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Article

Industry Concentration and Digital Process Innovation: Evidence from Chinese Rail Transit Firms

by
Yi Jin
1 and
Bo Liu
2,*
1
College of Management, Southwest Minzu University, Chengdu 610225, China
2
School of Economics and Management, Southwest University of Science and Technology, Mianyang 621010, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(9), 4116; https://doi.org/10.3390/su17094116
Submission received: 3 March 2025 / Revised: 29 April 2025 / Accepted: 29 April 2025 / Published: 2 May 2025
(This article belongs to the Special Issue Sustainable Transportation Systems Design and Management)
Versions Notes

Abstract

Market competition and industrial environment have a significant impact on firms’ innovation behavior. Hence, this study aims to uncover the connection between industry concentration and digital process innovation in Chinese rail transit firms. Grounded in innovation diffusion theory, we explore the effects of industry concentration on digital process innovation and analyze the contingent factors of firm size and environmental support on the above effects. Through empirical analyses of data from Chinese rail transit firms, this study reveals that industry concentration inhibits digital process innovation. Firm size strengthens the negative impacts of industry concentration, while environmental support weakens the main effect. Our findings offer a complementary framework for industry organization activities and practical implications for digital process innovation.

1. Introduction

Against the backdrop of global manufacturing’s imperative to decouple economic growth from resource depletion, digital process innovation has evolved beyond cost efficiency into a strategic enabler of sustainable industrial systems [1,2]. Process innovation refers to the introduction of new technologies, techniques, and equipment in manufacturing firms to improve product processes, enhance product efficiency, and reduce costs [3,4]. It has been regarded as an indispensable source for competitiveness-chasing manufacturing firms [5]. In the digital era, the structure, features, scope, and boundary conditions of process innovation have been adapted owing to the use of digital technologies [5,6]. Emerging evidence released in 2023 by United Nations Industrial Development Organization (UNIDO) suggests that digitally driven process innovations can concurrently achieve 18–34% reductions in material waste and 12–22% energy savings in capital-intensive industries, aligning operational excellence with planetary boundaries. Digital technologies, such as big data, cloud computing, and mobile devices, enable firms to ameliorate products and processes and pursue quality leapfrogging quickly, while simultaneously improving resource efficiency and reducing environmental footprints through precision manufacturing and energy optimization [7]. Therefore, the digital era awakes business practitioners, especially manufacturing businesses, to re-focus on the strategic roles and functions of process innovation to keep up with environmental sustainability [5,6,8].
Simultaneously, digital transformation changes not only affect the organizational structures of manufacturing firms [9], but also affect their market structures and competitive landscapes. In the long run, the relationships between market competitiveness and innovation have been debatable for business scholars and practitioners. In recent research, some scholars suggest that low market competitiveness and high concentration can facilitate innovation [10]; some, however, point out that a U-shaped relationship exists between industry concentration and innovation [11]. Regarding digital process innovation, a specific and significant innovation component in firm operations that massively affects innovation outcomes, its relationships with market structure or industry concentration are still ambiguous. In practice, the Chinses rail transit industry has made huge breakthroughs in digital process innovation. As digital transformation prevails in the Chinese rail transit industry, technologies such as big data, cloud computing, IoT, 5G, and satellite communications are increasingly integrated into product and process innovation. Simultaneously, the Chinese rail transit industry has its unique market structure and concentration, which tremendously affects its digital process innovation.
In parallel, the decarbonization demands of heavy industries necessitate examining how market architectures shape sustainable innovation trajectories—a critical yet understudied nexus in the Chinese rail transit sector where state-led consolidation coexists with technological leapfrogging. Industry concentration brings in innovation diffusion [12,13]. Recent studies on digital technology adoption in concentrated markets reveal that dominant firms’ process innovations can trigger industry-wide cascading effects, reducing sectoral carbon intensity [14]. Innovation diffusion theory reveals that innovation diffusion can strengthen technological understandings, transform development capability, and shape novel technological capability [15]. Industry concentration enables large firms to source collections, such as funding, technologies, and talents, which vastly affects digital process innovation [16,17]. This concentrated structure further accelerates the adoption of sustainable production practices, as dominant firms often set industry-wide standards for green technologies and circular economy integration. Then, demonstration effects from digital process innovation will diffuse and be adopted in the entire industry, and in turn re-affect digital process innovation [18]. Industry concentration generates a stable co-competitive landscape across the entire industry, thus impacting the diffusions, sharing, and adoptions of digital process technologies in the industry [19]. Therefore, we propose that innovation diffusion plays a crucial role in explicating the deep ties between industry concentration and digital process innovation, which has been sparsely addressed in previous research.
Certainly, industry concentration is significantly related to firm size [20], and firm size has been considered a vital moderator in digital contexts and innovation studies [21,22]. More specifically, firm size, as a variable, can influence how firms perform and profit from digital process innovation [23,24]. Moreover, innovation diffusion theory has revealed that firm size can affect its market structure, technology diffusion and adoption, and further its innovation outcomes [25,26]. Therefore, firm size appropriately deserves to be a contingent factor in explicating the link between industry concentration and digital process innovation.
Additionally, digital environmental supports are indispensable to pursuing digital process innovation [27,28]. The digital support environment generally contains firm-level and industry-level (such as digital infrastructure of the industry) support [27,28]. Environmental supports have been proven to be associated with industry concentration [16]. Simultaneously, the innovation diffusion literature reveals that environmental supports, such as network infrastructure construction, can facilitate technological diffusion and adoption in the pursuit of digital process innovation [16,29]. Thus, environmental support should be considered as a boundary condition to explore its roles in market structure and digital process innovation.
Hence, this article aims to explore how industry concentration affects the digital process innovation of a firm based on innovation diffusion theory and focuses on the boundary effects of firm size and environmental support on the above relationship. In our analysis, data from the Chinese rail transit industry are selected for the following considerations: First, the Chinese rail transit industry typically complements digital process innovation and acquires breakthroughs [30,31]. Second, the Chinese rail transit industry is equipped with and influenced by high concentration. It centralizes numerous rail digital technologies and process innovation outcomes, and benefits from the concentrated resource inputs and allocations of the Chinese government. Finally, we also consider the access to industry data, facilitating our exploration. Based on the panel data composed of 92 rail transit firms from the period of 2013 to 2021, we test all of our hypotheses.
Our findings have notable implications for theory. First, we contribute to the literature on the antecedents of digital innovations by revealing the influence of market structure on digital process innovation. Second, we extend existing works by connecting the meso factor with the micro outcome [32,33] and confirming the effect of industry concentration on firm-level digital process innovation varying across different firm sizes. Third, we enrich the digital innovation studies by revealing the boundary role of environmental support that mitigates the effects of industry concentration on innovation.
This study is organized as follows. Section 2 presents the theoretical background and hypotheses development. Section 3 depicts relevant methods used in our exploration. The analysis and results are shown in Section 4. Section 5 presents the discussions, including theoretical contributions, managerial implications, limitations, and future study.

