Digitalization and ESG Performance: How Strategic Digital Orientation Shapes Firms’ Sustainability Outcomes

Abstract

As digital technologies advance, companies increasingly use them to gain competitive and sustainability advantages. However, a firm’s digital development is not solely contingent on its technological assets; it is fundamentally shaped by the organization’s strategic posture toward digitalization. Despite this, extant empirical research lacks sufficient evidence linking a company’s strategic digital posture to its environmental, social, and governance (ESG) performance. This study investigates how a firm’s strategic digital orientation (SDO) – a firm’s strategic posture toward digital development – influences ESG performance. Analyzing data from Chinese A-share firms (2010–2019), we find that SDO positively impacts ESG performance, mediated by improved digital finance facilitating sustainability funding. The relationship is strongest in private firms, state-owned enterprises, politically connected firms, and emerging companies. Our findings, validated through various robustness tests, contribute to the literature by emphasizing the importance of developing multifaceted SDO to enhance ESG performance, demonstrating how strategic digitalization can create societal value.

Keywords

digital orientation digital transformation ESG performance digital finance China

Introduction

The rapid proliferation of digital technologies is fundamentally transforming how firms create and capture value, reshaping both their strategic decision-making paradigms and organizational contexts (Li & Shao, 2023; Yang et al., 2025). This digital transformation has elevated strategic digital orientation (SDO) – defined as an organization’s guiding principle to pursue digital technology-enabled opportunities to achieve competitive advantage (Kindermann et al., 2021) – as a critical strategic construct. Unlike traditional strategic orientations, SDO is uniquely tailored to the digital age, emphasizing agility, openness, and generativity – key attributes enabling firms to respond swiftly to emerging opportunities and challenges in the digital landscape (Maqsood et al., 2026; Nambisan et al., 2019). Simultaneously, Environmental, Social, and Governance (ESG) performance has become a critical benchmark for firm success in the 21st century, reflecting commitment to sustainable development and the ability to balance financial performance with social and environmental responsibility (Al Amosh, 2024). Stakeholders – including customers, employees, investors, and regulators – increasingly demand that firms demonstrate strong ESG performance as indicators of long-term resilience and ethical governance.

Despite growing literature on SDO and its impacts on sustainable innovation, financial performance, and environmental outcomes (Bendig et al., 2023; Kindermann et al., 2021; Yang et al., 2025), few studies holistically examine how multidimensional SDO influences ESG performance. Current understanding remains fragmented due to several limitations. First, existing studies predominantly focus on discrete – digital technology schemes’ impact on ESG performance rather than examining SDO as a unified construct (Khalid et al., 2024; S. Li et al., 2024). Second, some research assimilates Industry 4.0 technologies such as artificial intelligence (AI), blockchain, the internet of Things (IoT), and big data analytics to form a holistic measure for assessing the impact of digital transformation on ESG performance (e.g., Wang et al., 2023). Third, research methodologies often rely on firm-level case studies or surveys, overlooking the potential of longitudinal panel data analyses (Zhao et al., 2023). Fourth, the literature disproportionately focuses on digitalization in small and medium-sized enterprises (Wang & Esperança, 2023), neglecting the distinctive contexts of large corporations, which wield significant influence in digital transformation landscapes. Fifth, existing research predominantly interprets digital transformation from a technological standpoint (Fang et al., 2023; Tian et al., 2024), which should not only be measured by the usage of technology (Diófási-Kovács & Nagy, 2023) because it may overlook the critical role of organizational readiness, ecosystem coordination, and architectural configuration in driving successful digital initiatives. Finally, the mediating role of digital finance (DF) in this relationship remains underexplored, particularly in China, where DF has experienced rapid development and adoption (Mu et al., 2023).

This study addresses these gaps by examining three research questions:

Research Question 1 (RQ1): How does a firm’s SDO influence its ESG performance?

Research Question 2 (RQ2): What role does DF play in mediating this relationship?

Research Question 3 (RQ3): How do firm characteristics such as ownership, political connections, and maturity moderate the impact of SDO on ESG performance?

We argue that SDO serves as a central factor influencing ESG performance, particularly in the Chinese context, because it represents a firm’s comprehensive strategic positioning toward digital transformation, encompassing not just technological adoption but also organizational readiness, ecosystem integration, and architectural alignment – elements fundamental to achieving sustainable business practices in the digital age (Bendig et al., 2023; Yang et al., 2025).

This is particularly salient in China, where the government’s dual emphasis on digital advancement and sustainable development – manifested through policies promoting digital economy development and stringent ESG reporting requirements – has created a unique institutional environment emphasizing state-led technological advancement and sustainability goals (Hussain et al., 2024; Yang et al., 2025). Unlike discrete digital technologies or isolated sustainability efforts, SDO captures the holistic nature of a firm’s digital advancement journey, which is crucial in China’s rapidly evolving digital ecosystem where firms can better align their digital initiatives with broader ESG objectives. The significance of SDO is further amplified by China’s distinctive institutional characteristics, including varying degrees of state ownership and political connections that create diverse organizational contexts for leveraging digital technologies (Hussain et al., 2024; S. Li et al., 2024).

We theorize that SDO enhances ESG performance through multiple mechanisms grounded in dynamic capability theory (Teece et al., 1997). Digital technologies (IoT, blockchain, AI) enable real-time monitoring of environmental impacts, enhance social transparency, and strengthen governance through improved accountability (Bendig et al., 2023; S. Li et al., 2024). When combined with digital capabilities and ecosystem coordination, these foundations allow organizations to analyze ESG data, implement sustainability initiatives, and facilitate stakeholder collaboration (Crow & Millot, 2020; Sahut et al., 2021). Furthermore, the thoughtful configuration of digital architecture and leadership creates the structural foundation (Kindermann et al., 2021; Yang et al., 2025), necessary to systematically embed ESG considerations into organizational decision-making and operations. Collectively, these SDO dimensions represent dynamic capabilities that enable firms to sense ESG challenges, seize improvement opportunities, and transform operations to achieve superior ESG performance (Wang & Esperança, 2023; Xu et al., 2023).

In addition, DF mediates the SDO–ESG relationship. As firms develop SDO, they gain capabilities to implement advanced digital financial solutions for tracking ESG investments and performance metrics (S. Li et al., 2024). The development of specialized digital financial capabilities allows organizations to better analyze ESG data and create innovative financial solutions aligned with sustainability objectives (Hidayat-ur-Rehman, 2024), while digital ecosystem coordination provides access to broader networks of sustainable investment opportunities and alternative financing mechanisms (Hendrikse et al., 2020). These instruments mitigate financial constraints by enhancing capital access and resource allocation for ESG initiatives (Khalid et al., 2024; Mu et al., 2023).

Our empirical analysis employs a panel of A-share listed Chinese corporations (excluding financial firms) from 2010 to 2019. Using panel ordinary least squares (OLS) regression, we demonstrate that firms with higher SDO exhibit significantly improved ESG performance, even after controlling for industry and year fixed effects. Moreover, we uncover a partial mediating role of DF in this relationship, suggesting that SDO enhances ESG performance partly through facilitating digital financial solutions. Notably, these findings are validated through rigorous robustness tests, including alternative proxies for SDO and ESG measures, reduced sample analyses, firm-fixed effects, instrumental variables regression, and propensity score matching (PSM) – all consistently supporting the positive influence of SDO on ESG performance. Heterogeneity analysis reveals this positive impact is particularly pronounced among privately owned enterprises (POEs), centrally state-owned enterprises (SOEs), politically connected firms, and emerging companies. Subcomponent analysis indicates that SDO’s influence is most effective when considered as a multidimensional construct, with the most significant improvements observed in the social dimension of ESG metrics.

