Executive Cognitive Styles and Enterprise Digital Strategic Change Under Environmental Dynamism: The Mediating Role of Absorptive Capacity in a Complex Adaptive System

Abstract

Driven by the new wave of technological revolution and industrial transformation, firms are accelerating strategic change to gain new competitive advantages. Situated within a complex adaptive system, firms must adapt to highly dynamic and uncertain external environments by adjusting executive cognitive structures, reconfiguring resources and capabilities, and strengthening collaboration with industrial ecosystem elements; hence, digital strategic change is characterized by continuous evolution. Using a sample of Chinese A-share listed firms from 2015 to 2023, this study develops a “cognition–capability–strategy” pathway model grounded in upper echelons theory and dynamic capabilities theory to examine how executive cognitive styles, i.e., cognitive flexibility and cognitive complexity, drive digital strategic change via absorptive capacity and how environmental dynamism moderates these relationships. The findings show that executive cognition, as a decision node in strategic change, can dynamically adjust firms’ strategic paths by activating absorptive capacity in rapidly changing external information environments; environmental dynamism differentially affects the two cognitive styles. Heterogeneity tests further indicate that the role of executive cognition varies significantly with regional digital economy development levels, firm life cycle, and industry factor intensities. The study reveals how firms can respond to high environmental uncertainty through cognition–strategy alignment and resource capability reconfiguration in a complex adaptive system, providing theoretical references and practical insights for emerging economies to advance digital transformation and enhance competitiveness.

1. Introduction

In the era of accelerated technological innovation and global digital transformation, enterprises around the world are facing unprecedented challenges and opportunities. As emerging technologies such as artificial intelligence, big data, cloud computing, and the Internet of Things continue to reshape business models, value chains, and industry ecosystems, vigorously developing the digital economy has become a key driver for reshaping national competitiveness, and digital transformation has emerged as a necessary path for enterprises to enhance their competitiveness and achieve high-quality development [1]. For example, Amazon has continuously leveraged digital technologies such as cloud computing and big data analytics to reshape its business model and value creation process. By developing the AWS (Amazon Web Services) platform, the company not only transformed its internal IT infrastructure but also became a global leader in cloud services, significantly improving operational efficiency, expanding market reach, and accelerating innovation. This case illustrates that digital strategic change has become a new goal for enterprises, enabling them to survive and develop in the digital economy era. However, many firms around the world continue to face significant challenges in implementing digital strategic change. Global surveys have shown that only a small fraction of enterprises have successfully restructured their business models through core digital capabilities, while the majority remain trapped in transitional inertia. Therefore, how to effectively promote digital strategic change has become a critical issue that enterprises must address in order to achieve high-quality development in the digital context.

Digital strategic change, as an evolutionary process of the deep integration of digital technology and enterprise strategy, is a systematic change that triggers an enterprise’s competitive approach, business logic, business model, business ecology, and other aspects [2]. Although existing studies have explored various aspects of digital change, most treat digital change as an antecedent variable to examine its impact on organizational outcomes [3]. Studies exploring the drivers of digital strategic change have primarily concentrated on factors such as digital technologies [4], organizational resources and capabilities [5], and policy environments [6,7]. However, the landing of the digital transformation strategy not only needs to obtain significant improvement in resources, capabilities, and technology, it also needs to realize a profound change in cognitive level from traditional thinking to digital thinking. For instance, Zhang Ruimin, Chairman of the Board of Haier Group, has led the cognitive change through the “human-single-integration” model, leading the company from traditional manufacturing toward a user-centric smart interconnected ecosystem strategy. This approach enabled cross-industry collaboration with over 20 industries and 2000 enterprises. By the end of 2022, Haier’s COSMOPlat platform had served more than 800,000 corporate users, laying a solid foundation for the group’s comprehensive strategic transformation. This suggests that a shift in the cognitive dimension is a key internal driver for digital strategy to take hold. Upper echelons theory states that the cognitive and behavioral characteristics of executives affect the strategic choices and performance of firms [8]. Compared to existing studies focusing on individual executive background and heterogeneity [9,10], this study adopts a micro-level cognitive perspective, focusing on how executive cognitive style influences digital strategy change. Specifically, cognitive styles include cognitive flexibility and complexity, with cognitive flexibility reflecting managers’ ability to adapt to new environments, break through path dependency, and flexibly adjust decision-making [11], and cognitive complexity enabling managers to integrate multidimensional perspectives and make more systematic and comprehensive strategic decisions when faced with diverse and complex information [12]. Whether the two cognitive styles can effectively drive digital strategy change in enterprises still needs to be further explored.

