Digitalization and Firm Value: The Evidence from China’s Manufacturing Enterprises

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Here there are suggested Improvements to the Methodology

Clarify the Sampling Strategy:

• The study does not clearly explain how firms were selected for the sample or whether specific selection criteria were used to ensure a representative dataset of China’s manufacturing sector.
• It would be beneficial to include a sample breakdown in terms of size, industry distribution, and other characteristics to enhance credibility.

Address Data Outliers and Variability:

• The study does not discuss how outliers or extreme values were handled, which can significantly impact the accuracy of results.
• It is recommended to apply data-cleaning techniques such as outlier analysis or imputation methods for missing values to ensure robust findings.

Conduct Robustness and Reliability Tests:

• No robustness checks were performed to assess whether the findings remain consistent under different conditions.
• Sensitivity analysis should be used to test the stability of results by adjusting key parameters or reanalyzing data with a different sample.

Compare Results with Alternative Analytical Models:

• While fsQCA is a strong methodological approach, it would be useful to compare findings with other analytical techniques, such as multiple regression analysis or econometric modeling to verify consistency.

Ensure Generalizability of Findings:

• The study does not discuss whether the results can be generalized to other industries or geographic regions.
• A comparative analysis between firms in different sectors or regions could help confirm the broader applicability of the conclusions.

Additional Controls to Consider

Control for External Factors:

• The study should incorporate macro-economic indicators such as market volatility, regulatory changes, and technological disruptions as control variables, as these factors may influence the relationship between digitalization and firm value.

Incorporate a Dynamic Time-Series Analysis:

• Instead of focusing on a single period, analyzing digitalization trends over multiple years would provide a clearer picture of whether its impact is gradual or immediate.

Include a Comparison between Digital and Non-Digital Firms:

• To validate whether digital transformation has a true causal effect, the study should compare firms that have adopted digitalization with those that have not yet transitioned, ensuring that differences in value are truly due to digital adoption.

By implementing these methodological refinements and controls, the study will become more rigorous, enhancing its scientific and practical impact.

Comments for author File: Comments.pdf

Author Response

Response letter 1

Comment 1: The study does not clearly explain how firms were selected for the sample or whether specific selection criteria were used to ensure a representative dataset of China’s manufacturing sector.

Response 1: We agree that the previous description of sample selection was unclear, and we have rewritten it in detail on page 4, paragraph 5:

“The sample selection criteria were as follows: (1) Excluding automotive manufacturing enterprises listed after 2020, based on their post-transformation market valuation. (2) Eliminating enterprises that were delisted during the study period. (3) Excluding enterprises that underwent significant changes in their core business operations. (4) Removing enterprises whose annual reports did not disclose digital transformation-related information or exhibited abnormal data. (5) Excluding enterprise samples with missing or anomalous key data.”

Comment 2: It would be beneficial to include a sample breakdown in terms of size, industry distribution, and other characteristics to enhance credibility.

Response 2: we have revised accordingly, where the changes can be found in page 6, Table 1.

Table 1. Digital transformation status of sample enterprises.

Enterprises	Category	Establishment	Transformation	Digtal transformation overview
GEELY	Labor-technology-intensive automotive manufacturing	1986	2004	Geely has completed its entire digital transformation from the 1.0 era of comprehensive informatization and process reengineering, to the 2.0 era of deep SAP application in research and development, manufacturing, and marketing, and finally to the 3.0 era of comprehensive empowerment through the Internet of Things, cloud computing, and big data. Digital technology has become an important support for operations, manufacturing, and services, and the results of digital transformation are remarkable.
BAIC	Capital-intensive automotive manufacturing	1958	2020	Develop a three-step plan for digital transformation, collaborate with JD.com and Huawei to drive this transformation, and achieve certain results in digitizing industrial platforms, intelligent manufacturing, operations, marketing, services, and supply chains, among other areas.
BYD	Technology-intensive automotive manufacturing	1995	2011	A recent, medium-term, and long-term digital transformation plan has been formulated, a digital committee has been established, and digitization has been achieved in smart manufacturing, supply chains, sales, and other areas. The digital transformation has yielded good results.

 

Comment 3: The study does not discuss how outliers or extreme values were handled, which can significantly impact the accuracy of results.

It is recommended to apply data-cleaning techniques such as outlier analysis or imputation methods for missing values to ensure robust findings.

Response 3: we agree that we should detail the processing of outliers to enhance the clarity. The changes can be found in page 4, paragraph 5 and line 189:

“Furthermore, to mitigate the potential influence of outliers, all continuous variables in the econometric testing design were winsorized at the 1% and 99% levels.”

Comment 4: No robustness checks were performed to assess whether the findings remain consistent under different conditions. Sensitivity analysis should be used to test the stability of results by adjusting key parameters or reanalyzing data with a different sample.

Response 4: We thank you for this important observation. To address this concern, we have conducted additional robustness checks and sensitivity analyses to ensure the stability and reliability of our findings. Specifically, we have implemented the robustness check in section 5.4.:

“5.4. Robustness

5.4.1. Changing anchor points and adjusting thresholds

In quantitative analysis, robustness is of paramount importance. According to the steps in the QCA methodology, adjustments in sample selection, condition measurement, calibration, and threshold analysis (such as case frequency, PRI consistency, raw consistency, and consistency thresholds for frequency) can all affect the number of sufficient conditions for configuration analysis, the relationships among configuration sets, and related parameters. Therefore, to determine whether significant changes occur in the aforementioned indicators under different operational choices and to ensure the reliability of the research conclusions, it is necessary to test the robustness of the sample enterprises. To this end, this study examines the robustness of the 355 automotive manufacturing enterprise samples by altering calibration anchor points, adjusting analysis thresholds, and conducting endogeneity tests. The configuration results formed by the four calibration methods show no significant differences or substantive changes compared to the baseline regression model, and the adjusted parameters do not yield superior results, indicating that the baseline model is robust (see Table 7 for results).

Using two methods—changing anchor points and adjusting thresholds—the robustness of the baseline regression model was tested. The results show that threshold adjustments do not alter the overall solution coverage and consistency of configurations leading to high enterprise value enhancement. However, the coverage and consistency of configurations resulting in non-high enterprise value enhancement decline, failing to meet expectations. The configuration condition analysis results under altered anchor points and threshold adjustments do not reach the baseline of this study, and adjustments to related parameters do not improve the configuration results, though no substantive changes are observed. Therefore, the conditional configurations in this study exhibit robustness, indicating that the empirical results are highly reliable and consistent with the core conclusions.

5.4.2. Multiple regression analysis

Furthermore, to mitigate potential biases in the econometric tests that may arise from omitting important variables, this study also incorporates and controls for several potential factors that could influence the enhancement of manufacturing enterprise value. These include macroeconomic indicators such as market volatility, regulatory changes, and technological disruptions, as well as year fixed effects and industry fixed effects. The relationship model between digital transformation and enterprise value is specified as follows:

To account for unobservable macroeconomic factors and industry-specific characteristics that could affect the regression results, year fixed effects and industry fixed effects are included in the control variables.