This study contributes to existing literature in three ways. First, we advance strategic management theory by demonstrating how SDO, conceptualized as a multidimensional dynamic capability, enables firms to enhance ESG performance. While previous research has focused on discrete digital technology schemes or interpreted digital transformation as primarily technological (Fang et al., 2023; Tian et al., 2024), this work demonstrates that successful digital transformation requires coordinated development across technology scope, capabilities, ecosystem coordination, and architecture configuration dimensions. Second, we identify DF as a crucial mediating mechanism in the SDO–ESG relationship. While existing research has explored determinants of ESG performance (Khalid et al., 2024; Maqsood, Li, et al., 2024), the role of DF as a channel through which digital strategies enhance sustainability outcomes has not been explored. Our findings illuminate this pathway by showing how DF alleviates financial constraints, reduces information asymmetry, and improves ESG performance, adding a new perspective to both the digital transformation and ESG literature. Finally, we contribute to corporate sustainability literature by revealing important heterogeneities in the SDO–ESG relationship across different organizational contexts, particularly in ownership structure, political connections, and firm maturity.

The remainder of this work provides theoretical background on key variables, proposes hypotheses on the direct and mediated effects of SDO on ESG performance, introduces our data and methodology, presents results, and heterogeneity analyses, and concludes with discussion, implications, and limitations.

Literature Review

SDO

Digital technologies fundamentally reshape how firms create and capture value (Yoo et al., 2012), necessitating strategic orientations distinct from traditional approaches (Yang et al., 2025). While prior strategic orientations – entrepreneurial orientation (Covin & Slevin, 1989), market orientation (Narver & Slater, 1990), and learning orientation (Sinkula et al., 1997) – provide valuable capabilities, they may no longer sustain competitive advantage in digital contexts (Kindermann et al., 2021; Yang et al., 2025). To address this gap, Kindermann et al. (2021) proposed SDO as “an organization’s guiding principle to pursue digital technology-enabled opportunities to achieve competitive advantage” (p. 649). As a novel strategic orientation tailored to the digital environment, SDO aligns with three key digital themes identified by Nambisan et al. (2019): digital affordances (context-dependent properties requiring organizational fluidity; Yoo et al., 2012), generativity (continuous evolution of digital offerings in unpredictable ways; Kindermann et al., 2021), and openness (value creation through coordinating broader ecosystems rather than internal actions alone; Nambisan et al., 2019). SDO emphasizes rapid response to emerging affordances, open and distributed innovation, and modular structuring to harness generativity – distinguishing it from prior orientations rooted in fixed boundaries (Yang et al., 2025).

Kindermann et al. (2021) operationalize SDO across four interrelated dimensions. First, digital technology scope – “the set of digital technologies that allow the firm to realize strategic growth. This set can include technologies such as sensors, blockchain, and IoT solutions as both ingredients and outcomes of digitalization processes” (p. 648) – reflects leveraging technologies to provide digital product/service combinations enhancing customer value. Adopting emerging technologies allows reinforcing or redefining core competencies for competitive strength. Next, digital capabilities – “organizations’ efforts to develop and maintain routines that leverage human capital and knowledge assets to engage with a specific set of digital technologies” (p. 648) – involve cultivating skills and processes to effectively implement digital strategies (Yang et al., 2025). Specifically, this dimension captures the managerial acumen needed to translate technologies into strategic advantages. Finally, digital ecosystem coordination – “captures how effectively firms interact with stakeholders in open technological ecosystem” (p. 648) – signifies collaborating across the digital ecosystem to jointly pursue innovation opportunities (Yang et al., 2025; Yoo et al., 2012). Here, digitally oriented firms actively coordinate with diverse stakeholders to co-create digital offerings. Finally, digital architecture configuration – “encompasses how digitally orientated organizations enable digitalization by specifying organization structures and responsibilities that cater to technological change (e.g., having a chief digital officer), as well as by digitizing their internal processes (e.g., through algorithm-driven automation in Industry 4.0 settings)” (p. 649) – denotes formulating supportive strategies and configuring structures and procedures to facilitate digitalization (Kindermann et al., 2021). Essentially, this dimension represents the broader organizational changes required to pave the way for transformative technologies. Together, these dimensions represent a holistic approach to leveraging digitalization for competitive advantage in dynamic environments (Yang et al., 2025).

Firm’s ESG Performance

The significance of ESG performance has grown substantially as stakeholders increasingly expect companies to demonstrate strong sustainability practices alongside financial performance. ESG performance is typically assessed across three dimensions: Environmental (impact on natural resources and mitigation of environmental risks), Social (relationships with employees, suppliers, customers, and communities), and Governance (quality of leadership, transparency, and accountability) (S. Li et al., 2024; Maqsood, Li, et al., 2024).

Recent literature on ESG focuses primarily on identifying determinants of firm ESG performance (Maqsood, Li, et al., 2024; Tian et al., 2024). An emerging stream examines how digital technologies influence ESG outcomes. However, these studies exhibit several critical limitations: First, they tend to focus on either micro-level perspectives, such as specific digitalization initiatives (Fang et al., 2023), or macro-level perspectives, such as the digital economy (Tian et al., 2024), with some examining the effects of discrete digital technologies on ESG performance (Khalid et al., 2024; S. Li, et al., 2024). Others integrate Industry 4.0 technologies – such as AI, blockchain, IoT, and big data analytics – into holistic measures to assess digital transformation’s impact on ESG performance (Wang et al., 2023). Second, methodologically, research in this domain predominantly relies on firm-level case studies or questionnaire-based investigations, overlooking the potential of longitudinal panel data analyses. The literature also disproportionately focuses on digitalization in small and medium-sized enterprises (Wang & Esperança, 2023), neglecting the unique context of large corporations, which play a pivotal role in digital transformation due to their innovation capabilities, market influence, and stronger imperatives for change. Finally, while existing research interprets digital transformation primarily through a technological lens (Fang et al., 2023; Tian et al., 2024), it often overlooks the broader organizational and human dimensions. A comprehensive assessment should not only measure technology adoption (Diófási-Kovács & Nagy, 2023) but also incorporate organizational restructuring, internal process digitization, human capital readiness, and stakeholder engagement within the technological ecosystem. A unified, multidimensional index is needed to move beyond the current narrow focus on technology alone.

This fragmentation leaves a critical gap: existing research has not holistically examined how multidimensional SDO – encompassing technology adoption, organizational readiness, ecosystem coordination, and architectural configuration – influences firms’ ESG performance. Understanding this relationship is essential, as successful digital transformation requires coordinated development across all four SDO dimensions rather than isolated technological implementation. A comprehensive, multidimensional perspective is necessary to move beyond narrow technological focuses and capture how organizations systematically enhance ESG outcomes through strategic digital positioning.

Hypothesis Development

SDO and Firm’s ESG Performance

Recent empirical evidence supports a positive relationship between digital transformation and ESG performance. For instance, Tian et al. (2024) found that digital economy development significantly improves corporate ESG performance, particularly in environmental and governance dimensions. Fang et al. (2023) demonstrated that company digitization enhances ESG performance by reducing agency costs and improving reputation, with effects observed in governance and social dimensions, although environmental dimension effects were weaker. Wang et al. (2023) further corroborated the positive influence of digital transformation on ESG outcomes. These findings suggest that digital capabilities and strategic orientation toward digital technologies can drive improvements across the ESG spectrum.

We ground our theoretical argument in dynamic capabilities theory (Teece et al., 1997), which emphasizes that organizations must continuously adapt and reconfigure resources to maintain competitive advantage in rapidly changing environments. We conceptualize SDO as a dynamic capability enabling firms to sense ESG-related challenges through enhanced monitoring, seize improvement opportunities through sophisticated analysis, and transform operations through reconfigured processes to achieve superior ESG performance (Wang & Esperança, 2023; Xu et al., 2023). Drawing from this theoretical foundation, we posture that SDO may enhance corporate ESG performance through multiple mechanisms.

