Advanced Manufacturing Technologies and Digital Commerce Integration in Spanish Industry: Innovation Outcomes and Sustainability Pathways

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The topic is timely and well-motivated, but the paper currently reads as descriptive and correlation-driven, rather than mechanistic and theory-driven. The logical chain among constructs (AMT -> CE -> Innovation -> Performance, moderated by Digital Maturity / Policy) is present but not well justified or evidenced. The paper’s contribution would improve substantially with more rigorous modeling and sharper discussion.

1. Some statements seem too abstract. For example:

(1) “This study adopts a theoretically grounded, empirically rigorous approach to clarify these relationships.”

Need to specify the theory and approach, otherwise the audience will feel confused.

(2) “Examine the nuanced and conditional effects of AMTs and digital commerce on different innovation types in Spanish manufacturing firms.”

What does “nuanced effect” mean in this context? The term “nuanced and conditional effects” is too general. Specify which conditions (e.g., firm size? sectoral digital intensity? or policy instruments?) are hypothesized to moderate the effect. Each “condition” should later correspond to a testable moderator or subgroup analysis.

(3) The definition of AMT is cited but too generic. It lists technologies without clarifying what theoretical dimension is measured (automation? digital connectivity? learning capability?). It is unclear whether AMT represents a technological intensity construct or a capability construct. The boundary between AMT and “digital maturity” is blurred (both involve digital technologies but differ in scope).

2. The author mentioned “we test how AMT intensity, digital commerce integration, circular economy practices, and digital maturity interact to influence innovation and value-added outcomes”, but the results section only reports isolated regression coefficients.

Suggestions: (1) Expand the interpretation by discussing interaction patterns (e.g., whether digital maturity amplifies or mitigates AMT’s innovation effect, or whether circular economy mediates these relationships). (2) Use figures to visualize how these constructs interact.

3. The choice of indices for AMT intensity, digital commerce integration, circular economy, and digital maturity needs justification. Please justify why these specific indicators (variables) were selected and why they are sufficient to represent each construct.

4. The hypotheses are too general (for example, “AMT positively affects innovation”) and lack mechanistic reasoning. Each hypothesis should explain why and how such an effect is expected. (For instance, AMT may enhance process innovation through production flexibility but have limited effect on product innovation due to rigid design constraints.)

4. The paper employs cross-sectional Poisson regressions for innovation count variables but does not explain why this choice is appropriate. Please justify the reason you choose this model rather than others.

Author Response

Comment 1: 

1. Some statements seem too abstract. For example:

(1) “This study adopts a theoretically grounded, empirically rigorous approach to clarify these relationships.”

Need to specify the theory and approach, otherwise the audience will feel confused.

Response: The authors want to thank the reviewer for his/her valuable comments that help to increase the quality of the paper.

In page 2 such paragraph has been modified stating that “This study draws explicitly on resource orchestration theory and employs robust Poisson and OLS regression analyses on cross-sectional ESEE survey data from 1,813 Spanish manufacturing firms to clarify how advanced manufacturing technologies, digital commerce, circular economy practices, and digital maturity interact to shape innovation and value-added outcomes.”

Comment (2) “Examine the nuanced and conditional effects of AMTs and digital commerce on different innovation types in Spanish manufacturing firms.”

What does “nuanced effect” mean in this context? The term “nuanced and conditional effects” is too general. Specify which conditions (e.g., firm size? sectoral digital intensity? or policy instruments?) are hypothesized to moderate the effect. Each “condition” should later correspond to a testable moderator or subgroup analysis.

Response: We modified that part of the section in page 3 with the following explanation: “Examine how the effects of advanced manufacturing technologies (AMTs) and digital commerce on product, process, organizational, and commercial innovation in Spanish manufacturing firms are moderated by specific conditions—such as sectoral digital intensity, firm size, circular economy engagement, digital maturity, and the presence of policy instruments like Spain’s Startup Law. Each condition is measured and tested as a moderator or subgroup in our empirical analysis.”