The baseline model regression results, and the regression results incorporating control variables are shown in Table 8. The results indicate that, prior to including control variables, the regression coefficient between digital transformation and enterprise value enhancement is 0.059, with a test statistic of 5.197, passing the 1% significance test. After incorporating control variables, the regression coefficient between digital transformation and enterprise value enhancement in manufacturing enterprises increases to 0.132, with a test statistic of 11.941, also passing the 1% significance test. Additionally, the regression coefficients for year fixed effects and industry fixed effects are 0.047 (test statistic 6.704) and 0.039 (test statistic 5.021), respectively. This suggests that the enhancement of enterprise value in manufacturing enterprises follows a gradual, time-series progression and exhibits industry-specific variations. 

These test results validate the core conclusion that digital transformation alone is not a necessary condition for enhancing enterprise value. They also demonstrate that during the process of digital transformation, enterprises are more susceptible to macroeconomic indicators such as market volatility, regulatory changes, and technological disruptions, which can create positive anticipatory effects and significantly contribute to enterprise value enhancement. Thus, it can be concluded that a higher degree of digital transformation in manufacturing enterprises is more conducive to enhancing enterprise value, although it is not the sole necessary factor. These findings are consistent with the core conclusions of the study.”

Comment 5: While fsQCA is a strong methodological approach, it would be useful to compare findings with other analytical techniques, such as multiple regression analysis or econometric modeling to verify consistency.

Response 5: thank you for pointing this out. We have added the multiple regression analysis in section 5.4.2. to improve the robustness of the main results:

“The baseline model regression results, and the regression results incorporating control variables are shown in Table 8. The results indicate that, prior to including control variables, the regression coefficient between digital transformation and enterprise value enhancement is 0.059, with a test statistic of 5.197, passing the 1% significance test. After incorporating control variables, the regression coefficient between digital transformation and enterprise value enhancement in manufacturing enterprises increases to 0.132, with a test statistic of 11.941, also passing the 1% significance test. Additionally, the regression coefficients for year fixed effects and industry fixed effects are 0.047 (test statistic 6.704) and 0.039 (test statistic 5.021), respectively. This suggests that the enhancement of enterprise value in manufacturing enterprises follows a gradual, time-series progression and exhibits industry-specific variations. 

These test results validate the core conclusion that digital transformation alone is not a necessary condition for enhancing enterprise value. They also demonstrate that during the process of digital transformation, enterprises are more susceptible to macroeconomic indicators such as market volatility, regulatory changes, and technological disruptions, which can create positive anticipatory effects and significantly contribute to enterprise value enhancement. Thus, it can be concluded that a higher degree of digital transformation in manufacturing enterprises is more conducive to enhancing enterprise value, although it is not the sole necessary factor. These findings are consistent with the core conclusions of the study.”

Table 8. Baseline regression of digital transformation and manufacturing enterprise value enhancement.

Variables	Baseline regression coefficient	Baseline regression coefficient including control variables
Digital Transformation	0.059（5.197）*	0.132（11.941）*
R&D Digitalization	2.501（20.441）***	2.712（21.107）***
Marketing Digitalization	2.035（2.792）**	0.461（2.150）*
Middleware Digitalization	0.432（2.033）*	2.117（2.560）**
Environmental Digitalization	0.514（2.063）*	0.528（2.227）*
Service Digitalization	2.307（19.781）*	2.511（18.953）*
Year Fixed Effects	-	0.047（6.704）
Industry Fixed Effects	-	0.039（5.021）
Regulatory Changes	-	0.055（3.951）
Market Volatility	-	0.107（8.220）
Technological Disruption	-	0.359（13.058）

Note: Figures in parentheses represent t-test values. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

 

Comment 6: The study does not discuss whether the results can be generalized to other industries or geographic regions.

Response 6: We agree that we should add discussion on the generalization of our findings, which is detailed in section 5.6.:

“5.6. Generalization analysis by industry attributes

5.6.1. Labor-intensive manufacturing

The heterogeneity analysis of labor-intensive enterprises reveals that among the 28 samples, there is only one feasible path for configurations that enhance enterprise value in the high-value enhancement group: the combination of R&D digitalization, marketing digitalization, and service digitalization. The overall configuration consistency is 0.941, covering 21.4% of the sample. The presence of middleware digitalization and R&D digitalization as core antecedent variables is a critical condition for driving high-value enhancement in manufacturing enterprises. The absence of environmental digitalization has a minimal impact on the high-value enhancement of complete vehicle manufacturing enterprises.

In the non-high-value enhancement group, there are eight configurations leading to value enhancement, with an overall solution consistency of 0.871 and a sample coverage rate of 73.9% (configuration analysis table omitted). Longitudinally, the B1 configuration has the highest coverage at 49.3%, indicating that the absence of marketing digitalization and service digitalization are key factors affecting value enhancement in labor-intensive manufacturing enterprises. Horizontally, neither environmental digitalization nor middleware digitalization can reverse the state of non-high-value enhancement. The lack of R&D digitalization, service digitalization, and environmental digitalization are the primary reasons for non-high-value enhancement. R&D digitalization and service digitalization are critical for improving the marginal product of labor and overcoming demographic dividend constraints.

5.6.2. Capital-intensive manufacturing

There are two configurations driving high-value enhancement in capital-intensive enterprises (table omitted), with an overall solution consistency of 0.885 and a sample coverage rate of 34.9%. These configurations are: (1) middleware digitalization and service digitalization, and (2) service digitalization, environmental digitalization, and R&D digitalization. Among these, the core elements are R&D digitalization, service digitalization, middleware digitalization, and environmental digitalization. Middle-ware digitalization and service digitalization can complement each other, while R&D digitalization plays a more prominent role in enhancing the value of capital-intensive enterprises. 

In the non-high-value enhancement group for capital-intensive enterprises, there are eight possible configurations, with an overall solution consistency of 0.891 and a sample coverage rate of 61.7%. The absence of R&D digitalization and service digitalization, despite the presence of marketing digitalization and service digitalization as core elements, can still lead to non-high-value enhancement. The coverage rates for configurations lacking service digitalization and R&D digitalization are 78.1% and 48.3%, respectively, indicating their critical influence. Capital-intensive enterprises may possess one of the core capabilities—marketing digitalization, service digitalization, or middleware digitalization—but the absence of other core elements can still result in non-high-value enhancement.

This is because the role of leadership talent in enhancing enterprise value is more pronounced in capital-intensive enterprises than in labor-intensive or technology-intensive enterprises. The revenue of capital-intensive enterprises is driven by capital investment, and the direction of capital allocation is influenced by the abilities and preferences of leaders. Insufficient or short-sighted leadership can lead to significant crises and risks for technology-intensive enterprises, stagnating their development and hindering value enhancement.

5.6.3. Technology-intensive manufacturing

Among the technology-intensive configurations, there are three configurations driving high-value enhancement, with an overall solution consistency of 0.895 and a sample coverage rate of 50.7%. These three configurations share high similarity and are complemented by the presence or absence of other peripheral elements. Marketing digitalization plays a supportive role, while the absence of environmental digitalization does not hinder value enhancement.

