Digital Technology Deployment and Improved Corporate Performance: Evidence from the Manufacturing Sector in China

Abstract

With global supply chains being reshaped and costs surging, China’s manufacturing sector faces mounting pressure to retain its position as the world’s largest manufacturing center. Meeting this challenge demands the full mobilization of digital factors, which has attracted increasing academic attention. However, limited research has examined how the effective integration of digital factors with traditional production factors can improve corporate performance. With data on Chinese manufacturing enterprises from the A-share market, this study employs a fixed effect model and a mediating effect model to analyze how the synergies between digital and traditional factors enhance corporate performance. Further, it illustrates the heterogeneous impacts across different types of enterprises. The results reveal three key findings. First, the synergies between digital and traditional factors significantly enhance corporate performance, with digital–capital synergy proving more effective than digital–labor synergy. Second, this synergy promotes performance improvement through three primary mechanisms: strengthening internal control quality, fostering business model innovation, and increasing product differentiation. Third, the performance effects of multi-factor synergies vary considerably across enterprise types, being more pronounced in non-state-owned enterprises, firms with strong digital attributes, and firms without political connections. Overall, this study offers valuable insights for manufacturing firms seeking a competitive edge in high-end and intelligent manufacturing within an increasingly globalized competitive landscape.

1. Introduction

Breakthroughs in artificial intelligence have initiated a new wave of industrial transformation, exerting a disruptive impact on the global manufacturing sector. Studies indicate that the higher a country’s position in the manufacturing value chain, the greater the economic benefits it can attain [1,2]. Consequently, developed and developing countries are vying for a leading position in the global manufacturing competition. China stands out as the most representative manufacturing nation worldwide. According to the World Development Indicators (WDI) database of the World Bank, the Chinese manufacturing sector has maintained the most enormous scale in the world for fourteen consecutive years [3]. However, Chinese manufacturing enterprises have long faced the dilemma of being “big but not strong”. On the one hand, compared with developed countries Chinese manufacturers lack core technologies, and specific high-end technologies still need to be imported [4]. On the other hand, due to the advancement of digital technologies and the steady increase in the costs of labor, capital, and other production factors, the traditional advantages of Chinese manufacturers are gradually diminishing. A clear indication of this trend is that several prominent foreign companies, including Apple, Samsung, and Foxconn, have relocated their manufacturing facilities from China to countries such as India and those in Southeast Asia.

In recent years, the Chinese government has actively promoted the synergistic development of digital factors alongside traditional factors to enhance the overall performance of the manufacturing industry. Corporate performance has long been a topic of international academic interest. Scholars, however, have increasingly examined the effects of intelligent manufacturing, government intervention, innovation management, and the broader social environment [5,6,7,8]. With the advent of the digital age, although some scholars have examined how digital transformation and digital innovation can enhance corporate performance [9,10], the question of how the synergies among various factors affect corporate performance remains largely unexplored. In economics, the term “digital factor” refers to using digital technologies and data as a key input driving economic activity, productivity, and growth [11]. A pressing question, therefore, is how digital factors can synergize with traditional factors to enhance corporate performance. This issue has become particularly urgent for Chinese manufacturing enterprises.

This study draws on data from publicly listed manufacturing firms in China to examine how the synergies between the digital and traditional factors enhance corporate performance. The potential contributions of this study are primarily as follows: (1) it highlights that digital factors do not operate in isolation but require synergies with other production factors, providing a novel perspective for manufacturing enterprises seeking to secure a competitive advantage; (2) it develops a new theoretical framework to clarify how the synergistic effects of multiple factors influence corporate performance; (3) by addressing the challenges faced by manufacturing enterprises in digitization, it fills a research gap concerning the role of digital factors as productive inputs within the context of factor synergy.

The subsequent sections of this paper are organized as follows: Section 2 presents a comprehensive literature review. Section 3 describes the data sources, variable measurement indicators, and the research methodology. Section 4 reports the empirical findings. Finally, Section 5 and Section 6 provide the study’s findings, highlighting the main conclusions, contributions, practical implications, recognized limitations, and directions for future research.

2. Literature Review

Factors of production are central to shaping industry competitiveness [12]. Competitive advantages can be made and sustained when industry and technology choices align with the comparative advantages determined by factor endowments [13]. With the advent of the digital era, enterprises must establish a compatible structure that integrates new and traditional factors, thereby enhancing sustainable competitiveness [14] and improving corporate performance. Section 2 develops the theoretical framework and proposes the research hypotheses to reveal how multi-factor synergy promotes corporate performance in the digital economy era.