We believe that clarifies the condition and policies that are hypothesized to moderate the effect.

Comment (3): The definition of AMT is cited but too generic. It lists technologies without clarifying what theoretical dimension is measured (automation? digital connectivity? learning capability?). It is unclear whether AMT represents a technological intensity construct or a capability construct. The boundary between AMT and “digital maturity” is blurred (both involve digital technologies but differ in scope).

Response: We have more clearly explained the definition of AMT and the boundary between AMT and “digital maturity” in page 5 as:

“In this study, Advanced Manufacturing Technologies (AMTs) are operationalized as a technological intensity construct, emphasizing the degree of adoption of robotics, additive manufacturing/3D printing, machine learning/big data, and industrial IoT. The AMT index measures the extent to which these technologies are used to enhance process automation and interconnectivity within manufacturing operations. This construct captures the tangible implementation of digital production tools, focusing on automation and data-driven decision making, rather than broader organizational capabilities.

In contrast, 'digital maturity' is defined as an organizational capability construct, reflecting the firm's overall readiness and ability to integrate, scale, and manage digital technologies across business processes—beyond just the adoption of specific tools. Digital maturity includes factors such as cloud computing implementation, platform collaboration, and digital skills investment, enabling effective resource orchestration with AMTs and other digital assets.

Thus, AMT intensity in this research indicates the adoption level of key manufacturing technologies, while digital maturity differentiates firms by their strategic competence and capacity to leverage these technologies at scale."

In page 11 after the explanation of technical components, we added the following clarification: “The AMT index in this research specifically operationalizes technological intensity—the prevalence and degree of use of automation and digital connectivity technologies in manufacturing. It measures the implementation of digital production tools rather than broader organizational capabilities. This approach ensures AMT reflects the firm’s adoption of tangible technologies central to digital transformation.”

In addition, we clarified DMI with a deeper explanation in page 12:

 "In contrast, digital maturity represents an organizational capability construct. It assesses the firm’s overall readiness and ability to integrate, scale, and coordinate digital technologies, encompassing strategic leadership on digital transformation, cross-functional integration, cloud adoption, and investment in digital skills. While AMT intensity is confined to technology adoption and process automation, digital maturity reflects the firm’s comprehensive capacity to leverage digital resources for innovation and performance. Thus, AMT is about ‘what’ is adopted; digital maturity is about ‘how well’ it is orchestrated and integrated across the firm."

A new Table 2 was included in page 13.

Comment 2: The author mentioned “we test how AMT intensity, digital commerce integration, circular economy practices, and digital maturity interact to influence innovation and value-added outcomes”, but the results section only reports isolated regression coefficients.

Suggestions: (1) Expand the interpretation by discussing interaction patterns (e.g., whether digital maturity amplifies or mitigates AMT’s innovation effect, or whether circular economy mediates these relationships). (2) Use figures to visualize how these constructs interact.

Response: The following explanation has been included in page 19:

“To clarify the interaction patterns among core constructs, we find that digital maturity acts as a moderator by attenuating the (already modest) positive effect of circular economy intensity on process innovation – that is, at higher levels of digital maturity, the contribution of circular economy practices to process innovation diminishes rather than increases (Table 7, CEINT × DMI interaction: coefficient = -0.40, p < 0.05). Contrary to our initial hypotheses, digital maturity does not amplify the innovation benefits of sustainability practices but instead mitigates them, highlighting a potentially counterintuitive trade-off in resource allocation. Furthermore, mediation results in Table 6 indicate that circular economy intensity negatively mediates the effect of AMT and e-commerce on process innovation, suggesting that, in the Spanish manufacturing context, increased focus on sustainability practices may redirect resources away from process innovation. These findings show that the interplay of AMT, e-commerce, circular economy, and digital maturity is complex, sometimes producing negative or null interaction effects rather than straightforward synergies.”

