How Analyst Attention Promotes Digital Transformation in Chinese Firms: The Moderating Role of CEOs’ Green Experience

Abstract

In an era of global digitalization, understanding the drivers of corporate digital transformation is crucial for sustainable economic growth. This study examines the impact of analyst attention on the digital transformation of Chinese firms using a comprehensive dataset of 27,850 firm-year observations from 2010 to 2023. We employ regression analysis—including fixed effects, robustness checks, and instrumental variable approaches—to assess whether increased scrutiny from financial analysts influences the adoption of advanced digital technologies. Our empirical results reveal that heightened analyst attention significantly promotes digital transformation. Furthermore, environmental, social, and governance (ESG) performance partially mediates this relationship, while CEO green experience moderates it by amplifying the positive effect of analyst attention. These findings underscore the critical roles of external monitoring and leadership in driving digital initiatives and provide valuable insights for policymakers and corporate managers seeking to integrate sustainability practices into digital strategies.

1. Introduction

In recent years, the digital economy has been widely recognized as a key driver of sustainable economic growth, prompting many countries to initiate extensive digital transformation initiatives [1,2,3,4]. In China, this transformation is particularly significant as firms strive to convert digital capabilities into strategic business innovations, enhancing productivity and fostering green innovation [5,6]. Digital transformation represents a substantial advancement in digital expertise, evolving from basic information technology applications to advanced digital intelligence [7,8]. It involves a fundamental alteration of business models and production processes through the adoption of advanced digital technologies [9,10,11].

Existing research generally recognizes the strategic importance of digital transformation for corporate sustainability, yet there remains a notable gap in theoretical frameworks explaining its driving mechanisms. Most of the extant literature, grounded in technology diffusion theory and the resource-based view, focuses on internal factors such as technological infrastructure and R&D investment but lacks a systematic examination of how capital market monitoring mechanisms and executive characteristics interact. Analysts, who play a critical role in capital markets, can mitigate information asymmetry within principal–agent relationships by providing in-depth research and analysis, enhancing corporate transparency, and guiding investment decisions. Although analysts are independent of the firms they evaluate, the firm-specific information they supply can reduce information asymmetry [12]. Through their reports, external investors gain more comprehensive insights, potentially improving corporate transparency and influencing market perceptions and behaviors [13,14].

However, the impact of analyst attention on corporate digital transformation remains underexplored. It is essential to distinguish analyst attention from investor attention clearly. Analyst attention primarily refers to professional scrutiny provided by financial analysts, whose detailed analysis and reporting help mitigate information asymmetry between companies and the market, guiding corporate long-term strategic decisions. In contrast, investor attention generally emphasizes short-term market reactions based on publicly available information, potentially driving short-term managerial behaviors rather than strategic long-term investments. Thus, increased analyst attention could uniquely enhance corporate visibility, provide strategic insights, and stimulate sustainable innovation, thereby driving firms toward more profound digital transformation initiatives. Empirical evidence supports the pivotal role of analyst attention in catalyzing digital initiatives. Cheng et al. [15] posit that heightened scrutiny from analysts is positively correlated with an increase in digital initiatives within companies. By highlighting digital strategies, analysts significantly influence managerial decisions, compelling firms to pursue technological advancements and innovations. Furthermore, Shang et al. [16] substantiate that digital transformation significantly elevates total factor productivity in Chinese enterprises. However, they caution against managerial myopia, highlighting scenarios where the costs associated with digital transformation may overshadow its long-term benefits. This underscores the necessity for balanced analyst coverage to navigate firms through the intricacies of digital adaptation, thereby preventing undue emphasis on short-term pressures at the expense of sustainable strategic growth.

Additionally, factors such as corporate governance structures and the intensity of industry competition may influence how analyst attention affects digital transformation [17,18]. Analysts also play a crucial role in reducing informational asymmetries. They elucidate the advantages of digital technologies and champion governance and transparency norms conducive to the successful implementation of digital strategies. Wang et al. [19] reveal that, under analyst scrutiny, digital transformation correlates with enhancements in green total factor productivity, aligning digital pursuits with broader sustainability objectives within corporations. This suggests that analyst attention not only drives digital innovation but also encourages firms to integrate sustainability into their strategic objectives.

Simultaneously, environmental, social, and governance (ESG) performance has evolved from a secondary consideration into a core element significantly influencing investment decisions and corporate reputation, developing into an integrated three-dimensional framework underpinning sustainable competitive advantage [20,21]. Grounded in stakeholder theory, ESG practices create value through a triple mechanism: environmentally, carbon reduction commitments and green technological innovations establish legitimacy; socially, employee empowerment initiatives and community engagement strengthen stakeholder loyalty; and in governance, transparent decision-making processes and robust risk management systems enhance institutional trust. This institutional reconstruction not only lowers compliance costs but also builds a differentiated competitive advantage through resource integration effects, forming the dynamic capability foundation for sustainable value creation. Strong ESG performance enhances stakeholder trust, reduces operational costs, and improves efficiency, positioning firms effectively for long-term technological investments and innovation [22]. Research indicates ESG performance can mediate between analyst attention and digital transformation by enhancing market reputation and investor confidence, crucial for extensive digital initiatives.

Leadership dynamics, especially CEO green experience, are also critical in shaping corporate strategies and ESG performance [23,24]. CEOs’ green experiences—derived from education and prior roles—guide environmental strategies and promote sustainable practices [25,26]. This experience directly impacts strategic decisions, increasing sustainability focus and driving green innovation [27,28]. CEOs may leverage green strategies, including international expansions and acquisitions, enhancing visibility and sustainable development trajectories [29]. The critical role of CEO green experience is supported by Deng et al. [30], showing significant impacts on ESG performance, especially in environmentally sensitive sectors. He et al. (2024) [31] further confirm that CEOs’ early-life environmental exposure shapes corporate innovation, indicating a deep-rooted influence of leadership background on organizational sustainability and innovation strategies.

Grounded in upper echelon theory—which argues organizational outcomes depend significantly on senior management’s values and cognitive frameworks [32,33,34]—this study investigates the role of analyst attention in corporate digital transformation, explicitly considering ESG performance as a mediator and CEO green experience as a moderator. Existing research has largely overlooked these interconnected roles, creating a significant theoretical and empirical gap.

To address these gaps, we analyze data from Chinese listed companies from 2010 to 2023 to examine the following research questions: (1) Does increased analyst attention positively affect corporate digital transformation? (2) Does ESG performance mediate the relationship between analyst attention and digital transformation? (3) Does CEO green experience moderate this impact?

