1. Introduction
The 20th Party Congress report proposed that “we should adhere to the theme of promoting high-quality development, integrate the strategy of expanding domestic demand with deepening supply-side structural reform, enhance the endogenous power and reliability of the domestic circulation, improve the quality and level of the international cycle, accelerate the construction of a modern economic system, and focus on improving total factor productivity”. Promoting total factor productivity is important content and performance for high-quality development and an inevitable requirement to achieve economic sustainability. Being a vital pillar of the real economy, China’s total manufacturing output value has achieved first for many consecutive years in the whole world. However, at present, the main resources of the Chinese manufacturing industry are still concentrated in the middle and even low-end fields, while the high-end field has been blank for a long time, so it still faces problems such as unreasonable structure and production efficiency to be improved. The fundamental reason is that the ability for independent innovation is not strong since some key resources, such as talents, basic materials, technology, etc., are not mastered. It is still facing the risk of a “stuck neck” in many high-end fields. In addition, there are still many gaps with developed countries, such as resource utilization efficiency, service-oriented manufacturing, product quality, and efficiency, so the Chinese manufacturing industry is “big but not strong, comprehensive but not excellent” [
1]. Problems such as resource allocation and technological innovation have restricted the further progress of corporate total factor productivity in the Chinese manufacturing industry [
2,
3]. Young et al., (2003) calculated that during 1978–1998 China’s average annual growth rate of TFP (total factor productivity) was only 1.4% based on traditional statistical corrections, taking into account the effects of labor dividends and transfers and education improvements [
4]. Studies have shown that digitization can improve information transparency inside and outside enterprises by reducing communication costs [
5,
6] and can influence the innovation activities of enterprises [
7] to improve their productivity [
6].
As digital technology is easy to acquire for extensive applications, the digital economy has become the most dynamic and innovative new economic form at present and a “new engine” to promote economic development and progress [
8]. According to China’s 2022 Digital Economy Development Report, the digital economy accomplished CNY 45.5 trillion within the year 2021, accounting for 39.8 percent of China’s GDP, with a nominal increase of 16.2 percent over the previous year. Since the 18th Party Congress, the Chinese government has emphasized the vital role played by the real economy, particularly the manufacturing industry, and has put forward a bundle of policies to encourage them to accelerate implementing digital transformation. The eight strategies proposed in “Made in China 2025” include “promoting digital, networked, and intelligent manufacturing”. The “14th Five-Year Plan” of Intelligent Manufacturing Progress also proposes that by 2025, more than 70% of manufacturing enterprises above the scale should realize digital networking. The 20th Party Congress Report put forward “promote the high-end, intelligent and green development of the manufacturing industry”. The essence of intelligent manufacturing is to use the Internet, the Internet of Things, artificial intelligence, big data, and other technologies to carry out product design, production, and manufacturing based on scientific models. The basis is to have corresponding data and intelligent reasoning processes, and the reasoning process needs data in each link of the production process. Therefore, in this sense, digitization is the premise of intelligent manufacturing. In addition, the outbreak of the novel coronavirus in 2020 has had a huge impact on the production and lifestyle of the whole society, which accelerates companies’ application and dissemination of digital technology. A considerable quantity of firms carried out digital transformation and actively explored new growth points for their survival and development.
Digital transformation is a common sense and inevitable trend in the development of Chinese manufacturing enterprises. In the future, the digitalization of China’s manufacturing industry should not only be limited to simple business model innovation but also should carry out deeper penetration and upgrading in the production and operation, technology management, service strategy, and other aspects of the entire enterprise. We will continue to improve our ability to produce high-quality products and services that meet market demand, thus promoting the high-end and intelligent areas of the Chinese manufacturing industry. Objectively speaking, with data added in as a momentous production element, digital transformation has been likely to affect the manufacturing function to some extent [
9,
10,
11], thus causing changes in production efficiency. So, does—and how does—digital transformation impact the TFP of manufacturing firms? This is an issue of both theoretical significance and practical value and is worth exploring and pondering.
Research on the digital economy is extensive, mainly from the basic connotation [
12], the appraisal of the digital economy [
13], the business value and governance of data sharing [
14,
15], the sharing economy based on digital platforms [
16], and the value creation of corporate innovation activities [
17]. With the accelerated fusion of the real economy and the digital economy, our traditional economic paradigm is changing [
18,
19,
20], and more and more scholars are delving into the digital transformation of micro-enterprises. The process of enterprises applying digital technology to collect information, process data, and create a series of auxiliary decisions is enterprise digitalization [
21].