2. Theoretical Background and Hypothesis Development

2.1. Digital Process Innovation

Process innovation is defined as the introduction of new elements to the organization’s productions and service systems; it refers to the organization’s internal focus and actions to enhance the efficiency of internal processes, and further ameliorate products and services on the market [34]. The digital era endows process innovation with brand-new definitions and connotations and makes novel demands on it. Digital process innovation is not only improving operational process efficiencies through digitalization, but also applying digital technologies to nurture novel products and processes [35]. Previous digital process innovation studies mainly centered on the roles of information technology (IT) [5]. For instance, ref. [36] indicated that IT was significantly associated with a firm’s innovation strategies and low-cost strategies. Technology iteration upgrades digital process innovation [37]. As a result of the growth in technologies, firms attempt to adopt mobile internet and platforms to digitalize processes and advance their innovation capabilities [38]. Recent studies highlight the significance of data. For instance, ref. [39] mentions that data homogenization is one of the unique characteristics of digital innovation and can vastly affect digital processes.
Digital process innovation refers to the behaviors and strategies supporting novel business processes or improving existing innovation processes through digital technologies [40]. Innovation diffusion theories can contribute to discerning the factors affecting digital process innovation when firms are adopting digital technologies and implementing process innovation [33]. Without directly yielding competitiveness, process technology adoptions urge organizations to change business and function structures, and to improve the ties with their stakeholders, thus reducing innovation costs and intensifying their market responsiveness [40]. Resultingly, digital innovation researchers have been attempting to connect innovation diffusion with digital innovation to explore its roles in facilitating or impeding digital innovation. For instance, ref. [41] discusses how intermediaries can assist in diffusing digital technologies in healthcare ecosystems. Also, the authors discover several key barriers that hinder digital technology adoption. Ref. [19] indicates that organizational and technological factors can tremendously influence digital technology adoptions and further digital process innovation. These studies offer valuable insights into the theoretical explorations and business practices of digital technology diffusion and process innovation, laying a crucial foundation for our analysis. However, these studies mainly focus on micro factors, which spares us from exploring the link between innovation diffusion and digital process innovation at a relatively macro level, such as at the industry level.

