Exploring the Impact of Executives’ Digital Attention on Corporate Sustainable Development: Evidence from China

Abstract

Using the panel data of Chinese A-share firms from 2012 to 2023, we find that executives’ focus on digitalization is significantly and positively associated with corporate sustainability performance. The finding holds firm after a suite of endogeneity and robustness tests. Heterogeneity tests indicate that such a favorable impact is more salient for large enterprises, industry players with superior competitiveness, and entities situated in eastern China. The mechanism tests reveal that executives’ digital attention enhances corporate sustainable development by improving resource structuring capability, resource bundling capability, and resource leveraging capability. Additionally, financing constraints weaken, while media attention will enhance this promoting effect. Additional dimension-focused analyses demonstrate that the direct promotional impact of executives’ digital attention on corporate financial performance remains statistically insignificant, whereas it exerts a markedly positive catalytic effect on corporate environmental performance. This research offers novel theoretical interpretations and practical implications regarding the role of executive cognitive orientation in advancing corporate sustainable development against the backdrop of digital transformation.

1. Introduction

Amid the dual pressures of worsening global climate change and increasingly strict environmental regulations, corporate sustainable development has moved beyond the bounds of basic social responsibility to become a key measure of a company’s core competitiveness and long-term value (L. Li et al., 2021). As the largest developing economy globally, China has played a proactive role in propelling the global sustainable development initiative. In 2020, it put forward the “dual carbon” strategic goals, committing to reaching the carbon emission peak by 2030 and accomplishing carbon neutrality by 2060. As these goals advance, Chinese firms face an urgent demand to transform from traditional growth models to green, low-carbon, and efficient ones. Against this setting, identifying the critical factors driving corporate sustainable development has become a pressing issue drawing broad attention from academia and practice alike.

Existing literature examines corporate sustainable development from both internal and external perspectives. Internally, board oversight and talent incentive mechanisms are recognized as critical factors shaping sustainability strategies (Hussain et al., 2018; Chams & García-Blandón, 2019). Regarding external factors, market competition dynamics (Zou et al., 2024), industry technological progress (N. Wang et al., 2024), and external stakeholders (Alexopoulos et al., 2018) have a profound impact on corporate sustainable development. As digital technologies reshape the business ecosystem, corporate sustainable development has gained new impetus. Theoretically, executives’ digital attention is an important driving force for achieving corporate sustainable development. As an extended concept of the attention-based view in the digital context, executives’ digital attention specifically refers to the cognitive focus and resource allocation tendency of senior corporate managers towards digital issues during strategic decision-making processes (Ocasio, 1997; Song et al., 2025). As formulators and implementers of corporate strategies, the degree of senior executives’ understanding, acceptance, and attention to digital technologies directly determines the development fate of enterprises in the wave of digitalization. However, in previous studies on sustainable development, the crucial factor of executives’ digital attention has often been overlooked or oversimplified. At present, within the realm of studies probing the link between top management teams and corporate sustainable development, scholars predominantly adopt the analytical lenses of senior executive team diversity and leadership styles (Galbreath, 2018; Zada et al., 2025). Although these studies have revealed the influence of senior executives’ characteristics on corporate sustainable development, they mostly focus on static demographic characteristics or leadership styles, failing to deeply analyze the dynamic cognitive processes of senior executives in the digital age.

The resource orchestration theory provides a theoretical framework to break through this research bottleneck. This theory emphasizes that the acquisition of a firm’s sustainable competitive advantage not only depends on resource endowments (L. Li et al., 2018), but more importantly relies on the dynamic resource management process guided by managers’ cognition, highlighting the importance of managerial cognition for the effectiveness of resource orchestration (Sirmon et al., 2007). As the core subjects of resource orchestration, managers’ primary responsibility is to achieve optimal resource allocation through three key stages: resource construction, resource bundling, and resource utilization (Sirmon et al., 2011). Specifically, resource construction focuses on a firm’s acquisition and accumulation of resources while stripping away useless resources; resource bundling aims to integrate different types of resources to form unique resource combinations with synergistic effects; and resource utilization emphasizes the efficient deployment of existing resource combinations to create value and support the realization of strategic objectives. This theoretical perspective places managerial cognition at the core of resource allocation, providing a fitting analytical framework for decoding the influence mechanism of executives’ digital attention on corporate sustainable development.

Drawing on the foregoing analysis, this study explores three core research questions: (1) the impact of executives’ digital attention on corporate sustainable development; (2) the mediating mechanisms of resource orchestration capabilities; and (3) the moderating roles of financing constraints and media attention. Guided by resource orchestration theory, the research empirically tests these relationships using data from Chinese A-share listed firms from 2012 to 2023.

The study provides incremental contributions to current scholarly works in the aspects outlined below. First, by introducing the attention-based view, it provides a new theoretical perspective for understanding how digitalization drives corporate sustainable development. While existing studies have extensively explored the link between digital investment and firm performance, they often overlook the cognitive motivations behind investment behaviors. By measuring the frequency of digital keywords in the Management Discussion and Analysis section, this study captures executives’ dynamic cognitive focus on digitalization. This supplements the inadequacies of existing research, which mainly focuses on static characteristics or actual digital investments. This approach reveals the antecedent cognitive logic of digital strategy implementation, provides a micro-level analytical basis for predicting enterprises’ future digital transformation directions, and fills the research gap in the cognitive-driven dimension of existing studies.

Second, this study enriches the application scenarios and theoretical connotations of resource orchestration theory in the digital context. Resource orchestration theory clarifies that resource structuring, bundling, and leveraging are the core paths for enterprises to gain competitive advantages, but existing research rarely explores how managerial cognition guides these resource orchestration behaviors. By integrating resource orchestration theory with the attention-based view, this study confirms through mechanism tests that executives’ digital attention can sequentially enhance enterprises’ resource structuring capability, resource bundling capability, and resource leveraging capability, ultimately promoting sustainable development. This finding not only verifies the applicability of resource orchestration theory in explaining corporate sustainable development issues in the digital era but also identifies executives’ digital attention as a micro-cognitive driver of effective resource orchestration, expanding the theoretical boundary of the theory. It also responds to the academic call for exploring how digitalization reshapes resource orchestration logic, illustrating that cognitive focus on digital issues can promote the in-depth integration of traditional resources and digital elements, thereby creating synergistic effects for sustainable development.

Third, this study provides context-specific evidence from China and generalizable insights for the correlation between digitalization and sustainable development. Through heterogeneity analysis from three dimensions—firm size, industrial competitive advantage, and regional location—this study finds that the positive impact of executives’ digital attention on corporate sustainable development is more pronounced in large firms, firms with stronger market power, and firms in eastern China. These results clarify the boundary conditions for executives’ digital attention to exert its effect, provide a reference framework for research in other emerging economies or different institutional environments, and offer practical guidance for enterprises to formulate digital sustainable development strategies based on their own characteristics. In addition, this study identifies two contextual moderating variables: financing constraints and media attention. It confirms that financing constraints weaken while media attention strengthens the positive effect of executives’ digital attention, supplementing the inadequacies of existing studies in the analysis of contextual factors.

2. Literature Review and Research Hypothesis

2.1. Literature Review

2.1.1. Upper Echelons Theory

As one of the core theories in the fields of strategic management and organizational behavior, upper echelons theory holds that the traits of corporate managers exert a significant impact on firms’ strategic choices and operational performance. This theory extends the research focus from the corporate level to the individual manager level, proposing that managers’ cognitive bases, values, and demographic characteristics directly determine their judgment and interpretation of the external environment, thereby influencing the formulation and implementation of strategic decisions (Hambrick & Mason, 1984). In the research of corporate management and corporate finance, upper echelons theory is often used to explain the effects of factors such as top management team heterogeneity (Chen et al., 2019), CEO power (Saiyed et al., 2023), and CEO transcendence values (Cannavale et al., 2020) on corporate performance. With the progressive deepening of relevant research, scholars have started to probe how upper echelons theory applies to the digital era. Using a questionnaire survey approach, Yin et al. (2025) found that CEO digital embeddedness is positively associated with firm performance, which furnishes a fresh theoretical perspective for understanding and predicting strategic actions and performance in digital environments.

2.1.2. Attention-Based View

The attention-based view is rooted in organizational decision-making theory, regarding attention as a scarce and critical strategic resource for firms. Its core proposition is that a firm’s strategic choices and decision-making behaviors are essentially the outcomes of managers’ attention allocation. The theory posits that managers’ attention is limited, and their focus is dually influenced by external environmental stimuli and internal organizational contexts (Ocasio, 1997).