The theoretical mechanisms and economic impact of enterprise digital transformation have been studied from different perspectives. Porter and Heppelmann (2014) discussed the enterprise management transformation triggered by digitalization from the aspects of user value and competition [
22]. Chen et al., (2022) adopted a multivariate case study approach from the perspective of dynamic capabilities, exploring the data-driven mechanism of effective knowledge management and promoting enterprise innovation transformation [
23]. In addition, Chen and Kamal (2016) investigated how digital transformation affects enterprise boundaries in the view of the professional division of labor [
24]. In addition, digital transformation is considered to have some influence on enterprise performance [
25], corporate innovation performance [
26], corporate capital market performance [
27], and corporate market power [
28]. These studies provide a rich literature base and ideological inspiration for this paper. Existing studies on how digital transformation affects corporate total factor productivity are still general. Some studies focus on its prompting role, while few scholars discuss the moderate issue of enterprise digital transformation. This paper makes some beneficial attempts in the following aspects: (1) Considering that excessive or too rapid digitalization may also inhibit corporate total factor productivity, we put forward an inverted U-shape nonlinear hypothesis and conduct an empirical test. Further analysis finds that the positive promotion effect of digitalization varies with the deepening of digitalization, showing significant threshold characteristics. (2) Given the strategic transformation of the current manufacturing industry from product-oriented to consumer-centered service-oriented, this paper incorporates service-oriented manufacturing into the analysis framework, distinguishes it into embedded service-oriented manufacturing and mixed service-oriented manufacturing according to whether the service business and the core strategy of the enterprise are consistent. Then, this paper discusses its role in moderating the relationship between digitalization and corporate total factor productivity. At last, this paper empirically tests that. (3) Considering the network effect and synergy effect of digitalization, its positive impact on corporate technological innovation and also scale development are discussed, as well as their mechanism role in further enhancing corporate total factor productivity. Since the data from non-listed companies are hard to acquire, our study is based on only listed companies, so whether the conclusions apply to the whole manufacturing industry in China needs further in-depth discussion.
2. Theoretical Analysis and Research Hypotheses
Improving total factor productivity is one internal requirement for a sustainable and healthy economy, which is, in turn, an important embodiment of achieving advanced economic development. Scholars have explored the influencing factors of total factor productivity from multiple perspectives. From a macro perspective, researchers have examined its influencing factors from public infrastructure [
29], industrial structure change [
30,
31], exchange rate changes [
32,
33], and import and export Trade [
34,
35]. Macro research mainly explores the influencing factors of TFP at the regional or national level. As the essential units for the macro, corporate behaviors are increasingly valued by researchers. From a micro perspective, studies have revealed the effects of open capital accounts [
36], corporate cash holdings [
37], and equity liquidity [
38] on firm total factor productivity. The above studies, from macro to micro, provide good experience and inspiration for understanding the impact mechanism of total factor productivity.
Existing studies generally agree that, as a general-purpose technology, information technology has a wide range of vertical and horizontal external penetration [
39], as well as substitution of other elements such as labor and capital [
40] and synergy [
41] and plays a rapid substitution effect and synergy effect in corporate production and operation activities, thus significantly promoting their value creation activities. Information is essential to the operation of enterprises, not only facilitating managers to make various effective decisions but also improving the productivity of enterprises [
6]. Since Industry 4.0, information from production, R&D (research and development), and sales can be digitized and smartened through the Internet, the Internet of Things, artificial intelligence, and other key technologies to improve corporate production efficiency and quality. Accompanied by the development and application of digital technology, data as a resource in the operation of manufacturing enterprises has become increasingly prominent. Data added as a new element is likely to affect the traditional manufacturing function of micro-enterprises, and so does their total factor productivity [
9,
42]. The digital transformation of manufacturing enterprises is an important stage for the high fusion of the real and digital economy, which is a combination of manufacturing enterprises and digital technologies such as intelligent manufacturing, industrial Internet, and digital twin to comprehensively reconstruct business processes and business models, thereby improving the networking and digitalization level of their R&D, operation, and services. This innovation and transformation should be reflected in their total factor productivity. Researchers argue that the big data application has stimulated enterprises to invest in data solutions and other aspects, and further discussed its commercial value in the view of data asset use, found that enterprises’ BDA (big data application) assets have an obvious positive effect on the improvement of corporate practice [
43]. From the perspective of the industrial ecosystem, different enterprises form modular, symbiotic cooperation and highly autonomous organism by providing complementary products, services, or innovations [
44]. Data as the new production factor can optimize the allocation of traditional factors by improving the interaction efficiency between enterprises and further drive the improvement of industrial efficiency, therefore undoubtedly contributing to improving corporate total factor productivity. In addition, digitalization can also enhance enterprise productivity by reducing internal and external transaction costs and promoting specialization [
24]. It can be said that corporate digital transformation does effectively promote its total factor productivity to some extent. Considering that digital information overload may occur redundancies, make effective information annihilated and reduce decision-making efficiency, coupled with the huge cost of digital transformation investment, the cases of transformation failure such as GE, P&G, and other companies show that excessive or too fast digital transformation may also be detrimental to the development of enterprises [
45]. Accordingly, this paper proposes:
Hypothesis 1. Moderate digital transformation effectively improves enterprise total factor productivity, while digitalization beyond a certain degree may inhibit the increase of that, i.e., enterprise digital transformation and its total factor productivity show a nonlinear inverted U-shape correlation.