2.2. Industry Concentration and Digital Process Innovation

Industry concentration refers to the industry competition level and externally impacts firms’ strategy changes [42]. Consistent with previous studies, we find that industry concentration may hinder digital process innovation. First, industry concentration generates a monopoly and innovation inertia [43,44]. Innovation diffusion theory reveals that firms with higher concentration easily become market leaders, as their attitudes towards innovation adoption and refusal can markedly affect other firms [33,45]. These firms may create innovation inertia without external competitive pressure and shy from the early risks of digital process innovation [21], thus leading to demonstration effects and weakening innovation energies across the entire industry [33,42].
Second, concentration can result in uneven resource allocation, and large firms dominate more resources, which severely affects resource access and innovation adoption [46]. Consequently, uneven resource allocation hinders access to the requisite resources of other firms, limits innovation capabilities, and delays digital process innovation diffusion. Certainly, market concentration provides large firms with the rights to set digital process standards and principles, and forms innovation barriers [13]. Innovation diffusion theory claims that these innovation barriers can reduce market innovation diversity, and restrict digital process innovation adoptions, especially for disrupting the existing market landscape [47]. For these reasons, we propose the following hypothesis:
Hypothesis 1. 
Industry concentration inhibits digital process innovation.

2.3. Moderating Role of Firm Size

Firm size can be regarded as a crucial moderator in innovation studies [21,48]. More specifically, firm size can affect the firm’s innovation strategies [21]. Academically, we find that firm size can amplify the negative impacts of industry concentration on digital process innovation.
First, large firms may face resource dispersion and coordination issues [46], thus generating difficulties in diffusing and centralizing resources and information claimed by innovation diffusion theory [33]. Large firms thus have higher internal coordination costs [49], reducing the efficiency of diffusing key innovation resources under high industry concentration, hindering the innovation diffusion from innovators to early adopters, and to multiple adopters, and lowering the response rate to digital process innovation through swiftly adjusting resource deployment.
Second, higher firm size lowers speed and flexibility in strategy making and delays innovation adoptions [50,51], which profoundly affects the pace sensing and adoption of digital process innovation. Especially in high industry concentrations, innovation adoption delay may be magnified owing to firms’ excessive dependence on pre-existing business processes, and further neglecting the rapid changes in the market and technologies.
Finally, as per the industry organization and innovation diffusion literature, high industry concentration will generate organizational inertias and path dependences [33,49], which may be intensified by firm size [45]. Large firms will be conservative and restrain digital process innovation. Therefore, we propose the following hypothesis:
Hypothesis 2. 
Firm size negatively moderates the impact of industry concentration on digital process innovation.