In terms of application scenarios, the attention-based view is not only applicable to explaining firms’ digital transformation decisions. For instance, based on the attention-based view, F. R. Wang et al. (2025) took HIKVISION’s digital transformation process as a case study, exploring the impact mechanism of top management team attention on various stages of digital transformation in manufacturing enterprises from three perspectives: technology, organization, and environment. The ABV can also be used to analyze firms’ digital innovation. Y. W. Cui et al. (2024) found that the more attention the top management team allocates to digital innovation, the more they can stimulate firms’ digital innovation initiatives. Additionally, the attention-based view has been applied in the ESG field. Z. M. Zhang et al. (2025) revealed that although executives’ environmental attention does not significantly affect firms’ overall ESG performance, it exerts a notably positive impact on environmental performance. When stakeholders such as investors and consumers increase their attention to ESG performance, managers will allocate attention to areas like green production and public welfare donations to achieve long-term corporate sustainable development.

2.1.3. Resource Orchestration Theory

Resource orchestration theory emphasizes that an enterprise’s acquisition of a competitive edge hinges not only on its resource endowments but also on the dynamic processes through which managers govern and deploy these resources. The theory specifically identifies managers as central actors in resource orchestration, with their primary responsibility lying in optimizing resource allocation through three interconnected processes: resource structuring, resource bundling, and resource leveraging (Sirmon et al., 2011). In detail, resource structuring involves securing and building critical resources while eliminating those that no longer serve strategic purposes. Resource bundling focuses on integrating heterogeneous resources to create synergistic combinations. Resource leveraging emphasizes deploying these combined resources effectively to generate value and support strategic objectives.

In recent years, resource orchestration theory has attracted widespread attention from scholars and gradually emerged as a mainstream analytical perspective in multiple domains of business management research. M. Cui and Pan (2015) applied resource orchestration theory to explain how firms can accurately manage and effectively bundle internal and external resources in a dynamically changing market environment, thereby promoting the implementation of e-commerce businesses and the optimization of e-commerce governance processes. Carnes et al. (2017) further extended the research context to the dimension of the corporate life cycle, analyzing how firms can scientifically construct resource portfolios and integrate diverse resources at different developmental stages to cultivate and strengthen the innovation capabilities required in each phase. Bittencourt et al. (2021) focused on the aspect of resource coordination and explored how to stimulate corporate innovation vitality by coordinating various resource portfolios. With the digital economy and sustainable development increasingly intertwined, the application scenarios of this theory have continued to expand. Based on the dual perspectives of resource acquisition and allocation, X. Y. Zhang et al. (2025) introduced resource orchestration theory into the research on the relationship between digital transformation and corporate ESG performance. Their findings revealed that digital transformation can expand the scale of resource acquisition by broadening financing channels, strengthening technology collaboration networks, and optimizing relational capital allocation; meanwhile, it can improve the efficiency of resource allocation through the reorganization of innovation elements, the prioritization of green investment, and the reconstruction of production processes, thereby driving a significant improvement in corporate ESG performance.

2.1.4. Influencing Factors of Corporate Sustainable Development

Corporate sustainable development is a comprehensive development model that balances economic performance, environmental responsibility, and social value. Its influencing factors can be sorted out from two levels: the external macroenvironment and the internal micro-organization. At the external level, the constraints and guidance of policies and regulations serve as crucial driving forces. Environmental protection standards issued by the government (Y. Wang et al., 2022b), carbon emission reduction policies (H. C. Yu & Tsai, 2018), and ESG information disclosure requirements (Wu & Li, 2025) will directly force enterprises to adjust their production and operation models and increase green investments. Meanwhile, capital market liberalization (H. Y. Wang, 2023) and green finance (K. H. Wang et al., 2022) also exert impacts on enterprises’ sustainable development capabilities. At the internal level, corporate governance structure, resource endowments, managerial characteristics, and stakeholders are key influencing factors. Studies indicate that a well-structured corporate governance framework strengthens managerial supervision and facilitates enterprises’ implementation of sustainable development strategies (S. Li et al., 2021). Sufficient technological and financial reserves provide the essential material underpinning for corporations to pursue green innovation and perform social responsibilities (Yang, 2025). CEOs’ professional backgrounds, tenures, genders (Huang, 2013), and overseas experience (Y. Wang et al., 2022a) can influence corporate sustainable development. The demands of stakeholders affect the process and effectiveness of corporate sustainable development through multiple channels (F. Zhang & Zhu, 2019).

2.2. Research Hypothesis

2.2.1. Executives’ Digital Attention and Corporate Sustainable Development

Executives’ digital attention, as an extension of the attention-based view in the digital context, specifically refers to the cognitive focus and resource allocation tendency of top managers on digital issues in the strategic decision-making process (Ocasio, 1997; Song et al., 2025). sustain profitable growth alongside responsible resource stewardship and reduced operational environmental impacts, thereby ensuring long-term support from all stakeholders. When the executive team forms a digital cognition-driven mental model, its strategic attention will be continuously anchored in the field of digital transformation. This cognitively driven attention allocation injects new momentum into corporate sustainable development through three dimensions of technological empowerment, decision-making restructuring, and stakeholder relationship reconfiguration.

In the dimension of technological empowerment, the focus on digital attention drives enterprises to construct an intelligent technology matrix. Through sustained investment in cloud computing infrastructure, iterative upgrades of big data analytics capabilities, and in-depth development of artificial intelligence application scenarios, it achieves end-to-end digital restructuring of operational processes. This technological penetration not only enhances resource utilization efficiency but, more importantly, establishes a synergistic innovation mechanism between green production and digital technologies, providing technical support for environmentally friendly operational models. At the level of decision-making restructuring, the intensification of digital attention propels the transformation of decision-making systems toward a data-driven paradigm. The executive team deploys IoT-based environmental monitoring systems and establishes intelligent carbon emission management platforms, upgrading environmental risk identification from experience-based judgment to real-time dynamic early warning. This digital transformation of decision-making mechanisms significantly enhances the enterprise’s capability to respond to environmental regulations, fostering proactive environmental governance innovation beyond mere compliance management. In the realm of stakeholder management, digital cognition motivates enterprises to establish transparent information disclosure mechanisms and efficient communication channels, strengthening trust with shareholders, consumers, communities, and other stakeholders while improving corporate social responsibility fulfillment capabilities. The analysis presented above suggests the following hypothesis.

H1. 

Executives’ digital attention positively promotes corporate sustainable development.

2.2.2. The Mechanism of Resource Orchestration

From the perspective of resource orchestration theory, executives’ digital attention enhances firms’ resource structuring capability, thereby promoting sustainable development. Executives with strong digital cognition can more accurately identify trends in digital technologies and their strategic value. This cognitive advantage encourages greater investment in IT infrastructure and the development of robust data management systems. Enhanced resource structuring enables data-driven precision management, improves resource allocation efficiency, reduces waste, and strengthens productivity. Meanwhile, advanced environmental data monitoring systems allow firms to track energy use and emissions in real time, supporting evidence-based environmental governance. Together, these improvements in operational and environmental management enhance corporate sustainable development performance.

Executives’ digital attention also strengthens firms’ resource bundling capability. Digitally attuned executives better understand the synergies between digital technologies and existing resources and can integrate them more effectively. By combining human, production, and technological resources, firms can build intelligent production management systems that automate and optimize production processes. Such resource bundling improves resource utilization efficiency, generates distinctive competitive advantages, and ultimately facilitates sustainable development.

Furthermore, executives’ digital attention enhances sustainable development by improving resource leveraging capability. Executives with high digital cognition proactively drive innovative uses of existing resource portfolios. For example, applying artificial intelligence for in-depth customer analysis to inform product customization or using cloud computing to optimize workflows, improve efficiency, and reduce costs. Strengthening resource leveraging allows firms to create greater economic value while enhancing their ability to fulfill social responsibilities, such as reducing energy consumption and environmental impact through digitalized production. These improvements reinforce the foundation for green and sustainable development.

Therefore, this study proposes the following hypothesis:

H2. 

Executives’ digital attention promotes corporate sustainable development by enhancing the firms’ resource structuring capability, resource bundling capability, and resource leveraging capability.

2.2.3. The Moderating Role of Corporate Financing Constraints

From the resource-based theory, corporate financing constraints, as an important internal pressure variable, may affect the mechanism of the role of executives’ digital attention on corporate sustainable development. When enterprises face high financing constraints, the lack of funds makes it difficult to meet the financial needs of digital transformation and sustainable development strategies, and even if executives focus their attention on these areas, they are unable to effectively transform their strategic concepts into practical actions, which greatly weakens the ability of executives’ digital attention to be transformed into actual strategic results. When firms face lower financing constraints, adequate financial support can ensure the successful implementation of executive digital attention-driven sustainability strategies, thus strengthening the positive relationship between the two. Thus, the following hypothesis is derived:

H3. 

Corporate financing constraints weaken the positive effect of executives’ digital attention on corporate sustainable development.