The reason why digital transformation could improve corporate total factor productivity is not only through reducing costs and increasing efficiency across intelligent manufacturing processes but also through the mechanisms of service-oriented manufacturing, technological innovation, and scale effect. Among them, service-oriented manufacturing mainly refers to the embedded service activities closely related to its core business carried out by manufacturing enterprises after they have reached a certain level of development, which is conducive to expanding multi-dimensional value-added and thus enhancing the status of their industrial value chain [
46]. Technological innovation is mainly manifested as technological progress achieved by manufacturing enterprises relying on 4D printing, the Internet of things, and other technologies to activate innovation activities and improve innovation efficiency. The scale effect is manifested as the improvement of production efficiency achieved by digital manufacturing firms due to the gradual relaxation of their production scale restrictions.
In terms of digital transformation promoting service-oriented manufacturing, the Internet, Internet of Things, and artificial intelligence can quickly identify and respond to changes in user demand, enhance the interaction between enterprises and users, effectively alleviate information asymmetry, and reduce the uncertainty faced by enterprises in operation to promote corporate supply quality and efficiency. 4D printing and other digital technologies can promote the modular and flexible production of enterprises, enable enterprises to flexibly provide a series of product clusters, and continuously push back customer order separation points in the large-scale manufacturing process, thereby breaking the paradox of service-oriented manufacturing and manufacturing efficiency such as high cost of personalized, customized solutions, and drive the transition of traditional manufacturing enterprises to user value-oriented and service-oriented manufacturing. With the increasing demand for personalized and customized services, more and more manufacturing companies are carrying out product service business. The White Paper of China’s Digital Enterprise points out that improving the ability to serve customers is one of the core goals of enterprise digital transformation and that service-oriented manufacturing shifted from the past product-centric to customer-centric, such as collecting and processing user data through digital technology to better guide R&D activities, optimize the configuration of business processes, carry out precision marketing and provide personalized services in responding to user needs, thus further enhance customer satisfaction and loyalty, and finally improve the value creation capabilities and total factor productivity. With the concept of service relevance, the service business supplied by manufacturing enterprises can be subdivided into embedded services or mixed services. When an enterprise carries out embedded services, which are closely related to its core business, it will significantly promote the enhancement of enterprise value [
47]. By integrating product and service resources, embedded services can also extend to the upstream of the value chain around its core products, thus improving customer satisfaction and product-added value and also enhancing the competitive advantage of manufacturing enterprises [
46]. The embedded services carried out by manufacturing enterprises mainly include product installation and maintenance testing, solutions, software information solutions, technology development, and services, etc. Mixed services are services that are less relevant to its core business strategy but for entrance to a new industry or more profits, mainly including traditional logistics, retail, and other non-core businesses, as well as hotel catering, real estate development, property management, investment finance, and other service businesses unrelated to the main business. In short, by enhancing service-oriented manufacturing, especially embedded service-oriented manufacturing, digital transformation could promote corporate total factor productivity in the manufacturing industry.
In terms of digital transformation promoting corporate technological innovation, the widespread application of digital technology makes data a new production input, which is a technological advance in itself. Technological upgrading has changed the way of collaboration inside and outside enterprises, highlighting the role of data-driven innovation and promoting the improvement of enterprise productivity. First, enterprise digital transformation provides an open and shared information exchange platform, which can accelerate the flow of knowledge and information elements, lower the endeavor of information searching and matching, form an open also networked innovation ecosystem, promote the quality and efficiency of enterprises in absorbing and transforming knowledge into innovation results [
48], and also increase product innovation by promoting the scope economy within the enterprise [
7]. Second, under the current consumer-led trend, digital transformation is changing corporate innovation patterns. Traditional manufacturing enterprises are product-centric and lack timely responses to user needs. Digital technologies empower enterprises to establish extensive value connections and real-time interactions between enterprises and users and among enterprises so that enterprises can grasp immediate changes in user demand and market supply, clarify the direction of product innovation, reduce trial and error costs, and further improve innovation efficiency. Third, digitalization can also reconstruct the organizational structure and management innovation of enterprises, improve the organizational and management efficiency of R&D activities, and further promote enterprise innovation. With the application and penetration of digital technology, the communication and collaborative activities between various functional departments in the enterprise are increasing, and the corresponding organizational structure also needs to be adapted to the trend of faster communication, higher efficiency, responsive networking, and flattening, which is conducive to weakening the boundary barriers between different departments of the enterprise, promoting the blend of more innovation resources, then improving corporate innovation capability and productivity. Therefore, digital transformation could enhance corporate total factor productivity of the manufacturing industry with the aid of technological innovation.