First, the digital technology scope dimension enables real-time monitoring and optimization of resource consumption and emissions. For example, IoT-enabled smart grids facilitate real-time energy distribution adjustments, reducing wastage and optimizing consumption (Gungor et al., 2011), while smart connected products extend product lifecycles and mitigate e-waste (Bendig et al., 2023). Digital technologies like AI enhance transparency into labor practices, improving worker safety and wages (S. Li et al., 2024), and strengthen governance by improving information transparency and accountability (Kaggwa et al., 2024; Sadeghi et al., 2024), reducing fraud risks. Second, enhanced digital capabilities enable organizations to analyze ESG data more effectively and implement targeted sustainability initiatives. Advanced capabilities in data analytics reduce travel emissions through remote work support and greenhouse gas insights (Crow & Millot, 2020). Digital twins support employee training and development, enhancing personnel management and operational efficiency in firms where creativity and digital skills are becoming increasingly critical (Xu et al., 2023). System-use competencies enable better governance processes, including risk management and internal controls (Ferdous et al., 2023). Third, digital ecosystem coordination amplifies these effects by enabling stakeholder collaboration and knowledge sharing. Digital platforms facilitate environmental feedback and rapid response to environmental concerns (Sahut et al., 2021). Open-source platforms enable community communication and trust-building on social issues (Baek et al., 2010). Virtual meeting platforms like Zoom, Tencent, or Google Meetings allow stakeholders to exercise voting rights and participate in decision-making processes remotely (Denis & Blume, 2021), enabling firms to enhance stakeholder engagement in transparent and inclusive governance practices. Finally, digital architecture configuration – including digital leadership roles and process redesign – creates structural foundations for embedding ESG considerations into operations. Broadband infrastructure and execution management systems enable data-driven business process management, identifying inefficiencies and optimizing resource usage and reducing waste (Bendig et al., 2023). Digitalized document management and workflow automation improve efficiency and reduce decision errors (Wang et al., 2020), streamlining governance processes.

Collectively, these SDO dimensions enable firms to achieve superior ESG performance by systematically integrating digital capabilities into sustainability management. Thus, the following hypothesis is proposed:

Hypothesis 1 (H1): Ceteris paribus, a firm’s strategic digital orientation is positively related to firm’s ESG performance.

Digital Orientation, DF, and ESG Performance

Financial constraints significantly hinder organizational capacity to secure external funding and make strategic investments (Whited & Wu, 2006). Prior research has demonstrated that alleviating financial constraints facilitates the adoption of sustainable business practices (Khalid et al., 2024; S. Li et al., 2024). For example, Zhang (2023) has identified financial constraints as mediators in the relationship between DF and green innovation. In another study, Mu et al. (2023) identify financial constraints as a potential mechanism linking DF to corporate’s ESG performance. Our primary hypothesis posits a positive relationship between SDO and ESG performance. Building on the above insights, we further propose DF as a key mediating mechanism, encompassing innovative financial solutions enabled by digital technologies and offering enhanced processes for capital access and resource allocation. DF helps firms achieve non-financial objectives such as corporate environmental performance, ESG disputes, or overall ESG performance (Khalid et al., 2024; Mu et al., 2023).

The proposed hypothesis can also be established through the lenses of the dynamic capability theory, which provides a theoretical foundation for explaining how SDO facilitates improved ESG performance through DF. As explicated in the previous section, this study considers SDO as firms’ dynamic capability that empowers them to continuously adapt and innovate in response to changing ESG requirements. In the setting of mediation relationship, SDO represents a dynamic organizational capability that allows firms to sense technological opportunities in financial management and sustainability, seize these opportunities through strategic resource allocation, and transform financial processes to create more sustainable and efficient operational models.

We propose that DF mediates the SDO–ESG relationship through multiple pathways. First, the digital technology scope allows firms to adopt advanced digital financial technologies like blockchain, AI, and IoT, which streamline financial reporting, enhance transparency, and reduce operational inefficiencies (Mhlanga, 2020). The transparency and efficiency brought by DF enable firms to integrate sustainability and responsible practices into their operations through socially responsible investments and transparent reporting (Khalid et al., 2024; Mu et al., 2023). These technologies enable more sophisticated tracking and management of ESG-related financial investments and performance metrics (S. Li et al., 2024). Second, digital capabilities play a crucial role in developing the organizational competence required to effectively leverage financial technologies (Khin & Ho, 2019). Firms can more effectively analyze complex ESG data, develop targeted sustainability strategies, and create innovative financial solutions that align with environmental and social objectives by cultivating specialized skills and expertise in digital financial instruments (Hidayat-ur-Rehman, 2024). This skill development facilitates strategic approaches to integrating ESG considerations into core financial decision-making processes. Third, the digital ecosystem coordination dimension further amplifies the impact on DF by enabling more comprehensive and collaborative financial management. Through effective stakeholder coordination and open technological ecosystems, firms can access broader networks of financial expertise, sustainable investment opportunities, and innovative funding mechanisms (Hendrikse et al., 2020). In addition, their networks and connections within digital ecosystems also grant them greater awareness of and access to alternative online financing sources, such as P2P (peer-to-peer payments, person-to-person payments, private to private) (Hartmann, 2006) and blockchain-based capital raising (Rohr & Wright, 2018). This interconnectedness allows for more dynamic and responsive financial strategies that can quickly adapt to emerging ESG challenges and opportunities. Finally, digital architecture configuration provides the structural foundation for integrating DF into organizational strategy. By appointing specialized roles like Chief Digital Officers and proactively designing digital-first organizational structures, firms create dedicated mechanisms for managing the intersection of digital technologies, financial processes, and sustainability goals (Lim et al., 2024). This strategic configuration enables more holistic and integrated approaches to financial management that inherently consider ESG performance.

Through these mechanisms, SDO enables firms to strategically invest in ESG initiatives by alleviating financial constraints and providing advanced financial management tools. DF acts as a transformative mechanism, reducing financial barriers and facilitating more sophisticated resource allocation toward sustainability goals. Therefore, we propose the following hypothesis:

Hypothesis 2 (H2): The positive effect of a firm’s strategic digital orientation on firm’s ESG performance is mediated by the improvement in digital finance.

The theoretical analysis framework is summarized in Figure 1.

Figure 1.

Theoretical framework.

Research Data, Variable Measurement, and Methodology

Data Description and Source

The empirical analysis was carried out on A-share firms that are listed on Shenzhen and Shanghai stock exchanges from 2010 to 2019. We select 2010 as the starting year because ESG voluntary disclosure began that year and 2019 as the final year to avoid COVID-19-related distortions to normal operations. Data sources include Bloomberg (ESG data), management discussion and analysis (MD&A) sections from annual reports (SDO measurement via textual analysis), and the China Stock Market and Accounting Research database (CSMAR) (financial indicators). In order to ensure validity of the results, we exclude financial firms (banks, securities companies, insurances), firms under special treatment (ST/ST*), and observations with missing data. After merging data sources and applying 1% winsorization to continuous variables, our final sample comprises 6,295 firm-year observations.

Measurement of Dependent Variable

This study utilizes Bloomberg’s comprehensive ESG data to measure firm’s ESG performance. Bloomberg gathers granular ESG metrics across ESG factors from public disclosures, government data, and media reports. According to the study by Maqsood, Li, et al. (2024), Bloomberg’s ESG indicator system consists of 21 key indicators and 122 subindicators spanning in three dimensions: (a) Environment: Air quality, Ecological impact/climate change, Energy usage, water resources, and Material & waste management, (b) Social: Community and Customers relations, Diversity, Ethics and Compliance, Employees health & safety, Human Capital, and Supply Chain practices, and (c) Governance: Audit Risk & Oversight, board composition, Compensation practices, Diversity, Independence, Nominations and Governance oversight, Sustainability governance, and Tenure. Bloomberg weights these indicators based on a quantitative model reflecting their importance to firm’s ESG performance. The indicators are standardized for comparability, yielding an overall ESG score from 0 to 100 for each firm, with higher scores signaling stronger ESG performance.

Measurement of Independent Variable

To measure a Chinese firm’s SDO, we employ a comprehensive textual analysis approach to assess SDO, utilizing MD&A sections from annual reports. The MD&A provides valuable insights into a company’s digital strategies and initiatives, making it an appropriate source for our analysis (Yang et al., 2025). Our methodology builds upon recent research using text analysis (e.g., Hussain et al., 2024; Jiang et al., 2022; Yang et al., 2025) and adapts it to the Chinese business context. We implemented a computer-assisted textual analysis (CATA), developing our SDO measure through a rigorous multistage process:

Dictionary development: We adapted the keyword list from the framework proposed by Kindermann et al. (2021), which delineates four dimensions of SDO: digital technology scope, digital capabilities, digital ecosystem coordination, and digital architecture configuration. To ensure contextual relevance, all co-authors collaboratively reviewed, refined, and translated¹ the initial 148-word/phrase vocabulary, following Yang et al. (2025) and Maqsood et al. (2026).