2.2.4. The Moderating Role of Media Attention

From the perspective of stakeholder theory, media attention functions as an important external governance mechanism that can strengthen the link between executives’ digital attention and corporate sustainable development. This influence operates through three primary pathways: monitoring, reputation, and learning. At the level of the monitoring mechanism, the media’s continuous tracking of corporate digital transformation and sustainability practices creates strong external monitoring pressure, prompting executives to be more prudent in their strategic decisions to ensure that digital transformation is highly aligned with sustainability goals. For the reputation mechanism, positive media reports on the positive practices of the enterprise can enhance the social image of the enterprise and attract more high-quality resources. In order to maintain and enhance the reputation of the enterprise, the executives will take the initiative to increase the investment in digital attention and promote the sustainable development of the enterprise. Under the learning mechanism, industry success stories reported by the media provide valuable experience for enterprises, helping executives to optimize their digital strategies and sustainable development paths.

Based on the above analysis, this study proposes the following hypothesis.

H4. 

Media attention can reinforce the positive impact of executives’ digital attention on corporate sustainability performance.

2.2.5. Conceptual Model

Figure 1 summarizes our hypotheses. Executives’ digital attention is expected to positively promote corporate sustainable development (H1). This positive effect is mediated by firms’ resource orchestration capabilities, specifically resource structuring, resource bundling, and resource leveraging capabilities (H2). In addition, corporate financing constraints weaken the positive relationship between executives’ digital attention and corporate sustainable development (H3), while media attention reinforces this positive relationship (H4).

3. Research Design

3.1. Data Source and Sample Selection

This research takes A-share listed companies on the Shanghai and Shenzhen Stock Exchanges during 2012–2023 as its research sample. To guarantee the reliability and validity of the data, the sample is processed and refined via the following steps: (1) excluding enterprises in the financial sector; (2) eliminating firms labeled ST, *ST, or PT; and (3) removing observations with missing key variables. The research data are mainly obtained from the WIND Database, China Stock Market and Accounting Research Database (CSMAR), Chinese Research Data Services Platform (CNRDS), as well as official announcements released by the Shanghai and Shenzhen Stock Exchanges.

3.2. Variables

3.2.1. Corporate Sustainable Development

Drawing on literature that conceptualizes corporate sustainable development via the dual lens of environmental and financial performance (Alexopoulos et al., 2018; Xi & Zhao, 2022), this study measures corporate sustainable development performance (CSP) with an entropy-weighted index integrating these two aspects. Specifically, return on assets (ROA) is employed as the proxy variable for financial performance (Minutolo et al., 2019; Z. Zhang et al., 2022), whereas environmental performance is measured using Bloomberg’s ESG environmental score (W. H. Zhou et al., 2025). It is worth noting that this score spans a range from 1 to 100, with higher values signifying more transparent and comprehensive environmental information disclosure on the part of the firm. Financial performance reflects an enterprise’s economic value creation ability and long-term market potential, and environmental performance embodies its capacity to mitigate environmental impacts through green technology and products. Thus, this paper argues that CSP measurement should incorporate indicators from both financial and environmental dimensions.

-

The entropy weight method is an objective technique that assigns indicator weights according to the information each indicator provides. Drawing on the research of X. H. Zhang et al. (2024), the specific calculation steps for the corporate sustainable development performance (CSP) in this paper are as follows.

First, standardize financial performance and environmental performance.

$$Y_{i,j}={\displaystyle \frac{X_{ij}-\mathrm{m}\mathrm{i}\mathrm{n}\left(X_i\right)}{\max \left(X_i\right)-\mathrm{m}\mathrm{i}\mathrm{n}\left(X_i\right)}}$$

(1)

Second, calculate the information entropy of the above two indicators and determine their respective weights.

$${\rho }_{ij}={\displaystyle \frac{Y_{i,j}}{\sum _{t=1}^n{Y}_{i,j}}},$$

(2)

$$E_j=-\ln {\left(n\right)}^{-1}\sum _{i=1}^n{\rho }_{ij}\times \ln \left({\rho }_{ij}\right),$$

(3)

$${\omega }_j={\displaystyle \frac{1-E_j}{k-\sum _{j=1}^m{d}_j}}$$

(4)

Third, compute the corporate sustainable development performance (CSP) index based on the weight ratio of the two indicators.

$$Score(r_i=\sum _{j=1}^m{\omega }_j\times {\rho }_{ij})$$

(5)

3.2.2. Executives’ Digital Attention

Based on theoretical support, textual characteristics, and methodological consensus, this study adopts the word frequency statistical method on the Management Discussion and Analysis (MD&A) section to construct a proxy indicator for executives’ digital attention (EDA). According to the attention-based view (Ocasio, 1997), executives’ attention reflects their selective focus on and prioritization of specific strategic directions. Meanwhile, the core viewpoint proposed by Sapir (1944) provides a key theoretical basis for measurement, namely that an individual’s focus of attention can be reflected through the frequency of words they use. The degree of importance executives attach to digital transformation will inevitably be manifested by the frequency of relevant vocabulary in the texts they lead or review, which lays a logical foundation for measuring executives’ digital attention using the word frequency method.

The MD&A section is a high-quality carrier for capturing executives’ attention. Its core function is to disclose the company’s operating conditions during the reporting period and prospects for future development, and the drafting process requires direct composition by executives or rigorous review and finalization after being written by others (Guo et al., 2024). This characteristic enables the MD&A to not only convey executives’ forward-looking judgments on the business environment and strategic direction but also directly or indirectly reflect the direction and key focuses of executives’ attention (Guo et al., 2024). Compared with other sections of the annual report, the MD&A is closer to executives’ strategic cognition and decision-making orientation, serving as an effective textual source for capturing executive cognition and attention, which ensures the relevance and reliability of the measurement materials.

The word frequency method itself has a mature methodological consensus and has been widely used in relevant domestic and foreign studies to characterize executives’ attention related to digital transformation or corporate strategic orientation. For example, existing studies have used this method to explore the impact of digital transformation on corporate innovation performance (S. Li et al., 2023), trade credit financing (Z. Zhou & Li, 2023), and cost of equity capital (C. Zhang & Wang, 2024), fully verifying its effectiveness and scientificity in measuring such abstract strategic attention variables.

Based on the aforementioned multiple reasons, the specific measurement process of executives’ digital attention is as follows. First, Python (version 3.12) web scraping technology is used to collect A-share listed firms’ annual reports from 2012 to 2023 from the Juchao Information Network, and the content of the MD&A is extracted from each enterprise’s annual report. Second, with reference to the research of Zhao et al. (2021), the feature terms for executives’ digital attention are identified. Third, the Jieba library in Python (version 3.12) is utilized for text segmentation, and the number of all feature terms in the MD&A section is counted. Fourth, the word frequencies of the digital attention feature terms are aggregated and logarithmically transformed to ultimately obtain the proxy indicator for executives’ digital attention, denoted as EDA.

Table 1. Keywords of executives’ digital attention.

Dimension	Categorized Terms	Segmentation Lexicon
Digital Technology Application	Data, Digital, Digitalization	Data management, data mining, data network, data platform, data center, data science, digital control, digital technology, digital communication, digital network, digital intelligence, digital terminal, digital marketing, digitalization, big data, cloud computing, cloud IT, cloud ecosystem, cloud service, cloud platform, blockchain, Internet of Things (IoT), machine learning
Internet Application	Internet, Internet-based, E-commerce	Mobile Internet, Industrial Internet, Industrial Internet Solutions, Internet technology, Internet thinking, Internet initiatives, Internet business, mobile Internet, Internet applications, Internet marketing, Internet strategy, Internet platform, Internet model, Internet business model, Internet ecosystem, e-commerce, electronic commerce, Internet, “Internet +”, online-offline, online to offline, online and offline, O2O, B2B, C2C, B2C, C2B
Intelligent Manufacturing	Intelligent, Intellectualization, Automatic, Numerical Control, Integration, Integration	Artificial intelligence, high-end intelligence, industrial intelligence, mobile intelligence, intelligent control, intelligent terminal, mobile intelligence, intelligent management, smart factory, intelligent logistics, intelligent manufacturing, intelligent warehousing, intelligent technology, intelligent equipment, intelligent production, intelligent connectivity, intelligent system, intellectualization, automatic control, automatic monitoring, automatic supervision, automatic detection, automatic production, numerical control, integration, integration, integrated solutions, integrated control, integrated system, industrial cloud, future factory, intelligent fault diagnosis, lifecycle management, manufacturing execution system (MES), virtualization, virtual manufacturing
Modern Information System	Information, Informatization, Networkization	Information sharing, information management, information integration, information software, information system, information network, information terminal, information center, informatization, networkization, industrial information, industrial communication

presents the key words of executives’ digital attention.