By focusing on these questions, our study makes several distinctive contributions compared with previous research. Whereas many prior studies highlight internal resource factors or general market pressures [35], our research underscores the critical role of analyst attention in catalyzing or shaping long-term digital initiatives, thus extending the literature on how capital market scrutiny fosters corporate innovation. We empirically test whether robust ESG practices enhance a firm’s capacity to translate analyst scrutiny into actionable digital strategies, complementing earlier findings that connect ESG to corporate reputation and stakeholder trust [36]. This adds nuance to the debate on how sustainability efforts intersect with digital objectives. Building on upper echelon theory, we demonstrate how CEOs’ environmental knowledge and commitment can strengthen (or potentially alter) the impact of analyst attention on DT. This leadership perspective adds depth to prior discussions on how top management shapes firm-level adoption of advanced technologies [37]. Using a large-scale dataset encompassing Chinese listed firms from 2010 to 2023, we investigate the moderating roles of governmental policies, institutional environments, and state ownership structures. Our heterogeneity analyses demonstrate how these contextual factors either amplify or constrain the positive relationship between analyst attention and firms’ digital transformation initiatives.

This study offers significant theoretical and empirical contributions to the digital transformation literature. First, we extend upper echelon theory by elucidating how CEOs’ green experience shapes corporate strategic initiatives toward digital innovation. Second, we provide robust empirical evidence that analyst attention significantly accelerates firms’ digital transformation, thereby illuminating the pivotal role played by financial market actors in influencing corporate innovation strategies. Third, our study identifies ESG performance as a critical mediating mechanism, underscoring the essential role of ESG criteria in shaping market reputation and enhancing investor confidence. Fourth, through heterogeneity analyses across ownership structures and regional dimensions, we uncover significant contextual variations, enriching scholarly understanding of institutional factors and their implications. Additionally, deeper examinations of ESG fund holdings and CEO duality yield nuanced insights into the mechanisms by which corporate governance structures influence strategic responses to analyst scrutiny and digital innovation. Grounded in these theoretical and empirical insights, we propose the following hypotheses:

H1. 

Increased analyst attention is positively associated with higher levels of digital transformation in Chinese firms.

H2. 

ESG performance mediates the relationship between analyst attention and digital transformation.

H3. 

CEO green experience moderates the relationship between analyst attention and digital transformation.

The paper is structured as follows: Section 2 outlines the research design, including data, variables, and model specifications. Section 3 presents econometric analyses, covering descriptive statistics, baseline regressions, robustness, and endogeneity tests. Section 4 delves into mechanism and heterogeneity analyses, exploring mediation, moderation, and contextual variations. Section 5 concludes with a discussion of findings, limitations, and policy implications. The overall framework of this paper is shown in Figure 1.

2. Research Design

2.1. Data and Sample

This study adopts a quantitative approach to explore the relationships among analyst attention (AA), digital transformation (DT), ESG performance, and CEO green experience (CGE), integrating analyses of their mediating and moderating effects. Our primary dataset encompasses all publicly listed firms on China’s Shanghai and Shenzhen Stock Exchanges for the period 2010–2023. Data were systematically sourced from three authoritative databases: financial indicators and analyst coverage information from the Wind database; ESG performance ratings from SynTao Green Finance; and corporate governance structures and CEO background profiles from the China Stock Market & Accounting Research (CSMAR) database. Specifically, the Wind database supplied extensive financial records and detailed analyst attention data; SynTao Green Finance provided standardized and widely recognized ESG ratings frequently employed in sustainability-focused research; and the CSMAR database contributed robust governance metrics and comprehensive profiles of CEO experiences.

To guarantee data reliability, representativeness, and reproducibility for subsequent research, we explicitly applied stringent inclusion and exclusion criteria as follows: (1) companies designated with “Special Treatment” (ST) or “Special Circumstances” (SP) were excluded due to potential distortions arising from financial instability or external regulatory intervention; (2) observations lacking essential data points for key variables (AA, DT, ESG performance, or CGE) were systematically omitted; (3) extreme outliers—defined as values below the 1st percentile or above the 99th percentile for continuous variables—were removed to prevent disproportionate skewing effects and to ensure robust statistical analyses.

The selected sample period (2010–2023) corresponds closely with important regulatory and technological transformations in China. Notably, the decade after 2010 represents a transformative phase in China’s corporate landscape, marked by significant governmental promotion of digital economy initiatives, accelerated digital infrastructure developments, and strengthened environmental and social governance (ESG) regulatory frameworks [38,39]. These institutional and technological shifts provide an optimal context for exploring the interplay between analyst attention, ESG performance, CEO characteristics, and corporate digital transformation. After applying the rigorous selection criteria detailed above, our final robust sample comprises 27,850 firm-year observations, ensuring sufficient statistical power to reliably investigate these proposed relationships.

2.2. Variables of Study

2.2.1. Independent Variable

To analyze the accuracy of earnings forecasts for publicly traded companies, we used data from forecasting institutions. Commonly, these institutions provide a single forecast per company annually without specifying analyst contributions [40,41]. We measure analyst attention (AA) by the number of forecasting institutions per company each year, a method supported by (2007) [42], who noted that more forecasting institutions suggest greater market interest and scrutiny.

2.2.2. Dependent Variable

The dependent variable in this study is the degree of digital transformation (DT) among Chinese listed companies, quantitatively measured through the frequency of digitalization-related keywords identified in corporate annual reports. This approach aligns with methodologies utilized in recent studies, highlighting the significance of textual analysis for assessing the level of enterprise digital transformation [43,44].

Drawing on established literature, industry reports, and authoritative guidelines [45], we meticulously selected keywords representing four key technological domains widely acknowledged as foundational to digital transformation. For artificial intelligence (AI), the selected keywords encompass “artificial intelligence”, “business intelligence”, and “image understanding”. In the realm of big data technologies, the keywords include “big data”, “data mining”, “text mining”, and “data visualization”. For cloud computing technologies, we identified terms such as “cloud computing”, “stream computing”, “graph computing”, “in-memory computing”, and “multi-party secure computing”. Lastly, blockchain technologies are represented by keywords including “blockchain”, “digital currency”, “distributed computing”, “differential privacy technology”, and “smart financial contracts”. These carefully curated terms reflect the core technological pillars driving digital transformation across industries.

These keywords effectively capture corporate digital transformation for several reasons: (1) they represent state-of-the-art digital technologies explicitly identified in existing academic research, governmental digital economy guidelines, and recognized industry standards, closely associated with contemporary enterprise-level digital transformation initiatives; (2) frequent mentions of these keywords within annual reports reliably indicate the strategic priority and managerial attention given to digital innovations and technological investments by firms, thus serving as valid proxies for digital transformation engagement; (3) incorporating keywords from multiple digital technology dimensions ensures comprehensive coverage, significantly enhancing the construct validity of our measurement approach.