Figure 1 and 2 has been included to visualize construct interaction.

Comment 3: The choice of indices for AMT intensity, digital commerce integration, circular economy, and digital maturity needs justification. Please justify why these specific indicators (variables) were selected and why they are sufficient to represent each construct.

Response: In the Methodology—Variable Operationalization section, after introducing each construct and its measurement, a clear justification has been included.

4. The paper employs cross-sectional Poisson regressions for innovation count variables but does not explain why this choice is appropriate. Please justify the reason you choose this model rather than others.

This has been justified in page 15 as follows: Poisson regression was selected for modeling innovation outcomes because our dependent variables—such as product and process innovation intensity—are measured as non-negative integer counts (number of innovations implemented), with distributions that are highly skewed and right-tailed. Standard linear regression (OLS) assumes continuous, normally distributed outcomes and thus is not suitable for discrete count data, as it may produce biased estimates and invalid inferences. Logistic regression, meanwhile, is designed for binary or categorical outcomes, not counts. The Poisson model explicitly models the expected count conditional on covariates, making it the most statistically appropriate choice for this data structure (cf. Faraway, 2016; Raymond, 2005). Where overdispersion was detected, robust standard errors and, in sensitivity checks, negative binomial models were applied to confirm main results. This methodological choice ensures the correct treatment of the count nature, zero inflation, and overdispersion typical of innovation measurement in manufacturing (see Fox, 2015).

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript examines how Advanced Manufacturing Technologies (AMT), e-commerce integration, circular-economy intensity (CE_INT), and digital maturity relate to product and process innovation and to productivity among Spanish manufacturers (ESEE data, 2022–2023). The topic is timely and policy-relevant. However, several issues in measurement, model consistency, and reporting currently prevent confident interpretation of the results.

1. There is a clear inconsistency between the industry composition reported in the text and the figures presented in Table 1. The manuscript states that metal products (14.2%), food and beverages (13.1%), and machinery and equipment (11.7%) are the largest sectors, whereas Table 1 lists food and beverages (24.88%), metal products (22.17%), and machinery and equipment (7.83%). These discrepancies are substantial and could mislead readers regarding sample structure and representativeness. Please verify the original data, correct either the text or the table accordingly, and ensure consistent reporting of percentages and decimal formatting throughout the manuscript.

2. The composite AMT index is said to have five components, but only four are listed (RBI, I3D, MLBD, IIOT). Either add the missing component or rewrite as a four-item index. Report item wording, coding, and reliability.

3. In the variables section,COMERII is introduced as “Commercial Innovation,” but in descriptives/results it appears to be used as e-commerce integration. This is a fundamental mismatch. Please update all tables, notes, and text accordingly. Besides, use one form throughout (e.g., CE-INT; AMT; ECOMM; DMI). Define all at first mention.

4. PROCESSII is defined on a 0–2 scale and PRODUCTII on 0–3, yet the table of descriptives reports means and maxima above those ranges (e.g., PROCESSII mean >3, max 4; PRODUCTII mean >4, max 7). This implies re-coding or summation beyond the stated range. Please reconcile definitions and tables, and clearly explain the construction.

5. The manuscript currently employs different estimation families for conceptually similar dependent variables, which undermines comparability across models and weakens the statistical consistency of the findings. Specifically, certain innovation measures that are clearly count-type variables (e.g., number of process or product innovations, PROCESSII, PRODUCTII) are analyzed with Poisson regressions in some parts of the paper, while in other sections or tables the same variables—or highly related indices—are modeled using ordinary least squares (OLS).

Author Response

Comment 1: There is a clear inconsistency between the industry composition reported in the text and the figures presented in Table 1. The manuscript states that metal products (14.2%), food and beverages (13.1%), and machinery and equipment (11.7%) are the largest sectors, whereas Table 1 lists food and beverages (24.88%), metal products (22.17%), and machinery and equipment (7.83%). These discrepancies are substantial and could mislead readers regarding sample structure and representativeness. Please verify the original data, correct either the text or the table accordingly, and ensure consistent reporting of percentages and decimal formatting throughout the manuscript.