In terms of the corporate scale effect brought by digital transformation, manufacturing firms in traditional industrial economies are often constrained by factors such as operation and management capabilities, asset stock, and transaction costs and cannot quickly and flexibly adjust the production scale to the optimal scale, namely, the lowest long-term average cost, to obtain maximum production profits. At present, although the fixed cost investment is high in the early stage of digital transformation, the marginal cost of applying digital technology is very low or even negligible due to its non-competitive, reprogrammable, and mobile characteristics, which can continuously reduce the marginal production cost and average production cost of manufacturing enterprises’ products or services, thus relaxing the limitations on the scale expansion of manufacturing enterprises [
49]. Therefore, digital transformation is beneficial for manufacturing companies to acquire economies of scale to further expand the boundaries of their production possibilities. Digital technology also accelerates communication and cooperation between industries and enterprises. In these cases, the transformation of corporate organization, production, and management modes have changed the traditional scale return curve, to a certain extent showing the phenomenon of increasing return to scale, that is, the productivity of enterprises is improved with the expansion of company scale. In addition, that scale effect brought by corporate digital transformation is also reflected in the scope economy in some way. On the one hand, the application of digital technology accelerates information exchange, division, and cooperation of labor within the enterprise, promoting the migration of knowledge, technology, and other resources among different products or services. On the other hand, extensive digital connectivity enables manufacturing enterprises to quickly identify and actively respond to market information and then apply the feedback to their production behavior, thus increasing product diversity and improving production efficiency. Therefore, digital transformation can promote corporate total factor productivity by expanding the scale of enterprises. So, this paper puts forward:
Hypothesis 2. Digital transformation improves corporate total factor productivity mainly through technological innovation, service-oriented manufacturing—especially the embedded one closely related to its core strategy—and scale effects.
Furthermore, this paper argues digital transformation exerts an asymmetric impact on corporate total factor productivity. Companies carrying out digital transformation expend high fixed-cost investment at the beginning, and they cannot quickly reap the returns from that [
45]. With the deep integration of digitalization and manufacturing entities, on the one hand, the increase in collaboration among various departments of the company improves the operational efficiency of the enterprise; on the other hand, the constant application of digital technology may gradually bring about a series of transformation effects such as product and service upgrading, technological innovation capability improvement, and increasing returns on production scale through network effects and Metcalfe effects. If there is an inverted U-shape correlation between them, digital transformation within a certain range tends to exert a positive promotion effect, first rising and then decreasing to zero; that is, it first reaches the inverted U-shape apex, then turns into a negative inhibitory effect. When the degree of digital transformation rises to a particular inflection point, that is, the threshold, its positive effect is the strongest, and beyond that point, it still shows the promoting effect but is obviously weakened. In other words, the influence on corporate total factor productivity brought by digital transformation at different stages may differ to some extent, among which the positive effect shows the law of diminishing marginal effect. Therefore, this paper argues:
Hypothesis 3. The promotional influence on corporate total factor productivity brought by digital transformation at different stages is asymmetric.
4. Empirical Analysis
4.1. Baseline Regression Analysis
The baseline regression analysis is presented in
Table 2. Among them, models (1)–(4) control the fixed effects of enterprises, industries, and years. The models’
is between 0.298–0.391, indicating that the models’ explanatory power is well. Model (1) adds only the primary term, while model (2) also adds a bunch of control variables. The results show that, compared to model (1), the regression coefficient of the primary term
digital in model (2) decreases slightly after adding the control variables but remains positive at the 1% significance level, implying that corporate total factor productivity does be impacted by the current digital transformation vigorously promoted by manufacturing enterprises, which increases with the deepening of digital transformation. This study argues that only considering the primary linear impact from digital transformation to corporate total factor productivity is incomplete, so the quadratic term
digsq is added to models (3) and (4). The results in
Table 2 show that regardless of whether control variables are added, the coefficient of the first term
digital is positive at a 1% significant level, while that quadratic term
digsq is oppositely negative at a 1% significant level, manifesting that the digital paradox does exist and moderate digital transformation can indeed play a positive role on corporate total factor productivity. Moreover, there is a certain nonlinear inverted U-shape trend between them with the deepening of digital transformation. Therefore, our previous theoretical analysis is reasonable, and Hypothesis 1 is verified. After substituting the coefficient estimated in model (4) into the model for derivation, the estimation of digital transformation corresponding to that apex at the inverted U-shape curve is about 5.130, indicating that when corporate digital transformation is about 5.130 in our research samples. In other words, when the measured frequency of digitalization-related words is about 168, the corresponding corporate total factor productivity reaches its maximum. Below this level, digital transformation exerts a positive influence on promoting corporate total factor productivity. However, after exceeding that level, the positive influence turns into a negative. According to the previous descriptive statistics, the average value of digital transformation in the samples is 1.242, and the median value is 1.099, which is far less than 5.130, coupled with the maximum value of the samples is 6.14, showing that the digital transformation extent of listed manufacturing firms is relatively low, most of them are still in the rising stage of the inverted U-shape curve, and vigorously impelling the digital transformation of manufacturing firms in China at the current stage is conducive to elevate their total factor productivity.