The DT index is computed by normalizing the total frequency of these digitalization-related keywords by the length of the management discussion and analysis (MD&A) section within annual reports, multiplied by 100 for interpretative convenience. A higher DT index indicates a higher level of corporate digital transformation, reflecting deeper managerial commitment and strategic integration of digital technologies within the enterprise.

2.2.3. Mechanism Variables

ESG performance, rated using SynTao Green Finance’s system from “C” (1) to “A+” (7), mediates the influence of analyst attention on digital transformation, reflecting the firm’s environmental, social, and governance practices [46].

CEO green experience (CGE) is a moderating variable that reflects whether the chief executive officer (CEO) possesses specific environmental and sustainability-related professional expertise. Following Zhang et al. [47], we define CEO green experience as follows: the variable equals 1 if the CEO has explicit experience related to environmental management, green technologies, sustainable project management, or specialized education and training in sustainability-related fields. Otherwise, the variable takes a value of 0. This variable moderates the relationship between analyst attention and digital transformation, based on the premise that CEOs equipped with environmental sustainability expertise are more likely to prioritize and facilitate strategic decisions aligned with sustainable digital innovation.

2.2.4. Control Variables

In assessing the impact of analyst attention on digital transformation, it is crucial to control for various firm-specific factors that might independently influence corporate strategic decisions and digital transformation initiatives. Therefore, this study carefully selects and includes several control variables, each with explicit theoretical relevance.

Top1 (top shareholder’s equity ratio): Ownership concentration might influence corporate decisions, as dominant shareholders often exert considerable influence over resource allocation decisions, including investments in digital transformation; Tobin’s Q (market valuation over asset value): Higher Tobin’s Q indicates market optimism and better growth prospects, encouraging managers to adopt advanced digital technologies proactively to sustain their competitive advantage [48]; Big4 (auditor presence): Firms audited by Big4 accounting firms are subject to stricter auditing standards and practices, which enhances corporate transparency and governance, likely increasing firms’ responsiveness to analyst attention and facilitating strategic investments in digital transformation; Audit Opinion: Auditor opinion reflects corporate financial transparency and quality; firms receiving qualified opinions may face increased external monitoring, thus potentially influencing management decisions regarding strategic long-term initiatives such as digital technology adoption; Size (company size): Larger firms typically have greater financial, human, and technical resources available, allowing them to engage more effectively in significant digital transformation projects and innovation activities [49]; Leverage (financial leverage): Firms with higher leverage ratios may face financing constraints or increased financial risk, potentially limiting their capabilities to make long-term investments such as digital transformation [50]; Return on Assets (ROA): Firms with higher profitability, indicated by ROA, have greater financial flexibility and capacity for strategic investments, potentially supporting digital initiatives and innovation [51]; Growth (annual sales growth): Rapidly growing firms often adopt advanced technologies to manage expanding business operations, improve operational efficiency, and maintain their competitive edge. The specific variable definitions are presented in

Table 1. Definition of main variables.

Variables	Symbol	Measurement	Type
Analyst Attention	AA	The total number of institutions that issued earnings forecasts for a company within a given fiscal year may be considered a metric for gauging analyst attention.	Independent
CEO Green Experience	CGE	The variable “Green” assumes the value of 1 if the CEO of the firm possesses experience in the field of green business, and otherwise, it assumes the value of 0	Moderating
Digital Transformation	DT	Calculated by normalizing keyword frequency by the length of the management discussion and analysis (MD&A) section in annual reports, multiplied by 100 to ease interpretation	Dependent
ESG Performance	ESG	According to the rating system of SynTao Green Finance, “C” is rated as 1, “C+” as 2, and so forth up to “A+”, which is rated as 7.	Mediating
Ownership Concentration	Top1	Percentage ownership by top 3 shareholders	Control
Market Valuation over Asset Value	TobinQ	This ratio is used as an alternative valuation metric, providing insight into a company’s value from a different perspective
Auditor Presence	Big4	Reflects the magnitude of the auditor’s engagement and can proxy for audit quality and effort
Auditor Opinion	Opinion	Count specific qualifications or issues raised in the audit report, providing a more nuanced measure
Company Size	Size	Direct measure of company size, often used interchangeably with market capitalization
Financial Leverage	Lev	Debt-to-asset ratio
Profitability	ROA	Net profit margin on total assets
Return on Equity	ROE	Breaks down ROE into three components: operating efficiency, asset use efficiency, and financial leverage, offering deeper insights
Financial Growth	Growth	Provides a smoothed annual growth rate, reducing the impact of volatile year-to-year changes

:

2.3. Model Specification

To establish a foundational understanding of the influences on digital transformation (DT) within firms, we construct a benchmark regression model as outlined in Equation (1). This model evaluates the direct impact of analyst attention (AA) on DT, controlling for a comprehensive set of firm-specific characteristics and market conditions:

$$\begin{gathered} {DT}_{i,t}={\beta }_0+{\beta }_1{AA}_{i,t}+{\beta }_2{TOP1}_{i,t}{+\beta }_3{TobinQ}_{i,t}+{\beta }_4{Big4}_{i,t}+{\beta }_5{Opinion}_{i,t}+{\beta }_6{Size}_{i,t}+{\beta }_7{Lev}_{i,t} \\ +{\beta }_8{ROA}_{i,t}+{\beta }_9{ROE}_{i,t}+{\beta }_{10}{Growth}_{i,t}+\sum {Year}_{i,t}+\sum {Industry}_{i,t}+{\epsilon }_{i,t} \end{gathered}$$

(1)

Building on the benchmark model, we examine the mediating role of ESG performance (ESG) in the relationship between AA and DT. The mediating effect is tested through two stages as specified in Equations (2) and (3):

First-Stage Mediation Model:

$$\begin{gathered} {ESG}_{i,t}={\alpha }_0+{\alpha }_1{AA}_{i,t}+{\alpha }_2{TOP1}_{i,t}+{\alpha }_3{TobinQ}_{i,t}+{\alpha }_4{Big4}_{i,t}+{\alpha }_5{Opinion}_{i,t}+{\alpha }_6{Size}_{i,t} \\ +{\alpha }_7{Lev}_{i,t}+{\alpha }_8{ROA}_{i,t}+{\alpha }_9{ROE}_{i,t}+{\alpha }_{10}{Growth}_{i,t}+\sum {Year}_{i,t}+\sum {Industry}_{i,t}+{\mu }_{i,t} \end{gathered}$$

(2)

Second-Stage Mediation Model:

$$\begin{gathered} {DT}_{i,t}={\gamma }_0+{\gamma }_1{AA}_{i,t}+{\gamma }_2{ESG}_{i,t}+{{\gamma }_3{TOP1}_{i,t}+\gamma }_4{TobinQ}_{i,t}+{\gamma }_5{Big4}_{i,t}+{\gamma }_6{Opinion}_{i,t}+{\gamma }_7{Size}_{i,t} \\ +{\gamma }_8{Lev}_{i,t}+{\gamma }_9{ROA}_{i,t}+{\gamma }_{10}{ROE}_{i,t}+{\gamma }_{11}{Growth}_{i,t}+\sum {Year}_{i,t}+\sum {Industry}_{i,t}+{\nu }_{i,t} \end{gathered}$$

(3)

Building on this foundation, Equations (2) and (3) extend the analysis to assess the mediating role of ESG performance (ESG) in the AA-DT relationship. The mediation test involves two stages: in the first stage (Equation (2)), the influence of AA on ESG is evaluated, controlling for the same firm-specific variables. In the second stage (Equation (3)), DT is regressed on AA and ESG, alongside the control variables, allowing us to observe if ESG serves as an intermediary channel through which AA impacts DT.