In the non-high-value enhancement group for technology-intensive enterprises, there is only one configuration leading to non-high-value enhancement, with an over-all solution consistency of 0.861 and a sample coverage rate of 25.5%. This configuration is characterized by the absence of service digitalization, middleware digitalization, and R&D digitalization, combined with the presence of other peripheral elements. Technology-intensive enterprises are characterized by high product technology and knowledge content and significant R&D investment. If key technological aspects fall behind, it can constrain the enterprise's development. When external environments change rapidly and competitive challenges intensify, technology-intensive enterprises are the first to be affected. The lack of key core elements can prevent these enterprises from overcoming critical bottlenecks.

The analysis of manufacturing enterprises by industry attributes reveals strong heterogeneity among technology-intensive, labor-intensive, and capital-intensive enterprises. This indicates that the core conclusions of this study exhibit significant heterogeneity and generalizability.

 

Comment 7: A comparative analysis between firms in different sectors or regions could help confirm the broader applicability of the conclusions.

Response 7: we appreciate the comment on adding a comparative analysis between firms in different sectors. We have revised accordingly in Section 5.5.:

“5.5. Heterogeneity analysis

In the configurational analysis of sufficient conditions, the overall solution consistency of various configurations is 0.875, and the overall solution coverage is only 35.9%. It is necessary to explore whether heterogeneity among enterprises has weakened its coverage or whether group characteristics have caused differentiation in results. Therefore, the sample enterprises are classified according to the automotive industry report classification, dividing automotive manufacturing enterprises into full-vehicle manufacturing, parts manufacturing, and related industry manufacturing enterprises. Among them, there are 21 full-vehicle manufacturing enterprises (34.42%), 29 parts manufacturing enterprises (47.54%), and 11 related industry manufacturing enterprises (18.03%), indicating a significant heterogeneity in characteristics.

The necessity test results show that the consistency of conditional variables in both high and non-high enterprise value enhancement is less than 0.9, indicating that they are not necessary. According to the configurational thresholds set earlier, the calibration anchor points are set at 0.9, 0.5, and 0.1. The original consistency of the configurational conditions is set at 0.8, and the frequency threshold is set at 3 (note: the value "0.6" mentioned in the original text seems out of context here and is omitted for clarity).

5.5.1. Full-vehicle manufacturing enterprises

The consistency analysis results for full-vehicle manufacturing enterprises show that there is only one path for configurations leading to high enterprise value enhancement, which includes R&D digitization, marketing digitization, and service digitization. The overall solution consistency for this configuration is 0.927, with a sample coverage rate of 21.1%. The presence of core antecedent variables such as environmental digitization, middleware digitization, and R&D digitization are key conditions for driving high enterprise value enhancement in full-vehicle manufacturing enterprises. The absence of environmental digitization has a relatively small impact on high enterprise value enhancement in these enterprises.

The test results in Table 7 for non-high enterprise value enhancement show that there are seven configurational conditions affecting non-high enterprise value enhancement in full-vehicle manufacturing enterprises, with an overall solution consistency of 0.857 and a sample coverage rate of 71.8%. Configuration B1 has the highest coverage rate of 47.9%, which is due to the absence of service digitization and impact digitization. Configurations B5 and B6 explain the impact of the absence of service digitization and marketing digitization capabilities on enhancing the value of full-vehicle manufacturing enterprises. Horizontally, the presence of environmental digitization and middleware digitization cannot ensure the reversal of non-high enterprise value enhancement status. The absence of service digitization, R&D digitization, and environmental digitization are important factors contributing to non-high enterprise value enhancement in full-vehicle manufacturing enterprises.

5.5.2. Parts manufacturing enterprises

The configurational analysis of parts manufacturing enterprises as shown in Table 7 shows that there are two configurations for high enterprise value enhancement in this type of enterprise, with an overall solution consistency of 0.889 and a sample coverage rate of 36.1%. These configurations are middleware digitization and service digitization, as well as service digitization, environmental digitization, and R&D digitization. Service digitization, environmental digitization, R&D digitization, and middleware digitization are key factors for high enterprise value enhancement in parts manufacturing enterprises. However, there is complementarity between environmental digitization and middleware digitization, and they have a greater impact on high enterprise value enhancement in parts manufacturing enterprises.

There are six configurational conditions in the analysis of configurations driving non-high enterprise value enhancement in parts manufacturing enterprises, with an overall solution consistency of 0.877 and a sample coverage rate of 60.9%. Among them, the absence of R&D digitization and service digitization, although parts manufacturing enterprises possess service digitization and environmental digitization elements, the absence of middleware digitization and R&D digitization remains a key factor affecting their non-high enterprise value enhancement. The configurational coverage of the absence of service digitization capabilities reaches 79%, and the configurational coverage of the absence of R&D digitization capabilities is 49%. Both are core factors affecting non-high enterprise value enhancement in parts manufacturing enterprises. If an enterprise possesses one of the core capabilities such as service digitization, marketing digitization, or middleware digitization but lacks other elements, it will still result in non-high enterprise value enhancement.

5.5.3. Related industry enterprises

The configurational analysis of related industry enterprises shows that there are two configurations driving high enterprise value enhancement, namely marketing digitization and R&D digitization, with an overall solution consistency of 0.889 and a sample coverage rate of 50.9%. Overall, the two configurations driving high enterprise value enhancement have high similarity, and marketing digitization plays only an auxiliary role when matched with the presence or absence of other elements. Partially, the marginal absence of environmental digitization has little impact on high enterprise value enhancement.

There is only one configuration for non-high enterprise value enhancement in related industry manufacturing enterprises, with a consistency of 0.847 and a sample coverage rate of 26.1%. This is mainly due to the absence of middleware digitization, service digitization, and R&D digitization, matched with the presence of other marginal elements.”

Comment 8: The study should incorporate macro-economic indicators such as market volatility, regulatory changes, and technological disruptions as control variables, as these factors may influence the relationship between digitalization and firm value.

Response 8: To address this concern, we have incorporated macro-economic indicators in our control variables in page 14:

“The baseline model regression results, and the regression results incorporating control variables are shown in Table 8. The results indicate that, prior to including control variables, the regression coefficient between digital transformation and enterprise value enhancement is 0.059, with a test statistic of 5.197, passing the 1% significance test. After incorporating control variables, the regression coefficient between digital transformation and enterprise value enhancement in manufacturing enterprises increases to 0.132, with a test statistic of 11.941, also passing the 1% significance test. Additionally, the regression coefficients for year fixed effects and industry fixed effects are 0.047 (test statistic 6.704) and 0.039 (test statistic 5.021), respectively. This suggests that the enhancement of enterprise value in manufacturing enterprises follows a gradual, time-series progression and exhibits industry-specific variations.

These test results validate the core conclusion that digital transformation alone is not a necessary condition for enhancing enterprise value. They also demonstrate that during the process of digital transformation, enterprises are more susceptible to macroeconomic indicators such as market volatility, regulatory changes, and technological disruptions, which can create positive anticipatory effects and significantly contribute to enterprise value enhancement. Thus, it can be concluded that a higher degree of digital transformation in manufacturing enterprises is more conducive to enhancing enterprise value, although it is not the sole necessary factor. These findings are consistent with the core conclusions of the study.”

Table 3. Descriptive statistics and calibration values of variables.