2.4. Moderating Role of Environmental Support

Environmental support refers to the supporting conditions or requirements affecting digital process innovation, including industry-level support and city-level support [27,52]. For the Chinese rail transit industry, the China Stock Market and Accounting Research Database (CSMAR), as an authoritative, professional, and accurate research platform used in China, releases its specific conditions for environmental support, viz. internal support, such as the number of patents, R&D activities, new product development and marketing, the intensity of digital technology, the investment intensities of digital capital and human resources, external support, such as optical cable density, mobile switch capacity, and the scale of internet broadband user access, and that of mobile internet users in the city. Based on this, we reveal that as a significant contingent factor, environmental support can positively affect the relationship between industry concentration and digital process innovation.
On an industrial level, more patents show and strengthen innovation depth and quality [53], as well as hastening technological upgrades and innovation opportunities [54,55]. In high industry concentration, increasing patent numbers can generate innovation diffusion, stimulate the learning, imitations, and digital innovation of other firms, and eventually enhance the digital process innovation of the entire industry. R&D activities with high strength are seen as the key drivers of knowledge diffusion and technology in industries [56]. According to innovation diffusion theory, vibrant R&D activities can break the monopolies of large firms with high concentration, provide other firms with opportunities to access digital technologies, and encourage digital process innovation across the entire industry [32].
Moreover, regarding new product development and marketing, innovation diffusion researchers point out that in high industry concentration, new product development and marketing can augment product and service diversity, broaden market demands, and offer broader space for digital process innovation in technological applications and innovation experiments [33,57]. Then, firms with higher digital technology intensity are prone to adopting advanced digital technologies in high concentrations [58,59]. Innovation diffusion studies reveal that these firms can motivate industry digital process innovation through technical demonstration, collaborative R&D, and standard sharing [18,60], which facilitates digital resource flows and reconfiguration, and digital process innovation outcomes in the entire industry. For the investment intensity of digital capital, the innovation diffusion perspective indicates that firms with higher digital capital are not only the innovation implementors, but also the innovation communicators. Especially in high industry concentration, high digital capital can demonstrate the value of digital technologies, reduce the uncertainties of other firms adopting new digital technologies, and resultingly invigorate digital process innovation within the industry [61]. Finally, for the human capital intensity, innovation diffusion analysis states that skilled human capital can adjust to and apply new digital technologies [62]. Firms with high human capital can accelerate digital process improvement and nurture stronger flexibility and resilience, even in high industry concentration [63].
On a city level, firstly for the optical cable density, combined with the innovation diffusion literature, high optical cable density constructs stable digital infrastructure networks, thus nurturing the foundation of the digital process [64]. In high industry concentration, high optical cable density ensures the connections between large firms and other firms, vastly reducing the costs of creating digital process technologies. Second, the mobile switch capacity can not only facilitate the rapid flow and interactions of data resources, but enhance the integrative innovation and application of digital technologies [28]. Moreover, high mobile switch capacity can absorb more industry participants and resources, form a dynamic cycle of innovation diffusion in a concentrated industry, and contribute to digital process innovation [28,33]. Then, for the scale of internet broadband access users, a large scale of internet broadband access users ensures the breadth and depth of digital participation. In highly concentrated industry, these extensive digital participations expedite innovation flows, mitigate information asymmetry, and create more digital process innovation opportunities [16]. Lastly, the scale of mobile internet users enables users to participate in the whole process of product and service innovation. In highly concentrated industry, user participation more broadly diffuses and applies innovation resources, and drives the digital process innovation at the heart of users [64]. Out of these analyses, we propose that the following:
Hypothesis 3. 
Environmental support weakens the negative impacts of industry concentration on digital process innovation.

3. Methodology

3.1. Data and Sample

The transportation industry serves as infrastructure for other industrial sectors and accelerates national economic and social development [65]. As a vital component of transportation, rail transit is positioned by the Chinese government as a strategic emerging industry, and modernization goals were set for it in the fourteenth Five-Year Plan (2021–2025). Due to inherent connectivity, generativity, and affordance, digital technologies (e.g., the Internet of Things, Industry 4.0, big data, and cloud computing) have sparked the creation of novel products and services, reshaping business models, and enabled industrial upgrading [66,67], additionally creating new opportunities for modernization of the rail transit industry. Therefore, this study collected data from Chinese rail transit firms to test the hypotheses.
Rail transit is a large-scale series of projects that involve numerous related industries and sectors. The upstream includes rail transit design, consulting, and raw materials, the midstream includes engineering construction and equipment manufacturing, and the downstream includes operation services, maintenance, and repair. Going public is an important way for companies to raise funds, especially for rail transit projects that require significant capital investment. Many dominant companies in various industrial chains are listed companies, such as the rail transit equipment supplier CRRC and the mega construction firm China Railway Construction Corporation. Because the Shenzhen and Shanghai stock exchanges in China do not specifically list companies in the rail transit industry, we categorized the rail transit chain into upstream, midstream, and downstream categories based on industry research reports, and 107 sample companies were determined. Then, we collected industrial concentration, environmental support, and financial data from the CSMAR database. We collected digital process innovation data from company annual reports. Digitization is a major strategy for high-quality development of companies in the new era, and this type of characteristic information is more easily reflected in the annual reports that summarize companies’ performance and guide their development. The usage of vocabulary in annual reports can reflect the business philosophy, organizational strategy, and organizational behavior of the company. Digital process innovation is the process change brought about by digitization; therefore, it can be measured by the frequency of keywords. To eliminate the impact of financial anomalies on corporate innovation, this study referred to previous studies (e.g., reference [68]) and excluded ST, *ST, and PT samples. Next, we winsorized continuous variables at the 1% and 99% quantiles to control for the potential effect of outliers. The final sample consisted of 689 observations from 92 listed rail transit firms between 2013 and 2021.