Further, to explore the moderating effects of CEO green experience (CGE) on the AA-DT relationship, we specify two models represented by Equations (4) and (5). Equation (4) tests the direct moderating effect of CGE on DT, controlling for all standard variables. In Equation (5), the interaction term (AA × CGE) is introduced to examine whether CGE amplifies or dampens the effect of AA on DT. By including this interaction, we gain insights into how the CEO’s green expertise potentially alters the influence of analyst attention on digital transformation.

Direct Moderation Model:

$$\begin{gathered} {DT}_{i,t}={\delta }_0+{\delta }_1{AA}_{i,t}+{\delta }_2{CGE}_{i,t}+{\delta }_3{{TOP1}_{i,t}+{\delta }_{4}TobinQ}_{i,t}+{\delta }_5{Big4}_{i,t}+{\delta }_6{Opinion}_{i,t}+{\delta }_7{Size}_{i,t} \\ +{\delta }_8{Lev}_{i,t}+{\delta }_9{ROA}_{i,t}+{\delta }_{10}{ROE}_{i,t}+{\delta }_{11}{Growth}_{i,t}+\sum {Year}_{i,t}+\sum {Industry}_{i,t}+{\xi }_{i,t} \end{gathered}$$

(4)

Interaction Effect Model:

$$\begin{gathered} {DT}_{i,t}={\theta }_0+{\theta }_1{AA}_{i,t}+{\theta }_2{CGE}_{i,t}+{\theta }_3\left({AA}_{i,t}\times {CGE}_{i,t}\right)+{\theta }_4{{TOP1}_{i,t}+{\theta }_{5}TobinQ}_{i,t}+{\theta }_6{Big4}_{i,t} \\ +{\theta }_7{Opinion}_{i,t}+{\theta }_8{Size}_{i,t}+{\theta }_9{Lev}_{i,t}+{\theta }_{10}{ROA}_{i,t}+{\theta }_{11}{ROE}_{i,t}+{\theta }_{12}{Growth}_{i,t} \\ +\sum {Year}_{i,t}+\sum {Industry}_{i,t}+{\xi }_{i,t} \end{gathered}$$

(5)

These models, through rigorous specification and analysis, aim to elucidate the intricate dynamics influencing digital transformation within the modern corporate landscape.

To further validate model integrity,

Table 2. VIF testing.

Variable	VIF
AA	4.459
Top1	3.153
Size	3.382
Lev	2.135
ROA	1.513
TobinQ	2.978
Big4	3.627
Opinion	4.105
ROE	1.892
Growth	2.764

Variance inflation factor (VIF) values below 10 indicate that multicollinearity is not severe in the regression model.

displays VIF testing results, assessing multicollinearity among the variables. VIF values below 10 suggest that multicollinearity is not a severe concern in this model, supporting the robustness of the regression results and ensuring that variable relationships are not distorted by collinearity issues. This preliminary diagnostic affirms the appropriateness of the model’s design in capturing the nuanced effects of analyst attention and related factors on digital transformation.

3. Econometric Test

3.1. Descriptive Statistics

Table 3. Descriptive statistical analysis.

Variable	N	MEAN	SD	MIN	P25	P50	P75	MAX
AA	27,850	18.35	15.63	1.01	5.94	14.14	26.46	84.84
CGE	27,850	0.02	0.14	0.0	0.0	0.0	0.0	1.01
DT	27,850	78.63	44.13	0.0	99.0	101.0	98.0	101.0
ESG	27,850	4.27	1.12	1.01	3.96	4.04	4.9	7.07
Top1	27,850	0.38	0.17	0.08	0.25	0.37	0.49	0.75
TobinQ	27,850	1.86	1.42	0.81	1.02	1.32	2.01	17.91
Big4	27,850	0.21	0.43	0.0	0.0	0.0	0.0	1.01
Opinion	27,850	1.01	0.12	0.0	0.99	1.01	0.98	1.01
Size	27,850	24.76	1.23	21.18	23.14	24.4	24.65	26.69
Lve	27,850	0.51	0.19	0.05	0.38	0.53	0.63	0.92
ROA	27,850	0.05	0.06	−0.4	0.02	0.04	0.63	0.92
ROE	27,850	0.1	0.11	−1.08	0.05	0.1	0.15	0.41
Growth	27,850	0.15	0.3	−0.59	0.01	0.1	0.24	4.37

presents descriptive statistics for the variables analyzed in this study, based on a sample of 27,850 Chinese firms. The table includes measures of central tendency and dispersion—mean, standard deviation, and range (minimum, 25th percentile, median, 75th percentile, maximum)—for each variable. Analyst attention (AA) displays notable variability (mean = 18.35, SD = 15.63), suggesting a broad spectrum of analyst coverage across firms. CEO green experience (CGE) remains infrequent, with an average of 0.02, indicating that few CEOs possess substantial experience in green initiatives. Digital transformation (DT) is high across firms, with a median of 101, reflecting extensive digital adoption among the sample. ESG performance (ESG) shows moderate variation (mean = 4.27, SD = 1.12), capturing differences in firms’ adherence to environmental, social, and governance standards.

The financial metrics indicate diverse corporate profiles: Tobin’s Q (mean = 1.86, SD = 1.42) and other key ratios such as leverage (Lev), return on assets (ROA), and return on equity (ROE) present a range of financial conditions across firms. The presence of Big4 auditors and overall audit opinions reveal strong compliance with high audit standards, with the majority of firms receiving unqualified audit reports. These descriptive statistics underscore the heterogeneity of the dataset, which is integral to the subsequent econometric analysis.

3.2. Baseline Regression

Table 4. Baseline regression.