Dimension	Descriptive Statistics				Membership Value
	Mean	Min	Max	S.D.	0.1	0.5	0.9
Service Digitization	12.311	0.991	179.500	19.961	1.990	6.894	22.907
Environment Digitization	12.883	0.000	132.655	15.645	0.000	8.436	27.229
Marketing Digitization	29.952	1.185	79.011	7.186	19.825	27.558	37.862
R&D Digitization	53.961	0.003	2070.211	110.751	14.972	33.955	93.864
Middleware Digitization	7.977	0.031	15.983	4.497	1.918	8.175	15.046
Increase in Corporate Value	534.851	13.001	51013.044	2839.994	24.971	95.442	1830.002
Market Volatility	6.752	0.041	12.310	3.117	1.724	2.391	6.014
Regulatory Changes	7.251	0.107	13.021	4.306	2.004	3.115	5.933
Technological Disruption	12.956	0.022	17.588	3.961	4.025	5.007	7.182
Year Fixed Effects	5.891	0.017	7.551	5.211	2.682	4.033	4.601
Industry Fixed Effects	3.971	0.058	6.927	4.007	2.079	4.118	5.924

 

Comment 9: Instead of focusing on a single period, analyzing digitalization trends over multiple years would provide a clearer picture of whether its impact is gradual or immediate.

Response 9: We thank you for this valuable suggestion. To address this concern, we have expanded our analysis to examine digitalization trends and their impact on firm value over a multi-year period. Specifically, we have implemented the following revisions in page 14:

“Additionally, the regression coefficients for year fixed effects and industry fixed effects are 0.047 (test statistic 6.704) and 0.039 (test statistic 5.021), respectively. This suggests that the enhancement of enterprise value in manufacturing enterprises follows a gradual, time-series progression and exhibits industry-specific variations.”

Comment 10: To validate whether digital transformation has a true causal effect, the study should compare firms that have adopted digitalization with those that have not yet transitioned, ensuring that differences in value are truly due to digital adoption.

Response 10: We thank you for this important observation. While our original study focused exclusively on firms that have undergone digital transformation, we acknowledge that comparing these firms with non-digital firms would strengthen the causal inference of our findings. However, due to the scope and design of our study, we did not include non-digital firms in the analysis. To address this limitation, we have revised the discussion section to explicitly acknowledge the lack of a control group (non-digital firms) and its implications for interpreting the results. We have also highlighted this as a key area for future research, suggesting that subsequent studies could employ a quasi-experimental design or propensity score matching to compare digital and non-digital firms. This approach would help isolate the causal effect of digital transformation on firm value.

While we were unable to incorporate this analysis into the current manuscript due to data and time constraints, we believe this limitation does not detract from the study’s contributions. Our findings still provide valuable insights into the pathways and mechanisms through which digital transformation influences firm value, particularly within the context of China’s manufacturing sector.

Comment 11: No discussion of geographical limitations and their impact on the generalizability of the results. Clarify geographical constraints and their impact on generalizability. No mention of the applicability of findings to other industries. Discuss whether the findings can be applied to other industries.

Response 11: We appreciate the suggestion to address the applicability of our findings to other industries. We have added the generalization in section 5.6.:

“5.6. Generalization analysis by industry attributes

5.6.1. Labor-intensive manufacturing

The heterogeneity analysis of labor-intensive enterprises reveals that among the 28 samples, there is only one feasible path for configurations that enhance enterprise value in the high-value enhancement group: the combination of R&D digitalization, marketing digitalization, and service digitalization. The overall configuration consistency is 0.941, covering 21.4% of the sample. The presence of middleware digitalization and R&D digitalization as core antecedent variables is a critical condition for driving high-value enhancement in manufacturing enterprises. The absence of environmental digitalization has a minimal impact on the high-value enhancement of complete vehicle manufacturing enterprises.

In the non-high-value enhancement group, there are eight configurations leading to value enhancement, with an overall solution consistency of 0.871 and a sample coverage rate of 73.9% (configuration analysis table omitted). Longitudinally, the B1 configuration has the highest coverage at 49.3%, indicating that the absence of marketing digitalization and service digitalization are key factors affecting value enhancement in labor-intensive manufacturing enterprises. Horizontally, neither environmental digitalization nor middleware digitalization can reverse the state of non-high-value enhancement. The lack of R&D digitalization, service digitalization, and environmental digitalization are the primary reasons for non-high-value enhancement. R&D digitalization and service digitalization are critical for improving the marginal product of labor and overcoming demographic dividend constraints.

5.6.2. Capital-intensive manufacturing

There are two configurations driving high-value enhancement in capital-intensive enterprises (table omitted), with an overall solution consistency of 0.885 and a sample coverage rate of 34.9%. These configurations are: (1) middleware digitalization and service digitalization, and (2) service digitalization, environmental digitalization, and R&D digitalization. Among these, the core elements are R&D digitalization, service digitalization, middleware digitalization, and environmental digitalization. Middle-ware digitalization and service digitalization can complement each other, while R&D digitalization plays a more prominent role in enhancing the value of capital-intensive enterprises.

In the non-high-value enhancement group for capital-intensive enterprises, there are eight possible configurations, with an overall solution consistency of 0.891 and a sample coverage rate of 61.7%. The absence of R&D digitalization and service digitalization, despite the presence of marketing digitalization and service digitalization as core elements, can still lead to non-high-value enhancement. The coverage rates for configurations lacking service digitalization and R&D digitalization are 78.1% and 48.3%, respectively, indicating their critical influence. Capital-intensive enterprises may possess one of the core capabilities—marketing digitalization, service digitalization, or middleware digitalization—but the absence of other core elements can still result in non-high-value enhancement.

This is because the role of leadership talent in enhancing enterprise value is more pronounced in capital-intensive enterprises than in labor-intensive or technology-intensive enterprises. The revenue of capital-intensive enterprises is driven by capital investment, and the direction of capital allocation is influenced by the abilities and preferences of leaders. Insufficient or short-sighted leadership can lead to significant crises and risks for technology-intensive enterprises, stagnating their development and hindering value enhancement.

5.6.3. Technology-intensive manufacturing

Among the technology-intensive configurations, there are three configurations driving high-value enhancement, with an overall solution consistency of 0.895 and a sample coverage rate of 50.7%. These three configurations share high similarity and are complemented by the presence or absence of other peripheral elements. Marketing digitalization plays a supportive role, while the absence of environmental digitalization does not hinder value enhancement.

In the non-high-value enhancement group for technology-intensive enterprises, there is only one configuration leading to non-high-value enhancement, with an overall solution consistency of 0.861 and a sample coverage rate of 25.5%. This configuration is characterized by the absence of service digitalization, middleware digitalization, and R&D digitalization, combined with the presence of other peripheral elements. Technology-intensive enterprises are characterized by high product technology and knowledge content and significant R&D investment. If key technological aspects fall behind, it can constrain the enterprise's development. When external environments change rapidly and competitive challenges intensify, technology-intensive enterprises are the first to be affected. The lack of key core elements can prevent these enterprises from overcoming critical bottlenecks.