3.2. Measurements

Dependent variable: Referring to the method of [69] to depict digital transformation that counts the frequency of keywords, we utilize the keywords that represent the core content of digital process innovation we intended to measure. First, we used the Python 3.8 crawler function to download and organize the annual reports of Chinese-listed companies from 2013 to 2021. Second, considering the definition of digital process innovation and practical operation, rail transit firms have extremely high requirements for safety, reliability, and efficiency, and ask for digitalization to improve operational efficiency, reduce maintenance costs, enhance safety, and add customer value. Thus, the keywords should cover the key digitalization applications in production and operation, internal management, and customer interaction. Referring to [69], we constructed a digital dictionary containing 11 keywords to depict key features of digital process innovation, which include intelligent manufacturing, intelligent customer service, intelligent marketing, digital marketing, unmanned retail, unmanned factories, mobile payments, third-party payments, NFC payments, human–computer interaction, and social networks. Third, we used the jieba word segmentation tool in Python to segment the text of the annual reports. After data cleaning, we input the digital process innovation dictionary into the jieba user dictionary and then perform word segmentation. Fourth, we searched, matched, and statistically analyzed the text data of the annual reports, and finally obtained the corresponding annual digital process innovation word frequency for the enterprise. Fifth, we took the logarithm of the total frequency of relevant keywords appearing in the annual report after removing the content of MD and A (management discussion and analysis) to indicate the firm level of digital process innovation. The larger the value, the higher the degree of digital process innovation of the enterprise.
Independent variable: Industry concentration describes the degree to which industry output is produced by a few firms. The industry concentration is calculated by the proportion of the core business revenue of the top 20 companies in the industry to the total core business revenue of the entire industry. In the robust test, following Raguseo, Vitari, and Pigni [21], we lagged the variable to show the effect of the industry concentration of the previous year on the revenue of the year after, and we calculated the Herfindahl–Hirschman index to measure industry concentration.
Moderating variables: In line with previous studies (e.g., references [21,70]), we regarded total assets as a proxy of company size. The total assets were included in the logarithmic form in each model to measure the size of the company.
Firms cannot operate in a vacuum. The meso and macro levels of environmental factors compose the space in which firms operate, which affect the introduction, R&D, and commercialization of digital technologies. The environmental support data were obtained from the CSMAR database and computed using comprehensive indicators that include industry-level and city-level factors.
Control variables. To control for possible confounding factors that may influence the level of digital process innovation, we controlled the following variables. The measurement of controls is shown in Table 1.

4. Analysis and Results

4.1. Descriptive Statistics

Table 2 shows the descriptive statistics of the variables, including the minimum and maximum values, the means and standard deviations, and the correlation matrix. We can draw four main conclusions from an analysis of the correlation coefficients. First, the mean value of digital process innovation is 0.519, suggesting that overall half of rail transit firms have digital process innovation applications. Second, the mean value of industry concentration is 0.816, indicating a very high concentration in these firms. Third, the industry concentration is negatively associated with digital process innovation, indicating that the causal relationship between the two needs further examination. Another potential multi-collinearity problem has emerged concerning the negative correlation between industry concentration and environmental support. Thus, we conducted a VIF test, and the value is no more than 3, suggesting multi-collinearity is acceptable.

4.2. Panel Data Analysis

Non-stationary time series data for regression analysis may lead to spurious results. This study first conducted Fisher’s unit root test on the raw data of the core variables. The results showed that the statistical results of all variables rejected the null hypothesis (p < 0.001). Therefore, all variables have passed the unit root test and can be considered stationary sequence data. A Hausman specification test was performed to determine the suitability of random versus fixed-effects models [21], which basically tests whether the unique errors are correlated with the regressors. The Hausman test presented that the random effect was the most appropriate and effective estimation method for our case.
To examine Hypothesis 1, we first observed the direct effects between industry concentration in T−0 and T−1 periods and digital process innovation in the T−0 period. Second, we centered predictors, constructed interaction terms, and run models to test H2 and H3. Table 3 reports all regression analysis results. As shown in Models 1 and 2, the coefficients of industry concentration are negative and significant, just as Hypothesis 1 expected. The interaction effect of firm size on the relationship between industry concentration and digital process innovation is negative and significant too (shown in Models 3 and 4). These results mean that for larger rail transit firms, higher industry concentration exerts more negative impacts on its digital process innovation than smaller ones. For this reason, Hypothesis 2 is verified. In addition, Models 5 and 6 also show that environmental support weakens the negative influences of industry concentration on digital process innovation (β = 0.088 and 0.098, respectively, p < 0.01). Therefore, Hypothesis 3 is confirmed.