2.1. Multi-Factor Synergy and Corporate Performance

At the outset of this section, several clarifications are necessary. First, because the synergies between digital and traditional factors are essentially an intra-firm process of resource utilization and allocation, the digital factor in this study refers specifically to digital resources deployed internally by enterprises [15,16]. Second, this paper categorizes the synergies between digital and traditional factors into two dimensions: digital–labor factor synergy and digital–capital factor synergy. Third, within the same industry domain, enterprises typically build competitive advantages in two critical areas: cost and technology [13]. Moreover, gaining a competitive advantage is widely regarded as a key pathway for firms to expand market share, which translates into improved corporate performance.

The synergy effect of the digital–labor factor on corporate performance can be understood as a two-way process in which digital technologies optimize labor, and labor, in turn, transforms digital applications. On the one hand, the digital factor enhances labor force structure, creating cost-based competitive advantages. Digital office systems and intelligent production lines substitute repetitive tasks and low-skilled labor [17,18,19], facilitate labor division based on specialization, and reduce labor costs [20,21]. On the other hand, labor contributes to the transformation of the digital factor, generating technology-based competitive advantages. As workers’ digital skills improve, more decision-making power is delegated to machines, giving rise to advanced digital systems such as fully automated factories, unmanned supermarkets, and driverless vehicles. In summary, when enterprises establish a synergy between digital and labor factors, industrial upgrading can be aligned with competitive advantage, thereby improving corporate performance. So, it is hypothesized that:

H1a. 

Digital–labor factor synergy positively influences corporate performance.

Second, the synergy effect of the digital–capital factor on corporate performance can be understood as a dual process in which digital technologies enhance cost efficiency. In contrast, capital facilitates the commodification of digital resources. On the one hand, the digital factor generates cost-based competitive advantages. Implementing automation technologies optimizes production processes and supply chain management systems, improves capital utilization, and significantly reduces manufacturing and managerial costs [22]. On the other hand, capital markets promote the commodification of digital factors, thereby creating technology-based competitive advantages. Through leasing, equity participation, and other financial instruments, capital transforms digital resources into tradable commodities, reinforcing their technological value and expanding their application scope. In summary, when enterprises seek the synergies between digital and capital factors they gain greater affordability and initiative to sustain competitive advantage, thereby achieving sustainable performance. Accordingly, this study proposes the following hypothesis:

H1b. 

Digital–capital factor synergy positively influences corporate performance.

2.2. Synergistic Pathways for Increasing Corporate Performance

Firstly, Liu et al. (2020), Ameye et al. (2024), and Usai et al. (2021) [23,24,25] suggest that the synergies between digital and traditional factors can significantly enhance the internal control quality of enterprises. Such synergies not only reshape internal management processes [23] and foster a sustainable environment for cost reduction and efficiency gains [24], but also facilitate continuously updating risk control technologies. This enables firms to better identify and respond to internal control risks, improving overall performance [25]. Accordingly, this study proposes the following hypothesis:

H2a. 

Multi-factor synergies can improve the quality of internal control.

Secondly, Kazantsev et al. (2023), Chatterjee et al. (2023), and Abbas et al. (2021) [26,27,28] suggest that the effective synergies between digital and traditional factors can significantly foster business model innovation. Such synergies not only facilitate flexible employment modes and enable the commercialization of labor [26], but also ensure the seamless integration of personalized customization with large-scale production and turnover, enhancing performance levels through greater responsiveness and efficiency [27,28]. Accordingly, this study proposes the following hypothesis:

H2b. 

Multi-factor synergies can innovate the business model.

Thirdly, Obradovits et al. (2023), Kokkodis et al. (2023), and Durmusoglu et al. (2021) [29,30,31] emphasize that the synergies between digital and traditional factors can drive the development of product differentiation strategies. This synergy meets the diverse needs of consumers, who are willing to pay a premium for high-quality services [29,30], and achieves extreme cost efficiency through automated production lines and digital management platforms, thereby enhancing overall corporate performance [31]. Based on the above discussion, this study proposes the following hypothesis:

H2c. 

Multi-factor synergies can realize product differentiation.

With the arguments above, multi-factor synergies can improve the quality of internal control (H2a), drive business model innovation (H2b), and contribute to product differentiation (H2c). Moreover, Justin Lin (2023) [13] argues that to achieve an optimal production structure in the new environment changes in comparative advantage must be guided by the structure of factor endowments, thereby generating competitive advantage. Accordingly, changes in an enterprise’s comparative advantage are primarily reflected in its governance structure, business model, and product structure [13]. The closer these structures align with the Pareto-optimal state, the more likely it is that the enterprise will gain a competitive advantage and improve performance. Therefore, the cumulative effect of these influences (H2a–H2c) establishes the preconditions for enhancing corporate performance. Multi-factor synergies can enhance enterprise performance through three main pathways: strengthening internal control quality, fostering business model innovation, and achieving product differentiation. The specific mechanisms are illustrated in Figure 1.