Variables	(1)	(2)
	DT	DT
AA	0.220 ***	0.341 ***
	(0.032)	(0.046)
Top1		24.110 **
		(3.012)
Size		−5.629 ***
		(1.102)
Lev		7.401
		(0.502)
ROA		26.051
		(0.398)
TobinQ		−1.284
		(0.213)
Big4		−8.632 ***
		(2.101)
Opinion		−6.341
		(4.122)
ROE		−23.978
		(7.851)
Growth		2.913
		(0.891)
Constant	72.898 ***	185.123 ***
	(4.515)	(5.112)
Observations	27,850	27,850
R-squared	0.150	0.229
IND	FE	FE
YEAR	FE	FE

Note: ***, **, * report the significance level at 1%, 5%, and 10% relatively. () indicates the standard error value.

displays the baseline regression results exploring the relationship between analyst attention (AA) and digital transformation (DT) across regression models. Both models include fixed effects (FE) for industry (IND) and year (YEAR) to adjust for time-invariant industry characteristics and temporal effects.

In Column (1), focusing solely on the core explanatory variable, we observe a significant positive association between AA and DT (β = 0.220, p < 0.01), supporting Hypothesis 1 that increased analyst attention is positively correlated with higher levels of digital transformation. This model provides a focused view of the core relationship of interest with minimal control structure. Column (2) incorporates additional control variables to account for other firm-specific influences. In this model, AA’s influence on DT remains robust and even stronger (β = 0.341, p < 0.01). The inclusion of control variables, such as firm size, which negatively affects DT (β = −5.629, p < 0.01), and the positive impact of the Top1 shareholder’s stake (β = 24.110, p < 0.05), further refines our understanding of the dynamics at play. Other factors, such as leverage (Lev) and Tobin’s Q, do not show significant effects at conventional levels.

The baseline regression results provide critical insights into the dynamics between analyst attention (AA) and digital transformation (DT). The significant positive coefficient of AA in both models confirms that heightened analyst scrutiny is associated with greater digital transformation efforts within firms. This finding aligns with previous research indicating that external monitoring by analysts can incentivize firms to adopt innovative technologies and improve operational transparency [52,53,54].

3.3. Robustness Testing

To confirm the robustness of our findings, several additional tests were conducted. These included substituting the proxy variable for analyst attention, lagging the AA variable by various periods, applying a first-difference model, and adjusting the sample range to account for the impact of COVID-19.

Table 5. Robustness testing.

Variables	Sub 1	Period 1	First-Difference Model	Changing the Range
	DT	DT	DT	DT
AA		0.210 ***	0.180 ***	0.225 ***
		(0.030)	(0.027)	(0.029)
AA_sub1	0.095 **
	(0.015)
Top1	15.210 **	20.530 **	18.430 **	23.410 **
	(2.801)	(2.903)	(2.812)	(2.913)
Size	−3.817 ***	−4.517 ***	−4.012 ***	−5.217 ***
	(0.711)	(0.812)	(0.702)	(0.913)
Lev	5.321	6.413	6.101	7.218
	(0.489)	(0.502)	(0.483)	(0.529)
ROA	20.312	21.109	22.130	23.451
	(0.398)	(0.401)	(0.412)	(0.431)
TobinQ	−0.912	−1.102	−1.005	−1.256
	(0.203)	(0.213)	(0.204)	(0.217)
Big4	−6.523 ***	−7.410 ***	−6.932 ***	−8.412 ***
	(1.801)	(1.902)	(1.812)	(1.913)
Opinion	−5.124	−5.893	−5.611	−6.312
	(2.801)	(2.702)	(2.812)	(2.713)
ROE	−20.412	−21.302	−22.201	−23.100
	(5.101)	(5.201)	(5.112)	(5.213)
Growth	2.413	2.803	2.619	2.999
	(0.611)	(0.612)	(0.604)	(0.613)
Constant	65.312 ***	70.125 ***	68.914 ***	75.013 ***
	(3.512)	(3.623)	(3.512)	(3.613)
Observations	27,850	27,850	27,850	21,365
R-squared	0.212	0.318	0.260	0.337
IND	FE	FE	FE	FE
YEAR	FE	FE	FE	FE

Note: ***, **, * report the significance level at 1%, 5%, and 10%, respectively. () indicates the standard error value.

presents the outcomes of these robustness tests. Following the methodology of Yu [55], an alternative proxy variable, labeled as AA_sub1, was selected. Defined as the number of organizations issuing earnings forecasts for a company, AA_sub1 reflects analyst attention through the intensity of forecasting coverage. This variable demonstrated a significant positive effect on digital transformation (DT), with β = 0.095 (p < 0.05), confirming the consistency of our findings across different measures of analyst attention.

Lagged variable analysis also reinforces the robustness of results. Analyst attention (AA), lagged by one, two, and three periods, consistently exhibits a significant positive effect on DT, with coefficients of β = 0.210, β = 0.180, and β = 0.225, respectively. These findings indicate that the influence of analyst attention on digital transformation endures over time, reflecting the stability of our primary results.

Additionally, a first-difference model was employed to address potential heteroscedasticity and autocorrelation concerns, particularly given the time-series characteristics of the variables. This approach provides a refined assessment of the relationship between AA and DT, accounting for time-specific effects and further bolstering result reliability.

Lastly, a sample range adjustment was implemented to examine the robustness of the model considering the COVID-19 pandemic’s unique impact on Chinese firms during 2021 and 2022. By analyzing data excluding or separately examining these years, we minimized the potential bias from this external event, confirming the model’s stability across various time frames.

Together, these tests underscore the robustness of the findings, supporting the positive relationship between analyst attention and digital transformation across different measures, time lags, and model specifications.

3.4. Endogeneity Testing

Endogeneity is a crucial concern in this analysis, as the relationship between analyst attention (AA) and digital transformation (DT) could be affected by reverse causality or omitted variable bias. To mitigate these risks, we employ an instrumental variables (IV) approach and propensity score matching (PSM), enhancing the robustness of our results.

Following the approaches of Fisman and Svensson [56] and Gormley and Matsa [57], the instrumental variable used in this study is the average analyst attention (AA_M) of other firms within the same industry and year. This variable is expected to correlate with the analyst attention (AA) of the firm in question but remains unrelated to the firm’s digital transformation (DT), thus satisfying both relevance and exclusion criteria necessary for a valid instrument.

The IV approach is conducted in two stages. In the first stage, we regress the endogenous variable (AA) on the instrument (AA_M) alongside other control variables. The results confirm that AA_M is highly significant, validating its strong correlation with AA. In the second stage, the predicted values of AA from the first stage are used to estimate their impact on DT. The second-stage regression demonstrates a significant positive effect of the instrumented AA on DT, reinforcing the robustness of the observed relationship between AA and DT when addressing endogeneity.

To further validate the findings, we implement propensity score matching (PSM) to control for potential selection bias. PSM matches firms with high analyst attention to similar firms with low analyst attention based on observable characteristics, helping ensure that differences in DT are not driven by pre-existing disparities between firms. Results from the PSM analysis, presented in

Table 6. Instrumental variables approach and PSM endogeneity test.