From models (2) and (4), the coefficients of the control variables Age, Share, Lev, and Roa are positive, which all go through the test at a 1% significant level, manifesting that the establishment of an enterprise, the shareholding ratio of the top ten shareholders, the asset–liability situation, and the assets income all have a certain positive impact on the total factor productivity, while Intanr’s coefficient is negative at 10% level, showing that higher proportion of intangible assets in enterprises is unfavorable to corporate total factor productivity. Other variables such as enterprise nature (soe), cash holding level (Cash), and market power (Marketp) are not significant, implying their unobvious impact on the total factor productivity of enterprises.
4.2. Robustness Test
This paper examines the robustness from the following perspectives to verify the reliability of previous benchmark results.
As an important measure of enterprise productivity, the current estimation methods of total factor productivity mainly pertain to LP, OP, OLS (ordinary least squares), FE (fixed effects), and GMM (generalized method of moment). To mitigate the interference from different measurement methods on the test results, this study uses the total factor productivity estimated by OP, OLS, FE, and GMM methods as the replacement index of
TFP, respectively, expressed as
tfp_op,
tfp_gmm,
tfp_ols, and
tfp_fe, and regression analysis including the primary term, squared term, and control variables is conducted again. From
Table 3, we can see that the coefficients of
digital are positive and go through the 1% statistical significance test, while the coefficients of
digsq are negative by at least 10% significance, which supports the benchmark regression results. In a word, after changing the measurement method of total factor productivity, corporate digital transformation still tends to significantly promote their total factor productivity, and with the deepening of digital transformation, the two still show an inverted U-shape correlation, which supports the robustness of the benchmark results.
As for control variables, the coefficients of soe and Marketp are still insignificant, while that of Age, Share, Lev, and Roa are still positive, at least at 5% significance. However, unlike the benchmark analysis, in models (3) and (4), the Intanr’s coefficient is still negative but no longer significant. The coefficient for Cash in model (1) is not significant, while positive in model (2) and negative in models (3) and (4) significantly. Taken together, the results are generally robust after replacing the explained variables in the benchmark analysis with total factor productivity estimated by different methods.
- 2.
Change the measurement of digital transformation.
First of all, considering that the text words of the MD&A part in the annual reports of each enterprise vary to some extent, the ratio of total digital-related words number to its whole words of MD&A part in the corresponding annual report is added by 1 of the sample enterprise and then take that logarithm to measure the digitalization degree of the enterprise, which is expressed by
digR and the square term is expressed by
digRsq. Secondly, this paper forms a digital dictionary from five dimensions: big data, cloud computing, artificial intelligence, and blockchain, namely “underlying digital technology” and “digital technology application”, in which there are certain differences in the number of relevant words in each sub-dimension and may affect the measurement error of digital dictionary for different enterprises in different dimensions of digitalization. Therefore, this paper first counts the words frequency of digitization in each dimension to form a digital dimension indicator, then performs annual dimensionless processing on the five-dimensional indicators, and finally adds 1 to take the logarithm to construct the enterprise digital transformation index after adding together the standardized dimensional indicators, which is recorded as
dig_n, and the square term is
dig_nsq.
Table 4 reports the empirical results. The regression coefficients of
digR and
dig_n, the two replacement indicators of enterprise digital transformation, are both positive at a 1% significance, and that of the corresponding square term
digRsq and
dig_nsq are negative at a 1% significance, which are consistent with Hypothesis 1 in our previous theoretical analysis and support the robustness of the previous baseline regression conclusions.
As for control variables, the coefficients of Age, Share, Lev, and Roa are still positive at a 1% significance level, while that of Intanr is still negative at a 10% level. The coefficients of other variables (soe), cash holding level (Cash), and market power (Marketp) are not significant. These things considered, the coefficient values of each control variable are close to those in the benchmark analysis, indicating that Hypothesis 1 still holds after changing the measurement method of digital transformation.