3. Data and Methods

As the largest manufacturing country and a representative emerging economy, China contributes nearly 30% of global industrial output [3]. To ensure data availability and consistency of statistical standards, this study selects A-share-listed manufacturing firms in China from 2013 to 2022 as the research sample.

3.1. Data and Sources

The research sample consists of A-share-listed manufacturing firms in Shanghai and Shenzhen from 2013 to 2022. Except for internal control quality data obtained from the DIB Internal Control and Risk Management (DIB) database, all other data are sourced from the China Stock Market & Accounting Research (CSMAR) database and the annual reports of listed companies. The data were processed as follows: (1) firms with special treatment (ST), delisting risk warning (*ST), particular transfer (PT), as well as terminated or suspended listings were excluded; (2) non-manufacturing firms and firms with missing data were removed; (3) Winsorization was applied to mitigate the influence of outliers. After these steps, an unbalanced panel of 1650 listed firms was retained as the valid sample. A natural logarithm transformation was applied to the core variables after adding one to address the issue of zero values.

3.2. Variables

The key variables used in this study are defined as follows:

Dependent variable: Corporate performance (Per). Corporate performance is measured by operating income [32]. Since the synergistic effects of production factors are most directly reflected in production and operational activities, operating income provides a reliable proxy. To ensure consistency, a value of 1 is added before taking the natural logarithm, and the variable is denoted as Per.

Explanatory variables: Digital–labor factor synergy ($D·L$) and digital–capital factor synergy ($D·K$). These variables are constructed as the interaction terms of the digital factor with the traditional factor. (1) Digital factor (D): Following “International Accounting Standard 38—Intangible Assets”, the digital factor satisfies the criteria of identifiability, controllability, and reliably measurable cost. It is regarded as the book value of intangible assets associated with digital utilization [15,16]. (2) Labor factor (L): Measured by the number of employees. (3) Capital factor (K): Measured by total assets.

Control variable: Referring to influencing factors of corporate performance in existing studies [5,6,7,8], this study chooses the control variables as follows: ownership concentration (own1 and own10); the ratio of independent directors (Inde); dual-role occupancy (Dual); internal financial status (Cash, Inta, and Flow); and the growth condition (BTM). The measurement methods of all variables are summarized in

Table 1. Measurement of variables.

Variable Attribute	Variable Name	Symbol	Measure Method
Dependent variable	Corporate performance	Per	Operating income
Explanatory variables	Digital factor	D	The book value of intangible assets linked to digital utilization
	Capital factor	K	The total assets
	Labor factor	L	The number of employees
	Digital–capital factor synergy	$D·K$	The cross-multiplication term of the digital factor and the capital factor
	Digital–labor factor synergy	$D·L$	The cross-multiplication term of the digital factor and the labor factor
Intermediary variables	Internal control quality	ICI	The internal control index from the DIB database
	Business model innovation	BMI	Ratio of word frequencies associated with BMI in the enterprise to word frequencies in the industry
	Product differentiation	PD	Corporate selling expenses
Control variables	The current ratio	Flow	Ratio of current assets to current liabilities
	Enterprise value	BTM	Ratio of book-to-market
	The shareholding ratio	Own1	Percentage of total shares held by the biggest shareholder
		Own10	Percentage of total shares held by the top ten shareholders
	The ratio of intangible assets	Inta	Intangible assets divided by total assets
	Cash holdings	Cash	Cash and trading financial assets
	The proportion of independent board members	Inde	Divide the number of independent directors by the total number of board members
	Bi-power unison	Dual	Dual = 1 if the chairman and chief executive officer are the same person; otherwise, Dual = 0
Instrumental variables	Relief amplitude	iv1	Standard deviation of geographic elevation
	Digital policy shock	iv2	Frequency of digital-economy-related policies in government reports
	Industry average	iv3	Industry average of multi-factor synergies, excluding the enterprise itself
Variables in the robustness test	Replacing Per	Per1	Ratio of enterprise revenue to the total revenue of the industry
	Replacing K	K1	Net value of fixed assets
	Replacing L	L1	Wage of enterprise employees

.