The analysis of manufacturing enterprises by industry attributes reveals strong heterogeneity among technology-intensive, labor-intensive, and capital-intensive enterprises. This indicates that the core conclusions of this study exhibit significant heterogeneity and generalizability.”

Comment 12: Lack of justification for the sample size selection. Provide scientific reasoning for the chosen sample size to enhance credibility.

Response 12: we have detailed the sample selection in page 4, paragraph 5:

“The sample selection criteria were as follows: (1) Excluding automotive manufacturing enterprises listed after 2020, based on their post-transformation market valuation. (2) Eliminating enterprises that were delisted during the study period. (3) Excluding enterprises that underwent significant changes in their core business operations. (4) Removing enterprises whose annual reports did not disclose digital transformation-related information or exhibited abnormal data. (5) Excluding enterprise samples with missing or anomalous key data. Additionally, this study employed Python's web scraping, counting, and text segmentation functions to filter out invalid textual content from the financial reports of listed companies. Based on this, the frequency of all relevant keywords was statistically analyzed to describe the data sources for digital transformation in China's manufacturing enterprises. Furthermore, to mitigate the potential influence of outliers, all continuous variables in the econometric testing design were winsorized at the 1% and 99% levels.”

 

Comment 13: The research gap is not explicitly addressed. Clearly define how the study fills a specific research gap. No comparison with previous studies to clarify the study’s contribution. Provide a brief comparison with prior research to emphasize contributions. No direct mention of the practical impact on businesses. Explain how the findings can be practically applied in businesses.

Response 13: Thank you for your suggestion, we have revised accordingly in page 2, paragraph 5:

“Existing studies have not systematically discussed all relevant factors, nor have they measured the extent to which digital transformation impacts enterprise value. Furthermore, there is a lack of both quantitative and qualitative research on this topic. Therefore, the contributions of this study are as follows: (1) It verifies that a single digital factor cannot independently enhance enterprise value; (2) It enriches and expands the research on the pathways through which digital transformation influences enterprise value; and (3) Few scholars have focused on qualitative research regarding the relationship between digital transformation and enterprise value, and even fewer have explored the interconnected effects of various variables. This study employs fsQCA to investigate the relationships between different digital variables and enterprise value enhancement, thereby deepening the understanding of digital transformation issues. The findings of this study can provide theoretical references and foundations for enterprises in formulating digital transformation strategies, building digital capabilities, and enhancing enterprise value.”

 

Comment 14: Lack of precise quantitative details regarding the outcomes, such as key statistical values. Include specific statistical values to support the findings. No clear mention of the study's contribution compared to previous research. Clarify how this research differs from previous studies to highlight its originality. Absence of challenges or research limitations, which could limit the reader’s understanding of the study's scope. Mention key research challenges to ensure a more comprehensive evaluation.

Response 14: Thank you for the recommendations on the abstract. We have rewritten the abstract:

“In the context of digital transformation, the varying dimensions of digital maturity significantly influence value creation enhancement for enterprises. Optimizing these dimensions to augment corporate value represents an urgent challenge for manufacturing enterprises. This study examines 355 listed automotive manufacturing enterprises (including auto parts and related businesses) through multi-case analysis, grounded theory, and QCA methodology to investigate the intrinsic mechanisms and pathways linking digital transformation with value enhancement in automotive manufacturing. Key findings include: (1) Grounded theory identified service digitalization, environmental digitalization, middleware digitalization, marketing digitalization, and R&D digitalization as critical variables, with enterprise value enhancement requiring multi-dimensional synergies rather than single-factor determinants. (2) Configuration analysis revealed that comprehensive empowerment type (consistency >0.8, coverage 35.9%) drives high-value enhancement, while service-deficiency, R&D-deficiency, and marketing-deficiency configurations characterize non-high-value scenarios. Service, R&D, and marketing digitalization emerge as core value-enhancing competencies (consistency 0.817, coverage 75.9%). (3) Heterogeneous driving forces were observed across vehicle manufacturers, component manufacturers, and related industry manufacturers, though service digitalization constitutes a common value-enhancing element. This research provides theoretical insights into manufacturing digital transformation's value creation mechanisms and strategic implications, addressing current academic gaps. However, the automotive industry focus limits generalizability despite its concrete exploration of industry-specific digital transformation. Future studies should expand industry coverage and conduct comparative analyses to enhance theoretical robustness.”

 

Comment 15: Lack of a sufficient theoretical background on digital transformation and its impact on corporate value. Provide a more detailed theoretical background on digital transformation and corporate value.

Response 15: We agree that we should provide a more detailed theoretical background in the manuscript, and the changes have been made in Section 3:

“This study adopts the grounded theory model to collect relevant data, utilizing interview methods to obtain core data for coding analysis. Concepts are extracted from the raw data and developed in terms of dimensions and attributes. The coding process primarily involves open coding, axial coding, and selective coding, through which core concepts or categories are identified. These are then used to construct a narrative framework, ultimately forming a theoretical system and building a theoretical model. After completing the model construction, the concepts related to each described object are interpreted.

Based on existing literature and the practical digital transformation experiences of automotive manufacturing enterprises, this study examines the impact of digital service, digital experience, digital R&D, digital manufacturing, digital marketing, and digital middleware on the value of manufacturing enterprises. To further explore the intrinsic mechanisms of these elements, this research employs the grounded theory approach to analyze the characteristics, motivations, and implementation pathways of enterprise digital transformation.”

 

Comment 16: No explicit statement of the research gap, making it unclear why this study is necessary. Clearly identify the research gap to emphasize the study’s contribution.

Response 16: we have revised accordingly, and the changes can be found in page 2, paragraph 5:

“Existing studies have not systematically discussed all relevant factors, nor have they measured the extent to which digital transformation impacts enterprise value. Furthermore, there is a lack of both quantitative and qualitative research on this topic. Therefore, the contributions of this study are as follows: (1) It verifies that a single digital factor cannot independently enhance enterprise value; (2) It enriches and expands the research on the pathways through which digital transformation influences enterprise value; and (3) Few scholars have focused on qualitative research regarding the relationship between digital transformation and enterprise value, and even fewer have explored the interconnected effects of various variables. This study employs fsQCA to investigate the relationships between different digital variables and enterprise value enhancement, thereby deepening the understanding of digital transformation issues. The findings of this study can provide theoretical references and foundations for enterprises in formulating digital transformation strategies, building digital capabilities, and enhancing enterprise value.”

Comment 17: No mention of hypotheses or theoretical models that could guide the analysis. Introduce a theoretical model or hypotheses to support later analysis.

Response 17: We agree that we should provide a more detailed theoretical background in the manuscript, and the changes have been made in Section 3:

“This study adopts the grounded theory model to collect relevant data, utilizing interview methods to obtain core data for coding analysis. Concepts are extracted from the raw data and developed in terms of dimensions and attributes. The coding process primarily involves open coding, axial coding, and selective coding, through which core concepts or categories are identified. These are then used to construct a narrative framework, ultimately forming a theoretical system and building a theoretical model. After completing the model construction, the concepts related to each described object are interpreted.