Response: This has been corrected, and we appreciate the note remarking the incongruencies between the text and the table.

Comment 2: The composite AMT index is said to have five components, but only four are listed (RBI, I3D, MLBD, IIOT). Either add the missing component or rewrite as a four-item index. Report item wording, coding, and reliability.

Response: This has been corrected, and we appreciate the note remarking the mistake. We have added a new table with reliability indexes (Table 4)

Comment 3: In the variables section,COMERII is introduced as “Commercial Innovation,” but in descriptives/results it appears to be used as e-commerce integration. This is a fundamental mismatch. Please update all tables, notes, and text accordingly. Besides, use one form throughout (e.g., CE-INT; AMT; ECOMM; DMI). Define all at first mention.

Response: This has been corrected, and we appreciate the note remarking the mistake

Comment 4: PROCESSII is defined on a 0–2 scale and PRODUCTII on 0–3, yet the table of descriptives reports means and maxima above those ranges (e.g., PROCESSII mean >3, max 4; PRODUCTII mean >4, max 7). This implies re-coding or summation beyond the stated range. Please reconcile definitions and tables, and clearly explain the construction.

Response: PROCESSII was defined on a 2–4 scale, as the sum of IPRME (1-2) and IPRTM (1-2). This typo has been corrected.

This paragraph defining PRODUCTII has been corrected: “Product Innovation Intensity (PRODUCTII): This composite measure combines product innovations through new components (IPNC), new design features (ICODIS), and new products (IP), with IPNC measured on a 1-3 scale and ICODIS and IP in a 1-2 scale.

Comment 5: The manuscript currently employs different estimation families for conceptually similar dependent variables, which undermines comparability across models and weakens the statistical consistency of the findings. Specifically, certain innovation measures that are clearly count-type variables (e.g., number of process or product innovations, PROCESSII, PRODUCTII) are analyzed with Poisson regressions in some parts of the paper, while in other sections or tables the same variables—or highly related indices—are modeled using ordinary least squares (OLS).

Response: We thank the reviewer for the helpful comment. We have ensured full statistical consistency across models. Specifically, all dependent variables reflecting counts of innovations (PRODUCTII, PROCESSII, COMERII) are analyzed using Poisson regression models. We formally tested for overdispersion in all models, and in every case the dispersion parameter was close to 1 (range: 0.86–1.14), indicating that Poisson is appropriate and that more complex count specifications (e.g., negative binomial or zero-inflated models) are not required. OLS models are now used only where assume that the dependent variable is truly continuous (PBTN).

The following clarification has been introduced in section 3.3.

Poisson regression models were selected for the count data innovation variables (PRODUCTII, PROCESSII, and COMERII) as dispersion values (ranging from 0.86 to 1.14) confirmed the absence of overdispersion. Logistic regression was applied to the binary variable METODII, and Ordinary Least Squares (OLS) regression was used for the continuous performance measure, PBTN.

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

This manuscript investigates how advanced manufacturing technologies, digital commerce, circular economy intensity, and digital maturity interact to affect product and process innovation in Spanish manufacturing firms.

1. I don’t entirely agree with some of the statements, such as “AMT yields a small positive effect on process innovation, but the impact on product innovation is limited or negative.” “suggesting possible trade-offs between sustainability and innovation goals.” Additive manufacturing, as one of the typical advanced manufacturing technologies, can revolutionize product innovation (new structure design), process innovation and improve sustainability. Please refer to AMPOWER insight “Sustainability of Metal Additive Manufacturing”. (https://ampower.eu/insights/sustainability-of-metal-additive-manufacturing/)
2. It is suggested to add some summarizing figures to clarify the findings and conclusions of this paper.