3.2.3. Control Variables

Following prior studies (Jin & Wang, 2025; R. Cui et al., 2025), this study controls for the following variables: Age, measured by the logarithm of the number of years the enterprise has been listed; Top1, represented by the shareholding percentage of the company’s largest shareholder; TobinQ, calculated as the ratio of market value to total assets; Manag, defined as the ratio of administrative expenses to operating revenue; Indir, measured by the proportion of independent directors among all board members; Growth, indicated by the growth rate of operating income of the company; Lev, computed as the ratio of total liabilities to total assets; Cashflow, measured by the ratio of net operating cash flow to total operating revenue.

4. Model Construction

In order to test the effect of executives’ digital attention on corporate sustainable development, the following baseline regression framework is constructed using panel data:

$${CSP}_{i,t}={\propto }_0+{\propto }_1{EDA}_{i,t}+{\propto }_2{Controls}_{i,t}+\Sigma Ind+\Sigma Year+{\epsilon }_{i,t}$$

(6)

where subscript $i$ denotes firm and $t$ denotes year. ${CSP}_{i,t}$ denotes corporate sustainability performance, ${EDA}_{i,t}$ denotes executive digital attention, and ${Controls}_{i,t}$ denotes control variables. ${\propto }_0$ denotes the intercept, ${\propto }_1$ denotes the coefficients of the explanatory variables, and ${\propto }_2$ denotes the coefficients of the control variables. $\Sigma Ind$ is the industry fixed effect, $\Sigma Year$ is the year fixed effect, and ${\epsilon }_{i,t}$ is the random error term. To make the regression results more reliable, this study clusters at the firm level.

4.1. Empirical Analysis

4.1.1. Descriptive Statistics

Table 2. Descriptive statistics of variables.

VarName	Obs	Mean	SD	Min	P25	Median	P75	Max
CSP	8360	0.1391	0.1759	0.0002	0.0096	0.0750	0.1889	0.9920
EDA	8360	2.4948	1.3065	0.0000	1.6094	2.5649	3.4012	6.6821
Age	8360	2.5177	0.6672	0.6931	2.1972	2.7081	3.0445	3.4012
Top1	8360	36.3845	16.1058	7.9318	23.7478	34.6546	47.9264	76.9525
TobinQ	8360	1.9887	1.4179	0.8062	1.1201	1.4961	2.2577	8.8351
Manag	8360	7.2674	5.9150	0.6987	3.3766	5.7554	9.2199	36.7924
Indir	8360	0.3780	0.0554	0.3333	0.3333	0.3636	0.4286	0.5714
Growth	8360	9.5291	21.3706	−72.5836	0.3494	10.1669	20.8480	67.8227
Lev	8360	1.4389	1.1544	0.4717	0.9972	1.0969	1.3639	9.0194
Cashflow	8360	0.0648	0.0662	−0.1082	0.0231	0.0594	0.1019	0.2639

reports the descriptive statistics of the main variables. The mean value of the corporate sustainable development (CSP) is 0.1391 with a standard deviation of 0.1759, indicating a moderate degree of dispersion among enterprises. It ranges from 0.0096 to 0.9920, and the median of 0.0750 is slightly lower than the mean, suggesting that most enterprises have a moderately low level of sustainable development with a relatively concentrated overall distribution. The mean value of the corporate executives’ digital attention indicator is 2.4948 with a standard deviation of 1.3065, showing significant differences in the level of digital attention among executives of different enterprises.

4.1.2. Benchmark Regression Analysis

Table 3. Benchmark regression results.

Variables	(1)	(2)	(3)
	CSP	CSP	CSP
EDA	0.0212 ***	0.0095 ***	0.0085 ***
	(0.0015)	(0.0028)	(0.0028)
Age			−0.0051
			(0.0053)
Top1			0.0006 **
			(0.0002)
TobinQ			−0.0043 **
			(0.0020)
Manag			−0.0005 *
			(0.0003)
Indir			0.0303
			(0.0507)
Growth			0.0001
			(0.0001)
Lev			0.0000
			(0.0000)
Cashflow			0.1207 ***
			(0.0350)
cons	0.0863 ***	0.1153 ***	0.1012 ***
	(0.0041)	(0.0075)	(0.0271)
Adjusted R-square	0.025	0.329	0.336
Ind	No	Yes	Yes
Year	No	Yes	Yes
N	8360	8360	8360

Note(s): *** p < 0.01, ** p < 0.05, * p < 0.1, firm-level clustered robust standard errors in parentheses.

demonstrates the benchmark regression results for the effect of executives’ digital attention on corporate sustainable development. Column (1) reports the baseline results without control variables or fixed effects. The coefficient of EDA is statistically significant at the 1% level, initially verifying the H1. This positive relationship persists when control variables are introduced in column (2), where the coefficient of EDA remains significant at the 1% level. Furthermore, after incorporating industry and year fixed effects in column (3), the coefficient of EDA is statistically significant at the 1% level. Thus, it is corroborated that a higher level of executives’ digital attention is more positive for corporate sustainability, thereby verifying H1.

To clarify the economic significance of the regression results, this paper conducts a quantitative analysis based on the benchmark regression coefficients in column (3). In terms of the marginal effect, for every one-unit increase in EDA, the corporate sustainable development performance (CSP) increases by approximately 6.11% relative to its mean value. The calculation steps are 0.0085/0.1391 ≈ 6.11%. In terms of the practical impact of changes across quantiles, when EDA rises from the 25th percentile (0.0096) to the 75th percentile (0.1889), the corporate sustainable development performance increases by approximately 7.28% relative to its standard deviation. The calculation steps are 0.0085 × (0.1889–0.0096)/0.1759 ≈ 0.866%.

To further highlight the practical implications of the conclusions drawn in this paper, we compare the promotion effect of executives’ digital attention (EDA) on CSP with the relevant findings in existing literature. G. Wang et al. (2025) found that for every one-unit increase in new-quality productive forces, corporate sustainable development performance increases by 0.38% relative to the mean value. In comparison, the marginal promotion effect of EDA identified in this study demonstrates more prominent economic significance, which confirms that executives’ digital attention is a crucial factor driving corporate sustainable development. Meanwhile, unlike studies such as that of Huang (2013), which focus on CEO personal characteristics and rely on cross-sectional questionnaire data, this paper carries out the analysis based on a large-sample panel dataset. This approach enables more effective control of the interference caused by individual heterogeneity and time trends, thereby reflecting the long-term stable relationship between variables more accurately and enhancing the reliability and generalizability of the study’s conclusions.

4.1.3. Endogeneity Test

Instrumental variables method. To alleviate potential endogeneity issues stemming from reverse causality and omitted variables, we adopt an instrumental variable (IV) approach, drawing on the shift-share research design proposed by X. S. Li et al. (2022) as well as the broader body of quasi-experimental literature focusing on shift-share instruments (Adao et al., 2019; Borusyak et al., 2022). Specifically, the instrument is constructed in three steps. First, we calculate the annual average growth rate of executives’ digital attention across the full sample, which captures an aggregate digitalization shock common to all firms (shift). Second, for each firm, we compute the lagged industry-level average of executives’ digital attention, excluding the focal firm, which reflects the firm’s exposure to industry-specific digitalization trends (share). Third, we interact these two components to generate a simulated change in firm-level digital attention, and take the cube of the difference between the firm’s actual digital attention and this simulated value as the final instrument. This shift–share instrument satisfies the relevance condition, as industry-level digitalization trends are strongly correlated with firms’ own digital attention. Consistent with this expectation, the first-stage results in column (1) of

Table 4. Results of the endogeneity test.

Variables	(1)	(2)	(3)	(4)
	EDA	CSP	CSP	CSP
IV	0.0357 ***
	(0.0009)
EDA		0.0069 *	0.0072 **	0.0073 ***
		(0.0042)	(0.0029)	(0.0027)
Age	−0.0110	−0.0046	−0.0049	−0.0057
	(0.0229)	(0.0069)	(0.0056)	(0.0053)
Top1	0.0020 **	0.0007 **	0.0005 **	0.0005 **
	(0.0008)	(0.0003)	(0.0003)	(0.0002)
TobinQ	0.0044	−0.0045 **	−0.0071 ***	−0.0073 ***
	(0.0069)	(0.0023)	(0.0024)	(0.0022)
Manag	−0.0009	−0.0004 *	−0.0023 ***	−0.0026 ***
	(0.0013)	(0.0003)	(0.0005)	(0.0005)
Indir	0.2371	0.0269	0.0207	0.0283
	(0.2093)	(0.0580)	(0.0564)	(0.0534)
Growth	0.0008 ***	0.0001	0.0001	0.0001
	(0.0003)	(0.0001)	(0.0001)	(0.0001)
Lev	−0.0010	−0.0000	−0.0067 ***	−0.0060 ***
	(0.0019)	(0.0006)	(0.0018)	(0.0017)
Cashflow	0.0325	0.1142 ***	0.1368 ***	0.1190 ***
	(0.1548)	(0.0415)	(0.0400)	(0.0361)
Ind	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
N	6378	6378	6949	8352
Kleibergen-Paap rk LM Statistic	324.171 ***
Kleibergen-Paap rk Wald F Statistic	1689.613 [16.38]

Note(s): *** p < 0.01, ** p < 0.05, * p < 0.1, firm-level clustered robust standard errors in parentheses.

show a strong and statistically significant relationship between the instrument and executives’ digital attention.