Based on existing literature and the practical digital transformation experiences of automotive manufacturing enterprises, this study examines the impact of digital service, digital experience, digital R&D, digital manufacturing, digital marketing, and digital middleware on the value of manufacturing enterprises. To further explore the intrinsic mechanisms of these elements, this research employs the grounded theory approach to analyze the characteristics, motivations, and implementation pathways of enterprise digital transformation.”

 

Comment 18: No details on handling outliers in data, which could affect result accuracy. Include details on outlier handling to ensure robust data analysis.

Response 18: we agree that we should detail the processing of outliers to enhance the clarity. The changes can be found in page 4, paragraph 5 and line 189:

“Furthermore, to mitigate the potential influence of outliers, all continuous variables in the econometric testing design were winsorized at the 1% and 99% levels.”

 

Comment 19: No explanation of how companies were selected for the study, leaving sample selection unclear. Provide clear criteria for sample selection.

Response 19: We agree that the previous description of sample selection was unclear, and we have rewritten it in detail on page 4, paragraph 5:

“The sample selection criteria were as follows: (1) Excluding automotive manufacturing enterprises listed after 2020, based on their post-transformation market valuation. (2) Eliminating enterprises that were delisted during the study period. (3) Excluding enterprises that underwent significant changes in their core business operations. (4) Removing enterprises whose annual reports did not disclose digital transformation-related information or exhibited abnormal data. (5) Excluding enterprise samples with missing or anomalous key data.”

 

Comment 20: No mention of data validation tests or model stability checks. Add model validation and stability testing to confirm result reliability.

Response 20: We thank you for this important observation. To address this concern, we have conducted additional robustness checks and sensitivity analyses to ensure the stability and reliability of our findings. Specifically, we have implemented the robustness check in section 5.4.:

“5.4. Robustness

5.4.1. Changing anchor points and adjusting thresholds

In quantitative analysis, robustness is of paramount importance. According to the steps in the QCA methodology, adjustments in sample selection, condition measurement, calibration, and threshold analysis (such as case frequency, PRI consistency, raw consistency, and consistency thresholds for frequency) can all affect the number of sufficient conditions for configuration analysis, the relationships among configuration sets, and related parameters. Therefore, to determine whether significant changes occur in the aforementioned indicators under different operational choices and to ensure the reliability of the research conclusions, it is necessary to test the robustness of the sample enterprises. To this end, this study examines the robustness of the 355 automotive manufacturing enterprise samples by altering calibration anchor points, adjusting analysis thresholds, and conducting endogeneity tests. The configuration results formed by the four calibration methods show no significant differences or substantive changes compared to the baseline regression model, and the adjusted parameters do not yield superior results, indicating that the baseline model is robust (see Table 7 for results).

Using two methods—changing anchor points and adjusting thresholds—the robustness of the baseline regression model was tested. The results show that threshold adjustments do not alter the overall solution coverage and consistency of configurations leading to high enterprise value enhancement. However, the coverage and consistency of configurations resulting in non-high enterprise value enhancement decline, failing to meet expectations. The configuration condition analysis results under altered anchor points and threshold adjustments do not reach the baseline of this study, and adjustments to related parameters do not improve the configuration results, though no substantive changes are observed. Therefore, the conditional configurations in this study exhibit robustness, indicating that the empirical results are highly reliable and consistent with the core conclusions.

5.4.2. Multiple regression analysis

Furthermore, to mitigate potential biases in the econometric tests that may arise from omitting important variables, this study also incorporates and controls for several potential factors that could influence the enhancement of manufacturing enterprise value. These include macroeconomic indicators such as market volatility, regulatory changes, and technological disruptions, as well as year fixed effects and industry fixed effects. The relationship model between digital transformation and enterprise value is specified as follows:

To account for unobservable macroeconomic factors and industry-specific characteristics that could affect the regression results, year fixed effects and industry fixed effects are included in the control variables.

The baseline model regression results, and the regression results incorporating control variables are shown in Table 8. The results indicate that, prior to including control variables, the regression coefficient between digital transformation and enterprise value enhancement is 0.059, with a test statistic of 5.197, passing the 1% significance test. After incorporating control variables, the regression coefficient between digital transformation and enterprise value enhancement in manufacturing enterprises increases to 0.132, with a test statistic of 11.941, also passing the 1% significance test. Additionally, the regression coefficients for year fixed effects and industry fixed effects are 0.047 (test statistic 6.704) and 0.039 (test statistic 5.021), respectively. This suggests that the enhancement of enterprise value in manufacturing enterprises follows a gradual, time-series progression and exhibits industry-specific variations. 

These test results validate the core conclusion that digital transformation alone is not a necessary condition for enhancing enterprise value. They also demonstrate that during the process of digital transformation, enterprises are more susceptible to macroeconomic indicators such as market volatility, regulatory changes, and technological disruptions, which can create positive anticipatory effects and significantly contribute to enterprise value enhancement. Thus, it can be concluded that a higher degree of digital transformation in manufacturing enterprises is more conducive to enhancing enterprise value, although it is not the sole necessary factor. These findings are consistent with the core conclusions of the study.”

Table 8. Baseline regression of digital transformation and manufacturing enterprise value enhancement.

Variables	Baseline regression coefficient	Baseline regression coefficient including control variables
Digital Transformation	0.059（5.197）*	0.132（11.941）*
R&D Digitalization	2.501（20.441）***	2.712（21.107）***
Marketing Digitalization	2.035（2.792）**	0.461（2.150）*
Middleware Digitalization	0.432（2.033）*	2.117（2.560）**
Environmental Digitalization	0.514（2.063）*	0.528（2.227）*
Service Digitalization	2.307（19.781）*	2.511（18.953）*
Year Fixed Effects	-	0.047（6.704）
Industry Fixed Effects	-	0.039（5.021）
Regulatory Changes	-	0.055（3.951）
Market Volatility	-	0.107（8.220）
Technological Disruption	-	0.359（13.058）

Note: Figures in parentheses represent t-test values. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

 

Comment 21: No discussion of research limitations and challenges faced. Include a discussion on research limitations and challenges encountered. Future research directions are not explicitly stated. Add suggestions about future research areas based on the findings.

Response 21: we agree that we should detail the challenges we faced and future research directions. We have added them in page 20, paragraph1:

“Since this study focuses solely on the relationship between digital transformation and enterprise value in the automotive manufacturing industry, the findings exhibit strong typicality. However, due to the limited sample size and research scope, the study does not address regional differences among manufacturing enterprises or variations in digital transformation behaviors. Consequently, the generalizability of the results is not sufficiently evident. Future research should incorporate larger sample sizes and comprehensively consider various differentiating factors to ensure broader applicability. Additionally, this study has several limitations, such as the lack of in-depth analysis of time-series data, the relationship between digital transformation and enterprise value at the industry level, and comparative analysis between digital and non-digital enterprises. These limitations narrow the scope of factors influencing enterprise value enhancement and, to some extent, affect the completeness and systematicity of the conclusions. Therefore, further exploration and analysis will be conducted in subsequent studies.”

Comment 22: Limited discussion on how results contribute to existing economic theories.Explain how the study contributes to the development of current economic theories.