Metric calculation: We develop web crawlers in Python to extract MD&A section from the annual financial reports of all the companies recorded in our dataset. Utilizing the CAT Scanner tool (McKenny et al., 2012), we counted/calculated the frequency of keywords for each dimension within the MD&A sections. This process yielded individual dimension scores² (continuous values), which we then aggregated to create an overall SDO score for each firm per year. The final SDO³ is log-transformed after adding 1 for comparability with other variables in this study. For robustness, we employ two alternative specifications following Yang et al. (2025) and Maqsood et al. (2026): (a) SDO_c⁴ without logarithmic transformation, as the aggregated sum of each dimension-specific occurrence of keywords for each firm per year and (b) we normalized frequencies by dividing the dimension-specific occurrence of keywords by the total word count in each MD&A, yielding the alternative proxy SDO_n,⁵ to account for reporting length variations of MD&A sections as consistent with the prevailing literature (Fang et al., 2023; Li & Shao, 2023; Yang et al., 2025).

Construct validation: We validated the multidimensionality of the SDO construct using multiple methods. The correlation matrix between dimensions (presented in Supplemental Appendix A) shows significant but imperfect intercorrelations (all below 0.454), indicating related yet distinct dimensions of SDO. Furthermore, we confirmed convergent validity by verifying that each dimension correlates with the overall construct at r > 0.5 (ranging from 0.644 to 0.783). In addition, we implement several methodological safeguards and validation checks to address potential concerns regarding reporting style and terminology inconsistencies in MD&A sections.⁶

Measurement of Mediating Variable

In this study, DF is modeled as a mediating variable, representing the application of digital technologies to financial activities that alleviate financial frictions by enhancing capital access, operational efficiency, and resource allocation (Ding et al., 2023; Mu et al., 2023). At the firm level, DF is measured as a composite index reflecting firms’ advancement across seven subdimensions: digital credit, DF technology, digital marketing, digital payment and transactions, digital identity, data encryption, and e-commerce. This measure is constructed using a text-based approach that combines the Peking University Digital Financial Inclusion Index⁷ with corporate disclosures. Specifically, textual analysis is performed on firms’ MD&A sections, extracting the frequencies of 411 DF-related keywords corresponding to the seven subdimensions. These frequencies are aggregated to generate a firm-year DF score, with higher values indicating greater adoption and integration of DF.

Measurement of Control Variables

Following prior ESG research (Maqsood, Li, et al., 2024), we control for firm-specific characteristics: firm size (SOF), market-to-book ratio (TOBQ), leverage (LEVF), R&D expenditure (RADE), asset tangibility (FASSETS), ownership concentration (HHIND), Big 4 auditor status (B4AF), financial analyst coverage (FINAN), and state ownership (STATEO). We also incorporate board characteristics: board size (SOB), board independence (BIN), and CEO duality (CEODUL). In robustness tests, we include regional and industry controls, including economic development (EcoDev), financial development (FinDev), openness (Openness) environmental regulations (EnvReg), and industrial structure (IndStruc), to account for contextual and industry-specific influences. Detailed variable definitions appear in Supplemental Appendix B.

Model Specification

We estimate the direct effect of SDO on ESG performance using fixed effects OLS regression:

ESG {performance}_{it} = β_{0} + β_{1} {SDO}_{it} + \sum_{k = 1}^{12} β_{k} {Controls}_{it} + δ_{t} + γ_{j} + ε_{it} \dots \dots

(1)

where subscript $i$ denotes the individual firms, $t$ signifies the specific years, and $j$ represents the distinct industries. $ESG {performance}_{it}$ is the firm ESG performance, $S D O_{i t}$ is the strategic digital orientation, ${Controls}_{it}$ includes 12 firm and board variables (detailed in Supplemental Appendix B), $δ_{t}$ represents the year fixed effects, $γ_{j}$ represents the industry fixed effects, and $ε_{i t}$ is the error term.

To test the mediating role of DF, we estimate a three-equation mediation model:

ESG {performance}_{it} = β_{0} + \partial {SDO}_{it} + \sum_{φ = 1}^{12} φ_{k} {Controls}_{it} + δ_{t} + γ_{j} + ε_{it} \dots \dots

(2)

{DF}_{it} = β_{0} + η {SDO}_{it} + \sum_{φ = 1}^{12} φ_{k} {Controls}_{it} + δ_{t} + γ_{j} + ε_{it} \dots \dots

(3)

ESG {performance}_{it} = β_{0} + θ {SDO}_{it} + λ {DF}_{it} + \sum_{φ = 1}^{12} φ_{k} {Controls}_{it} + δ_{t} + γ_{j} + ε_{it} \dots \dots

(4)

where $D F_{i t}$ is the digital finance index. Mediation exists when ∂ in Equation (2) is significant, and both $η$ in Equation (3) and $λ$ in Equation (4) are significant too. Partial mediation occurs if $θ$ in Equation 4 remains significant; complete mediation occurs if $θ$ is insignificant.

Empirical Results

Descriptive Statistics

Table 1 reports descriptive statistics for key variables. ESG performance averages 20.02 (SD = 6.44) with a range of 1.24 to 64.11, indicating considerable ESG performance with substantial variation across firms. The mean and median are nearly identical, suggesting an approximately normal distribution. Similarly, SDO averages 0.85 (SD = 1.15) with a range of 0 to 5.78, indicating relatively low levels with considerable heterogeneity across firms. Finally, control variables fall within expected ranges consistent with prior Chinese firm research (Maqsood, Li, et al., 2024; Maqsood, Wang, et al., 2024).

Table 1.

Summary Statistics.

	Mean	Median	Standard deviation	Maximum	Minimum	Observations
ESG	20.0208	19.4215	6.4364	64.1148	1.2397	6,295
WESG	1.8456	1.0000	1.7744	6.0000	1.0000	6,295
HXRATE	3.6216	4.0000	0.7854	5.0000	1.0000	6,295
SDO	0.8536	0.0000	1.1416	5.7838	0.0000	6,295
SDO_c	5.1692	0.0000	15.8567	324.0000	0.0000	6,295
SDO_n	0.0349	0.0000	0.1071	2.1892	0.0000	6,295
SOF	22.2458	22.1583	1.4126	27.8133	16.3805	6,295
TOBQ	1.9423	1.5067	1.4559	27.3380	0.0000	6,295
LEVF	0.4573	0.4500	0.4856	33.3333	0.0050	6,295
RADE	0.0025	0.0000	0.0110	0.2207	0.0000	6,295
HHIND	0.3053	0.3124	0.2603	1.0000	0.0000	6,295
FASSETS	0.2351	0.1947	0.1835	4.0769	0.0002	6,295
B4AF	0.0770	0.0000	0.2667	1.0000	0.0000	6,295
FINAN	0.7628	0.8451	0.5324	1.8129	0.0000	6,295
SOB	9.0634	9.0000	1.8862	20.0000	0.0000	6,295
BIN	0.3724	0.3333	0.0562	0.8000	0.1818	6,294
CEODUL	0.2108	0.0000	0.4079	1.0000	0.0000	6,295
STATEO	0.5022	1.0000	0.5000	1.0000	0.0000	6,290

Source: Authors’ calculations.

Note: Data are winsorized at 1% tails. All variables are defined in Supplemental Appendix Table B.

Correlational Analysis

Table 2 presents correlation coefficients for all variables. Pairwise correlations are below|0.40|, and variance inflation factors are well below conventional thresholds (unreported). To further assess multicollinearity, we estimated the model with control variables alone and then with variables of interest, comparing coefficient direction, magnitude, and significance. Results remained consistent (unreported), confirming the absence of multicollinearity issues.

Table 2.

