Holistic Approach to Value Chain Creation: From Human Resources Management Towards Customer Satisfaction

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1.- The paper states, "Given that the overall predictable power (R²) for most endogenous variables is fairly low (10–20%)," which implies that, due to its low predictive power, the model is not adequately explaining the phenomenon.

2.- A linear regression model can explain correlation, but not causality. Evidence must be provided to support the existence of a causal relationship between the variables.

Author Response

We appreciate the careful review and constructive suggestions provided by reviewers. Comments and suggestions from all reviewers were of high importance. It is our belief that the manuscript is substantially improved after making the suggested edits.

In the following section, we addressed the reviewers’ comments. Each reviewer’s comment is followed by the author’s response. The changes to our manuscript within the document are highlighted in red colored text.

Thank you very much for the Reviewers’ participation and provided support.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

It is meaningful to study the relationship between human resource management and customer satisfaction, but it is necessary to think about and improve these issues: 1. Whether the holistic approach is suitable for the research method of this paper, and how the paper should be an SEM method, how to reflect the concept of Holistic; 2. There are several different types of supply chains from sample sources, the industry occupies 186, and what is the role of other places here? 3. The paper can also enhance the completeness of the analysis of managerial behavior.

Author Response

We appreciate the careful review and constructive suggestions provided by reviewers. Comments and suggestions from all reviewers were of high importance. It is our belief that the manuscript is substantially improved after making the suggested edits.

In the following section, we addressed the reviewers’ comments. Each reviewer’s comment is followed by the author’s response. The changes to our manuscript within the document are highlighted in red colored text.

Thank you very much for the Reviewers’ participation and provided support.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

This paper addresses a timely and relevant topic, proposing a model that links human resources management (HRM) with customer satisfaction through intermediary value chain actors under the Industry 5.0 paradigm. The manuscript is well-structured and demonstrates good methodological awareness, especially with its use of PLS-SEM and confirmatory factor analysis. The clarity of objectives and logical flow from conceptual framework to empirical validation are commendable. However, to meet the standards of a high-impact publication, the paper requires major revisions.

The introduction section of the manuscript provides a broad overview of Industry 5.0, emphasizing its human-centric, sustainable, and resilient dimensions (lines 30–34), and outlines the transition from technology-focused paradigms to those prioritizing human involvement, such as the concepts of Resilient Operator 5.0 and Consumer 5.0 (lines 39–55). While the contextual background is adequately introduced, the section would benefit from a more focused articulation of the research gap. Currently, the novelty of the proposed model—linking human resources management (HR) to customer satisfaction via intermediate value chain components—is implied but not clearly contrasted with existing studies. For instance, the manuscript references that "the literature recognizes both Resilient Operator 5.0 and Consumer 5.0 terms, but does not examine their relation" (line 54), which could be more explicitly developed into a critical gap statement. Additionally, the research questions (RQ1 and RQ2, lines 59–61) are relevant but would be stronger if framed to reflect the mediating role of organizational processes, suppliers, inventory, and delivery. To enhance academic rigor, the authors should better integrate existing literature with their conceptual aims and clarify how their study extends previous work, such as those discussed later in the paper (e.g., [59–61]).

The literature review section offers a structured examination of human-centric engineering, production organization and management, and supply chain management, aligning with the six constructs analyzed in the empirical model. The discussion of Industry 5.0 and Society 5.0 (lines 77–83) effectively frames the shift toward human-centricity, emphasizing collaborative robotics, personalization, and the integration of customer needs into value creation (lines 93–122). However, the review remains largely descriptive and would benefit from a more critical synthesis of existing research. For example, while relevant concepts such as Education 5.0 (lines 101–105) and Consumer 5.0 (lines 117–121) are introduced, their direct connection to the model’s constructs—particularly the mechanisms through which HR practices influence downstream outcomes—are insufficiently interrogated. Similarly, while digital technologies and continuous improvement practices are reviewed in the context of Industry 5.0 (lines 130–158), the literature does not explicitly position the proposed study within known theoretical debates or empirical gaps. The authors could strengthen this section by clarifying where the proposed model builds upon, diverges from, or fills voids in prior work (e.g., [40], [42], [43], [61]).