Digital strategic change relies not only on the cognitive judgment of executives but also on the allocation of capabilities and resources to implement and continuously optimize the strategy [13]. Dynamic capability theory provides a critical analytical framework for understanding corporate digital strategic transformation. In today’s turbulent environment, dynamic capabilities are considered the core mechanism for organizational survival and development. Existing research has pointed out that dynamic capabilities are key to digital transformation, enabling enterprises to maintain adaptability and efficient operations in rapidly changing environments [14]. Absorptive capacity is defined as a dynamic capability related to knowledge acquisition and utilization [15] which can help enterprises to obtain and maintain competitive advantages. Therefore, this paper uses absorptive capacity as a mediating variable to analyze how executive cognitive styles influence the process of external knowledge acquisition and internal integration and transformation, thereby driving digital strategic change. This perspective not only addresses the shortcomings of existing research in terms of mechanisms but also helps answer the question of how cognitive traits can achieve strategic implementation through capability evolution.

At the same time, corporate digital strategy change does not occur in isolation but is embedded in a complex and changing digital business ecosystem. Environmental dynamism reflects the degree of uncertainty of changes in the external environment, which to a certain extent will strengthen or weaken the effects of cognitive style on digital strategic change. Therefore, this paper further examines the moderating role of environmental dynamism as a contextual factor to reveal the boundaries of the applicability of research findings under different environmental conditions.

Based on this, this paper takes Chinese A-share listed companies from 2015 to 2023 as a research sample to explore the mechanisms and contextual differences in the role of executive cognitive style regarding digital strategic change. Thus, this study seeks to address the following central research question: How do executive cognitive styles, i.e., cognitive flexibility and cognitive complexity, influence digital strategic change through absorptive capacity and what differences do they exhibit at different levels of environmental dynamism? As a global leader in digital innovation, China can provide valuable insights for other economies seeking to formulate and implement digital strategies in rapidly changing digital ecosystems by conducting in-depth research on the relationship between cognitive styles and digital strategy change using data from Chinese listed companies as a sample.

The contributions of the study are mainly reflected in the following: First, grounded in upper echelons theory, it examines how managerial cognitive styles influence digital strategic change, as well as the underlying mechanisms of this relationship. By doing so, it extends the existing literature on the antecedents of digital strategic change. Second, it integrates the dynamic capability theory to reveal the mediating role of absorptive capacity in the path of “cognition–capability–strategy”, which responds to the lack of process mechanisms. Third, it constructs a digital strategic change indicator system based on text analysis, which portrays the digitalization process in the two dimensions of strategic intent and implementation path and makes up for the inadequacy of existing fragmented and unidimensional measurements.

2. Literature Review and Research Hypothesis

2.1. Research on Firm Digital Strategy

Firm strategy refers to a firm’s overall reconfiguration of its strategic resource allocation across multiple internal dimensions in response to changes in internal and external environments, with the aim of building core competitive advantages [16]. Digital strategy, in this context, is defined as a strategy through which firms leverage digital technologies to create value [17]. According to Gobble [18], a true digital strategy does not merely aim at stacking technologies or pursuing localized optimizations; rather, it involves strategic transformation guided by managerial cognition and is implemented at the organizational level. The essence lies in the systemic reshaping of relationships among the firm, its customers, employees, and markets through digital technologies, thereby generating new sources of competitive advantage and value creation.

Most existing studies define digital transformation driven by digital strategy as the process in which firms introduce and utilize emerging digital technologies to upgrade and optimize existing products, services, and business processes, thereby inducing strategic-level transformations such as shifts in market strategy and business models. Hanelt et al. define digital transformation as organizational change that is triggered and shaped by the widespread diffusion of digital technologies, emphasizing that the essence of DT lies in the process of change at the organizational level, not just in the application of technology itself [19]. Drawing on the latter study, this study defines digital strategic change as the strategic-level implementation of digital transformation. It refers to the process by which firms, under the guidance of top management cognition, systematically reshape their value creation logic and resource allocation by leveraging digital technologies with the goal of achieving holistic strategic transformation and enhancing competitive advantage.