Author Response

Comment 1: I don’t entirely agree with some of the statements, such as “AMT yields a small positive effect on process innovation, but the impact on product innovation is limited or negative.” “suggesting possible trade-offs between sustainability and innovation goals.” Additive manufacturing, as one of the typical advanced manufacturing technologies, can revolutionize product innovation (new structure design), process innovation and improve sustainability. Please refer to AMPOWER insight “Sustainability of Metal Additive Manufacturing”. (https://ampower.eu/insights/sustainability-of-metal-additive-manufacturing/)

Response: Thank you for the info. The citation has been added. In addition, after empirical findings the following has been included:

While the aggregate analysis in our Spanish manufacturing sample finds only modest effects of AMT adoption on product innovation, it is important to recognize that distinct AMT tools—particularly metal additive manufacturing—may offer strong and simultaneous gains in product and process innovation as well as sustainability. For example, recent research by AMPOWER (2024) demonstrates that metal additive manufacturing can revolutionize product innovation through novel structure design, enable highly efficient process innovations, and significantly reduce material waste and environmental footprint, especially when optimized for design and alloy selection. In certain use cases, AM methods produce components with both lower lifetime carbon emissions and higher functional performance compared to conventional manufacturing. This nuance suggests that, while some results in our aggregate regression models indicate negative or limited effects (potentially subgroup-sensitive), best-practice deployment of AMT—particularly additive manufacturing—can enable synergy, not a trade-off, between sustainability and innovation goals.

In section 5.2 about Practical Implications and Policy Insights it has been included that our findings suggest that realizing the simultaneous benefits of innovation and sustainability depends on technology-specific adoption, production context, and the degree to which best-practice methods such as metal additive manufacturing are implemented.

Comment 2: It is suggested to add some summarizing figures to clarify the findings and conclusions of this paper.

Figures 1 and 2 as well as a new table (2) have been included for clarification. Thank you once again.

Author Response File: Author Response.docx

Reviewer 4 Report

Comments and Suggestions for Authors

Overall Comments

This manuscript addresses how advanced manufacturing technologies, digital commerce, circular economy intensity, and digital maturity interact to affect product and process innovation in Spanish manufacturing firms during the post-pandemic period. The study provides Design/Methodology/Approach-Drawing on resource orchestration theory to test the direct and mediated effects of AMT, e-commerce adoption and sustainability practices. However, several issues related to “theoretical framing and Language & Expression” need to be addressed to meet the publication standards of this journal. Below are specific revision suggestions for the authors’ reference.

Revisions

Theoretical Integration and Contribution

1. The link between ROT and circular economy practices should be elaborated.
2. The cross-sectional design limits causal inference. Although instrumental variables are used, their strength is questionable.

Format & References

Check all DOI correspond to entries reference, and ensure the reference format complies with the journal’s guidelines.

Comments on the Quality of English Language

Language & Expression

1. Academic tone & precision: Replace informal or vague phrases: (1) “modest improvements” → “marginal improvements” (more precise in academic contexts); (2) “Unexpectedly” → “Notably” (avoids subjective judgment and maintains objectivity); (3) “joint technology adoption effects are minimal” → “the synergistic effects of joint technology adoption are negligible” (more academically standard).
2. Sentence structure: Simplify overly complex sentences. For example, the sentence “The economic benefits of sustainability orientation are most evident in export-oriented firms, not across the wider sample” can be revised to “The economic benefits of circular economy practices are most pronounced in export-oriented firms rather than in the overall sample” (clearer and more concise).

Author Response

Comment 1: The link between ROT and circular economy practices should be elaborated.