The methodology section outlines a clear and systematic approach to instrument development, data collection, and modeling, reflecting appropriate alignment with the study’s exploratory aims. The use of previously validated constructs from literature to operationalize the six dimensions (HR, PRC, INV, SUP, DEL, CUS) enhances the content validity of the questionnaire (lines 217–220), and the application of a five-point Likert scale is consistent with standard practices in organizational research (line 225). Moreover, the study’s sampling strategy—targeting 240 organizations across various industry sectors—provides a broad empirical base (lines 232–235). However, the rationale for choosing a variance-based method (PLS-SEM) over a covariance-based approach requires further elaboration. Although the authors mention a lack of empirical evidence as a motive for an exploratory method (lines 244–245), it would strengthen the methodological justification to explicitly relate this choice to specific model characteristics, such as formative constructs, sample size, or the predictive nature of the analysis, as recommended by Hair et al. (reference [56]). Additionally, while the study employs appropriate validation techniques such as confirmatory factor analysis and reliability indices (lines 250–256), more detail on the pilot testing process and sample characteristics (e.g., firm size, geographic scope, response rate) would increase transparency and reproducibility.

The modelling results section presents a comprehensive analysis of both the measurement and structural components of the proposed model using Partial Least Squares Structural Equation Modeling (PLS-SEM). The authors report confirmatory factor analysis (CFA) metrics, including Cronbach’s alpha, Dillon-Goldstein’s ρ, AVE, and outer loadings, all of which indicate strong construct reliability and validity (Table 3, lines 259–260). This strengthens confidence in the measurement model. The structural model results (Table 4, lines 274–275) show statistically significant direct relationships among key constructs—particularly from HR to PRC, and from PRC to SUP, DEL, and INV—as well as indirect effects that support the hypothesized value chain interdependencies (Table 5, lines 276–279). However, while the significance of paths is well established, the overall explanatory power of the model is relatively weak, with low R² values for most endogenous constructs (lines 264–265). This limitation is acknowledged but insufficiently discussed; the authors should offer a more detailed interpretation of what these low R² values imply for the practical utility and generalizability of the model. Additionally, the visual representation in Figure 1 (line 280) is helpful, but it would be beneficial to include standardized path coefficients and R² values directly in the figure for clarity. Overall, while the results are statistically robust, their theoretical and practical implications could be more deeply explored, particularly regarding the relatively weak indirect effects (e.g., HR → CUS = 0.0470) and how these align with or deviate from existing literature.

The discussion section effectively restates the main findings of the structural model, emphasizing the interdependencies among value chain elements and the indirect influence of human resources management (HR) on customer satisfaction (CUS) through intermediate constructs such as processes (PRC), inventory (INV), suppliers (SUP), and delivery (DEL) (lines 284–304). The authors provide a coherent narrative around the cascading effects within the value chain, highlighting that every element must perform adequately to ensure overall customer satisfaction. However, the discussion tends to reiterate empirical results without sufficiently linking them back to the theoretical foundations or critically engaging with existing literature. Although previous studies are mentioned (e.g., [59]–[61], lines 308–316), these references are briefly acknowledged rather than systematically compared with the current findings. A more thorough reflection on how the model advances, contradicts, or confirms prior conceptualizations would enhance the scholarly depth of this section. Additionally, the managerial implications of these relationships are underdeveloped; the discussion could be strengthened by outlining specific strategies firms might adopt based on the identified linkages. Finally, the statement that constructs are "independent but affect each other’s performance" (line 304) warrants further elaboration, especially in the context of system dynamics or complex adaptive systems literature, to better position the study within broader theoretical discourses.