Bivariate Correlation Coefficients.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)
	ESG	SDO	SOF	TOBQ	LEVF	RADE	HHIND	FASSETS	B4AF	FINAN	SOB	BIN	CEODUL	STATEO
ESG	1
SDO	−0.0654^***	1
SOF	0.375^***	−0.0318^*	1
TOBQ	−0.157^***	0.108^***	−0.357^***	1
LEVF	0.0563^***	−0.0607^***	0.217^***	−0.151^***	1
RADE	−0.0358^**	0.0618^***	−0.0987^***	0.136^***	−0.0486^***	1
HHIND	−0.133^***	−0.0294^*	−0.0360^**	−0.0454^***	0.0514^***	−0.0700^***	1
FASSETS	0.129^***	−0.278^***	0.114^***	−0.135^***	0.144^***	−0.0879^***	−0.117^***	1
B4AF	0.242^***	−0.0388^**	0.248^***	−0.0601^***	0.0272^*	−0.0313^*	0.0316^*	−0.00508	1
FINAN	0.0750^***	0.102^***	0.308^***	0.122^***	−0.0409^**	0.0383^**	0.0533^***	−0.0853^***	0.0796^***	1
SOB	0.109^***	−0.101^***	0.217^***	−0.163^***	0.0625^***	−0.0315^*	0.0418^***	0.173^***	0.0580^***	0.0431^***	1
BIN	0.0293^*	0.0654^***	0.0351^**	0.0395^**	−0.00697	0.0109	−0.00189	−0.0314^*	0.0348^**	0.0201	−0.371^***	1
CEODUL	−0.107^***	0.116^***	−0.134^***	0.0855^***	−0.0464^***	0.0730^***	−0.0342^**	−0.115^***	−0.0719^***	0.0878^***	−0.205^***	0.0953^***	1
STATEO	0.215^***	−0.185^***	0.282^***	−0.189^***	0.112^***	−0.0834^***	0.0652^***	0.194^***	0.123^***	−0.112^***	0.288^***	−0.0121	−0.316^***	1

Source: Authors’ calculations.

Note: Pearson’s correlation coefficients for all study variables. Variables are defined in Supplemental Appendix Table B. ***, **, and * denote the significance at 1%, 5%, and 10% levels.

Baseline Regression

Prior to conducting our main regression analysis, we performed a Hausman test to determine the most appropriate estimation method. The resulting p-value (.000) confirmed the appropriateness of fixed effects OLS estimation. Table 3 presents baseline results examining SDO’s impact on ESG performance.⁸ Column (1) shows a significant positive relationship between SDO and ESG performance (β = 0.59, p < .001). After incorporating year and industry fixed effects in Column (2), this relationship remains significant and stable, indicating robustness to time- and industry-specific factors. Economically, a one standard deviation increase in SDO corresponds to a 3.36%⁹ increase in ESG performance, demonstrating meaningful real-world impact and supporting H1.

Table 3.

Baseline Regression: Impact of Firm’s Strategic Digital Orientation on Firm’s ESG Performance.

	(1)	(2)
	ESG	ESG
SDO	0.5904*** (0.0761)	0.1472** (0.0748)
SOF	1.8567*** (0.0976)	0.6056*** (0.1035)
TOBQ	0.0441(0.0471)	0.0007(0.0475)
LEVF	−1.3197*** (0.3235)	−0.5911* (0.3077)
RADE	21.7747*** (7.0878)	7.3905(6.6698)
HHIND	−2.5369*** (0.1971)	−0.0060(0.2230)
FASSETS	1.1029* (0.6164)	0.5154(0.5955)
B4AF	1.2094*** (0.4409)	0.8925** (0.4139)
FINAN	−0.3459** (0.1444)	0.6117*** (0.1426)
SOB	−0.3170*** (0.0550)	−0.0903* (0.0524)
BIN	−1.4372(1.4559)	−0.7346(1.3696)
CEODUL	−0.2293(0.1771)	−0.3581** (0.1665)
STATEO	0.9918** (0.4006)	0.9139** (0.3812)
Constant	−17.6780*** (2.3331)	4.3318(4.3604)
Observations	6289	6289
R-squared	0.1555	0.2682
Year & Industry FE	No	Yes

Source: Authors’ calculations.

Note: Baseline regression results for SDO impact on ESG performance using Equation (1). Standard errors are clustered at the firm level in parentheses. All models include controls and year/industry fixed effects. ***, **, and * denote the significance at 1%, 5%, and 10% levels. Variables are defined in Supplemental Appendix Table B.

Validation Tests

This study validates the main findings through multiple robustness checks presented in Table 4.

Table 4.

Robustness tests.

Panel A: replaced dependent variable, replaced independent variable, and lagged independent variable
	Replaced-dependent variable		Replaced-independent variable		Lagged-independent variable
	(1)	(2)	(3)	(4)	(5)	(6)
	WESG	HXRATE	ESG	ESG	ESG	ESG
SDO	0.1474*** (0.0326)	0.0995*** (0.0159)
SDO_c			0.0233*** (0.0057)
SDO_n				3.4414*** (0.8423)
SDO_t-1					0.5773*** (0.0782)
SDO_t-2						0.4839*** (0.0844)
Constant	−22.0317*** (1.8251)	−0.5991(0.8891)	−19.8372*** (4.3033)	−19.8372*** (4.3033)	−17.3890*** (4.1219)	−14.7098*** (4.3287)
Observations	6,289	6,289	6,289	6,289	5,190	4,338
R-squared	0.1779	0.1006	0.1557	0.1557	0.1395	0.1233
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Year & Industry FE	Yes	Yes	Yes	Yes	Yes	Yes

Panel B: sample period adjustment, firm-fixed effects, and other potential drivers.

	Sample period adjustment	Firm-fixed effects	Other potential drivers
	(1)	(2)	(3)
	ESG	ESG	ESG
SDO	0.2982*** (0.0830)	0.5904*** (0.0761)	0.2849*** (0.0859)
EcoDev			1.6891*** (0.3299)
FinDev			0.6634*** (0.1311)
Openness			−19.8043*** (5.4720)
EnvReg			−2.2404(1.9000)
IndStruc			10.6077*** (1.6694)
Constant	−15.0794*** (4.1704)	−17.6780*** (2.3331)	−34.1824*** (4.2909)
Observations	4,553	6,289	4,505
R-squared	0.1274	0.1555	0.2128
Controls	Yes	Yes	Yes
Year & Industry FE	Yes	No	Yes
Firm FE	No	Yes	No

Source: Authors’ calculations.

Note: Panel A: Alternative measures for ESG (WESG, HXRATE), SDO (SDO_c, SDO_n), and lagged SDO (SDO_t-1, SDO_t-2). Panel B: Sample period (2013–2019), firm-fixed effects, and additional regional/industry controls (EcoDev, FinDev, Openness, EnvReg, IndStruc). Standard errors in parentheses. ***, **, and * denote the significance at 1%, 5%, and 10% levels.

Alternative Measures of ESG Performance

Following existing studies (Fu et al., 2024), we employ two alternative metrics to address measurement inconsistencies in ESG performance: WIND ESG rating (WESG), which categorizes performance into six levels (A, B, B+, B-, C+, and C-) and HEXUN CSR rating (HXRATE), which employs a five-tier classification system (A, B, C, D, and E). Results in Panel A, Columns (1–2), show that SDO coefficients remain positive and significant, confirming baseline findings.

Alternative Measures of SDO

Our baseline SDO score aggregates keyword frequencies across dimensions and applies log-transformation. In robustness, we test two alternatives to our baseline log-transformed¹⁰ keyword frequency approach: SDO_c (raw aggregated keyword count without log-transformation) and SDO_n (normalized by MD&A word count to address length bias). Both alternatives in Panel A, Columns (3–4), show positive and significant SDO effects, corroborating baseline results.

Using Lagged Values of SDO

The use of lagged independent variables is a common econometric approach to endogeneity issues (Bellemare et al., 2017), particularly the possibility of reverse causality (firms with stronger ESG may adopt SDO). We establish causal inference by introducing temporal separation between SDO measurement and ESG performance. If prior-period SDO significantly influences current-period ESG outcomes, this strengthens evidence that SDO drives ESG rather than reverse causality. Therefore, following established research methodologies (Wang et al., 2023; Yang et al., 2024), we employ 1-year (SDO_t-1) and 2-year (SDO_t-2) lagged SDO values as sensitivity tests. Columns (5–6) in Panel A show that both lagged specifications maintain positive and significant relationships with ESG performance, strengthening causal inference by establishing temporal separation between SDO and ESG measurement.