Although the previous study takes into account different measurements of the core variables, the research may still have endogenous problems. On the one hand, promoting digital transformation could improve the corporate business environment and promote enterprise reform and innovation, thus enhancing their total factor productivity. On the other hand, companies with higher total factor productivity may also have more strength and willingness to carry out high-level digitalization to further improve their market position and profitability. Since the digital influence transformation exerted on corporate total factor productivity may objectively have a certain lag, we adopt the lag period of
digital for regression analysis to mitigate the potential impact of this reverse causal relationship on the research conclusions.
Table 5 reports the regression results that contain only the primary term
L.digital, both primary term
L.digital and quadratic term
L.digsq, respectively. It can be found that model (1) performs re-regression merely on the lagged core explanatory variable
L.digital, which coefficient is still positive at the 1% level of statistical significance while slightly smaller than in previous baseline regression. Except for
Intanr is no longer significant, other control variables remain the same as in previous results. In model (2), the coefficient of
L.digital is significantly positive, while that of
L.digsq remains negative, with the significance level dropping to 10%. In addition, except for
Intanr becomes negative and no longer significant, other control variables are in line with previous benchmark analysis. In summary, after analyzing the hysteresis core explanatory variables, the obtained results still support the robustness of our baseline analysis and verify hypothesis 1 convincingly.
- 4.
Different sampling methods.
The digital transformation variables depicted through text analysis in this paper can comprehensively reflect the specific corporate digital transformation operation, but as an important carrier of public disclosure information of listed companies, the specific disclosure content of annual reports is affected by the public willingness of enterprises to a certain extent, and there may be certain differences among different enterprises. For example, some enterprises may exaggerate the implementation of digital transformation, while others may be more cautious in describing the specific transformation operation. To decrease the sample selection bias on test results, we first use simple random sampling to select 50% of the total samples in this paper and re-test. Second, considering that samples with too high or too low total factor productivity may have large sample differences from the overall, the samples of the top 20% and the bottom 20% of total factor productivity are excluded from testing, respectively.
Table 6 reports the test results for different sampling methods. In models (1)–(3), the coefficients of core explanatory variables are significantly positive for the primary term
digital and significantly negative for the quadratic term
digsq, and the coefficient values are not much different from the benchmark regression results. For control variables, coefficients are in line with benchmark results, except for intangible assets (
Intanr), firm nature (
soe), and market power (
Marketp), whose coefficients are slightly different from the benchmark results. It shows that digital transformation could indeed promote corporate total factor productivity. Furthermore, there is an inverted U-shape relationship between them. Therefore, different sampling methods do not affect our basic conclusions.
4.3. Heterogeneity Testing
This study probes the effect that the digital transformation of manufacturing firms exerted on their total factor productivity, using our whole manufacturing samples in the previous test. The previous research verifies that digital transformation could indeed promote corporate total factor productivity; moreover, with the deepening of transformation, the two have a nonlinear inverted U-shape relationship, which passes several robustness tests. It is worth noting that enterprises with different property rights have great differences in financial conditions, policy support, and risk bearing, so the effect of digital transformation in different firms may vary, which requires considering the heterogeneity of the digital impact of enterprises with different property rights in the manufacturing industry. Combined with the urgent requirements of the current green and low-carbon development, considering that some enterprises have been subject to key pollution monitoring by relevant government departments, exploring the impact of digital transformation on total factor productivity by distinguishing whether they are key pollution monitoring enterprises is of immense practical guiding significance. These things considered, since we propose that digital transformation could improve corporate total factor productivity through that scale effect, we should specify the company size to examine whether larger firms show a better transformation effect. Our classification is based on the Statistical Standards for the Division of Large, Small, and Micro-Enterprises published by the National Bureau of Statistics. To be specific, firms with more than 1000 employees are divided into large firm groups, otherwise into small firm groups. So, this paper divides the whole samples by property rights attribute, pollution attributes, and company size and conducts group tests.
Table 7 describes the results. In models (1)–(2), the coefficients of
digital are 0.3489 and 0.2527, both positive at a 1% level significantly, while the quadratic coefficients are −0.4968 and −0.3533 on at least a 5% significant level. However, the absolute values of the coefficients in the state-owned enterprise group are bigger than that in the opposite group. After calculation, the degree of digitization at the inverted U-shape vertex of the two models is 5.278 and 5.299, which are similar to each other and slightly higher than the benchmark analysis value of 5.130, indicating that although digital transformation exerts an obvious promoting effect on corporate total factor productivity in the manufacturing industry with different ownerships, state-owned enterprises seem to own much more strength and determination in promoting digital transformation under their natural reputation, policy, capital, and other advantages, and have achieved relatively better transformation results. Of course, while the extent of digital transformation is too high, the two in both groups show a similar inverted U-shape nonlinear relationship. Among control variables, the coefficient of
Intanr in (1) is insignificant while significantly negative in (2), indicating that state-owned enterprises have relatively sufficient funds and stronger strength in implementing digital transformation, and their total factor productivity is not greatly affected by the proportion of intangible assets. In contrast, many non-state-owned enterprises often face more financial problems. Perhaps the excessive proportion of intangible assets in them inhibits their total factor productivity from increasing. The coefficient of market power (
Marketp) is significantly negative in (1) while significantly positive in (2), indicating that due to the natural advantages, state-owned enterprises with too large market power may lack innovation motivation, which restrains their total factor productivity improvement, while non-state-owned enterprises often have more sense of distress after obtaining certain market power, and will continue to increase innovation investment to maintain market power they achieved, thereby their market power exerts active impact on their total factor productivity.