The conclusion section provides a concise summary of the study's key findings, reiterating that human resources management (HR) indirectly influences customer satisfaction (CUS) through a sequence of interrelated value chain elements—namely processes (PRC), inventory (INV), suppliers (SUP), and delivery (DEL) (lines 326–337). The authors restate the answers to the research questions and assert the significance of Industry 5.0’s human-centric paradigm in shaping value chain performance (lines 338–345). While these points are consistent with the study’s objectives, the conclusion primarily recapitulates earlier content without offering deeper theoretical reflection or new insights. Theoretical contributions are presented in general terms and would benefit from clearer articulation, particularly in terms of how this model extends or refines existing frameworks in HRM, supply chain, or Industry 5.0 research. Similarly, practical implications are mentioned briefly (lines 346–350) but lack specificity regarding how managers can apply the findings to improve organizational performance. The stated limitation—that external contextual factors are not considered (lines 352–354)—is valid, but future research directions could be more ambitious by proposing interdisciplinary or sector-specific extensions, comparative models, or longitudinal approaches.

Author Response

We appreciate the careful review and constructive suggestions provided by reviewers. Comments and suggestions from all reviewers were of high importance. It is our belief that the manuscript is substantially improved after making the suggested edits.

In the following section, we addressed the reviewers’ comments. Each reviewer’s comment is followed by the author’s response. The changes to our manuscript within the document are highlighted in red colored text.

Thank you very much for the Reviewers’ participation and provided support.

I look forward to receiving your answer on the revised manuscript.

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

The manuscript sets out to model the links between human-resources practices and customer satisfaction through a chain of intermediate constructs. Using a cross-sectional questionnaire survey of 240 organizations and variance-based PLS-SEM, the authors report statistically significant paths and argue that Industry 5.0’s “human-centricity” should be embedded across the value chain. Please try to address the following issues to improve:

1. The paper largely reiterates established concepts of HRM, lean/Industry 4.0 and customer satisfaction, offering no genuinely new theory or integrative framework beyond a linear chain of well-known constructs.The finding that value chain elements are interconnected is not novel. The paper does not extend theory or provide substantial practical insights beyond what is already known about value chain management.
2. The paper inadequately defines key constructs such as HR, PRC, INV, SUP, DEL, and CUS. While parameters are listed, there is no robust theoretical grounding or justification for why these specific measures were chosen.
3. The manuscript does not provide the full questionnaire or the exact wording of the measurement items used for each construct (e.g., HR, PRC, CUS), nor does it report how these items were adapted or validated from prior research. Without this transparency, it is difficult to assess content validity or judge whether the items accurately capture the intended constructs. Including an appendix with the survey instrument and discussing item development and adaptation would greatly improve methodological clarity.
4. All variableslike predictors and outcomes come from the same respondent at one point in time, inflating path coefficients and threatening internal validity.
5. The sample is heavily skewed toward manufacturing firms (77.5% from the industrial sector), limiting generalizability to other sectors. The article does not justify this focus or address how findings might apply to non-manufacturing contexts.
6. The choice of a variance-based method over a covariance-based approach is justified only by the study’s exploratory nature, without discussing trade-offs or providing evidence that this method is optimal for the research questions.
7. The structural model shows low R2 values (10-20%) for most endogenous variables, indicating weak explanatory power. The article acknowledges this but does not sufficiently address its implications or propose ways to improve the model.

Author Response

We appreciate the careful review and constructive suggestions provided by reviewers. Comments and suggestions from all reviewers were of high importance. It is our belief that the manuscript is substantially improved after making the suggested edits.

In the following section, we addressed the reviewers’ comments. Each reviewer’s comment is followed by the author’s response. The changes to our manuscript within the document are highlighted in red colored text.

Thank you very much for the Reviewers’ participation and provided support.

I look forward to receiving your answer on the revised manuscript.

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

The authors have thoroughly and thoughtfully addressed all the comments and suggestions provided in the initial review. The revised manuscript demonstrates significant improvements in the articulation of the research gap, theoretical grounding, methodological justification, and integration of literature. The refinement of the research questions, enhanced clarity in the conceptual model, and expanded discussion of the model’s implications contribute to a stronger and more coherent scholarly contribution. Overall, I am satisfied with the revisions made and find the manuscript suitable for publication in its current form. I wish the authors continued success in their research and good luck with the further stages of the review process.

Author Response

Dear reviewer,

Thank you for your kind words.

Best regards!

Reviewer 4 Report

Comments and Suggestions for Authors

This paper has been revised accordingly and can be considered for publication. 

Author Response

Dear reviewer,

Thank you for your kind words.

Best regards!