Controlling for Temporal Effects, Firm-Fixed Effects, and Other Potential Drivers

We implement three additional robustness checks. First, we restrict the sample to 2013–2019 to account for China’s big data era onset in 2013 (Tian et al., 2024). Second, we incorporate firm-fixed effects beyond baseline year and industry effects. Third, we include regional controls (economic development measured as log GDP per capita [EcoDev], financial development as foreign investment-to-GDP ratio [FinDev], openness as deposit-loan-to-GDP ratio [Openness]) and industry controls (industrial structure as tertiary industry share of regional GDP [IndStruc]). Columns (1–3) in Panel B demonstrate that SDO coefficients remain consistently positive and significant across all specifications, validating robustness to temporal effects, firm heterogeneity, and contextual factors.

Endogeneity Analysis

Endogeneity concerns in our SDO–ESG relationship may arise when the error term of the regression in Equation (1) is correlated with SDO due to omitted variables, simultaneity, measurement error, or selection bias (Wooldridge, 2010). We employ three complementary approaches to address these concerns based on existing literature (Hill et al., 2021; Wooldridge, 2010).

First, we employ two-stage least square which is particularly well-suited for this scenario, as it allows us to mitigate the bias (using instruments) caused by omitted variables, simultaneity, or reverse causality that may be present in our model (Hill et al., 2021; Wooldridge, 2010). Following the established methodologies in the recent literature (Hussain et al., 2024; Yang et al., 2025), we use the industry-year average of SDO as an instrument to generate IV_SDO. This instrument is correlated with firm-level SDO while remaining uncorrelated with the error term, as industry-wide SDO’s trends are likely to influence firm-level strategic digital decisions, while the reverse (individual firms significantly shaping industry-level trends) is less probable (Hussain et al., 2024; Wang et al., 2025; Yang et al., 2025).

Table 5, Panel A, reports first-stage results showing strong correlation with the Cragg–Donald Wald F-statistic well exceeding the benchmark of 10 (1,147), alleviating weak instrument concerns. The Wu–Hausman and Durbin tests confirm significant endogeneity, validating the need for IV estimation. Panel B presents second-stage results where SDO maintains positive and significant effects on ESG performance, confirming our baseline findings.

Table 5.

Endogeneity Analysis: Two-Stage Least Square (2SLS).

Panel A: first-stage regression results
	SDO
IV_SDO	0.850*** (0.865)
Constant	−1.804*** (0.431)
Observations	6,180
R-squared	0.483
Cragg–Donald Wald F-statistic	1147
p-value (Wu)	.046
p-value (Durbin)	.045
Controls	Yes
Year & Industry FE	Yes
Panel B: second-stage regression results
	ESG
SDO	0.622*** (1.222)
Constant	−11.40* (5.943)
Observations	6,180
R-squared	0.226
Controls	Yes
Year & Industry FE	Yes

Source: Authors’ calculations.

Note: Two-stage least squares (2SLS) using industry-year average SDO as instrument (IV_SDO). Panel A: First-stage results (F-statistic = 1,147). Panel B: Second-stage results. Standard errors in parentheses. ***, **, and * denote the significance at 1%, 5%, and 10% levels.

Second, to address reverse causality through quasi-experimental design (Hill et al., 2021), we leverage the 2016 AlphaGo breakthrough as an exogenous shock. This pivotal event, where Google’s AI program defeated world champion Lee Se-dol in the game of Go, sparked widespread interest in applying deep learning to complex problems (Zhang & Yang, 2022). The timing of this technological disruption creates a natural experiment, enabling a difference-in-differences (DID) analysis. The study categorizes firms based on their pre-event SDO levels, with those showing higher SDO forming the treatment group (treat) and those with low SDO serving as the control group. Following the study of Beck et al. (2010), we then construct the interaction term “post_treat” and estimate the following DID model comparing groups before and after 2016:

ES G_{i t} = β_{0} + β_{1} post_{treat}_{it} + \sum_{φ = 1}^{12} φ_{k} {Controls}_{it} + δ_{t} + γ_{j} + ε_{it} \dots \dots

(5)

Table 6, Column 1, shows a significant positive post_treat coefficient, indicating treatment firms experienced higher ESG performance after the event. Moreover, the parallel trends test validates the DID analysis. Parallel trends tests (Column 2 and Figure 2) reveal insignificant pre-treatment coefficients (Pre_3, Pre_2, Pre_1), confirming similar baseline trends between groups. Significant effects emerge post-2016, establishing a clear temporal sequence where SDO changes precede ESG improvements. This pattern strongly suggests that the relationship flows from SDO to ESG performance rather than the reverse, validating the baseline results.

Table 6.

Endogeneity Analysis: Difference-In-Difference and Parallel Trend Test.

	DID	Parallel trend
	(1)	(2)
	ESG	ESG
Post_treat	1.472*** (.152)
Pre_3		1.621(1.96)
Pre_2		2.017(1.958)
Pre_1		2.972(1.96)
Current		3.496* (1.95)
Post_1		4.326** (1.956)
Post_2		4.879** (1.953)
Post_3		5.408*** (1.956)
Constant	−14.263*** (4.542)	4.138(4.659)
Observations	6,289	6,289
R-squared	.175	.268
Controls	Yes	Yes
Year & Industry FE	Yes	Yes

Source: Authors’ calculations.

Note: Difference-in-differences (DID) analysis using the 2016 AlphaGo breakthrough as an exogenous shock. Column (1): Main DID results. Column (2): Parallel trends test (Pre_3 to Post_3 relative to 2016). Standard errors in parentheses. ***, **, and * denote the significance at 1%, 5%, and 10% levels.

Figure 2.

Parallel trends test validating DID analysis using the 2016 AlphaGo breakthrough as an exogenous shock. Pre-treatment coefficients (Pre_3, Pre_2, Pre_1) are insignificant, confirming similar baseline trends between treatment and control groups.

Third, to address selection bias from non-random treatment assignment (Roberts & Whited, 2013; Rosenbaum & Rubin, 1983), we employ the PSM methodology. Using the same treatment and control groups as in the previous analysis, we employ one-to-one nearest neighbor PSM based on observable covariates (SOF, TOBQ, LEVF, RADE, HHIND, FASSETS, B4AF, FINAN, SOB, BIN, and CEODUL). The t-statistics in the matched sample (Panel A of Table 7) demonstrate successful matching with substantially reduced covariate differences post-matching. To visually validate the effectiveness of the PSM process, we present standardized percentage bias across covariates in Figure 3 and propensity score density plots before and after matching in Figure 4. Overall, these plots demonstrate how PSM minimizes bias by aligning the treatment and control groups based on the common support assumption. Panel B shows that the SDO coefficient remains positive and significant (β = 0.331, p < .01) in the matched sample, consistent with baseline results.

Table 7.

Endogeneity Analysis: Propensity Score Match (PSM).

Panel A: Match sample
	Mean			T-test		V(T)/V(c)
	Treated	Control	%bias	T-statistic	p > t	V(T)/V(c)
SOF	22.999	23.023	−1.900	−12.500	.000	1.010
TOBQ	1.909	1.990	−5.800	7.140	.000	0.890*
LEVF	0.434	0.436	−1.100	−11.320	.000	0.920
RADE	0.002	0.003	−8.100	0.000	.720*	.
HHIND	0.287	0.291	−1.800	0.310	.757	0.860*
FASSETS	0.211	0.216	−2.900	−23.390	.000	1.060
B4AF	0.095	0.104	−3.200	−8.340	.000	.
FINAN	0.814	0.822	−1.600	4.420	.000	1.020
SOB	8.979	8.903	4.100	−4.730	.000	0.860*
BIN	0.376	0.375	0.500	2.500	.013	0.990
CEODUL	0.199	0.212	−3.300	9.250	.000	.
Panel B: PSM regression results
			(1)		(2)
			ESG		ESG
SDO			0.331*** (0.109)		0.282** (0.110)
Constant			15.044*** (4.107)		-5.546(6.476)
Observations			2,353		2,353
Within R-squared			0.249		0.266
Controls			Yes		Yes
Year & Industry FE			Yes		Yes

Source: Authors’ calculations.