3.3. Methods

In order to control time and individual, a fixed effect model is adopted. To understand whether multi-factor synergies can improve corporate performance and to verify Hypotheses 1a,1b, this study builds the following regression model:

$${Per}_{it}={\beta }_0+{\beta }_1{D}_{it}·F_{it}+{\beta }_2{X}_{it}+{\delta }_i+{\epsilon }_t+{\mu }_{it}$$

(1)

where ${Per}_{it}$ is the performance level; D is digital factor; $F=K,L$, respectively, which are the capital and labor factors invested by the firm; the synergy effect between digital factor and traditional factor is denoted as $D·F$; X is control variables; ${\delta }_i$ and ${\epsilon }_t$ are the individual and year fixed-effect; and ${\mu }_{it}$ is the random disturbances. Further, to test the function route of the two and verify Hypotheses 2a–2c, this study builds the following model for the mechanism test:

$$M_{it}={\alpha }_0+{\alpha }_1{D}_{it}·F_{it}+{\alpha }_2{X}_{it}+{\delta }_i+{\epsilon }_t+{\mu }_{it}$$

(2)

where $M_{it}$ stands for intermediary variables, including three indicators: internal control quality, business model innovation and product differentiation degree. In summary, a methods graph is shown in Figure 2.

4. Empirical Results

This section constructs an empirical analysis framework that incorporates the key variables and enables us to provide transparent and credible interpretations of corporate behaviors through descriptive statistics, benchmark regression, mechanism testing, and heterogeneity analysis.

4.1. Descriptive Statistics

Table 2. Descriptive statistics.

Variable Attributes	Variable	N	Mean	Sd.	Min	Max
Dependent variable	Corporate performance (Per)	13,961	21.522	1.362	18.362	25.63
Explanatory variables	Digital–capital synergy ($D·K$)	13,961	15.835	1.862	10.669	21.038
	Digital–labor synergy ($D·L$)	13,961	19.967	53.589	0.008	447.399
Control variables	The current ratio (Flow)	13,961	2.364	2.105	0.284	13.836
	Enterprise value (BTM)	13,961	32.164	13.814	8.16	73.19
	The shareholding ratio of the biggest shareholder (Own1)	13,961	56.797	14.37	23.1	90.62
	The shareholding ratio of top ten shareholders (Own10)	13,961	0.045	0.039	0	0.323
	The ratio of intangible assets (Inta)	13,961	0.187	0.124	0.014	0.66
	Cash holdings (Cash)	13,961	3.101	0.501	2	5
	The proportion of independent board members (Inde)	13,961	4.917	7.39	0	28.068
	Bi-power unison (Dual)	13,961	21.522	1.362	18.362	25.63

presents the descriptive statistics. The standard deviation of corporate performance (Per) is 1.362, indicating a pronounced imbalance in performance across the manufacturing sector. The mean value of digital–capital factor synergy ($D·K$) is 15.835 with a standard deviation of 1.862, suggesting considerable variation in digital–capital factor synergy among Chinese manufacturing firms. In comparison, the variation in digital–labor factor synergy ($D·L$) is even larger, implying that Chinese manufacturing enterprises are relatively more proficient in achieving digital–capital synergy and have established a more sustainable mechanism for integrating digital and capital factors.

4.2. Benchmark Result

This paper employed a fixed-effect model to examine the nexus between multi-factor synergy and corporate performance. The benchmark regression results are reported in

Table 3. Benchmark results of multi-factor synergies on corporate performance.

Variable	Per
	H1a			H1b
	(1)	(2)	(3)	(4)	(5)	(6)
$D·K$	0.3028 ***	0.2812 ***	0.1901 ***
	(0.0143)	(0.0128)	(0.0142)
$D·L$				0.0076 ***	0.0056 ***	0.0030 ***
				(0.0005)	(0.0006)	(0.0005)
Flow	−0.1285 ***	−0.0595 ***	−0.0531 ***	−0.1586 ***	−0.0943 ***	−0.0641 ***
	(0.0112)	(0.0071)	(0.0069)	(0.0112)	(0.0080)	(0.0071)
BTM	0.0114 ***	−0.0038 *	−0.0015	0.0122 ***	−0.0102 ***	−0.0025
	(0.0020)	(0.0019)	(0.0019)	(0.0020)	(0.0023)	(0.0020)
Own1	0.0000	0.0005	0.0053 ***	0.0007	−0.0024	0.0067 ***
	(0.0017)	(0.0016)	(0.0016)	(0.0017)	(0.0018)	(0.0018)
Own10	−2.2922 ***	−2.3630 ***	−2.0103 ***	−3.1153 ***	−2.6526 ***	−1.9554 ***
	(0.4631)	(0.3996)	(0.3947)	(0.4722)	(0.5112)	(0.4490)
Inta	0.3475 **	0.2130 **	0.0371	0.4873 ***	0.5048 ***	0.0545
	(0.1739)	(0.0869)	(0.0899)	(0.1840)	(0.0954)	(0.0901)
Cash	0.3807 ***	0.0498 **	0.0755 ***	0.4226 ***	0.0662 **	0.1004 ***
	(0.0401)	(0.0232)	(0.0227)	(0.0437)	(0.0261)	(0.0237)
Inde	0.0115 ***	0.0016	0.0015	0.0132 ***	0.0026	0.0018
	(0.0025)	(0.0018)	(0.0017)	(0.0027)	(0.0020)	(0.0018)
Dual	−0.1285 ***	−0.0595 ***	−0.0531 ***	−0.1586 ***	−0.0943 ***	−0.0641 ***
	(0.0112)	(0.0071)	(0.0069)	(0.0112)	(0.0080)	(0.0071)
Individual fixed	No	Yes	Yes	No	Yes	Yes
year fixed	Yes	No	Yes	Yes	No	Yes
N	13,750	13,645	13,645	13,749	13,644	13,644
R2	0.493	0.906	0.911	0.440	0.883	0.905