Regarding the exclusion restriction, the identifying assumption is that conditional on firm fixed effects, year fixed effects, and a rich set of control variables, industry-level digitalization shocks affect corporate sustainable development primarily through their influence on executives’ digital attention, rather than through alternative firm-specific channels. To alleviate concerns that the instrument may capture sector-wide shocks directly affecting sustainability outcomes (e.g., regulatory changes or industry-wide ESG initiatives), we explicitly control for industry and year fixed effects, thereby absorbing time-invariant industry characteristics and common macroeconomic trends. Under this specification, remaining variation in the instrument reflects differential exposure to aggregate digitalization shocks driven by pre-existing industry structures, which is plausibly exogenous to contemporaneous firm-level sustainability outcomes.

Columns (1) and (2) of Table 4 report the results. The first-stage results in column (1) confirm a strong relationship between the IV and EDA. The second-stage results in column (2) show that the coefficient of EDA remains statistically significant at the 10% level, indicating that the baseline finding is robust.

Diagnostic tests further validate instrument variables. The Kleibergen-Paap rk LM statistic of 324.171 rejects the null of underidentification at the 1% level. Additionally, the Kleibergen-Paap Wald rk F statistic of 1689.613 exceeds the Stock-Yogo critical value of 16.38, ruling out the problem of weak instruments. Collectively, these tests confirm the validity of the chosen IV.

Propensity score matching. To further mitigate the potential bias arising from sample selection, this study employs the propensity score matching (PSM) method. Specifically, we first adopt the one-to-one nearest-neighbor matching approach, with all control variables included in the baseline model specified as covariates. After confirming the validity of the matching results through a balance test, we re-estimate the core model using the matched sample. In addition, we also implement the kernel matching method for additional robustness, aiming to rule out the interference of matching method selection on the research conclusions. Columns (3) and (4) of Table 4 report the results of one-to-one nearest neighbor matching and kernel matching, respectively. The regression coefficient of EDA remains significantly positive, confirming the robustness of our core findings.

4.1.4. Robustness Tests

Placebo test. To ensure the observed effect is not driven by other potential factors, a placebo test is employed. In this procedure, sample firms are randomly reassigned into pseudo ‘treatment’ and ‘control’ groups. This randomization process is repeated 1000 times, and the original estimation model is applied for regression analysis on each of the randomly generated samples.

Figure 2 shows the kernel density plot of placebo test coefficients, which cluster around zero and differ sharply from the significant baseline estimate. This indicates that the estimates from the randomized experiment are not statistically significant, confirming that the improvement in corporate sustainable development is indeed driven by executives’ digital attention.

Replacement of dependent variable. This study re-measures environmental performance using the Bloomberg ESG total score and re-measures financial performance using ROE. Similarly, the entropy approach is used to construct a new corporate sustainable performance indicator (CSP2), which is used to robustly test the main effects of the paper. The significantly positive coefficient in column (1) of

Table 5. Robustness test results.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
	Replacement of Dependent Variable	Replacing the Independent Variable	One-Period Lagged Independent Variable	Excluding High-Tech Industries	Adding Individual Effects	Adding City Effects
	CSP2	CSP	CSP	CSP	CSP	CSP
EDA	0.0053 ***	0.0048 *	0.0090 ***	0.0084 **	0.0077 ***	0.0075 ***
	(0.0011)	(0.0027)	(0.0032)	(0.0035)	(0.0026)	(0.0028)
Age	−0.0026	−0.0055	−0.0051	−0.0133 *	0.0379 ***	−0.0031
	(0.0020)	(0.0053)	(0.0069)	(0.0072)	(0.0134)	(0.0057)
Top1	0.0003 ***	0.0006 ***	0.0006 **	0.0004	0.0000	0.0005 *
	(0.0001)	(0.0002)	(0.0003)	(0.0003)	(0.0004)	(0.0003)
TobinQ	0.0035 ***	−0.0045 **	−0.0045 **	−0.0045	0.0037 **	−0.0036 *
	(0.0008)	(0.0020)	(0.0023)	(0.0029)	(0.0016)	(0.0019)
Manag	−0.0003	−0.0006 *	−0.0005 *	−0.0004	−0.0001	−0.0005 *
	(0.0002)	(0.0003)	(0.0003)	(0.0003)	(0.0001)	(0.0003)
Indir	0.0153	0.0328	0.0296	0.1095 *	0.0737	0.0332
	(0.0190)	(0.0509)	(0.0575)	(0.0627)	(0.0456)	(0.0540)
Growth	0.0002 ***	0.0001	0.0001	0.0001	−0.0000	0.0001
	(0.0000)	(0.0001)	(0.0001)	(0.0001)	(0.0000)	(0.0001)
Lev	−0.0001 **	−0.0000	−0.0001	−0.0000	0.0000	−0.0002
	(0.0000)	(0.0000)	(0.0006)	(0.0000)	(0.0004)	(0.0003)
Cashflow	0.2938 ***	0.1205 ***	0.1127 ***	0.1203 ***	0.0353	0.1059 ***
	(0.0186)	(0.0352)	(0.0410)	(0.0437)	(0.0273)	(0.0346)
cons	0.1657 ***	0.1140 ***	0.1071 ***	0.0958 ***	−0.0127	0.1018 ***
	(0.0100)	(0.0265)	(0.0328)	(0.0352)	(0.0415)	(0.0285)
Adjusted R-square	0.468	0.335	0.317	0.346	0.656	0.399
Ind	Yes	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes	Yes
N	8360	8360	6472	4880	8360	8354

Note(s): *** p < 0.01, ** p < 0.05, * p < 0.1, firm-level clustered robust standard errors in parentheses.

provides empirical support for Hypothesis 1.

Replacing the independent variable. Referring to the evaluation system of the enterprise digital transformation index constructed by Zhen et al. (2023), the measure of executive digital attention is replaced and regressed. The results in column (2) of Table 5 further validate the robustness of the main finding.

One-period lagged treatment of the independent variable. To further ensure that the findings are not driven by unobserved time trends or serial correlation, an additional robustness check is conducted. Specifically, the model is re-estimated by incorporating the dependent variable lagged by one period. In column (3) of Table 5, the research conclusion remains unchanged.

Excluding high-tech industries. Considering that executives of firms in high-tech industries usually possess a higher natural focus on digital technology, this study excludes firms in high-tech industries and regresses them. As shown in column (4) of Table 5, the findings remain robust.

Incorporating high-dimensional fixed effects. Adding high-dimensional fixed effects involves factoring in unobservable heterogeneous factors across multiple dimensions, allowing stricter identification and estimation of the regression model. Two high-dimensional fixed-effect regressions are performed to strengthen result robustness. First, on top of the year and industry fixed effects in the benchmark regression, firm fixed effects are added to control for time-invariant unobservable firm heterogeneity, preventing the core explanatory variable’s estimated effect from being confounded by inherent firm characteristics. Column (5) of Table 5 shows the coefficient remains significantly positive. Additionally, city fixed effects are incorporated into the benchmark model to control for local macro and regional factors, ruling out systematic biases from geographic clustering of samples. As column (6) of Table 5 displays, the coefficient stays significantly positive.

4.1.5. Heterogeneity Analysis

Firm size. At the firm level, this study further examines whether enterprise size influences the relationship between executives’ digital attention and sustainable development. With the median firm size as the cutoff, the sample is divided into large and small enterprise groups for subgroup regressions.

