Technological Adoption Sequences and Sustainable Innovation Performance: A Longitudinal Analysis of Optimal Pathways

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

I have the following comments:

1) The study offers practical, actionable guidance for manufacturing firms by identifying distinct patterns of Industry 4.0 technology adoption that significantly influence sustainable innovation outcomes. This enables firms to make more informed decisions when planning their digital transformation journeys.

2) By employing longitudinal data and sequence analysis methods, the research provides a nuanced and evidence-based understanding of how technology adoption unfolds over time. This adds depth and rigor to the findings, enhancing their relevance for both academics and practitioners.

3) The study effectively bridges the gap between digital transformation and sustainability transitions, showing how different adoption sequences build specific organizational capabilities that mediate innovation outcomes. This interdisciplinary approach advances theoretical development and supports holistic policy-making.

 

Based on the above comments, I recommend this paper for publication in this journal.

Author Response

Response to Reviewer 1 for TECHNOLOGICAL ADOPTION SEQUENCES AND SUSTAINABLE INNOVATION PERFORMANCE: A LONGITUDINAL ANALYSIS OF OPTIMAL PATHWAYS

Comment 1: The study offers practical, actionable guidance for manufacturing firms by identifying distinct patterns of Industry 4.0 technology adoption that significantly influence sustainable innovation outcomes. This enables firms to make more informed decisions when planning their digital transformation journeys.

Response 1: The authors want to thank the reviewer for this comment.

2) By employing longitudinal data and sequence analysis methods, the research provides a nuanced and evidence-based understanding of how technology adoption unfolds over time. This adds depth and rigor to the findings, enhancing their relevance for both academics and practitioners.

Response 2: Once more, the authors want to thank the reviewer for this comment.

3) The study effectively bridges the gap between digital transformation and sustainability transitions, showing how different adoption sequences build specific organizational capabilities that mediate innovation outcomes. This interdisciplinary approach advances theoretical development and supports holistic policy-making.

Response 3: Thank you, this will encourage us to keep on working n this research line.

Final Comment: Based on the above comments, I recommend this paper for publication in this journal.

The authors are very thankful for this outcome.

Reviewer 2 Report

Comments and Suggestions for Authors

This paper presents an interesting and important topic for research, aiming to shed light on the transformation toward Industry 4.0 through the lens of sequence analysis. While the study offers potential contributions, several issues require in-depth reconsideration to ensure the scientific soundness and clarity of the manuscript. The following suggestions are intended to help the authors refine their work:

1)The introduction section is overly long and lacks a focused, concise articulation of the main research objectives and the existing research gaps the study intends to fill. Some redundant or tangential information dilutes the clarity of the research narrative. The authors are advised to streamline this section by eliminating unnecessary content and emphasizing the core motivations and research questions.

2) Due to the excessive length of the introduction, some content overlaps with the literature review, particularly regarding the theoretical contributions of the study. A well-structured literature review should not only clarify the theoretical foundations but also contextualize the research design by summarizing how similar studies have used sequence analysis to monitor Industry 4.0 transformation. A more thorough synthesis of prior work, along with a clear statement of theoretical and practical contributions, would strengthen the manuscript's scholarly grounding.

3) In the methodology section, the rationale for selecting specific variables requires further elaboration. For instance, the authors propose eight pillar technologies to assess Industry 4.0 adoption. However, some variables may be conceptually or empirically overlapping. For example, the distinction between robotics for industrial applications (RBI) and advanced robotics and automation (RAV) should be clarified—if the latter represents a more advanced evolution of the former, this relationship should be explicitly discussed.

4) The explanation of the clustering method applied to the sequence data is insufficient. The authors should provide mathematical formulations or algorithmic descriptions to clarify how firms were grouped into clusters. At a minimum, one or two illustrative figures should be included to visualize the clustering results and aid reader comprehension.

5) The discussion section should be enhanced by highlighting the novelty and priority of the proposed approach. This could be achieved by comparing the findings with those from existing studies, thus reinforcing the value and implications of the results. Subsections addressing theoretical, methodological, and managerial implications would also enrich the discussion.

6) Important details appear to be missing from Table 1. The authors should ensure that all variables and metrics are clearly defined, and relevant statistics are reported for full transparency and reproducibility.

7) The process or mechanism through which the five clusters were derived should be described in more detail. This includes specifying the clustering criteria, interpretation of clusters, and how they align with the overarching research objectives.

Author Response

Please, see the attachment

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Major Contributions

Theoretical Innovations:

• Addressed the theoretical gap in understanding the dynamic relationship between technology adoption temporality and sustainable innovation, synthesizing Resource-Based View (RBV), dynamic capabilities, and sustainable transition theories (Section 3).
• Proposed the "technology adoption sequences as capability-building trajectories" framework (Section 3.1), advancing path-dependency perspectives in sustainable innovation research.

Missing Cluster Visualization:

While referencing Figure 1 to illustrate sequence patterns (§4.2), the manuscript omits this figure. A sequence index plot should be added to visually contrast adoption timing differences across the five clusters.

Inadequate Industry Heterogeneity Analysis:

Table 5 documents performance disparities across high/medium/low-tech sectors but lacks causal explanations (e.g., why high-tech industries derive greater benefits from data infrastructure).

Recommendation:

In the Discussion section, integrate industry technological intensity (§3.10) to elucidate underlying mechanisms. For example, high-tech firms may exhibit superior capabilities in translating data into resource optimization strategies due to pre-existing digital maturity and R&D infrastructure.

Key Terms Clarified:

Sequence Index Plot: A visualization tool mapping temporal adoption trajectories across organizational cohorts.

Technological Intensity: The density of R&D investment, advanced technology utilization, and knowledge-based assets within an industry sector (§3.10).

Figure/Table Captions:

The † symbol in Table 3 lacks explanation (a notation should clarify: † denotes statistical significance at p < 0.10).

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have made significant revisions in response to the first-round comments, substantially addressing many of the original manuscript's shortcomings and improving the overall quality of the paper. However, several issues still require attention for further refinement:

1）Regarding clusters used in both Tables 2 and 3，what is the relationship between the clusters presented in these two tables? Are they derived from the same clustering units or different datasets? If these represent distinct clustering results, please clarify their potential connections or correlations. Additionally, please explain why the cluster numbering in Table 2 does not begin with 1, as this appears inconsistent with conventional presentation formats.

2）Concerning the perfect five clusters in Table 3: What are the precise boundary conditions defining these clusters? Please address the possibility of inter-cluster overlaps or transitional cases that might challenge this clear-cut classification.  The manuscript should provide more robust theoretical justification for such distinct clustering results to preclude any perception of potential data manipulation.

3) For methodological validation: Could the authors select five representative enterprises (one from each cluster) for detailed case analysis? A more thorough examination of how these enterprises exemplify their respective clusters would significantly strengthen the empirical validation of the proposed methodology. Such concrete examples would help readers better understand the practical implications and applications of your classification framework. 

Author Response

Please see the attachment

Author Response File: Author Response.pdf