Note(s): the robust standard errors are enclosed in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

. Columns (1)–(3) present the effects of digital–capital synergy on corporate performance after controlling for year fixed effects, individual fixed effects, and two-way fixed effects, respectively. The results indicate that digital–capital synergy positively impacts corporate performance, supporting H1a. Similarly, columns (4)–(6) report the digital–labor synergy results under the same fixed-effect settings. The findings demonstrate that digital–labor synergy also exerts a significantly positive effect on corporate performance, thus verifying H1b.

4.3. Robustness Test

Referring to Lee et al. (2022) [33], this paper further employs the robustness tests by replacing the explained variable and the explanatory variables, and strengthening the fixed effect and the instrumental variable test. The definitions of variables are shown in Table 1 of Section 3.2. According to the instrumental variables of the digital factor in existing studies [14,15,16], this paper chooses the instrumental variables as follows: relief amplitude (iv1), digital economy policy shock (iv2), and industry average (iv3).

The regression results are listed in

Table 4. Robustness test.

Variable	Per1		Per
	H1a	H1b	H1a	H1b	H1a	H1b
	Replace the dependent variable		Replace the explanatory variables		Enhance the fixed effect
	(1)		(2)		(3)
$D·K$	0.0008 **				0.1959 ***
	(0.0004)				(0.0165)
$D·L$		0.00002 ***				0.0029 ***
		(0.0000)				(0.0005)
$D·K1$			0.00007 ***
			(0.0000)
$D·L1$				0.00001 ***
				(0.0000)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Individual fixed	Yes	Yes	Yes	Yes	Yes	Yes
year fixed	Yes	Yes	Yes	Yes	Year # City	Year # City
N	13,645	13,645	13,645	13,645	12,544	12,543
R2	0.820	0.819	0.919	0.9183	0.939	0.934
Variable	Per
	H1a			H1b
	Instrumental variable test
	(4)
	iv1	iv2	iv3	iv1	iv2	iv3
$D·K$	0.3549 ***	0.3959 ***	0.1592 ***
	(0.0130)	(0.0108)	(0.0206)
$D·L$				0.0048 ***	0.0053 ***	0.0051 ***
				(0.0022)	(0.0002)	(0.0006)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Individual fixed	Yes	Yes	Yes	Yes	Yes	Yes
year fixed	Yes	Yes	Yes	Yes	Yes	Yes
N	10,889	10,897	10,800	10,886	10,894	10,797
R2	0.998	0.998	0.998	0.997	0.997	0.997
Anderson LM	1988.919 *** (0.000)	2955.724 *** (0.000)	860.933 *** (0.000)	4606.928 *** (0.000)	9262.296 *** (0.000)	819.777 *** (0.000)
Cragg–Donald Wald F	2429.141	4048.765	933.863	7973.071	6200	885.572

Note(s): the robust standard errors are enclosed in parentheses. *** p < 0.01, ** p < 0.05.

. According to column (1)–(3) in Table 4, the regression coefficients of digital–capital factor synergy and digital–labor factor synergy are all significantly positive at the 1% level (0.0008 for $D·K$ and 0.00002 for $D·L$ when replacing the dependent variable; 0.00007 for $D·K$ and 0.00001 for $D·L$ when replacing the explanatory variable; and 0.1959 for $D·K$ and 0.0029 for $D·L$ when enhancing the fixed effect).

According to column (4) in Table 4, the Anderson LM value and the Cragg–Donald Wald F value have passed the under-identification test and the weak instrumental variable test. This suggests that the instrumental variables are all strong predictors of multi-factor synergy. Additionally, there is a significant positive effect of multi-factor synergy on corporate performance (0.3549 for $D·K$ and 0.0048 for $D·L$ when using iv1; 0.3959 for $D·K$ and 0.0053 for $D·L$ when using iv2; and 0.1592 for $D·K$ and 0.0051 for $D·L$ when using iv3). The results are consistent with the previous results: H1a and H1b are further supported.