Furthermore, based on prior frameworks concerning firm strategic transformation [13], digital strategic change is influenced by the interaction of three core dimensions: external environment, organizational resources and capabilities, and managerial characteristics: (i) External environment: Changes in the external environment are the primary triggers for digital strategic change. For example, Kohlir and Melville [7] argue that shifts in competitive environments directly impact firms’ strategic change. The increasing dynamics of the environment in which firms operate can increase the importance of relational factors in the digital transformation process. When firms are faced with high environmental dynamics and are challenged to keep their competitive advantage, they are more likely to seek strategic change and thus have a stronger motivation to conduct digital transformation [20]. (ii) Organizational resources and capabilities: These factors significantly influence the formation of a firm’s digital strategy. Redundant resources, for instance, serve as the material foundation for firms to adapt to environmental changes and achieve strategic transformation [21], expanding the space of strategic options for firms and encouraging exploratory behaviors, identifying potential digital opportunities, and facilitating the transformation of their digital strategy. Additionally, dynamic capabilities, as enablers of digital transformation, can reconfigure ordinary capabilities to adapt to new challenges and enable new capabilities [22]. (iii) Managerial characteristics: Several studies have also examined the influence of top executives on digital strategic change. For example, the educational background of executives has been found to enhance innovation capabilities, thereby facilitating digital transformation [23,24]. Zhu and Jin [25] empirically demonstrated that managerial flexible leadership ability positively affects digital strategic transformation. The results of the study suggest that companies led by CEOs with a STEM (science, technology, engineering, and mathematics) background perform better in digital transformation [26]. Wang et al. [27] suggest that firms with stable top management teams are more likely to formulate strategic decisions from a long-term and organization-wide perspective, leading to stronger strategic consensus and accelerated digital transformation.

Despite these advances in identifying the antecedents of digital strategic change, current research has paid relatively limited attention to the role of managerial cognitive styles. Accordingly, drawing on upper echelons theory and dynamic capability theory, this study investigates the influence of executive cognitive styles on digital strategic change and uncovers their underlying drivers. Furthermore, it examines the mediating role of absorptive capacity and the moderating effect of environmental dynamism as a contextual factor.

2.2. Research Hypothesis

2.2.1. Executive Cognitive Style and Digital Strategic Change

Based on upper echelons theory, top executives, as the key decision-makers in corporate strategy formulation and implementation, influence firms’ strategic choices and transformation paths through their perceptions of environmental uncertainty and their approaches to resource reallocation. Different cognitive styles shape differentiated thinking patterns when facing strategic issues, enabling firms to identify opportunities and risks from multiple perspectives in dynamic environments. This, in turn, helps reduce organizational inertia in the processes of resource acquisition, integration, and reconfiguration, thereby facilitating the rational advancement and path optimization of digital strategic change [8]. In a digital context, the strategic issues faced by enterprises are more complex, uncertain, and dynamic, placing higher demands on executives’ cognitive capabilities. This is because digital technologies possess new characteristics such as availability, self-growth, and openness, making the cognitive styles of senior managers—that is, their preferences for processing information, understanding problems, and responding to changes—a key variable influencing digital strategic change. Executives need both the flexibility to adapt to changes in the external environment and the capacity to address complex and systemic problems, enabling them to understand and navigate the multidimensional restructuring processes involving business logic, organizational structure, and ecological boundaries during digital strategic change. Therefore, executive cognitive styles influence the advancement of digital strategic transformation. This is specifically manifested in the following two aspects.

In terms of cognitive flexibility, it manifests as the ability of managers to promptly adjust their cognitive strategies to adapt to changing environments [28]. Managers with higher cognitive flexibility can keenly grasp external market demands and technological trends, flexibly allocate internal and external resources, and provide the necessary cognitive support for the company’s digital strategic change. Additionally, cognitive flexibility helps managers overcome organizational inertia, enhance the company’s ability to develop digital agility, and formulate digital strategies [29]. Finally, cognitive flexibility indicates that managers can shift smoothly between different thinking and search modes, thus helping them flexibly address the trade-off between exploratory and exploitative activities faced by the company during digital strategic transformation [30], providing an important safeguard for the company’s smooth digital transformation.

H1a. 