Response 22: Thank you for your suggestion, we have revised accordingly in page 2, paragraph 5:

“Existing studies have not systematically discussed all relevant factors, nor have they measured the extent to which digital transformation impacts enterprise value. Furthermore, there is a lack of both quantitative and qualitative research on this topic. Therefore, the contributions of this study are as follows: (1) It verifies that a single digital factor cannot independently enhance enterprise value; (2) It enriches and expands the research on the pathways through which digital transformation influences enterprise value; and (3) Few scholars have focused on qualitative research regarding the relationship between digital transformation and enterprise value, and even fewer have explored the interconnected effects of various variables. This study employs fsQCA to investigate the relationships between different digital variables and enterprise value enhancement, thereby deepening the understanding of digital transformation issues. The findings of this study can provide theoretical references and foundations for enterprises in formulating digital transformation strategies, building digital capabilities, and enhancing enterprise value.”

Thank you again for all the constructive comments!

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Thank you for the opportunity to review this manuscript. The topic is highly relevant, and the study is well-structured, with a solid theoretical foundation and a clear focus on the relationship between digitalization and firm value in China’s manufacturing sector. The literature review is comprehensive, and the empirical approach is interesting. However, there are some areas that need improvement to strengthen the paper’s clarity, rigor, and contribution.

One key issue is the abstract, which does not clearly describe the methodological approach. It would be helpful to specify the number of cases analyzed, the criteria for their selection, and how the data were processed. This would enhance transparency and make the study’s methodological rigor clearer to the reader.

The study provides valuable empirical insights, but the discussion on its unique contribution could be more explicit. While it builds on existing research, it is not entirely clear what gap it fills. Making the originality of the study more evident would add to its impact.

The methodological section is generally well developed, but some details are missing. The selection of the three case studies (Geely, BYD, and BAIC) could be better justified, and the coding process for data analysis should be explained more clearly. The configurational analysis (fsQCA) is a strong point of the paper, but the criteria for defining thresholds and consistency levels should be better justified.

The empirical findings are relevant, but some results are not fully explained in the discussion. Certain dimensions of digitalization have inconsistent effects on firm value, and these findings could be explored in greater depth. Additionally, the robustness checks are useful, but their implications for the study’s conclusions should be more clearly articulated.

The discussion and policy implications, while relevant, could be more practical and actionable. It would be helpful to provide more concrete recommendations for managers and policymakers based on the findings.

Finally, the writing is clear, but some sections are somewhat repetitive, especially in the theoretical background and discussion. Streamlining the text would improve readability and make the argumentation more direct.

Overall, this is a well-conceived study, but it requires major revisions to clarify the methodology, highlight its original contribution, and provide a deeper interpretation of the results. I look forward to seeing the revised version.

Comments on the Quality of English Language

The English language quality is generally good, but there are some awkward phrasings and minor grammatical issues that could be improved for better readability. A careful proofreading or a professional language review would enhance clarity and fluency.

Author Response

Reponses letter 2

Comment 1: One key issue is the abstract, which does not clearly describe the methodological approach. It would be helpful to specify the number of cases analyzed, the criteria for their selection, and how the data were processed. This would enhance transparency and make the study’s methodological rigor clearer to the reader.

Response 1: Thank you for the recommendations on the abstract. We have rewritten the abstract:

“In the context of digital transformation, the varying dimensions of digital maturity significantly influence value creation enhancement for enterprises. Optimizing these dimensions to augment corporate value represents an urgent challenge for manufacturing enterprises. This study examines 355 listed automotive manufacturing enterprises (including auto parts and related businesses) through multi-case analysis, grounded theory, and QCA methodology to investigate the intrinsic mechanisms and pathways linking digital transformation with value enhancement in automotive manufacturing. Key findings include: (1) Grounded theory identified service digitalization, environmental digitalization, middleware digitalization, marketing digitalization, and R&D digitalization as critical variables, with enterprise value enhancement requiring multi-dimensional synergies rather than single-factor determinants. (2) Configuration analysis revealed that comprehensive empowerment type (consistency >0.8, coverage 35.9%) drives high-value enhancement, while service-deficiency, R&D-deficiency, and marketing-deficiency configurations characterize non-high-value scenarios. Service, R&D, and marketing digitalization emerge as core value-enhancing competencies (consistency 0.817, coverage 75.9%). (3) Heterogeneous driving forces were observed across vehicle manufacturers, component manufacturers, and related industry manufacturers, though service digitalization constitutes a common value-enhancing element. This research provides theoretical insights into manufacturing digital transformation's value creation mechanisms and strategic implications, addressing current academic gaps. However, the automotive industry focus limits generalizability despite its concrete exploration of industry-specific digital transformation. Future studies should expand industry coverage and conduct comparative analyses to enhance theoretical robustness.”

Comment 2: The study provides valuable empirical insights, but the discussion on its unique contribution could be more explicit. While it builds on existing research, it is not entirely clear what gap it fills. Making the originality of the study more evident would add to its impact.

we have revised accordingly, and the changes can be found in page 2, paragraph 5:

“Existing studies have not systematically discussed all relevant factors, nor have they measured the extent to which digital transformation impacts enterprise value. Furthermore, there is a lack of both quantitative and qualitative research on this topic. Therefore, the contributions of this study are as follows: (1) It verifies that a single digital factor cannot independently enhance enterprise value; (2) It enriches and expands the research on the pathways through which digital transformation influences enterprise value; and (3) Few scholars have focused on qualitative research regarding the relationship between digital transformation and enterprise value, and even fewer have explored the interconnected effects of various variables. This study employs fsQCA to investigate the relationships between different digital variables and enterprise value enhancement, thereby deepening the understanding of digital transformation issues. The findings of this study can provide theoretical references and foundations for enterprises in formulating digital transformation strategies, building digital capabilities, and enhancing enterprise value.”

Comment 3: The methodological section is generally well developed, but some details are missing. The selection of the three case studies (Geely, BYD, and BAIC) could be better justified, and the coding process for data analysis should be explained more clearly. The configurational analysis (fsQCA) is a strong point of the paper, but the criteria for defining thresholds and consistency levels should be better justified.

Response 3: We agree that we should provide more details, and the changes have been made on page 4, paragraph 5 and page 10, paragraph 7:

“The sample selection criteria were as follows: (1) Excluding automotive manufacturing enterprises listed after 2020, based on their post-transformation market valuation. (2) Eliminating enterprises that were delisted during the study period. (3) Excluding enterprises that underwent significant changes in their core business operations. (4) Removing enterprises whose annual reports did not disclose digital transformation-related information or exhibited abnormal data. (5) Excluding enterprise samples with missing or anomalous key data.”

“The explanatory and control variables were input into fsQCA for calibration to obtain anchor points, full membership, crossover points, and full non-membership. These were represented by the maximum, minimum, mean, and 90%, 50%, and 10% levels of the anchor points. After calibration, if the consistency of necessary conditions for the variables exceeded 0.9, further testing was required. Ultimately, the consistency threshold was determined. Based on current research findings and relevant literature, this study employed existing literature results to calibrate the data using the direct calibration method. The full membership calibration standard was set at 0.9, the crossover point calibration standard at 0.5, and the full non-membership calibration standard at 0.1. A configuration consistency greater than 0.8 was considered acceptable, with a threshold of greater than 0.5.”