4.4. Mechanism Test

Based on the literature review presented earlier, this study believes multi-factor synergies can enhance internal control quality, facilitate business model innovation, and foster product differentiation, as empirically examined in Model (2). These factors collectively contribute to improving corporate performance. The mechanism test results are shown in

Table 5. Mechanism effect.

Variable	ICI		BMI		PD
	H2a		H2b		H2c
	(1)	(2)	(3)	(4)	(5)	(6)
$D·K$	12.0371 ***		0.8239 ***		0.1978 ***
	(2.7203)		(0.3172)		(0.0183)
$D·L$		0.1120 ***		0.0338 ***		0.0037 ***
		(0.0582)		(0.0126)		(0.0005)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Individual fixed	Yes	Yes	Yes	Yes	Yes	Yes
Year fixed	Yes	Yes	Yes	Yes	Yes	Yes
N	13,645	13,644	13,645	13,644	13,607	13,606
R2	0.489	0.487	0.636	0.637	0.905	0.901

Note(s): the robust standard errors are enclosed in parentheses. *** p < 0.01.

.

First, columns (1) and (2) in Table 5 test the mechanism effect of the internal control quality ICI, which employs the internal control index from the DIB database for measurement [34]. Multi-factor synergies have a positive impact on ICI (12.0371 for $D·K$ on ICI, 0.1120 for $D·L$ on ICI), which shows that both digital–labor synergy and digital–capital synergy can significantly improve ICI, thereby improving corporate performance. The results support H2a.

Second, columns (3) and (4) in Table 5 examine the mediating effect of business model innovation (BMI), which is measured as the ratio of keyword frequency related to BMI within an enterprise to the total word frequency in the industry [27]. The specific method follows: First, the authors compiled a keyword list capturing BMI: internet, mobile internet, e-commerce, electronic commerce, internet marketing, internet thinking, internet-based applications, and related terms. Second, Python 3.10 was employed to scrape the annual report text for each firm. Third, the occurrences of BMI-related keywords in each report were tallied. Fourth, these counts were aggregated to the industry level using two-digit industry codes. Finally, a firm’s BMI intensity was proxied by its BMI keyword frequency ratio to the corresponding industry total. The results indicate that multi-factor synergies positively influence BMI (0.8239 for $D·K$ on BMI, 0.0338 for $D·L$ on BMI), suggesting that both digital–labor and digital–capital synergies significantly enhance BMI. This, in turn, enables firms to develop value-added new business models and improve corporate performance. These findings provide support for H2b.

Finally, columns (5) and (6) in Table 5 examine the mechanism effect of product differentiation (PD), which employs corporate selling expenses for measurement [35]. The coefficient of multi-factor synergy is significant at 1% level (0.1978 for $D·K$ on PD, 0.0037 for $D·L$ on PD), which suggests that both digital–labor synergy and digital–capital synergy can optimize the PD, satisfy the differentiated needs of consumers, and increase revenue and market share, thereby enhancing corporate performance. The results obviously support H2c.

4.5. Heterogeneity Test

Furthermore, the authors investigate the following question: Does this positive impact have heterogeneity across firms with different attributes, such as ownership, industry digital attribute, and political connection? This issue is addressed here.

The authors first examine the heterogeneity across ownership types. Column (1) of

Table 6. Heterogeneity of ownership attribute.

Variable	State-Owned	Non-State-Owned	State-Owned	Non-State-Owned
	(1)	(2)	(3)	(4)
$D·K$	0.1593 ***	0.1992 ***
	(0.0269)	(0.0165)
$D·L$			0.0023 ***	0.0035 ***
			(0.0008)	(0.0006)
Control variables	Yes	Yes	Yes	Yes
Individual fixed	Yes	Yes	Yes	Yes
Year fixed	Yes	Yes	Yes	Yes
N	3662	9929	3662	9929
R2	0.944	0.912	0.941	0.906
Fisher’s permutation test (p-value)	0.041		0.011

Note(s): the robust standard errors are enclosed in parentheses. *** p < 0.01.

shows that multi-factor synergies positively contribute to corporate performance for both the state-owned and the non-state-owned enterprises. The coefficients pass Fisher’s permutation yest at the 10% level (p-values: 0.041 for $D·K$ and 0.011 for $D·L$). Moreover, the synergies between digital and traditional factors have a more pronounced effect in non-state-owned enterprises, with coefficients of 0.1992 ($D·K$) and 0.0035 ($D·L$). A possible explanation is that, due to the rigidity of ownership and entrenched development models, state-owned enterprises tend to be less adaptive, which can dampen the performance-enhancing effects of factor synergy.

The authors next investigate heterogeneity across industries with different digital attributes. Following Li et al. (2023) [36], this study classifies Printing and Recorded Media Reproduction and Manufacture of Cultural, Educational, Arts-and-Crafts (as defined in the National Economic Industry Classification, GB/T 4754-2017 [37]) as manufacturing sectors with digital attributes. The results, presented in

Table 7. Heterogeneity of industry digital attribute.