In models (3)–(4), the regression coefficients of digital in both groups are 0.1884 and 0.2399, all passing the statistical significance test of at least 10%, while the coefficients of the quadratic terms digsq are negative but not significant in (3), showing that promoting digital transformation does significantly improve the total factor productivity of both types of firms. The relationship between digital transformation and total factor productivity in key pollution monitoring unit (pmu) enterprises is more linear and positive, while in non-key pollution monitoring units, a certain inverted U-shape nonlinear relationship appears, indicating that key pollution monitoring units are limited to the influence of the current green development situation as they consume higher energy and emit more pollutants. The empowering role of digital transformation has not yet been fully exerted, and it is necessary to continue to explore and promote the digitalization process, especially for key polluting enterprises in the future. Among the control variables, Intanr is insignificant in (3) and significantly negative in (4), indicating that it has little impact on key pollution monitoring units but has a suppressive impact on general non-key pollution monitoring units. Marketp is positive in (3) and negative in (4) significantly, indicating that since key pollution monitoring enterprises are more supervised if they have certain market forces—that may help them improve the efficiency of pollution control and promote their total factor productivity, while the expansion of market power of general non-key pollution monitoring enterprises may not conducive to market competition and inhibit their total factor productivity.
As for the effect of company size, the coefficients of digital and digsq are 0.2834 and −0.4480 on at least a 1% significant level in model (5), which are similar to the baseline results, demonstrating that digital transformation in large firms exerts an obvious promoting effect on their total factor productivity in the manufacturing industry and the two show a similar inverted U-shape nonlinear relationship with the deepening of transformation. In contrast, the corresponding coefficients in model (6) are no longer significant, implying that digital transformation in small firms does not play the expected role in enhancing their total factor productivity. According to our previous analysis, firms implementing digital transformation cost high investment and endure plenty of risks, and perhaps small firms need more time and endeavor to benefit from that. Therefore, the effect digital transformation exerts on corporate total factor productivity has a significant difference between large and small firms; that is, company size is an important factor while implementing digital transformation. Among the control variables, Cash and Intanr are insignificant in (5) and significantly negative in (6), indicating that small firms have more fund shortage problems compared with large firms. Marketp is negative in (5) and positive in (6) significantly, indicating that since small firms often have various inadequate resources, if they have certain market forces, that may help them improve their reputation and capabilities to acquire more advantages to improve their performance and promote their total factor productivity, while the expansion of market power of large firms may not conducive to market competition and inhibit their total factor productivity.
4.4. Sensitivity Analysis
This study uses observational data to evaluate the effect on manufacturing total factor productivity exerted by its digital transformation. We conclude that moderate digitalization improves corporate total factor productivity, while digitalization beyond a certain level instead reduces it. To evaluate the influence of objective unmeasured potential confounders on the study results, sensitivity analysis is conducted in this paper. Referring to the regression results of model (2) in our previous baseline analysis, the point estimate between digital transformation and total factor productivity is 0.1878, the 95% confidence interval is [0.1307, 0.2449], and the sensitivity analysis results are described in
Table 8 below. The point estimating E-value for the zero-value effect is 10.124, and the E-value for the upper CI (confidence interval) limit is 7.632 in column (1), indicating that the minimum correlation strength between unmeasured confounders and digitization and total factor productivity is 10.124 or 7.632 for the transfer of point estimates or CI to zero, while weaker confounders fail to do so. Column (2) shows that if the RR (risk ratio) estimate is converted to 0.2879, the E-value of the required point estimate is 2.436, and the E-value of the CI upper limit is 1.629. This indicates that an unmeasured confounder related to digitalization and total factor productivity could move RR from 0.18979 to 0.2879 if the minimum correlation strength reaches 2.436 times, while weaker confounders could not.
Similarly, to change the upper confidence limit from 0.2449 to 0.2879, unmeasured confounders with a strength of 1.629 associated with digitization and total factor productivity can do, while weaker confounders cannot. Column (3) shows if the estimate is converted to 0.3879, the E-value for the point estimate is 3.548, and the E-value for the CI upper limit is 2.545. This indicates that when a confounder associated with digitalization and total factor productivity shifts the observed risk ratio RR estimate from 0.1879 to 0.3879, the risk ratio RR of the confounder is at least 3.548. Similarly, an unmeasured confounder-related risk ratio RR of 2.545 for digitalization and total factor productivity can change the upper confidence limit from 0.2449 to 0.3879, while weaker confounders cannot. The results of comprehensive sensitivity analysis show that the benchmark regression in this paper is relatively robust.