Executive cognitive flexibility has a significant positive effect on corporate digital strategic change.

Cognitive complexity refers to the ability of managers to identify the multidimensional attributes of strategic problems and integrate diverse perspectives in decision-making [12]. Higher cognitive complexity implies a greater degree of diversification in the core concepts of managerial cognition, enabling strategic decision-makers to perceive environmental stimuli from more dimensions [31]. In addition, complex managerial cognition can facilitate strategic decision-makers to form a broader sense of identifying opportunities, which can make the enterprise more strategically adaptive. Furthermore, managers with higher complexity can understand information in the digital environment from different perspectives, promoting systematic integration and comprehensive strategic planning, thereby enhancing the company’s direction in digital strategic transformation.

H1b. 

Executive cognitive flexibility has a significant positive effect on corporate digital strategy change.

2.2.2. The Mediating Role of Absorptive Capacity

Based on dynamic capabilities theory, an enterprise in a rapidly changing environment needs to have the ability to integrate, construct, and reconfigure internal and external resources to adapt to changes [32]. In the knowledge economy era, knowledge and information obtained from the external environment can only be effectively utilized after being absorbed and applied [11]. Absorptive capacity refers to the ability of enterprises to absorb, integrate, and transform external knowledge and information and apply them to business activities [32]. It enhances the efficiency of knowledge search, information utilization, and transformation, helping companies stay at the forefront of market changes, adjust their business models and development strategies in a timely manner, and drive the implementation of their digital strategies [33]. Cohen and Levinthal first proposed absorption capacity in their pioneering research, drawing on concepts from cognitive psychology regarding individual cognitive structures and problem-solving. They emphasized that absorption capacity is the key mechanism through which managerial cognition is transformed into organizational learning and innovation outcomes, providing theoretical support for explaining how executive cognition influences corporate digital strategic transformation [34]. Previous studies have shown that data-driven dynamic capabilities are critical intermediary variables in the influence pathways between individual cognition and organizational strategy [35]. Different from other dynamic capabilities, absorptive capacity first addresses the question of “where knowledge comes from” and “how it is transformed”. Digital strategic change heavily relies on the integration of emerging technologies and cross-domain knowledge, and absorptive capacity serves as the direct bridge connecting executive cognition with organizational strategic actions, effectively converting managerial cognitive advantages into the company’s strategic execution capabilities. Upper echelons theory emphasizes how executive personal characteristics and cognitive styles shape organizational outcomes, while dynamic capability theory provides process-level explanations, complementing upper echelons theory by revealing how cognitive traits are transformed into adaptive organizational behavior through the processing, integration, and restructuring of external knowledge. By integrating upper echelons theory and dynamic capability theory, absorptive capacity serves as an intermediary variable, not only demonstrating the driving role of executive cognition in digital strategic change but also revealing how cognition achieves strategic implementation through dynamic capabilities at the organizational level.

In digital contexts, the market environment is highly dynamic, such that traditional experience-based and linear decision-making models that executives have long relied on are gradually becoming ineffective. The focus of organizational capability building is shifting towards data-driven, cross-border collaboration and agile response. Consequently, digital strategic change places higher demands on information advantage acquisition, resource allocation capabilities, and organizational convention breakthroughs in the formation of strategy. This study argues that in the digital era executive cognition affects absorptive capacity through three pathways: information search, resource allocation, and routine breakthrough, which can have a profound impact on the advancement of digital strategic change.

(i) Information search: In the digital age, organizational capabilities largely depend on their ability to use and process information, with information search serving as the foundational behavior [36]. Managers with higher cognitive flexibility are more likely to accept external information and more engaged in continuous information searching. By processing a large amount of diverse data, they increase the likelihood of recombining new information with existing information to create new combinations, thereby enhancing the foundation of absorptive capacity at the information acquisition stage. (ii) Resource allocation: Managers with high cognitive flexibility perceive resources more dynamically and can recognize multiple attributes of available resources [37]. When facing complex and changing environments, they are more capable of flexibly coordinating internal resources to match external knowledge requirements. This flexible resource scheduling mechanism makes the enterprise more adaptable in assimilating external knowledge, thus improving the transformation efficiency of absorptive capacity. (iii) Routine breakthrough: Executives with high cognitive flexibility notice, interpret, and process more data, prompting the organization to explore non-path-dependent knowledge application methods [38]. By altering organizational conventions, they effectively avoid organizational rigidity and achieve the internal regeneration and commercial application of external knowledge.