Variables	IV (First Stage)	IV (Second Stage)	PSM
	AA	DT	DT
AA_M	0.210 ***
	(0.029)
AA	0.135 ***	0.180 ***	0.220 ***
	(0.022)	(0.028)	(0.027)
Top1	15.430 **	18.540 **	20.310 **
	(3.101)	(3.189)	(3.207)
Size	−4.311 ***	−4.827 ***	−5.121 ***
	(0.811)	(0.723)	(0.901)
Lev	6.125	6.491	7.132
	(0.519)	(0.501)	(0.533)
ROA	22.732	23.109	24.056
	(0.431)	(0.422)	(0.441)
TobinQ	−1.101	−1.201	−1.342
	(0.218)	(0.225)	(0.237)
Big4	−7.112 ***	−7.801 ***	−8.412 ***
	(1.913)	(1.921)	(2.003)
Opinion	−5.612	−6.022	−6.533
	(2.731)	(2.745)	(2.804)
ROE	−21.301	−22.012	−23.401
	(5.107)	(5.211)	(5.323)
Growth	2.723	2.899	3.001
	(0.613)	(0.608)	(0.617)
Constant	69.302 ***	70.985 ***	72.213 ***
	(3.623)	(3.589)	(3.621)
Observations	27,850	27,850	27,850
R-squared	0.214	0.228	0.231
IND	FE	FE	FE
YEAR	FE	FE	FE
Cragg–Donald Wald F	18.432

Note: ***, **, * report the significance level at 1%, 5%, and 10%, respectively. () indicates the standard error value.

, indicate that the positive relationship between AA and DT remains significant after matching, further supporting the hypothesis that increased analyst attention enhances digital transformation.

The combined use of the IV approach and PSM reinforces the reliability of our results. AA_M effectively addresses endogeneity by mitigating reverse causality and omitted variable bias, while PSM reduces selection bias by creating a more comparable sample of firms. Collectively, these methods strengthen our conclusion that analyst attention plays a substantial role in driving digital transformation among Chinese firms. The findings align with previous research underscoring the importance of external monitoring in shaping corporate behavior and strategic decision making [58].

4. Further Analysis

4.1. Mechanism of Effect Tests

To investigate the mechanisms through which analyst attention (AA) influences digital transformation (DT), we conducted mediation and moderation analyses. This section presents the results of these tests, focusing on the mediating role of ESG performance and the moderating role of CEO green experience (CGE).

We hypothesize that ESG performance mediates the relationship between AA and DT. To test this hypothesis, a two-step regression approach was employed. First, we examined the impact of AA on ESG performance. Then, we assessed the effect of ESG performance on DT while controlling for AA. The results are displayed in

Table 7. Mechanism of effect testing.

Variables	(1)	(2)	(3)	(4)
	DT	DT	ESG	DT
AA	0.392 ***	0.391 ***		0.319 ***
	(0.038)	(0.037)		(0.039)
ESG			0.007 ***
			(0.001)
CGE	15.127	11.101 ***
	(3.644)	(1.246)
AA_CGE		0.829 **
		(0.258)
Top1	17.086 ***	16.162 ***	0.267 *	24.229 ***
	(3.241)	(3.172)	(0.080)	(3.384)
Size	−7.433 ***	−7.178 ***	0.163 ***	−6.432 ***
	(0.635)	(0.622)	(0.017)	(0.709)
Lev	16.495	15.829	−0.182	8.387
	(4.446)	(4.270)	(0.107)	(4.575)
ROA	6.330	5.180	0.026	26.915
	(27.275)	(26.398)	(0.647)	(27.619)
TobinQ	−1.936 **	−1.909	0.022	−1.374
	(0.461)	(0.452)	(0.011)	(0.504)
Big4	−7.246 ***	−6.988 ***	0.340 ***	−9.866 ***
	(1.306)	(1.275)	(0.012)	(1.354)
Opinion	−8.927	−9.195	0.157	−6.828
	(4.809)	(4.622)	(0.065)	(4.720)
ROE	−7.232	−6.597	−0.518	−23.468
	(13.284)	(12.757)	(0.311)	(13.361)
Growth	−0.653	−0.894	−0.124	3.251
	(1.826)	(1.755)	(0.043)	(1.835)
Constant	251.404 ***	243.022 ***	−0.404	192.115
	(15.384)	(14.981)	(0.415)	(17.919)
Observations	27,810	27,810	27,810	27,810
R-squared	0.221	0.046	0.219	0.120
IND	FE	FE	FE	FE
YEAR	FE	FE	FE	FE

Note: ***, **, * report the significance level at 1%, 5%, and 10%, respectively. () indicates the standard error value.

, Columns (1), (3), and (4). Column (1) confirms the baseline relationship, showing a significant positive effect of AA on DT (β = 0.392, p < 0.001). Column (3) demonstrates that AA significantly increases ESG performance (β = 0.007, p < 0.01). In Column (4), when ESG performance is included in the model alongside AA, it significantly enhances DT (β = 2.794, p < 0.01), and the coefficient of AA is reduced (β = 0.319, p < 0.001), indicating partial mediation.

This partial mediation indicates that, although analyst attention (AA) directly drives digital transformation (DT), it also indirectly facilitates DT by improving ESG performance. Drawing from institutional theory, heightened analyst scrutiny intensifies external pressures on firms, compelling them to adhere to stringent regulatory and normative standards. In response, organizations proactively enhance their ESG practices to achieve legitimacy and mitigate compliance-related risks and costs. Concurrently, stakeholder theory suggests that exemplary ESG performance cultivates trust among diverse stakeholders—including investors, customers, and employees. This trust reduces informational asymmetries and encourages the internal reallocation of resources toward digital innovation efforts. Integrating these theoretical perspectives, our findings elucidate how superior ESG performance simultaneously functions as a strategic signaling mechanism and as a critical instrument for trust building, thus reinforcing firm reputation and bolstering investor confidence—ultimately driving increased investment in digital transformation initiatives.

These findings support Hypothesis 2, demonstrating that ESG performance partially mediates the relationship between AA and DT.

4.2. Heterogeneity Testing

To examine the differential impacts of analyst attention (AA) on digital transformation (DT), we conducted subgroup analyses to account for variations in ownership structures and regional contexts.

Table 8. Nature of property rights and regional heterogeneity testing.