Variable	With the Digital Attribute	Without the Digital Attribute	With the Digital Attribute	Without the Digital Attribute
	(1)	(2)	(3)	(4)
$D·K$	0.2958 ***	0.1787 ***
	(0.0481)	(0.0146)
$D·L$			0.0041 ***	0.0025 ***
			(0.0008)	(0.0005)
Control variables	Yes	Yes	Yes	Yes
Individual fixed	Yes	Yes	Yes	Yes
Year fixed	Yes	Yes	Yes	Yes
N	1375	12,254	1375	12,254
R2	0.942	0.925	0.936	0.920
Fisher’s permutation test (p-value)	0.000		0.004

Note(s): the robust standard errors are enclosed in parentheses. *** p < 0.01.

, indicate that enterprises in digital-intensive industries exhibit more substantial synergy effects between digital and traditional factors on corporate performance (0.2958 for $D·K$, 0.0041 for $D·L$). This finding suggests that, in the context of the digital economy, firms embedded in digital-intensive industries are more adaptive to shifts in comparative advantage, thereby gaining a competitive edge earlier.

The authors further examine the heterogeneity concerning political connection. Following Cheng et al. (2023), directors are defined as politically connected if they have previous experience in government or related departments [38].

Table 8. Heterogeneity of political connection.

Variable	With the Political Connection	Without the Political Connection	With the Political Connection	Without the Political Connection
	(1)	(2)	(3)	(4)
$D·K$	0.1632 ***	0.1939 ***
	(0.0233)	(0.0176)
$D·L$			0.0024 ***	0.0028 ***
			(0.0007)	(0.0006)
Control variables	Yes	Yes	Yes	Yes
Individual fixed	Yes	Yes	Yes	Yes
year fixed	Yes	Yes	Yes	Yes
N	3630	9805	3630	9805
R2	0.952	0.927	0.949	0.922
Fisher’s permutation test (p-value)	0.023		0.091

Note(s): the robust standard errors are enclosed in parentheses. *** p < 0.01.

shows that the effect of multi-factor synergy on corporate performance is more pronounced in firms without a political connection (0.1939 for $D·K$, 0.0028 for $D·L$). A plausible explanation is that politically connected firms can more easily obtain preferential resources, such as policy subsidies or financial support, and often prioritize short-term gains. Consequently, they may invest less effort in the long-term integration of digital and traditional factors.

5. Discussion

This study investigates the synergistic effect of digital and traditional factors on corporate performance in the manufacturing industry. It employs a fixed effect model and a mediating effect model to examine how the synergies between digital factors and traditional production factors enhance corporate performance, and further illustrates the heterogeneous impacts across different types of enterprises.

5.1. Findings

First, the direct effect of the synergies between digital factors and traditional production factors on corporate performance was examined. The benchmark results reported in Section 4.2 support hypotheses H1a and H1b, indicating that the synergistic interaction between digital (new) and traditional (existing) factors can generate a virtuous cycle of sustainable competitive advantages, thereby positively influencing corporate performance. Furthermore, it was found that the synergies between digital and capital factors has a greater impact on corporate performance than the synergies between digital and labor factors. Manufacturing enterprises appear to be more effective at leveraging the “multiplier effect” of digital factors in combination with capital accumulation. In contrast, achieving synergies between labor and digital factors is more challenging, particularly for labor-intensive enterprises where substituting digital factors for labor can substantially influence internal organizational structures.

Second, the mediating effect of the synergies between digital and traditional factors on corporate performance was examined. The mechanism tests reported in Section 4.4 support hypotheses H2a–H2c, indicating that the synergies between digital and traditional factors positively enhance internal control quality, facilitate business model innovation, and promote product differentiation. These mediating effects help the comparative advantage following the endowment structure, influencing corporate performance.

Among these mechanisms, the enhancement of internal control quality appears to be the most significant. On the one hand, the synergies of multiple factors fundamentally reshape internal management processes, establish an environment conducive to governance and decision-making, streamline interactions across various operational links, and drive cost reduction and efficiency gains. On the other hand, the synergies between digital and traditional factors can cultivate high-quality risk assessment teams with digital competencies, ensuring that the enterprise can effectively identify and respond to internal control risks through digital means.

Notably, this study found that the synergies between digital and traditional factors can also enhance corporate performance by driving business model innovation. In terms of labor, this synergy fosters flexible employment arrangements and increases the value derived from the workforce. Regarding capital, the accumulation of digital capital enables rapid interpretation of market signals and ensures seamless integration between personalized customization and large-scale production, thereby improving corporate performance through greater responsiveness and operational efficiency.