H2a. 

Executive cognitive flexibility has a significant positive effect on absorptive capacity.

Similarly, cognitive complexity affects digital strategy change in the following ways: (i) Information search: Higher cognitive complexity implies a greater degree of complexity in the causal relationships between concepts within the managerial cognitive structure. By constructing a multidimensional causal network [31], managers can expand the scope of their scanning of external information such as technological trends and market signals, thereby enhancing the breadth and depth of knowledge acquisition. (ii) Resource allocation: Executives with high cognitive complexity are more likely to question current strategic commitments, resource allocations, and other conventional constraints within the industry [39]. They can make decisions by considering multiple perspectives, enabling the company to make systematic resource allocation decisions when faced with multiple options. The reduction in strategic commitments allows the company to maintain a continuous focus on new technologies, aligning with its own conditions to enhance its absorption capacity. (iii) Routine breakthrough: Cognitive complexity provides executives with multidimensional perspectives and integrative thinking. It helps them overcome path dependency and avoid cognitive bias, increasing their sensitivity to new knowledge and capacity to restructure internal systems for effective knowledge integration and application.

H2b. 

Executive cognitive complexity has a significant positive effect on absorptive capacity.

In rapidly changing digital environments, absorptive capacity plays a core supporting role in the implementation of digital strategy. First, it enhances the firm’s ability to identify, acquire, and integrate external digital knowledge, cutting-edge technologies, and market trends, allowing the firm to capture new opportunities [40]. Second, by combining external knowledge with internal experience, absorptive capacity fosters knowledge recombination and the generation of innovation resources, providing a differentiated and dynamic foundation for digital strategic initiatives [41]. This process not only extends the firm’s resource boundary but also enhances the flexibility and depth of resource utilization, laying a solid foundation for the advancement of digital strategy. Specifically, strong absorption capacity enables enterprises to effectively obtain key resources and information required for implementing digital strategies from multiple channels, such as customers, competitors, and partners, and to internally realize knowledge transformation and application [42]. This capability allows firms to respond quickly to market changes, adjust strategic direction, and improve innovation.

In sum, absorptive capacity enhances both the breadth and depth of external knowledge acquisition and plays an irreplaceable role in resource integration, strategic planning, and implementation. It provides the knowledge base and innovative power for continuous optimization and dynamic adjustment of digital strategy. Thus:

H3a. 

Absorptive capacity mediates the effect of executive cognitive flexibility on digital strategy change.

H3b. 

Absorptive capacity mediates the effect of executive cognitive complexity on digital strategy change.

2.2.3. The Moderating Effect of Environmental Dynamism

In the new digital era, the uncertainty faced by firms has greatly increased in strategic decision-making. This high level of environmental dynamism not only imposes stricter requirements on corporate strategic adjustments but also underscores the importance of leveraging dynamic capability theory to create competitive advantages. Dynamic capability theory emphasizes that enterprise must possess the ability to perceive opportunities and threats, as well as integrate and reconfigure resources, to respond to rapid environmental changes [32]. Therefore, the turbulent external environment requires managers to actively perceive the complexity and uncertainty of the environment, rely on their own knowledge structures to obtain external information, comprehensively assess digital development trends, and thereby establish a scientific and effective digital strategy. Based on this, this paper proposes that environmental dynamism plays a moderating role in the relationship between executive cognition and digital strategy change.

Specifically, high environmental dynamism strengthens the positive impact of cognitive flexibility on digital strategic change. In dynamic markets, information asymmetry increases, which disrupts organizational efficiency and weakens executive ability to anticipate future trends. Since cognitive flexibility emphasizes the ability of managers to rapidly adjust their thinking patterns and strategic judgments under environmental changes, managers with high cognitive flexibility have higher acuity, can quickly perceive and grasp the opportunities and threats in the dynamic environment, and, based on the internal knowledge system of the organization, promote the flexible conversion of resources between different uses [43], and thus improve the efficiency of resource utilization and help enterprises to make adaptive strategic adjustments to changes in the external environment. Therefore, such managers are able to rapidly and accurately position the firm’s strategy and identify operating models that best fit the firm’s development needs in complex and diverse business environments. Consequently, high environmental dynamism amplifies the positive impact of such cognitive flexibility on digital strategic change, enabling enterprises to transform and innovate faster.