4.3. Robustness Test

We examined the sensitivity of the results in several ways. First, we lagged the independent variable to avoid omitted variable bias (shown in Table 3). Second, we replaced the measurement of industry concentration with the Herfindahl–Hirschman index (HHI). We calculated the HHI by the proportion of the book value of an individual company’s equity to its market share in the industry. Then, we re-preformed the analysis, and the results in Models 7, 8, and 9 were highly consistent with the findings shown above (see Table 4).
Third, we considered the endogeneity problem caused by reverse causality. We ran a random-effects regression model in which the dependent variable was the use of industry concentration, and the independent variable was the lagged digital process innovation. The results in Models 10 and 11 support the absence of reverse causality or simultaneity in industry concentration and digital process innovation, since the effect of digital process innovation at time t − 1 is not significant for the industry concentration at time t.

5. Conclusions and Discussion

5.1. Conclusions

Market structure is consumingly associated with digital process innovation [37,71], which still holds ambiguous conclusions. Based on innovation diffusion theory, this study explores the impacts of industry concentration on digital process innovation. Simultaneously, we analyzed the boundary roles of firm size and environmental support. The data were collected from the Chinese rail transit industry, which is representative of market concentration and digital process innovation activities. Based on the 9-year panel data, this study concludes that (1) industry concentration inhibits digital process innovation; (2) firm size intensifies the negative impacts of industry concentration on digital process innovation; and (3) environmental support weakens the blocking effects of industry concentration on digital process innovation.

5.2. Theoretical Implications

This study contributes to the digital innovation and industry organization literature in several ways. First, we explore the impacts of industry concentration on digital process innovation, reflecting the attempt to further bridge the distances between market structure and digital innovation and to link meso antecedents with micro outcomes, which responds to the previous call for exploring how to overcome digital technological and organizational constraints in an industrial context [19]. In response to previous studies stating the positive or U-shaped effects of industry concentration on innovation [10,11], we reveal the negative effects of market structure on innovation, which offers a valuable extension to existing analyses. More specifically, we focus on digital process innovation and try to explicate digital technology diffusions and adoptions brought in industry concentration when firms conduct digital process innovation, which is claimed by innovation diffusion theories [32,33]. Grounded in the firms’ relatively macro market characteristics, we connect the diffusions and adoptions of digital technologies with process innovation, indicating in essence that we care more about the impacts of the diffusions and interactions of digital technologies on innovation outcomes, especially process innovation. This strengthens a deeper understanding and extends a scene analysis of innovation diffusion theories.
Next, we focus on the key moderator of firm size. We show that firm size is associated with and influences market structure [11,21]. In response to the call for assessing city- and industry-level effects on innovation activities [21,22], we intensify the interactions between firm size, the inner factor, and industry concentration, the outer factor. Moreover, this study discovers that firm size can intensify the negative impacts of industry concentration on digital process innovation, actually confirming the limitations of diffusions and adoptions of digital technologies at the firm and industry levels, thus hindering digital process innovation. Also, this provides new insights for exploring digital innovation issues through innovation diffusion theories.
Finally, our considerations include supporting environments. It goes without saying that digital process innovation necessitates environmental support, including at the firm level and the industry one [27]. This study centers on the details of Chinese digital process innovation and analyzes its roles in the relationship between market structure and digital process innovation in the Chinese rail transit industries, thus indicating that scenario-specific environmental supports can be highly effective. Moreover, we reveal that environmental supports can impair the negative effects of industry concentration on digital process innovation, manifesting positive interactions between environmental support and industry concentration. This facilitates the diffusions and adoptions of digital technologies, accelerates our understanding of innovation diffusion outside firms, and further extends a theoretical explanation of innovation diffusion in digital innovation.