6. Discussion and Conclusions
In the current society, China’s economy has entered a fresh, high-quality phase in which the manufacturing industry occupies a vital strategic position because of its characteristics of rapid technological progress, economies of scale, and significant technology diffusion effects. With the popular development of the digital economy, deeply discussing the influence manufacturing digital transformation exerts on its total factor productivity is conducive to fully understanding its influence on promoting the development of manufacturing firms so as to further guide enterprises’ investment decisions in digital capabilities and form important policy insights to guide their high-quality development. On the basis of existing literature, this paper theoretically analyses and empirically examines the influence that digital transformation exerts on corporate total factor productivity and internal mechanisms from the perspective of micro-enterprises, which uses 2013 to 2020 of China’s listed A-share manufacturing firms and constructs the digital transformation index from their crawled annual reports by text analysis method.
The study finds that manufacturing digital transformation does obviously enhance their total factor productivity; moreover, as digital transformation deepens, the promotion role of digitalization gradually weakens after reaching the inflection point of maximum value until it turns to inhibition, that is, the two have a nonlinear inverted U-shape relationship, which stands up several robustness tests and sensitivity analysis. We propose three mechanisms through which digital transformation can exert influence on corporate total factor productivity. First, it can promote the level of service-oriented manufacturing, especially the embedded one that is closely related to the core business of firms, to enhance their competitiveness. Second, it can improve the input–output efficiency of technological innovation, as well as the quality and efficiency of their operations. Third, it can expand the boundaries of production possibilities and achieve economies of scale, which all contribute to their total factor productivity. Moreover, in firms with different ownership properties, pollution attributes, and company size, the effect on manufacturing total factor productivity exerted by digital transformation is significantly heterogeneous. Specifically, the digital transformation of state-owned, non-key pollution monitoring and large firms exploit a better effect on total factor productivity enhancement. In addition, there is an asymmetry in the positive promotion influence digital transformation exerts on corporate total factor productivity; that is, there is a threshold value (digital = 4.094), and the positive effect reaches the maximum when digital transformation reaches this point, while the positive effect gradually decreases after exceeding this threshold, showing the law of diminishing marginal effect.
At present, the overall digital transformation degree in China’s manufacturing firms has been in the rising area of that inverted U-shape curve, and most of them have not yet reached the threshold of the greatest promotion effect, so the current should strengthen the investment in digital infrastructure such as artificial intelligence and industrial Internet and improve the serviceability of digital infrastructure for manufacturing enterprises. The effect of digital transformation lies in the capability to obtain and comprehensively utilize data. Constructing digital infrastructure can expand data sources and promote data flow velocity over different nodes so as to promote the quality and efficiency of enterprise data utilization, which is an important condition for corporate digital transformation. At present, manufacturing digitalization in China is still generally not high, and there is still a lot of room for development in the future. As far as manufacturing enterprises themselves are concerned, they should follow the principle of differentiation, carry out digital transformation abiding by their own business characteristics, and pay attention to the powerful role digital transformation plays in enhancing embedded service-oriented manufacturing, technological innovation, and economies of scale. In the process of digital transformation, manufacturing companies can continuously learn and explore the integration and adaptation of embedded service-oriented manufacturing, technological innovation, and digitalization to elevate their products’ quality and operation efficiency. As far as we are concerned, firms that belong to the key pollution monitoring units do not implement adequate transformation and have more disadvantages compared with the non-key ones to conquer. So, the government may create more friendly policies to help the key ones to grow stronger to accelerate their digital transformation. In addition, the government and society also should pay attention to guide and help small firms to acquire the essential resources they urgently need in their digital transformation. Since many small and medium-sized firms are not strong enough, they can start with informatization and intelligence, then steadily promote transformation and enhance efficiency. In addition, this paper also finds evidence that excessive digitalization may indeed damage the productivity of enterprises, so some large and already high-transformed firms should not blindly pursue extensive excessive digital transformation and be vigilant against the occurrence of digital paradox.
Although this paper portrays the whole extent of corporate digital transformation using the text analysis method, it is still necessary to further consider how to measure the digitalization of each process within an enterprise more accurately to study the consequences of digital transformation more specifically. However, considering the availability of data, our paper is based on 2013 to 2020 of A-share listed manufacturing firms and does not include unlisted manufacturing firms whose data are difficult to obtain, so it is worth exploring whether the results obtained are also applicable to unlisted manufacturing enterprises in the future.