In addition, the synergies between digital and traditional factors can promote product differentiation, enhancing corporate performance. On the one hand, digital–labor synergies support service differentiation. Services delivered via digital platforms rely on high-quality labor, encompassing individualized services, home delivery, immersive experiences, and intelligent after-sales services. Such services cater to the diverse needs of consumers, and when customers are willing to pay a premium for these high-quality services, corporate performance is improved. On the other hand, digital–capital synergies facilitate extreme cost efficiency. To meet consumer preferences enterprises invest capital to amplify the multiplier effect of digital factors, achieving cost reduction and operational efficiency, which in turn enhances overall corporate performance.

Third, heterogeneity was further examined based on different firm attributes. The heterogeneity tests reported in Section 4.5 indicate that the positive effect of digital–traditional factor synergies on corporate performance is more pronounced in non-state-owned firms, those without political connections, and firms operating in digitally advanced industries. Firms with lower levels of government intervention and higher digital investment exhibit stronger performance in leveraging digital–traditional factor synergies.

5.2. Contributions

This study, for the first time, elucidates the impact of digital–traditional factor synergies. Conventional wisdom holds that the synergies of multiple factors can not only enhance innovation performance [39], stimulate economic growth [40], and boost urban vitality [41] at the regional level, but also improve total factor productivity [42], advance technological innovation [43,44], and promote environmental sustainability [45] at the enterprise level. The theoretical framework demonstrates that the synergies between digital and traditional factors are an inevitable outcome of comparative advantage, as determined by the structure of factor endowments. Furthermore, this study empirically examines the direct and mediating effects of multi-factor synergies on corporate performance. The insights from this study may prompt a theoretical reassessment of production factor synergies in the digital era. More importantly, they could inspire scholars to explore the interactions between new and traditional factors, thereby enhancing corporate performance.

6. Conclusions, Policy Implications, and Limitations

6.1. Conclusions

This paper first develops a theoretical framework to explain the relationship between multi-factor synergies and corporate performance, and then employs panel data from 1650 listed companies in China to empirically examine the effect of multi-factor synergies on corporate performance and its transmission mechanisms. The main conclusions are as follows:

(1) The synergies between digital and traditional factors significantly improve corporate performance, and this result is robust to a range of robustness checks; (2) the synergies between digital and capital factors exert a more substantial effect on corporate performance than that between digital and labor factors; (3) the mediating-effects analysis identifies that multi-factor synergy can improve the quality of internal control, drive business model innovation, and contribute to product differentiation; (4) heterogeneity tests show that the performance-enhancing effects of multi-factor synergies are more pronounced in non-state-owned enterprises, firms in digitally oriented industries, and enterprises with political connection.

6.2. Practical Implications

Based on the above conclusions, this paper provides practical and strategic guidance for promoting the integration of digital factors into the real economy to enhance corporate performance (Figure 3).

First, policies should emphasize and encourage the synergistic allocation of digital and traditional factors. Existing policy frameworks often promote the adoption of digital technologies in a rather superficial manner, which may dampen firms’ incentives for effective utilization. Instead, regulations should focus on guiding enterprises to enhance corporate performance through three mechanisms: internal control quality, business model innovation, and product differentiation. Moreover, given that digital–capital synergies exert a more substantial influence on corporate performance, policymakers should consider increasing subsidies and targeted support measures to facilitate its effective implementation.

Second, policymakers should stress to enterprises that digital integration is an incremental and long-term process, without shortcuts. To accelerate the penetration of digital factors, firms can adopt a digital–traditional synergy strategy through three approaches. (1) Improve the internal organizational environment and cultivate a high-quality digital workforce. (2) Based on mature digital capabilities, strengthen the development of digital platforms to foster business model innovation. (3) By leveraging business model innovation, firms can expand diversified application scenarios and stimulate product innovation, thereby reducing the risk of competitive homogenization.

6.3. Limitations and Future Research

This study has the following limitations: one possible limitation is the focus on the listed manufacturing firms, which may not fully represent the broader manufacturing sector, particularly small- and medium-sized enterprises (SMEs) that encounter distinct challenges and opportunities in digital transformation. Another limitation is the restricted set of digital attributes considered. Although the paper treats industry digital attributes as one input of multi-factor synergies, a broader taxonomy of digital technologies, each with distinct mechanisms and performance implications, would sharpen the understanding of the transformation process.

Future research could expand the scope to include the SMEs and non-listed firms, thereby providing a more comprehensive understanding of multi-factor synergies across different organizational contexts. In parallel, research might disaggregate digital technologies to assess how each interacts with traditional production factors, tracing the resulting effects on productivity, innovation, and market competitiveness.