5.3. Managerial Implications

This study provides some valuable managerial implications for the rail transit companies’ operation and sustainability. First, as a traditional industry, the rail transit firms face issues of resource consumption and environmental pollution during construction, operation, and maintenance. Digital process innovation, such as intelligent scheduling systems, predictive maintenance, and energy management systems, can effectively improve operational efficiency, reduce energy consumption and emissions, and help rail transit companies achieve environmental sustainability goals. However, we reveal that industry concentration will impede digital process innovation due to monopoly and innovation inertia, which does not mean that firms cannot live without high market structures. Thus, managers should be cautious about the potential impact of market structure on digital process innovation of companies and, more importantly, conduct digital process innovation activities actively with a focus on integrating digital process technologies. For example, rail transit manufacturing companies should introduce intelligent manufacturing technology to achieve high automation in the production of rail transit components, thereby improving production efficiency and significantly reducing energy and material consumption.
Second, we reveal that firm size can exacerbate the negative impacts of industry concentration on digital process innovation, which signifies that firms may face higher resource integration costs, and operate with business model and innovation inertia. Managers should simplify decision processes to encourage intrapreneurship in developing sustainable digital solutions, and consolidate cooperation to reduce integration costs and motivate digital process innovation.
Third, environmental supports significantly impair the negative influences of industry concentration on digital process innovation. This requires rail transit firms to actively strive for and utilize various resources (e.g., digital technology, funds, and talents) provided by the government, industry, and other entities in the external environment. For example, rail transit companies can improve their digital level and operational efficiency by participating in R&D activities and new product development in the industry and making use of local infrastructure (e.g., optical cables and internet). Additionally, the government, especially the Chinese government, is a powerful external entity that supports firms’ digital innovation. The government can create and optimize an environment for digital innovation by strengthening the construction of digital infrastructure and increasing policy support, guiding the digitalization and sustainable development of rail transit companies.

5.4. Limitation and Future Study

Although valuable contributions are made by this article, some limitations necessitate follow-up analyses. First, although we justify the rationality for the measurement of digital process innovation, we admit that measuring digital process innovation is novel and challenging. The construct of digital process innovation may involve multiple dimensions, such as innovation processes and innovation inputs and outputs. Thus, when the availability of data increases, more measurement strategies in future studies may emerge. Second, even if this study connects the meso factor, industry concentration, with the organizational micro outcome, digital process innovation, some micro antecedents of digital process innovation, such as organizational digital strategies and innovation decisions from top management teams, still require more exploration. Third, we select the data from the Chinese rail transit industry. Differentiated conclusions may exist when data from other industries that can match our research model is selected. Fourth, we center this study on two meaningful boundary conditions, namely firm size and environmental support. However, other contingent factors, such as information technology capability and institutional logic, can impact on firms’ concentrations and further affect digital innovation. These factors will be intriguing and valuable in future analyses.