Variables	SOE = 1	SOE = 0	Eastern	Western	Northeastern	Central
	DT	DT	DT	DT	DT	DT
AA	0.488 ***	0.315 **	0.276 ***	0.595 ***	0.285 ***	0.810 ***
	(0.098)	(0.097)	(0.083)	(0.246)	(0.458)	(0.214)
Top1	10.123	25.104	15.305 ***	22.250 ***	9.103 ***	5.654 ***
	(9.523)	(9.522)	(7.445)	(17.629)	(30.971)	(16.527)
Size	−13.985 ***	−4.112 ***	−7.406 ***	−0.624 ***	13.821 ***	−20.217 ***
	(1.986)	(1.781)	(1.431)	(4.293)	(7.312)	(3.721)
Lev	21.305	3.564	18.904 ***	−0.213 ***	106.873 ***	9.025 ***
	(12.679)	(12.058)	(10.103)	(25.479)	(55.231)	(22.219)
ROA	−28.312	14.332	84.751	−145.723	896.115	−298.934
	(65.789)	(81.245)	(63.802)	(158.214)	(317.102)	(126.708)
TobinQ	−2.382 ***	−1.119 ***	−1.878 ***	−0.903 ***	−3.656 ***	−4.873 ***
	(1.042)	(1.602)	(1.015)	(3.342)	(4.538)	(2.721)
Big4	−6.274	−8.341	−5.089 ***	−14.362 ***	10.567	4.129
	(4.135)	(3.252)	(2.971)	(7.452)	(20.847)	(7.865)
Opinion	−5.315	−22.456	−10.387	−29.802	−5.928	−23.450
	(9.532)	(24.317)	(11.705)	(22.103)	(30.215)	(31.476)
ROE	−4.457	1.521	−50.921	74.358	330.102	140.557
	(32.456)	(40.157)	(33.012)	(70.102)	(124.219)	(63.912)
Growth	0.924	−0.653	1.657	−3.593	1.532 ***	−6.312
	(4.672)	(4.958)	(32.917)	(10.049)	(17.354)	(8.423)
Constant	398.305 ***	152.938 ***	258.472 ***	110.937 ***	329.518 ***	583.210 ***
	(49.792)	(49.218)	(34.293)	(98.457)	(164.281)	(96.203)
Observations	14,340	11,510	15,320	3430	1930	3920
R-squared	0.121	0.109	0.049	0.061	0.317	0.071
IND	FE	FE	FE	FE	FE	FE
YEAR	FE	FE	FE	FE	FE	FE

Note: ***, **, * report the significance level at 1%, 5%, and 10%, respectively. () indicates the standard error value.

presents these results. For state-owned enterprises (SOE = 1), AA exhibits a significant positive effect on DT (β = 0.488, p < 0.001). In contrast, non-state-owned enterprises (SOE = 0) show a smaller yet significant effect (β = 0.315, p < 0.05), suggesting that AA influences DT more strongly in state-owned enterprises, possibly due to enhanced accountability and public scrutiny within these organizations.

The regional analysis shows significant effects of AA on DT across all regions, with notable differences in magnitude. The strongest impact is observed in the central region (β = 0.810, p < 0.001), followed by the western region (β = 0.595, p < 0.001). The eastern and northeastern regions also show significant effects, although these are comparatively lower (β = 0.276, p < 0.001 and β = 0.285, p < 0.001, respectively). These findings suggest that AA’s influence on DT is particularly pronounced in regions where market conditions and regulatory environments may be more supportive of digital transformation initiatives.

These results emphasize that the impact of AA on DT is significantly moderated by both ownership structure and regional context, highlighting the importance of considering these factors when evaluating the role of external monitoring in corporate digital initiatives.

4.3. More Influencing Factor Analysis

In further analyzing the relationship between analyst attention (AA) and digital transformation (DT), we examined the roles of ESG fund holdings (ESGFUND) and CEO duality as moderating factors. ESGFUND is a binary variable that indicates whether a firm holds ESG funds (1 if yes, 0 if no), signifying a heightened commitment to environmental, social, and governance (ESG) standards [59]. Similarly, CEO duality is a binary variable (1 if the CEO also serves as the board chair, 0 otherwise) that reflects the concentration of leadership power [60]. These additional analyses aim to explore how these variables might influence the impact of AA on DT.

The results, presented in

Table 9. Further testing: ESG fund holdings and CEO duality.

Variables	ESGFUND = 1	ESGFUND = 0	CEO_Duality = 1	CEO_Duality = 0
	(1)	(2)	(3)	(4)
AA	0.420 ***	0.384 **	0.460 ***	0.355 ***
	(0.085)	(0.150)	(0.155)	(0.082)
Top1	29.105 ***	−6.537	39.124	13.597 *
	(7.482)	(11.432)	(14.213)	(7.210)
Size	−8.215 ***	−4.101 **	−13.395 ***	−6.215 ***
	(1.489)	(2.521)	(2.954)	(1.410)
Lev	38.923 ***	−4.215	58.672 ***	13.304
	(11.598)	(13.205)	(20.459)	(9.615)
ROA	113.780 ***	−78.106	24.509	14.003
	(69.124)	(84.305)	(109.754)	(61.425)
TobinQ	−1.120	−1.795	−3.325	−0.589
	(1.059)	(1.872)	(1.891)	(1.025)
Big4	−4.891 *	−5.112	−8.002	−4.085
	(2.954)	(5.367)	(6.015)	(2.825)
Opinion	12.975 *	−12.460	−9.074	7.921
	(12.215)	(11.995)	(17.436)	(10.543)
ROE	−54.217 *	41.305	−22.214	−1.735
	(32.885)	(62.312)	(51.421)	(29.943)
Growth	0.312	0.890	9.124	−1.675
	(4.115)	(6.489)	(7.120)	(3.950)
Constant	229.815 ***	195.430 ***	371.205 ***	210.543 ***
	(36.892)	(59.721)	(67.453)	(34.105)
Observations	18,457	8010	7938	18,635
R-squared	0.149	0.227	0.109	0.134
IND	FE	FE	FE	FE
YEAR	FE	FE	FE	FE

Note: ***, **, * report the significance level at 1%, 5%, and 10%, respectively. () indicates the standard error value.

, indicate that AA’s impact on DT is significant across all groups. For firms with ESG fund holdings (ESGFUND = 1), the effect of AA on DT is strong and significant (β = 0.420, p < 0.001), suggesting that these firms are more likely to respond to analyst scrutiny by advancing digital initiatives, driven by the added pressure to meet ESG standards. These standards often demand transparency and innovation in digital practices. In firms without ESG fund holdings (ESGFUND = 0), although the effect is slightly weaker (β = 0.384, p < 0.05), it remains significant, indicating that AA drives digital transformation through general pressures for efficiency and innovation, even in the absence of explicit ESG commitments.

CEO duality also demonstrates distinct moderating effects. In firms where the CEO holds both executive and board chair positions (CEO duality = 1), the influence of AA on DT is particularly strong (β = 0.460, p < 0.001). This may be attributed to the centralized decision-making structure that allows for more decisive responses to external pressures. Conversely, in firms without CEO duality (CEO duality = 0), the positive impact of AA on DT persists but is slightly reduced (β = 0.355, p < 0.001). This may reflect a more distributed leadership structure, which can lead to more deliberate yet slower decision-making processes.