As reported in column (1) of

Table 6. Heterogeneity results.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	Large-Scale Enterprises	Small-Scale Enterprises	Stronger Industry Competitive Advantage	Weaker Industry Competitive Advantage	Eastern Region	Central Region	Western Region
	CSP	CSP	CSP	CSP	CSP	CSP	CSP
EDA	0.0081 *	−0.0005	0.0135 ***	0.0024	0.0084 ***	0.0106	0.0091
	(0.0045)	(0.0029)	(0.0036)	(0.0036)	(0.0032)	(0.0085)	(0.0059)
Age	−0.0149	−0.0091 *	−0.0063	−0.0036	0.0006	−0.0162	−0.0334 *
	(0.0094)	(0.0055)	(0.0064)	(0.0069)	(0.0059)	(0.0146)	(0.0182)
Top1	0.0004	0.0003	0.0004	0.0009 ***	0.0007 **	0.0008	−0.0003
	(0.0004)	(0.0003)	(0.0003)	(0.0003)	(0.0003)	(0.0007)	(0.0006)
TobinQ	−0.0059	0.0011	−0.0046 *	−0.0038	−0.0031	−0.0099 **	0.0007
	(0.0041)	(0.0018)	(0.0025)	(0.0029)	(0.0021)	(0.0046)	(0.0060)
Manag	−0.0004	−0.0005	−0.0004	−0.0008 *	−0.0004 *	−0.0017	−0.0034 **
	(0.0005)	(0.0003)	(0.0003)	(0.0004)	(0.0002)	(0.0012)	(0.0014)
Indir	0.0411	−0.0220	0.0281	0.0382	0.0767	−0.0952	−0.0801
	(0.0713)	(0.0591)	(0.0638)	(0.0658)	(0.0605)	(0.1312)	(0.1148)
Growth	0.0002	0.0000	0.0001 *	0.0000	0.0001	−0.0000	0.0000
	(0.0001)	(0.0001)	(0.0001)	(0.0001)	(0.0001)	(0.0002)	(0.0002)
Lev	−0.0005	0.0000	0.0000	−0.0002	0.0002	0.0000	−0.0014
	(0.0007)	(0.0000)	(0.0000)	(0.0006)	(0.0003)	(0.0000)	(0.0028)
Cashflow	0.0766	0.0899 **	0.0917 *	0.1498 ***	0.1020 ***	0.1362	0.2252 **
	(0.0574)	(0.0380)	(0.0477)	(0.0442)	(0.0385)	(0.0936)	(0.1026)
cons	0.1644 ***	0.1242 ***	0.1061 ***	0.0941 ***	0.0631 **	0.1914 ***	0.2607 ***
	(0.0449)	(0.0298)	(0.0346)	(0.0345)	(0.0308)	(0.0710)	(0.0724)
Adjusted R-square	0.391	0.318	0.331	0.347	0.355	0.361	0.414
Ind	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	4126	4234	4359	4001	6011	1243	1106

Note(s): *** p < 0.01, ** p < 0.05, * p < 0.1, firm-level clustered robust standard errors in parentheses.

, the regression results for large enterprises reveal a positive and statistically significant coefficient for EDA, indicating that executives’ digital attention can significantly drive corporate sustainable development practices in large-scale enterprises. Large firms tend to have more adequate resources, better management systems, and greater risk tolerance, giving them an advantage in translating digital strategies into sustainability actions.

In contrast, for small-scale enterprises, executives’ digital attention has no significant effect on sustainable development, as indicated by the insignificant coefficient in column (2) of Table 6. Small-scale enterprises are limited by resource constraints such as capital, technology and human resources, which make it difficult to effectively transform digital attention into sustainable development actions, and more resources may be prioritized to maintain basic business operations and surviving.

Industry competitive advantage. From the industry level, this study further explores the impact of industry competitive advantage on the relationship between executives’ digital attention and corporate sustainable development. Lerner’s index is usually used to measure the market power or monopoly degree of a firm, and a high Lerner’s index implies that a firm has strong pricing power and industry competitive advantage in the market (Tang et al., 2022). To examine heterogeneous effects, the sample is partitioned by the median Lerner index into two subsamples representing firms with stronger and weaker industry competitive advantages, which are then analyzed separately.

In column (3) of Table 6, the coefficient of executive digital attention among firms with stronger industry competitive advantage is significantly positive at the 1% level. This illustrates the fact that in industries with stronger market dominance or a higher degree of monopoly, the digital attention of executives is more likely to be transformed into actual sustainable development actions. These firms have accumulated more resources and capabilities by virtue of their market position and are able to invest large amounts of money in digital transformation and sustainability programs. At the same time, with less competitive pressure in the market, companies are more motivated to transform their digital attention into sustainable development strategies to strengthen their market position and enhance their corporate image.

In column (4) of Table 6, the effect of executives’ digital attention on sustainable development is not significant in firms with weaker competitive advantages in the industry. This may be due to the fact that, in a competitive market environment, firms are always facing survival challenges, and their resources are more often used to cope with short-term market competition demands such as price wars and new product development, which makes it difficult to effectively transform digital attention into sustainable development actions.

Geographical location. Starting from the regional level, this study further explores the impact of the geographic location of a firm on the relationship between executives’ digital attention and sustainable development. In view of distinct regional variations in China’s economic development, resource endowments, industrial structure, and institutional environment, this research categorizes the sample into eastern, central, and western groups to conduct heterogeneity tests (L. Li et al., 2021).

The effectiveness of executive digital attention in promoting sustainable development appears to be contingent on the regional market environment. In the eastern region, characterized by a high degree of marketization, the coefficient is positive and significant, as detailed in column (5) of Table 6.

On the contrary, columns (6) and (7) of Table 6 indicate that the regression coefficients corresponding to the central and western regions lack statistical significance. This phenomenon can likely be ascribed to the relatively underdeveloped marketization processes and insufficient infrastructure development in these regions. When attempting to transform digital strategies into sustainable development actions, firms in the central and western regions confront a greater number of obstacles. Consequently, the positive influence of top executives’ digital attention on sustainable development is less pronounced in these areas.

5. Further Analysis

5.1. Mechanism Analysis

5.1.1. Resource Structuring Mechanism

Following Liu and Tian (2019), this study adopts the input of enterprise software capital and hardware capital to measure the resource structuring capability, which is denoted as Structuring. This indicator is appropriate because, in the digital economy, such capital represents the core elements for a firm’s digital transformation and the foundation of its digital resources. The scale of their investment directly reflects the degree of importance that executives attach to digital technology infrastructure and the ability to reserve resources. The more investment in software capital and hardware capital, the stronger the enterprise’s resource structuring capability.

In column (1) of

Table 7. Mechanism analysis results.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
	Structuring	CSP	Bundling	CSP	Leveraging	CSP
EDA	0.0926 *	0.0073 ***	0.0049 ***	0.0061 **	0.0363 ***	0.0078 ***
	(0.0533)	(0.0027)	(0.0008)	(0.0028)	(0.0074)	(0.0028)
Structuring		0.0127 ***
		(0.0016)
Bundling				0.4770 ***
				(0.0782)
Leveraging						0.0186 *
						(0.0102)
Age	0.1330	−0.0068	−0.0044 ***	−0.0030	0.0411 ***	−0.0059
	(0.0813)	(0.0052)	(0.0015)	(0.0052)	(0.0122)	(0.0053)
Top1	0.0079 **	0.0005 **	−0.0000	0.0006 **	0.0032 ***	0.0006 **
	(0.0040)	(0.0002)	(0.0001)	(0.0002)	(0.0006)	(0.0002)
TobinQ	−0.2934 ***	−0.0006	−0.0005	−0.0040 **	−0.0078 *	−0.0041 **
	(0.0416)	(0.0017)	(0.0004)	(0.0020)	(0.0045)	(0.0020)
Manag	−0.0181 *	−0.0003	0.0001	−0.0006 **	−0.0052 *	−0.0004 *
	(0.0099)	(0.0002)	(0.0001)	(0.0003)	(0.0029)	(0.0003)
Indir	0.5576	0.0232	0.0001	0.0302	−0.1809	0.0336
	(0.8928)	(0.0490)	(0.0159)	(0.0499)	(0.1342)	(0.0504)
Growth	0.0029 ***	0.0000	0.0000 **	0.0001	0.0008 ***	0.0001
	(0.0010)	(0.0001)	(0.0000)	(0.0001)	(0.0001)	(0.0001)
Lev	0.0006	−0.0000	0.0000 *	−0.0000	0.0000	0.0000
	(0.0006)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)
Cashflow	1.4687 **	0.1021 ***	0.0465 ***	0.0985 ***	0.8680 ***	0.1045 ***
	(0.6320)	(0.0338)	(0.0112)	(0.0340)	(0.1090)	(0.0356)
cons	19.3394 ***	−0.1438 ***	0.0342 ***	0.0849 ***	0.3941 ***	0.0939 ***
	(0.4392)	(0.0398)	(0.0074)	(0.0270)	(0.0695)	(0.0274)
Adjusted R-square	0.281	0.360	0.227	0.348	0.426	0.338
Ind	Yes	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes	Yes
N	8360	8360	8360	8360	8360	8360
Percentage of indirect effects	16.08%		38.23%		8.69%
95% confidence interval	[0.004, 0.010] (Z = 4.55)		[0.002, 0.009] (Z = 3.14)		[0.005, 0.011] (Z = 4.75)

Note(s): *** p < 0.01, ** p < 0.05, * p < 0.1, firm-level clustered robust standard errors in parentheses.