Author Contributions

Conceptualization, Y.J. and B.L.; methodology, Y.J.; validation, Y.J. and B.L.; investigation, Y.J.; writing—original draft preparation, B.L.; writing—review and editing, Y.J. and B.L.; funding acquisition, Y.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Natural Science Foundation of China (grant number 72202186), “the Fundamental Research Funds for the Central Universities”, Southwest Minzu University (grant number ZYN2025006).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Table 1. Measurement of the variables.
Table 1. Measurement of the variables.
Variables SymbolMeasurement
Digital process innovationDPILn (the total frequency of relevant keywords + 1)
Industry concentrationICProportion of the core business revenue of the top 20 companies/the total core business revenue of the entire industry
Firm sizeSizeLn (total assets)
Environmental supportES0.1157 × Digital technology intensity of the industry + 0.114 × Digital capital investment intensity of the industry + 0.0789 × Human capital investment intensity of the industry × 0.1923 × Number of invention patents of the national economic industry + 0.1779 × R&D activities of the national economic industry + 0.1498 × Development and sales of new products of the national economic industry + 0.0477 × Fiber optic cable density of the city that the firm locates + 0.0403 × Mobile switch capacity of the city + 0.04 × Number of internet broadband access users of the city + 0.0434 × Number of mobile internet users of the city
Firm ownershipOwnershipMark 1 if the firm is state-owned; otherwise, 0
Financial leverageLeverageTotal liabilities/total assets
Current assetsLITotal current assets/total assets
Intangible assetsIAIntangible assets /total assets
Table 2. Descriptive statistics and Spearman’s correlation matrix.
Table 2. Descriptive statistics and Spearman’s correlation matrix.
VariableMeanS.D.Min.Max.1234567
DPI0.5190.80303.638-
IC0.8160.1250.5851−0.177 ***-
Size22.9541.75419.89427.9610.097 **0.370 ***-
EA0.4690.1960.1830.8990.169 ***−0.773 ***−0.367 ***-
Ownership0.3900.488010.0250.381 ***0.561 ***−0.327 ***-
Leverage0.4670.2030.0601.1170.072 *0.253 ***0.595 ***−0.252 ***0.387 ***-
LI0.6420.1710.0190.9800.059−0.298 ***−0.204 ***0.366 ***−0.106 ***−0.074 *-
IA0.0500.05100.364−0.0090.096 **0.238 ***−0.117 ***0.072 *0.199 ***−0.342 ***
* p < 0.1, ** p < 0.05, *** p < 0.01.
Table 3. Results of the random-effects regressions.
Table 3. Results of the random-effects regressions.
VariableModel 1Model 2Model 3Model 4Model 5Model 6
IC (t − 0)−1.284 ***
(0.395)		 −1.622 *** (0.378) −0.024
(0.497)
IC (t − 1) −1.177 **
(0.454)		 −1.508 ***
(0.435)		 −0.139
(0.545)
Size (t − 0) 0.170 *** (0.040)
Size ( t− 1) 0.143 ***
(0.044)
IC × Size (t − 0) −1.194 *** (0.203)
IC × Size (t − 1) −1.168 ***
(0.230)
EA(t − 0) 1.190 *** (0.345)
EA(t − 1) 0.010 **
(0.004)
IC × EA (t − 0) 0.088 *** (0.026)
IC × EA (t − 1) 0.098 *** (0.028)
Ownership0.144
(0.109)		0.137
(0.119)		0.061
(0.107)		0.083
(0.118)		0.201 *
(0.107)		0.197 *
(0.117)
Leverage−0.067
(0.215)		−0.181
(0.242)		−0.315
(0.228)		−0.370
(0.256)		−0.111
(0.212)		−0.228
(0.239)
LI−0.483 * (0.270)−0.541 *
(0.301)		−0.109
(0.264)		−0.176
(0.295)		−0.528 ** (0.267)−0.487
(0.0.298)
IA1.119
(0.823)		2.038 **
(0.919)		1.432 *
(0.803)		2.209 **
(0.897)		1.280
(0.810)		1.987 ** (0.903)
Yearsincludedincludedincludedincludedincludedincluded
Constant2.217 *** (0.390)2.238 ***
(0.451)		−1.469
(0.919)		−0.855
(1.009)		0.886 *
(0.532)		1.079 *
(0.601)
R-squared0.2420.2130.2700.2250.2580.223
N666576666576666576
* p < 0.1, ** p < 0.05, *** p < 0.01; standard errors appear in parentheses.
Table 4. Results of the robustness test.
Table 4. Results of the robustness test.
VariableModel 7Model 8Model 9Model 10Model 11
DPIDPIDPIICHHI
DPI (t − 1) −0.005
(0.003)		−0.002
(0.002)
HHI (t − 1)−2.455 ***
(0.697)		−2.094 ***
(0.773)		1.029
(1.603)
Size (t − 1) 0.081 *
(0.044)
HHI × Size (t − 1) −0.524 *
(0.283)
EA (t − 1) 0.013 ***
(0.004)
HHI × EA (t − 1) 0.147 **
(0.073)
Ownership0.125
(0.113)		0.089
(0.120)		0.176
(0.113)		0.005
(0.011)		−0.007
(0.006)
Leverage−0.227
(0.234)		−0.433 *
(0.257)		−0.205
(0.236)		0.072 ***
(0.020)		0.002
(0.010)
LI−0.571 **
(0.299)		−0.480
(0.292)		−0.584 **
(0.288)		0.019
(0.025)		−0.040 ***
(0.013)
IA1.618 *
(0.879)		1.543 *
(0.888)		1.722 **
(0.873)		0.092
(0.072)		−0.056
(0.037)
YearsIncludedIncludedIncludedIncludedIncluded
Constant1.474 ***
(0.257)		−0.335
(1.008)		0.715 **
(0.357)		0.750 *** (0.023)0.097 ***
(0.012)
R-squared0.2110.2140.2160.1780.102
N594594594578594
* p < 0.1, ** p < 0.05, *** p < 0.01.
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Jin, Y.; Liu, B. Industry Concentration and Digital Process Innovation: Evidence from Chinese Rail Transit Firms. Sustainability 2025, 17, 4116. https://doi.org/10.3390/su17094116

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Jin Y, Liu B. Industry Concentration and Digital Process Innovation: Evidence from Chinese Rail Transit Firms. Sustainability. 2025; 17(9):4116. https://doi.org/10.3390/su17094116

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Jin, Yi, and Bo Liu. 2025. "Industry Concentration and Digital Process Innovation: Evidence from Chinese Rail Transit Firms" Sustainability 17, no. 9: 4116. https://doi.org/10.3390/su17094116

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Jin, Y., & Liu, B. (2025). Industry Concentration and Digital Process Innovation: Evidence from Chinese Rail Transit Firms. Sustainability, 17(9), 4116. https://doi.org/10.3390/su17094116

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