Comment 4: The empirical findings are relevant, but some results are not fully explained in the discussion. Certain dimensions of digitalization have inconsistent effects on firm value, and these findings could be explored in greater depth. Additionally, the robustness checks are useful, but their implications for the study’s conclusions should be more clearly articulated.

Response 4: We thank you for this important observation. To address this concern, we have conducted additional robustness checks and sensitivity analyses to ensure the stability and reliability of our findings. Specifically, we have implemented the robustness check in section 5.4.:

“In quantitative analysis, robustness is of paramount importance. According to the steps in the QCA methodology, adjustments in sample selection, condition measurement, calibration, and threshold analysis (such as case frequency, PRI consistency, raw consistency, and consistency thresholds for frequency) can all affect the number of sufficient conditions for configuration analysis, the relationships among configuration sets, and related parameters. Therefore, to determine whether significant changes occur in the aforementioned indicators under different operational choices and to ensure the reliability of the research conclusions, it is necessary to test the robustness of the sample enterprises. To this end, this study examines the robustness of the 355 automotive manufacturing enterprise samples by altering calibration anchor points, adjusting analysis thresholds, and conducting endogeneity tests. The configuration results formed by the four calibration methods show no significant differences or substantive changes compared to the baseline regression model, and the adjusted parameters do not yield superior results, indicating that the baseline model is robust (see Table 7 for results).

Using two methods—changing anchor points and adjusting thresholds—the robustness of the baseline regression model was tested. The results show that threshold adjustments do not alter the overall solution coverage and consistency of configurations leading to high enterprise value enhancement. However, the coverage and consistency of configurations resulting in non-high enterprise value enhancement decline, failing to meet expectations. The configuration condition analysis results under altered anchor points and threshold adjustments do not reach the baseline of this study, and adjustments to related parameters do not improve the configuration results, though no substantive changes are observed. Therefore, the conditional configurations in this study exhibit robustness, indicating that the empirical results are highly reliable and consistent with the core conclusions.”

 

Comment 5: The discussion and policy implications, while relevant, could be more practical and actionable. It would be helpful to provide more concrete recommendations for managers and policymakers based on the findings.

Response 5: We thank you for this important observation. To address this concern, we have revised the research implications in page 19:

“6.2. Research implications

Digital transformation in enterprises can drive the enhancement of enterprise value. In terms of enterprise revenue, digital transformation in the manufacturing sector can expand markets, transform business models, and improve the ability of enterprises to create value. This study reveals that there are seven configuration paths through which digital transformation in automotive manufacturing enterprises enhances enterprise value, and it analyzes the impact of each path. These findings provide a reference for various enterprises to leverage digital transformation for value enhancement. In other words, if enterprises achieve service digitalization, marketing digitalization, and R&D digitalization, they are more likely to drive significant improvements in enterprise value. Conversely, the absence of service digitalization, marketing digitalization, and R&D digitalization can hinder high-value enhancement. Based on these insights, the following recommendations are proposed:

First, align with the trend of digital transformation and establish policy support mechanisms to promote digital transformation in manufacturing enterprises. Currently, accelerating the development of the digital economy and making it a new growth driver has become an international consensus. However, the external characteristics and scalability of the digital economy make it highly dependent on policy support. In other words, its full potential and ability to drive the development of traditional industries can only be realized with the backing of economic policies. This is particularly true for the digital transformation of individual enterprises, which heavily relies on policy guidance and support. Only through such measures can enterprises deeply integrate digital technologies into their organizational structures and technological innovations, enhancing their digital capabilities in marketing, services, R&D, middleware, and environmental aspects, thereby solidifying the role of digital transformation in driving enterprise value enhancement.

Second, clarify the challenges and pain points of digital transformation and identify the transmission pathways of its driving effects. Whether from the perspective of digital transformation or enterprise value enhancement, the driving relationship between the two is not achieved overnight. Instead, it is realized through optimizing various attributes of manufacturing enterprises to enhance their overall value. This study primarily examines how digital transformation enhances enterprise value through the synergistic effects of service digitalization, R&D digitalization, marketing digitalization, middleware digitalization, and environmental digitalization. The results show that the synergy, refinement, and optimization of these capabilities can drive digital transformation and, in turn, enhance enterprise value. Manufacturing enterprises can leverage their digital technology capabilities to improve data governance, market adaptability, and risk control.

Third, strengthen digital technology R&D capabilities and create an internal mechanism and external environment conducive to transformation. Value enhancement is a key focus of enterprise development. During the digital transformation process, greater emphasis should be placed on integrating and promoting digital technologies with traditional core business operations. Attention should be given to digital technology R&D capabilities, combining the application and R&D of digital technologies with enterprise value enhancement. By using R&D to drive application and value enhancement, the internal governance systems of manufacturing enterprises can be continuously optimized. Externally, the synergistic effects of policies and markets should be leveraged to guide the flow of talent and capital, as well as societal attention, toward the digital transformation of manufacturing. This can be achieved through talent development, policy support, patent protection, cultural recognition, and social acceptance, thereby concentrating limited resources to create an external environment conducive to transformation and driving R&D capabilities, ultimately promoting enterprise value enhancement.

Therefore, managers of manufacturing enterprises should advance digital transformation based on their developmental realities, selecting transformation models and pathways that align with their specific circumstances.”

 

Comment 6: Finally, the writing is clear, but some sections are somewhat repetitive, especially in the theoretical background and discussion. Streamlining the text would improve readability and make the argumentation more direct.

We thank you for this observation. We have carefully reviewed the manuscript and streamlined repetitive sections, particularly in the theoretical background and discussion. Redundant content has been removed or consolidated to enhance clarity and directness.

The English language quality is generally good, but there are some awkward phrasings and minor grammatical issues that could be improved for better readability. A careful proofreading or a professional language review would enhance clarity and fluency.

We appreciate the constructive suggestions. In response, we have made revisions to enhance the clarity and fluency.

Thank you again for all the constructive comments!

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

I would like to express my sincere gratitude for the tremendous effort you have invested in enhancing the quality of your research. Your comprehensive and thoughtful responses to all the reviewers’ comments and suggestions have significantly improved the clarity, methodology, and overall robustness of your study.

The detailed revisions—including the clear explanation of your sample selection criteria, the rigorous treatment of outliers, the incorporation of robustness checks and sensitivity analyses, and the expanded theoretical background—are highly commendable. These modifications not only address the concerns raised by the reviewers but also substantially enrich the paper’s contribution to the field of digital transformation and firm value.

I am confident that the current version of your manuscript will make a significant impact in the research community and open up new avenues for further investigation.

Thank you again for your outstanding work and dedication.

Author Response

We sincerely thank you for your invaluable guidance and constructive feedback throughout the revision process of our manuscript!

Reviewer 2 Report

Comments and Suggestions for Authors

Thank you for the opportunity to review this manuscript. I appreciate the authors' efforts to address my previous comments and suggestions adequately. After carefully evaluating the revised version, I find that the manuscript has improved significantly, and all concerns have been satisfactorily resolved.

In my opinion, the article is now ready for approval.

Congratulations!

Author Response

We sincerely thank you for your invaluable guidance and constructive feedback throughout the revision process of our manuscript!