for digital platform innovation research. However, the paper requires further refinement in theoretical contributions, methodological rigor, and literature coverage.

1.Literature review

Some cited literature is outdated. It is recommended to supplement the discussion with cutting-edge studies on digital platform ecosystems from the past five years, such as: platform envelopment strategy；ecosystem governance and the impact of digital technologies on category boundaries. Additionally, the distinction between "cross-category innovation" and "cross-boundary innovation" needs to be clarified. Relevant theories, such as niche theory, could be cited to deepen the discussion.

2.Research method

While the manuscript claims to adopt the Gioia methodology, Figure 3 (the Gioia model’s structure) remains overly generalized and fails to explicitly demonstrate the extraction process of first-order concepts and second-order themes through typical textual evidence (e.g., direct quotes from raw data). Although the paper mentions multi-source data (e.g., coding tables), it lacks concrete examples of coding procedures or data refinement processes.

3.Discussion

The manuscript focuses solely on a single case study of ByteDance. Although the longitudinal analysis is detailed, it does not address whether the proposed model is generalizable to other digital platforms. It is recommended to include a discussion on “theoretical generalization,” elaborating on the applicability and boundary conditions of the model in the context of other platforms such as Amazon or Tencent, in order to enhance the explanatory breadth of the study.

Furthermore, while the manuscript is grounded in categorization theory, it does not sufficiently articulate how it extends or refines existing theoretical frameworks. It is suggested that the discussion section more explicitly highlight how the technology-driven perspective supplements traditional categorization theory, and clarify the theoretical contributions of cross-category innovation strategies—such as platform envelopment and open innovation—within the context of categorization theory.

4.Conclusion

The managerial implications section is relatively general. It is recommended that the authors provide differentiated strategic suggestions based on industry type or firm size—for example, how startups can leverage modular technologies for rapid deployment, and how established firms can balance ecosystem control with open innovation.

Author Response

Response to Reviewers’ Comments

Reviewer 3’s comments:

Reviewer #3: It is a great honor to have the opportunity to review this manuscript. The manuscript employs ByteDance as a case study to explore cross-category innovation strategies and the evolutionary mechanisms of digital platform ecosystems, a topic of significant theoretical and practical importance. The research framework is clear, integrating categorization theory…

 

Response to Reviewer 3’s Comments

 

Dear Reviewer 3,

 

Thank you for your detailed reading and helpful suggestions to revise our manuscript. We hope that you will find the revision to be an improvement.

 

The changes in the revised manuscript are highlighted in “red”. Below we outline the changes we made, in response to your comments item by item.

 

Comments 1: Literature review

 

Some cited literature is outdated. It is recommended to supplement the discussion with cutting-edge studies on digital platform ecosystems from the past five years, such as: platform envelopment strategy; ecosystem governance and the impact of digital technologies on category boundaries. Additionally, the distinction between "cross-category innovation" and "cross-boundary innovation" needs to be clarified. Relevant theories, such as niche theory, could be cited to deepen the discussion.

 

Response 1:

Thank you for your insightful feedback. We deeply appreciate your recommendation to enhance the literature review with cutting-edge studies and clarify conceptual distinctions. We have incorporated recent research to address the gaps identified. In Section 2.3, we now explicitly differentiate. In Section 2.3, we now more explicitly differentiate cross-category vs. cross-boundary Innovation and have expanded the relevant content.

 

Comments 2: Research method

While the manuscript claims to adopt the Gioia methodology, Figure 3 (the Gioia model’s structure) remains overly generalized and fails to explicitly demonstrate the extraction process of first-order concepts and second-order themes through typical textual evidence (e.g., direct quotes from raw data). Although the paper mentions multi-source data (e.g., coding tables), it lacks concrete examples of coding procedures or data refinement processes.

 

Response 2:

Thank you very much for your insightful suggestion. Thank you for your comments. The Gioia model's structure emphasizes a systematic evidence chain, forming a verifiable process model through layered abstraction via first-order coding (phenomenon description), second-order coding (theme induction), and third-order coding (theoretical framework). Raw data citations are provided in Tables 2 and 3, which present stage-specific raw evidence citation tables. We acknowledge potential imperfections and look forward to further improvements in the future.

 

Comments 3: Discussion

The manuscript focuses solely on a single case study of ByteDance. Although the longitudinal analysis is detailed, it does not address whether the proposed model is generalizable to other digital platforms. It is recommended to include a discussion on “theoretical generalization,” elaborating on the applicability and boundary conditions of the model in the context of other platforms such as Amazon or Tencent, in order to enhance the explanatory breadth of the study.

Furthermore, while the manuscript is grounded in categorization theory, it does not sufficiently articulate how it extends or refines existing theoretical frameworks. It is suggested that the discussion section more explicitly highlight how the technology-driven perspective supplements traditional categorization theory, and clarify the theoretical contributions of cross-category innovation strategies—such as platform envelopment and open innovation—within the context of categorization theory.

 

Response 3:

Thank you for your insightful suggestions, which have significantly strengthened the theoretical rigor and generalizability of our study. We acknowledge the importance of discussing how our model applies beyond ByteDance. To address this, we have added this content in 5.2. Management Significance. Thank you for the suggestions on categorization theory, which have also deepened the depth of our research. We greatly appreciate have incorporated this content into Section 5.2 accordingly.

 

Comments 4: Conclusion

The managerial implications section is relatively general. It is recommended that the authors provide differentiated strategic suggestions based on industry type or firm size—for example, how startups can leverage modular technologies for rapid deployment, and how established firms can balance ecosystem control with open innovation.

 

Response 4:

We are sincerely indebted to you for your invaluable contributions, which have significantly shaped the quality and direction of our research, in particular, your suggestions elevated the depth of the research summary and enhanced the Management Significance section.

 

Once again, we thank you for your encouragement, valuable feedback and detailed suggestions. We hope that you find this updated version of the paper has addressed your suggestions/concerns well.

 

Sincerely,

The Authors

Shuo Sun, Bing Gu, and Fangcheng Tang