, the regression coefficient of EDA is positive and significant, which implies that the increase in executives’ attention to the digital domain will promote the enhancement of enterprises’ resource building capacity in terms of software capital, hardware capital investment, and the acquisition of patents in digital technology. In column (2) of Table 7, the coefficients for both EDA and Structuring are positive and statistically significant at the 1% level. This finding indicates that executive digital attention promotes corporate sustainable development by enhancing resource structuring capability.

5.1.2. Resource Bundling Mechanism

Following X. Yu et al. (2025), this paper adopts the coupling synergy between the proportion of employees in R&D and technical positions and enterprise innovation to measure the resource bundling capacity, which is labeled as Bundling. From the perspective of human resource-innovation synergy, R&D and technical employees constitute the core workforce driving corporate innovation activities, with their proportion directly reflecting the firm’s strategic talent reserves for innovation. Meanwhile, corporate innovation spans multiple dimensions, including technological advancement, product development, and managerial improvement. The coupling coordination degree between these two elements precisely captures an organization’s capability to organically integrate human capital with innovation initiatives and leverage synergistic effects. A higher coupling coordination value indicates more effective integration between human resources and innovation activities, corresponding to stronger resource bundling capacity within the enterprise.

The results in columns (3) and (4) of Table 7 support the mediating role of resource bundling capability. Column (3) shows that executive digital attention (EDA) has a significantly positive effect on this capability. Column (4) demonstrates that when both EDA and Bundling are included, both coefficients are positive and significant. This evidence indicates that executive digital attention promotes corporate sustainable development by enhancing the firm’s resource bundling capability.

5.1.3. Resource Leveraging Mechanism

This paper refers to the research of Shen et al. (2024), using the total asset turnover ratio to measure the resource leveraging capacity, which is recorded as Leveraging. As a core indicator for measuring the operational efficiency of enterprise assets, the total asset turnover ratio directly reflects the comprehensive utilization effect of the enterprise on all assets, which is highly compatible with the essential connotation of resource leveraging capacity. In corporate operations, efficient resource utilization signifies an organization’s ability to generate greater output with relatively fewer asset investments, achieving optimal resource allocation. A higher total asset turnover ratio indicates superior resource utilization efficiency and stronger leveraging capability.

Columns (5) and (6) support the mediating role of resource leveraging capability. Column (5) shows that executive digital attention (EDA) has a significantly positive effect on this capability, as indicated by improved total asset turnover and resource utilization. Column (6) demonstrates that when both EDA and Leveraging are included in the model, both have positive and statistically significant coefficients. This evidence confirms that executive digital attention promotes corporate sustainable development by enhancing the firm’s resource leveraging capability.

To enhance the robustness of the results, the Sobel test and Bootstrap test were also conducted. Table 6 demonstrates the results of the indirect effect shares and 95% confidence intervals, which also supports H2.

5.2. Moderating Effects Analysis

5.2.1. Financing Constraints Pressure

In line with the approach of C. Li et al. (2023), financing constraints are proxied by the SA index, with a greater absolute value of this index indicating more severe financing constraints. To alleviate the possible impact of multicollinearity on regression outcomes, both the explanatory variable and the moderating variable are subjected to mean-centering treatment prior to the construction of the interaction term. As detailed in column (1) of

Table 8. Results of moderating effects.

Variables	(1)	(2)
	CSP	CSP
EDA	0.0066 **	0.0084 ***
	(0.0027)	(0.0028)
SA	−0.1061 ***
	(0.0139)
EDA × SA	−0.0228 ***
	(0.0082)
Media		0.0004 ***
		(0.0001)
EDA × Media		0.0002 ***
		(0.0001)
Age	0.0138 **	−0.0034
	(0.0056)	(0.0053)
Top1	0.0005 *	0.0006 ***
	(0.0002)	(0.0002)
TobinQ	−0.0037 *	−0.0039 **
	(0.0020)	(0.0020)
Manag	−0.0005 *	−0.0005 *
	(0.0003)	(0.0003)
Indir	−0.0173	0.0263
	(0.0500)	(0.0504)
Growth	0.0001	0.0001
	(0.0001)	(0.0001)
Lev	−0.0000	0.0000
	(0.0000)	(0.0000)
Cashflow	0.1255 ***	0.1139 ***
	(0.0343)	(0.0348)
cons	0.0978 ***	0.1175 ***
	(0.0255)	(0.0259)
Adjusted R-square	0.360	0.340
Ind	Yes	Yes
Year	Yes	Yes
N	8360	8360

Note(s): *** p < 0.01, ** p < 0.05, * p < 0.1, firm-level clustered robust standard errors in parentheses.

, the interaction term EDA × SA exhibits a significantly negative coefficient. This finding suggests that the positive impact of executives’ digital attention on corporate sustainable development is more pronounced among enterprises with lower financing pressure, thereby providing empirical support for H3.

5.2.2. Media Attention Pressure

In this study, with reference to T. Wang et al. (2025), the logarithm of the total coverage of newspapers and online media was used to measure media attention, denoted as Media. To mitigate the potential impact of multicollinearity on regression results, both the explanatory variable (executive digital attention, EDA) and the moderating variable (Media) are subjected to centering processing before constructing the interaction term. As shown in column (2) of Table 8, the coefficient on the interaction term EDA × Media is statistically significant at the 10% level. This result indicates that the positive effect of executives’ digital attention on sustainable development is more pronounced in environments with higher media visibility. Such external oversight appears to reinforce managerial commitment to sustainability goals, which is consistent with the prediction of H4.

5.3. Dimension-Wise Tests

Corporate sustainability performance is a composite index integrating financial performance and environmental performance. To accurately identify the specific influence of executives’ digital attention on different dimensions of sustainability, this section decomposes the dependent variable into financial performance and environmental performance and separately tests the independent effects of EDA on each dimension.

Table 9. Results of dimension-wise tests.

Variables	(1)	(2)
	Financial Performance	Environmental Performance
EDA	0.0010	0.7658 ***
	(0.0006)	(0.2509)
Age	−0.0079 ***	−0.4629
	(0.0013)	(0.4772)
Top1	0.0001 ***	0.0557 **
	(0.0000)	(0.0225)
TobinQ	0.0085 ***	−0.3866 **
	(0.0011)	(0.1809)
Manag	−0.0001	−0.0483 *
	(0.0001)	(0.0275)
Indir	−0.0015	2.7373
	(0.0122)	(4.5885)
Growth	0.0001 ***	0.0078
	(0.0000)	(0.0052)
Lev	−0.0000	0.0002
	(0.0000)	(0.0013)
Cashflow	0.3374 ***	10.9161 ***
	(0.0195)	(3.1669)
cons	0.0302 ***	9.1347 ***
	(0.0075)	(2.4537)
Adjusted R-square	0.486	0.336
Ind	Yes	Yes
Year	Yes	Yes
N	8360	8360

Note(s): *** p < 0.01, ** p < 0.05, * p < 0.1, firm-level clustered robust standard errors in parentheses.

presents the results of dimension-wise tests. The regression results in column (1) show that the coefficient of EDA is 0.0010 but fails to pass the significance test, indicating that its direct promoting effect on firms’ financial performance is not yet apparent. The results in column (2) reveal that the coefficient of EDA is 0.7658 and significantly positive at the 1% level, demonstrating that executives’ digital attention can significantly drive the improvement of corporate environmental performance. These findings illustrate that the promoting effect of executives’ digital attention on corporate sustainable development is mainly achieved through enhancing environmental performance, providing empirical support for the differential impacts of sustainable development performance across dimensions.

6. Discussion

By fundamentally transforming the business environment, the rapid evolution of digital technology provides a pathway for companies to integrate such advancements into their sustainable growth models, thereby promoting both economic expansion and ecological protection (Ali, 2024). Feng et al. (2025) argued that executives’ understanding and acceptance of digital technology and their ability to integrate it into corporate strategies directly determine the fate of enterprises in the digital wave. Our research indicates that the digital attention of executives is an important driving factor influencing corporate sustainable development, which is consistent with previous research findings.

Contrary to prior research, this study shifts the focus from technology application to executive cognition in the context of corporate sustainable development. On the one hand, while earlier studies explored the impact of digital tools at the implementation level (Duch-Brown & Rossetti, 2020; Guandalini, 2022), they largely ignored the pivotal role of senior executives’ cognitive frameworks. Even though Yin et al. (2025) proposed a conceptual model regarding CEO digital embeddedness and corporate performance, their research perspective focuses on short-term performance and fails to consider the long-term impact of corporate sustainable development. This study focuses on the attention of executives in the context of digital transformation, incorporating the cognitive factor of executives’ digital attention into the theoretical framework of enterprise sustainable development, further enriching the driving factors of corporate sustainable development. On the other hand, most existing studies adopt the questionnaire survey method to explore the relationship between executives and corporate sustainable development (Galbreath, 2018; Zada et al., 2025). Galbreath (2018) employed the questionnaire survey method and found that board attention exerts an impact on corporate sustainable development. However, this study is a cross-sectional one, which limits the in-depth analysis of causal relationships. By contrast, this paper uses the panel data of A-share listed companies from 2012 to 2023. By including industry and year fixed effects, the model accounts for unchanging industry traits and broad temporal shocks. Compared with cross-sectional studies, this approach can more effectively mitigate the omitted variable bias, thereby providing more reliable empirical evidence for identifying the causal relationship between executives’ digital attention and corporate sustainable development.