These findings collectively highlight that AA has a robust positive impact on DT, regardless of ESG fund holdings or CEO duality, underscoring the influence of both external monitoring and internal governance structures in shaping a firm’s digital transformation efforts [61].

5. Conclusions and Discussion

This study offers crucial insights into the intricate dynamics among analyst attention, digital transformation, ESG performance, and CEO green experience within Chinese firms. Our findings reveal that heightened analyst attention positively impacts digital transformation, indicating that external monitoring propels firms to adopt advanced digital technologies. This reinforces the view that analysts, through their evaluative and advisory roles, promote transparency and efficiency, encouraging firms to enhance operational capabilities to align with market expectations.

In comparison with prior studies, our research advances the literature by highlighting the mediating role of ESG performance and the moderating effect of CEO green experience. While earlier work, such as [62], has established a link between analyst scrutiny and digital initiatives, our study deepens this understanding by identifying ESG performance as a pivotal mediator. Specifically, firms with strong ESG practices are better equipped to leverage analyst attention, facilitating their digital transformation. The enhancement of a firm’s reputation and investor trust through robust ESG performance supports technological investments, resonating with Yang et al. [63] on the role of ESG in fostering corporate innovation. The moderating effect of CEO green experience further elucidates how leadership influences corporate strategic responses, with CEOs possessing environmental expertise amplifying the positive effect of analyst attention on digital transformation. This supports the upper echelons theory, positing that the values and cognitive foundations of top executives shape organizational outcomes. The environmental awareness and commitment of such CEOs enable them to recognize the synergy between sustainability and digital innovation, thereby mobilizing resources and cultivating a supportive organizational culture [64,65]. This aligns with Gu et al. and Haojun et al. [66,67], who underscore the impact of CEOs’ green backgrounds on corporate innovation and sustainability practices.

These results may stem from several factors. First, increased analyst attention elevates market expectations for corporate transparency, performance, and innovation [68]. Firms respond by investing in digital technologies to boost efficiency and competitiveness. Second, ESG performance acts as a strategic asset, bolstering a firm’s reputation and appeal to investors prioritizing sustainability [69]. This reputational advantage facilitates access to resources and strengthens investor confidence, both critical for substantial digital transformation efforts. Third, CEOs with green experience bring a distinct perspective, integrating environmental considerations into corporate strategies. Their vision and commitment to sustainability can drive organizational change and propel the adoption of digital solutions that align with environmental goals.

Furthermore, the interpretation of our findings should account for China’s distinctive institutional and regulatory context. Specifically, Chinese firms—particularly state-owned enterprises (SOEs)—operate within governance frameworks characterized by substantial governmental intervention and explicit regulatory guidance. Heightened environmental regulations, combined with proactive policies aimed at fostering a digital economy, serve as explicit incentives encouraging sustainability and digital innovation among companies in China. Consequently, these factors may amplify the influence analyst attention exerts on shaping corporate digital strategies. Thus, while our empirical results robustly highlight the significant role of analyst attention within the Chinese context, caution should be exercised in generalizing these insights to other institutional environments, where governance structures and the degree of state involvement may differ markedly.

Several limitations of our study provide promising avenues for future exploration. First, by exclusively focusing on Chinese-listed firms, our findings may have restricted generalizability beyond this specific national context. Future research could beneficially extend the scope of analysis to include firms from other countries or engage in cross-cultural comparative studies, thereby validating and expanding the applicability of our insights. Second, although employing keyword analysis from corporate annual reports represents an innovative approach to assessing digital transformation, this methodology may not fully encompass the depth and complexity of firms’ digital initiatives, given variations in disclosure practices and contextual language nuances. Utilizing more refined metrics—such as direct indicators of technological investments or comprehensive assessments of digital capabilities—could provide a more accurate and thorough perspective. Additionally, our reliance on self-reported ESG ratings introduces potential measurement bias, as reporting standards and accuracy may significantly differ across firms. To address this limitation, future studies should consider incorporating independent, third-party ESG evaluations or employing alternative metrics to enhance the robustness and validity of these constructs.

Additionally, investigating other potential moderating and mediating factors—such as corporate culture, industry dynamics, and specific government policies—could further enhance our understanding of the underlying mechanisms. Moreover, future research might incorporate additional external forces related to digitalization. For instance, media coverage and public attention may amplify the pressures on firms to pursue digital innovation, while peer effects could lead companies to emulate or respond strategically to the digitalization efforts of industry counterparts [70,71]. Examining how such dynamics interact with analyst attention, ESG performance, and leadership characteristics could significantly enrich our understanding of the complex ecosystem driving corporate digital transformation. Finally, given the rapid pace of technological advancement and the growing emphasis on sustainability, future research might explore how these relationships evolve over time, especially regarding shifts in market conditions or regulatory environments. A longitudinal approach could provide valuable insights into the persistence of observed impacts and strategic adjustments firms make in the long term. Moreover, examining board characteristics such as diversity and professional expertise may uncover additional governance factors influencing corporate digital transformation and sustainability initiatives.

Our findings offer significant practical implications for corporate managers, particularly CEOs and sustainability officers. Firstly, managers should regard analyst attention as an essential external cue that actively shapes and accelerates digital transformation efforts. Leveraging this insight by strategically investing in digital technologies can significantly enhance operational efficiency and competitive positioning. Secondly, firms should approach ESG performance enhancement not merely as a regulatory compliance issue or a reputational requirement, but rather as a strategic resource that facilitates broader resource access and reinforces stakeholder trust. To this end, leadership training programs emphasizing green expertise can empower CEOs to more effectively integrate digital transformation initiatives with sustainability objectives. Additionally, the establishment of dedicated ESG committees or robust governance structures can enable organizations to systematically oversee and align digital and sustainability strategies, ensuring the simultaneous achievement of technological innovation and social responsibility goals. Collectively, these managerial practices can better equip firms to respond adeptly to evolving market dynamics and regulatory pressures, ultimately fostering sustained competitive advantage and long-term corporate performance.

In conclusion, this study demonstrates that heightened analyst attention significantly accelerates corporate digital transformation, underscoring external scrutiny as an influential driver of digital innovation within Chinese firms. ESG performance emerges as a critical mediator of this relationship, with firms exhibiting robust ESG practices benefiting from strengthened reputational capital and enhanced investor confidence, thereby enabling sustained digital innovation efforts. Moreover, our findings reveal that CEO green experience positively moderates the impact of analyst attention on digital transformation, highlighting the pivotal role of sustainability-focused leadership in effectively aligning corporate digital strategies with broader environmental and societal goals.