Note: Propensity score matching (PSM) with one-to-one nearest neighbor matching. Panel A: Covariate balance statistics before/after matching. Panel B: PSM regression results on matched sample. Standard errors in parentheses. ***, **, and * denote the significance at 1%, 5%, and 10% levels.

Figure 3.

Matching outcomes for propensity score matching.

Figure 4.

Common support test graph for propensity score matching.

Exploring Influencing Channel

Based on a rigorous research survey (Ding et al., 2023; Khalid et al., 2024), we propose DF as a key mediating mechanism through which SDO enhances ESG performance. Table 8, Panel A, presents mediation analysis results. Column (1) shows that SDO significantly increases ESG performance (∂, p < .01). Column (2) demonstrates that SDO significantly enhances DF (η, p < .01). Column (3) reveals that DF significantly improves ESG performance (λ, p < .10), indicating SDO’s effect on ESG flows through DF. Panel B reports Sobel, Aroian, and Goodman tests confirming the indirect effect is significant (z-statistics > 1.88, p < .05). These results support H2: SDO promotes ESG performance through enhanced DF.

Table 8.

Channel Analysis: Mediating Effect of Digital Finance.

Panel A: Mechanism analysis
	(1)	(2)	(3)
	ESG	DF	ESG
SDO	0.147** (0.075)	0.369*** (0.039)	0.195** (0.087)
DF			0.058* (0.03)
Constant	4.332(4.36)	−3.82(3.231)	−5.618(5.636)
Observations	6,289	10,669	3,603
R-squared	0.268	0.032	0.258
Controls	Yes	Yes	Yes
Year & Industry FE	Yes	Yes	Yes
Panel B: Sobel, Aroian, and Goodman test
Sobel test-DF			1.894** (0.011)
Aroian test-DF			1.884** (0.011)
Goodman test-DF			1.904** (0.011)

Source: Authors’ calculations.

Note: Mediation analysis with digital finance (DF) as mediator. Panel A: principal mechanism analysis results. Panel B: Sobel, Aroian, and Goodman tests for indirect effect significance. Standard errors in parentheses. ***, **, and * denote the significance at 1%, 5%, and 10% levels.

Heterogeneity Analysis

Firm’s Ownership

In China, SOEs retain competitive advantages through political connections and preferential access to financial resources (Wang et al., 2025), while POEs must rely on strategic acumen given their limited political ties and capital access (S. Li et al., 2024). Developing SDO may be vital for POEs to sustain competitiveness. Consequently, SDO’s positive association with ESG performance is expected to be more pronounced among private firms lacking state backing compared to SOEs. To assess potential heterogeneous effects, we segmented the sample into subsamples of SOEs and POEs.

Table 9, Panel A, shows that the SDO coefficient is significant for POEs but insignificant for SOEs. This reflects POEs’ greater management autonomy and strategic flexibility, enabling rapid adjustment of digital strategies to enhance ESG performance (S. Li et al., 2024). In contrast, SOEs operate within established legislative frameworks prioritizing economic growth and political stability, which constrains their ability to fully leverage digital technologies for sustainability (Hussain et al., 2024). In addition, SOEs face soft budget constraints that redirect resources based on political agendas (Liang et al., 2012), whereas POEs typically allocate higher proportions of revenue to R&D and strategic initiatives (S. Li et al., 2024).

Table 9.

Heterogeneity Analysis: Effects of Ownership Type.

	Panel A		Panel B
	Privately-owned firms	State-owned firms	Local SOEs	Central SOEs
	ESG	ESG	ESG	ESG
SDO	.176* (.097)	.046(.12)	.23(.155)	1.196* (.704)
Constant	.951(4.752)	1.577(6.852)	−61.615*** (6.027)	−11.633(19.198)
Observations	3,131	3,159	1,886	183
R-squared	.261	.281	.224	.151
Controls	Yes	Yes	Yes	Yes
Year & Industry FE	Yes	Yes	Yes	Yes

Source: Authors’ calculations.

Note: Heterogeneity analysis by ownership type. Panel A: Privately owned enterprises (POEs) versus state-owned enterprises (SOEs). Panel B: Local SOEs versus central SOEs. All models include controls and year/industry fixed effects. Standard errors in parentheses. ***, **, and * denote the significance at 1%, 5%, and 10% levels.

Panel B further explores heterogeneity among SOEs by separately analyzing central and local SOEs. Results reveal that the SDO coefficient is significantly positive for central SOEs but insignificant for local SOEs. CEOs in Central SOEs hold higher political status, and their career advancement opportunities are closely tied to firm performance and alignment with national development objectives (S. Li et al., 2024), motivating strategic decisions on sustainable practices (Q. Li, et al., 2024). Central SOEs additionally benefit from stronger regulatory backing and better alignment with national development objectives (Hussain et al., 2024; S. Li et al., 2024), enabling more effective deployment of digital capabilities for ESG initiatives. Conversely, local SOEs encounter challenges including short-term political priorities, risk-averse leadership, and inadequate political incentives for innovation and sustainability (S. Li et al., 2024).

Firm’s Political Connection

We hypothesize that realizing the benefits of SDO and ESG engagement requires substantial firm resources, including capital, expertise, and complementary technologies. Firms with political connections benefit from preferential access to these inputs compared to politically unaffiliated peers. Hence, the SDO–ESG relationship is expected to be more pronounced among politically connected firms.

Table 10, Panel A, reports regression results supporting our hypothesis. Politically connected firms – where the chairperson/CEO served as a party representative, NPC deputy, CPPCC member, or in military/government roles – exhibit stronger SDO effects on ESG performance compared to politically unconnected firms. To validate the distinctiveness of our subgroup analysis, we conducted a Chow test comparing coefficients between politically connected and unconnected groups. The F-statistic of 74.64 substantially exceeds the 5% critical value, with p-value = .000, strongly rejecting the null hypothesis of coefficient equality. This outcome confirms that the impact of SDO on ESG performance differs significantly between the two groups, supporting our decision to partition the sample.

Table 10.

Heterogeneity Analysis: Effects of Political Affiliation and Risk Tolerance.

Panel A: Impact of political connection
	Politically attached firms	Politically detached firms
	ESG	ESG
SDO	.403** (.192)	.053(.083)
Constant	9.826(10.001)	5.325(4.799)
Observations	750	5,544
R-squared	.263	.261
Controls	Yes	Yes
Year & Industry FE	Yes	Yes
F-statistic	74.640
Critical F-value at 5%	0.0020
p-value	.000
Panel B: Impact of firm’s maturity level
	Mature	Emerging
	ESG	ESG
SDO	.034(.112)	.222** (.112)
Constant	8.305(5.821)	1.165(5.614)
Observations	3,095	2,551
R-squared	.313	.231
Controls	Yes	Yes
Year & Industry FE	Yes	Yes
F-statistic	46.192
Critical F value at 5%	0.0023
p-value	.000

Source: Authors’ calculations.

Note: Heterogeneity analysis by political connection and firm maturity. Panel A: Politically attached versus detached firms (Chow test: F = 74.64, p < .001). Panel B: Emerging versus mature firms (Chow test: F = 46.19, p < .001). All models include controls and year/industry fixed effects. Standard errors in parentheses. ***, **, and * denote the significance at 1%, 5%, and 10% levels.

Firm’s Maturity Effect

Prior research indicates that emerging firms tend to exhibit higher risk tolerance than mature firms, which tend to be more risk-averse (Harymawan et al., 2022). Given the substantial risks associated with adopting new technologies and cultivating SDO, emerging firms may be especially motivated to pursue ESG improvements through digital orientation. Consequently, the SDO–ESG relationship is expected to be more pronounced among emerging firms compared to mature firms. To test this, we segmented the sample into emerging and mature subsamples based on the median firm age.