Regarding the resource orchestration theory, although it has been widely applied in the field of strategic management, relevant empirical research remains relatively limited, and a single theoretical perspective is insufficient to fully address complex management issues. This also echoes the viewpoint of Soleymanzadeh and Hajipour (2025), who contend that subsequent research can expand empirical examinations of the theory and merge it with complementary theories to form a fuller analytical framework, such as focusing on core issues like the micro-foundations of how managers orchestrate resources to achieve firm growth. Drawing on this academic insight, this study combines the resource orchestration theory with the upper echelons theory and the attention-based view, which not only enriches the empirical research on the resource orchestration theory but also responds to the practical need for reconstructing firm resource orchestration logic in the digital era.

Furthermore, this study extends resource orchestration theory to the digital context by exploring the mechanisms of resource structuring, bundling, and leveraging. This analysis refines the theory’s applicability and addresses a key gap in the existing literature. Amit and Han (2017) emphasized that the digital era requires enterprises to reconstruct the logic of resource orchestration and called on academic circles to pay attention to the evolutionary direction of this theory in the digital context. Drawing on the resource orchestration theory, existing studies have explored its realization paths in such aspects as corporate value creation (Noviaristanti et al., 2024), digital transformation (Cheng et al., 2024), and corporate growth mechanisms (B. Wang et al., 2024). However, most of these studies have focused on the direct effects of resource orchestration, rarely incorporated executives’ cognitive factors into the analytical framework, and neglected the guiding role of executives’ attention throughout the entire process of resource construction, bundling and utilization. By integrating executives’ digital attention with the resource orchestration theory, this study demonstrates that executives’ attention to digital technologies can improve the level of corporate resource orchestration, promote the in-depth integration of digital resources and traditional resources, and thus facilitate corporate sustainable development. This research perspective not only expands the studies of resource orchestration theory at the micro-cognitive level but also responds to the research proposition put forward by S. Q. Wang et al. (2022) that top management team attention affects resource orchestration and strategic renewal, thereby providing important insights for subsequent scholars to deepen the application of resource orchestration theory from a cognitive perspective.

7. Conclusions and Implications

7.1. Conclusions

This study investigates the causal relationship between executive digital attention and corporate sustainable development, utilizing a comprehensive sample of A-share listed companies in China from 2012 to 2023. Grounded in resource orchestration theory, the research systematically analyzes the primary effect of executive digital attention and elucidates the underlying transmission mechanisms. Specifically, it traces a three-stage pathway of “resource structuring—resource bundling—resource leveraging.” Furthermore, the analysis extends to explore heterogeneity across different firm characteristics and examines the moderating roles of financing constraints and media attention.

The principal conclusion is that executive digital attention is a powerful driver of corporate sustainable development. The validity of this finding is rigorously confirmed through a battery of robustness tests, including instrumental variable approaches, placebo tests, and alternative model specifications, which collectively substantiate the reliability of the results.

Heterogeneity tests conducted at the firm, industrial and regional dimensions demonstrate that this favorable impact tends to be more salient among large-sized enterprises, those with a more robust competitive edge in their respective industries, as well as entities located in the eastern regions. Specifically, large firms, with their abundant resources, well-established management systems, and strong risk-bearing capacity, can more effectively translate executives’ digital attention into sustainable development practices. Companies with strong competitive industry advantages possess more resources to invest in digital transformation and sustainable development projects, and they are more motivated to convert digital attention into long-term strategies to consolidate market position and enhance corporate image. Firms in the eastern region benefit from higher marketization levels and efficient resource allocation, making the positive impact of executives’ digital attention on sustainable development more significant.

Mechanism tests indicate that executives’ digital attention enhances corporate sustainable development by improving firms’ capabilities in resource structuring, bundling, and leveraging. Specifically, executives’ focus on digital domains drives firms to increase investments in software and hardware capital, strengthening resource structuring capabilities. It also promotes the coupling synergy between the proportion of R&D and technical staff and corporate innovation, enhancing resource bundling capabilities, while improving total asset turnover to optimize resource leveraging.

This study also pinpoints two critical moderating factors that exert an influence on the correlation between executives’ digital attention and corporate sustainable development. For one thing, financing constraints are found to mitigate the aforementioned association, since financial limitations curtail a firm’s capacity to implement digital strategy-focused initiatives. As a result, the positive effect of executives’ digital attention on corporate sustainability is notably more pronounced among enterprises with abundant financial reserves and alleviated financing strains. For another, media attention serves to reinforce this relationship by heightening reputational incentives for firms. Under intense public scrutiny, executives are more compelled to translate their digital focus into tangible sustainability outcomes to safeguard corporate reputation, thereby enhancing the overall effect and confirming the critical role of external governance.

Further, dimension-specific tests show that the direct facilitating effect of executives’ digital attention on corporate financial performance is not yet significant, while it exerts a notably positive driving effect on corporate environmental performance. This divergence implies that the value of executives’ digital attention tends to materialize first in the realm of environmental governance rather than short-term financial gains. It also highlights the necessity for firms to establish a long-term perspective when leveraging executives’ digital attention to advance sustainable development, rather than overemphasizing immediate financial returns.

7.2. Practical Implications

This study provides important insights for corporate sustainable development and government decision-making. For enterprises, priority should be given to enhancing executives’ digital attention by encouraging them to closely monitor digital technology trends and deeply integrate digital technologies into corporate strategies. At the resource structuring level, companies should increase investment in digital technology infrastructure, prioritizing intelligent energy management systems, carbon emission monitoring platforms and other infrastructure. In terms of resource bundling, cross-departmental collaboration platforms should be established using digital means to integrate various resources through digital technologies and create resource synergy effects. For resource utilization, big data analytics should be employed to optimize supply chain processes, reduce inventory backlog and logistics waste, and improve resource turnover efficiency. Moreover, the findings highlight the necessity of strategically managing media relations. Rather than passively receiving media coverage, companies should actively initiate communication and build rapport with press channels. This deliberate approach allows a firm to leverage the media narrative to strengthen and disseminate the positive impacts of executive digital attention on corporate sustainable development performance.

For government departments, proactive policy guidance and support should be provided. In cultivating executives’ digital attention, digital transformation demonstration projects could be established to encourage corporate executives’ participation, helping them deepen understanding of digital technology applications in business operations through hands-on project implementation and develop their ability to translate digital attention into effective strategic decisions. To address corporate financing constraints, governments should improve multi-level capital market development, encourage venture capital and private equity investment institutions to increase support for corporate digital transformation projects, promote financial products such as green bonds and sustainability-linked loans to provide dedicated financing channels for sustainable development projects. Furthermore, governments can play a pivotal role in accelerating regional convergence by establishing cross-regional resource-sharing platforms. These digital ecosystems would be specifically designed to facilitate the systematic exchange of transformation experiences and best practices. By channeling the advanced digital initiatives from the more developed eastern regions to their central and western counterparts, these platforms can effectively narrow the existing disparities in digital transformation and sustainable development, paving the way for more balanced and inclusive national socioeconomic progress.

7.3. Limitations and Future Directions

While this study offers certain insightful contributions, it is not without limitations. First, this research sample is restricted to Chinese A-share listed companies. Moreover, throughout the study period, the Chinese government introduced robust regulatory interventions targeting digital transformation and sustainable development initiatives. Although the resource orchestration mechanism boasts theoretical applicability across different contexts, its actual efficacy could be influenced by cross-regional variations in infrastructure refinement, regulatory rigor, and cultural value systems. Thus, it cannot be directly generalized to market economies without similar institutional environments, and its applicability needs further verification in different institutional contexts. Second, there is a variable measurement limitation. Executives’ digital attention relies on textual frequency analysis of the Management Discussion and Analysis section in annual reports. While this method is consistent with academic consensus, it fails to fully capture the depth of cognition, strategic priority, and real attention level. Future research can improve the measurement by combining mixed methods such as questionnaire surveys and executive interviews. Finally, there is a sample scope limitation. Non-listed companies and small and medium-sized enterprises (SMEs) are not included. Such enterprises differ from listed companies in terms of resource constraints and policy response models, which may affect the performance of the core relationship. Subsequent studies can expand the sample to different countries and institutional environments, test the generalizability boundary of the conclusions through cross-country comparisons, and include non-listed companies and SMEs to enrich the application scenarios of the research conclusions.