Response: The authors want to thank the reviewer for his/her insightful comments. The following expanded theoretical linkage (after discussing green resource orchestration or ROT in sustainability context) has been included:

Resource Orchestration Theory (ROT) provides a powerful lens to understand how firms achieve sustainable competitive advantage, not simply through passive resource possession but by actively structuring, bundling, and leveraging resources in dynamic ways (Sirmon et al., 2011). Circular economy practices—such as resource recovery, recycling, remanufacturing, and eco-design—demand substantial reconfiguration of routines, inter-organizational linkages, and knowledge bases. ROT postulates that valuable outcomes (like innovation and sustainable performance) arise when organizations purposefully orchestrate tangible (material, technological) and intangible (knowledge, network, managerial) resources in alignment with strategic objectives.

The integration of circular economy principles thus becomes an active process of resource orchestration: firms must (1) structure their assets to enable take-back, reverse logistics, and design-for-reuse, (2) bundle their capabilities to support cross-functional and cross-firm collaboration for sustainability, and (3) leverage new routines to maximize both innovation and ecological value (Andersén, 2023). In this way, circular economy adoption is not a static adoption of green routines, but a dynamic, strategic process that tests a firm's ability to reconfigure and combine resources, as prescribed by ROT, for superior sustainable outcomes.

Also, it has been clarified that in our model, circular economy intensity is theorized as an outcome of effective resource orchestration and as a critical mediator between technological resources (AMT, e-commerce) and innovation results. Following ROT, the ability to implement circular practices depends on managerial capacity to realign and deploy existing assets for environmental and innovation objectives simultaneously.

And added In the Discussion/Theoretical Contributions section, the following:

Our findings extend ROT by demonstrating that the implementation and impact of circular economy initiatives depend on orchestrating both technological and organizational capabilities. Simply adopting green practices does not guarantee innovation gains; instead, the returns to circular economy practices hinge on a firm's ability to purposefully structure, bundle, and leverage resources towards integrated innovation and sustainability goals – a central premise of ROT (Sirmon et al., 2011; Andersén, 2023).

Comment 2. The cross-sectional design limits causal inference. Although instrumental variables are used, their strength is questionable.

Response: In the limitattions section we acknowledge that the cross-sectional design limits causal inference. While instrumental variables are employed to strengthen the analysis, we are aware that their strength may be limited, and findings should be interpreted as associations rather than definitive causal effects.

Comments 3:

1. Academic tone & precision: Replace informal or vague phrases: (1) “modest improvements” → “marginal improvements” (more precise in academic contexts); (2) “Unexpectedly” → “Notably” (avoids subjective judgment and maintains objectivity); (3) “joint technology adoption effects are minimal” → “the synergistic effects of joint technology adoption are negligible” (more academically standard).
2. Sentence structure: Simplify overly complex sentences. For example, the sentence “The economic benefits of sustainability orientation are most evident in export-oriented firms, not across the wider sample” can be revised to “The economic benefits of circular economy practices are most pronounced in export-oriented firms rather than in the overall sample” (clearer and more concise).

Response: 

All these issues have been corrected. Thank you very much once again.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have addressed my previous questions clearly.
The figures appear somewhat rough; increasing the font size would enhance readability for the audience.

Author Response

Comment 1: The authors have addressed my previous questions clearly.

Response: The authors want to thank the reviewer for his/her insightful comments.

Comment 2: The figures appear somewhat rough; increasing the font size would enhance readability for the audience.

Response: The figura has been remade and the font size increased

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have addressed all my previous questions.

Author Response

Comment 1: The authors have addressed all my previous questions.

Response: The authors want to than the reviewer for his/her insighful comments.

Reviewer 4 Report

Comments and Suggestions for Authors

The authors reflected on all of my comments and made extensive improvements to the article. The final authors' changes are satisfactory. Upon comprehensive review, the manuscript fulfills the publication criteria, and we advise expediting the subsequent procedures for its publication.

Author Response

Comment 1: The authors reflected on all of my comments and made extensive improvements to the article. The final authors' changes are satisfactory. Upon comprehensive review, the manuscript fulfills the publication criteria, and we advise expediting the subsequent procedures for its publication.

Response: The authors want to thank the reviewer for his/her insightful comments.