Table 10, Panel B, confirms that SDO has a significant positive association with ESG performance in emerging firms but not in mature firms, consistent with our expectations. To validate this heterogeneity, we conducted a Chow test for these subgroups. The F-statistic of 46.192 substantially exceeds the 5% critical value with p-value = .000, enabling us to confidently reject the null hypothesis of coefficient equality between emerging and mature firms. This result strongly supports separate analysis of these groups, confirming that firm maturity significantly moderates the impact of SDO on ESG performance.

Subcomponent Analysis

SDO measurement is based on four interrelated dimensions as discussed earlier. An interesting question is how these individual dimensions relate to ESG performance. While extant research has largely viewed digital transformation through a technological lens, assessing it solely by technology adoption metrics (Diófási-Kovács & Nagy, 2023), we argue that multidimensional conceptualization better captures the complexity of enterprise-wide digital transformation compared to unidimensional digital maturity models. Therefore, individual SDO dimensions alone may not enhance ESG performance.

The results in Table 11 demonstrate that individual SDO components have positive but insignificant effects on ESG performance, confirming that multidimensional SDO is more impactful than its individual dimensions in boosting ESG performance.

Table 11.

Heterogeneity Analysis: Digital Orientation Subcomponents.

	ESG	ESG	ESG	ESG
	(1)	(2)	(3)	(4)
Digital technology scope	.017(.012)
Digital capabilities		.002(.015)
Digital ecosystem coordination			.022(.015)
Digital architecture configuration				.014(.014)
Constant	4.369(4.363)	4.156(4.363)	4.205(4.36)	4.063(4.361)
Observations	6,289	6,289	6,289	6,289
R-squared	.268	.268	.268	.268
Controls	Yes	Yes	Yes	Yes
Year & Industry FE	Yes	Yes	Yes	Yes

Source: Authors’ calculations.

Note: Individual effects of SDO subcomponents on ESG performance. Columns show: (1) digital technology scope, (2) digital capabilities, (3) digital ecosystem coordination, and (4) digital architecture configuration. All models include controls and year/industry fixed effects. Standard errors in parentheses. ***, **, and * denote the significance at 1%, 5%, and 10% levels.

Conclusion

Discussion of Main Findings

Our study investigates how SDO influences ESG performance in Chinese listed firms. We find that SDO significantly enhances ESG performance, aligning with recent research on digital transformation and corporate sustainability (e.g., Tian et al., 2024; Wang & Esperança, 2023). Furthermore, we identify DF as a key mediating mechanism through which SDO drives ESG improvements. This finding supports emerging evidence positioning DF as a driver of green innovation, green investment, environmental performance, corporate social responsibility, and ESG outcomes (Khalid et al., 2024; Mu et al., 2023). Our study is among the first to empirically confirm this mediation pathway, demonstrating how digital strategies translate into sustainability outcomes.

Our heterogeneity analyses reveal important contextual variations. SDO exhibits stronger positive effects on ESG performance in private firms and centrally owned SOEs compared to local SOEs (Hussain et al., 2024; S. Li et al., 2024). Contrary to the notion that political connections reduce environmental accountability (Fang et al., 2023), politically affiliated firms demonstrate more pronounced SDO–ESG relationships. Emerging firms show heightened motivation to leverage SDO for ESG improvements due to higher risk tolerance in technology adoption (Harymawan et al., 2022). Finally, individual SDO dimensions alone are insufficient; rather, the impact operates synergistically across all four dimensions.

Theoretical Contributions

Our study advances information systems, strategic management, and sustainability literatures in three ways. First, we extend the digitalization literature by demonstrating that successful digital transformation extends beyond technology adoption to encompass adaptive capabilities, ecosystem coordination, and architectural configuration (Bendig et al., 2023; Diófási-Kovács & Nagy, 2023; Kindermann et al., 2021). We provide one of the first holistic analyses of how multidimensional SDO drives ESG performance in emerging economies like China, bridging critical gaps in information systems research. Second, we advance dynamic capability theory by conceptualizing SDO as a strategic capability enabling firms to sense external demands (ESG pressures), seize opportunities (digital-enabled solutions), and transform operations (restructured processes). We specifically illuminate the mediating role of DF in enabling sustainability initiatives, bridging information systems and corporate finance literatures. Third, we clarify boundary conditions shaping the SDO–ESG relationship. We demonstrate how ownership structure, political connections, and firm age influence SDO’s effectiveness – a relatively underexamined intersection of technology adoption and sustainability research (Hussain et al., 2024; Wang et al., 2025). Our findings on China’s distinctive institutional context (state ownership prevalence and political connections; Maqsood, Li, et al., 2024; Wang et al., 2025) contribute to innovation research in developing economies. In addition, by revealing that synergistic interactions among SDO dimensions drive ESG performance rather than individual components, we support systems thinking in digital transformation literature (Diófási-Kovács & Nagy, 2023).

Practical Implications

Our findings offer actionable insights for business leaders and policymakers seeking to harness digital transformation for sustainability. We provide the following specific recommendations for firms and policymakers based on our findings.

For firms: Managers should adopt a comprehensive SDO encompassing technology, capabilities, ecosystem coordination, and architecture configuration. Specifically: (1) invest in DF as a strategic enabler – adopting blockchain for ESG reporting, AI for sustainable resource allocation, and digital platforms for green financing; (2) private firms and central SOEs should capitalize on agility and resources to implement SDO-driven ESG strategies; (3) politically connected firms should leverage networks for policy incentives and funding; (4) emerging firms should aggressively adopt SDO given their risk tolerance; and (5) integrate all four SDO dimensions synergistically rather than pursuing individual components in isolation.

For policymakers: (1) Design targeted policies encouraging SDO adoption for ESG – including subsidies for green technologies, tax breaks for ESG investments, and grants for digital financial innovation projects; (2) foster collaboration between firms, financial institutions, technology providers, and academia to build robust digital ecosystems and public–private partnerships for ESG data sharing; (3) mandate transparent, standardized ESG reporting frameworks incorporating digital transformation metrics; (4) invest in training programs equipping managers and employees with skills for SDO implementation and digital sustainability tools; and (5) recognize SDO–ESG heterogeneity across firm types and develop tailored policies supporting local SOEs and smaller firms in overcoming digital transformation barriers.

These recommendations bridge academic findings and real-world applications, ensuring research contributes to corporate practice and policy formulation.

Limitation and Future Directions

While offering valuable insights, our study has limitations. First, our sample comprises publicly listed Chinese A-share firms, limiting generalizability. China’s distinctive context – rapid DF development, SOE prevalence, and strong government influence on sustainability – shapes the SDO–ESG relationship in ways potentially non-transferable to contexts with different digital adoption levels, regulatory frameworks, and firm characteristics. Future research should examine these dynamics across diverse geographical and institutional contexts. Second, our multidimensional SDO construct may not fully capture digital transformation distinctions in less digitally advanced contexts or those where organizational readiness plays a lesser role. Applicability may vary across industries with different digital maturity and sustainability pressures, warranting investigation in diverse sectoral settings. Third, while DF emerges as a key mediator, other organizational mechanisms – innovation capabilities, stakeholder engagement, and corporate governance structures – may also influence the SDO–ESG relationship. Future research should explore these additional pathways for comprehensive understanding. Finally, our reliance on textual analysis of MD&A sections to measure SDO may be subject to strategic reporting bias. Alternative approaches – longitudinal case studies, interviews, or direct observation of digital initiatives – could provide more robust measurement and mitigate potential biases.

Supplemental Material

sj-docx-1-brq-10.1177_23409444261441322 – Supplemental material for Digitalization and ESG Performance: How Strategic Digital Orientation Shapes Firms’ Sustainability Outcomes

Supplemental material, sj-docx-1-brq-10.1177_23409444261441322 for Digitalization and ESG Performance: How Strategic Digital Orientation Shapes Firms’ Sustainability Outcomes by Murtaza Hussain, Shaohua Yang, Umer Sahil Maqsood and R. M. Ammar Zahid in BRQ Business Research Quarterly

Footnotes

ORCID iDs

Murtaza Hussain

Umer Sahil Maqsood

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental Material

Supplemental material for this article is available online.

Notes

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