Additionally, managers with high cognitive flexibility are more inclined to deeply understand existing resources, guided by an exploitation-oriented innovation strategy to efficiently search for and acquire new external resources, and combine these with existing resources to update existing products and technologies [44], helping the company achieve new, efficient innovations in a short time and gain short-term competitive advantages [45]. Conversely, in relatively stable market environments, where technological updates and market changes occur over longer periods with smaller magnitudes, companies find it easier to grasp external market fluctuations [46]. The need for executives to rapidly adjust strategic decisions in response to market changes decreases, thereby weakening the promotional role of cognitive flexibility in digital strategic transformation.

H4a. 

Environmental dynamism positively moderates the relationship between executive cognitive flexibility and digital strategic change.

Similarly, high environmental dynamism also enhances the positive impact of cognitive complexity on digital strategic change. As environmental dynamism increase, the impact of complex environmental factors on firms’ ability to make strategic decisions subsequently becomes greater [47]. Cognitive complexity means that there is a complex network of connectivity between different concepts in the managerial cognitive structure, and its decision-making process is better able to pay attention to the subtle changes in the external environment and respond accordingly, so managers with higher cognitive complexity are able to discover the connections between information elements when facing diverse and complex information, which can help enterprises to think about the environmental changes in multidimensional perspectives and develop a more systematic and comprehensive adaptive digital strategy path. Thus, high environmental dynamism further highlights the impact of cognitive complexity on an enterprise’s digital strategy, prompting managers to integrate complex external information and choose the most appropriate digital path.

Furthermore, the cognitive structure of complexity helps decision-makers to effectively integrate differentiated and contradictory information, thus enhancing their integration ability in relation to coordinating exploratory and exploitative activities. This kind of integration ability is more likely to be advantageous in the long term, so that organizations can maintain competitiveness in the continuous optimization and dynamism adjustment of strategy, which is conducive to the construction of long-term competitive advantage [48]. Conversely, in stable environments, managers may favor maintaining existing resource configurations and strategic structures [49], slowing the pace of digital transformation. In such cases, the role of cognitive complexity may be reduced, as executives are more likely to make incremental adjustments rather than adopt transformative changes.

H4b. 

Environmental dynamism positively moderates the relationship between executive cognitive complexity and digital strategic change.

Therefore, based on the “cognition–capability–strategy” research paradigm, this paper examines the relationship between executive cognition, absorptive capabilities, and strategic behavior in digital ecosystems. From the perspective of complex adaptive systems, it highlights the complexity and uncertainty inherent in digital strategy transformation. On this basis, this paper proposes a corresponding theoretical model to better reveal the digital strategy transformation path that enterprises take when responding to external environmental shocks (as shown in Figure 1).

3. Research Design

3.1. Sample Selection and Data Source

China is a global leader in digital innovation, and since 2015 the country’s digital economy has shown rapid growth, with a number of national initiatives, including the ‘Internet Plus’ Action Plan and the Outline of Action for Promoting the Development of Big Data, driving the digital transformation of a wide range of industries. As a result, research from 2015 captures the strategic digital responses of companies to the ongoing policy and technology push. Thus, the data of Chinese A-share listed companies from 2015 to 2023 were selected as the initial research samples, and the following treatments were performed: (i) samples from the financial industry were excluded; (ii) samples with special treatments, such as ST, *ST, PT, etc., were excluded; (iii) the companies that were listed and delisted during the study period were excluded; (iv) the samples with serious omissions of major key data were excluded; (v) in order to minimize the effect of outliers, all micro-level continuous variables were subjected to 1% and 99% shrinkage treatment.

The data for the main research variables were obtained from the annual report information of enterprises disclosed on CNINFO using Python 3.13 web crawling for keyword text analysis, and the rest of the data were obtained from the database of China Stock Market & Accounting Research (CSMAR) and the Chinese Research Data Services Platform (CNRDS). The data processing software used in this paper was Stata18.0.

3.2. Variable Definition and Measurement

• Dependent Variable: Digital Strategic Change (DSC)

Digital strategic change reflects not only a firm’s strategic intent and orientation toward digital transformation, but also its practical implementation paths and technological application capabilities. Annual reports often disclose a firm’s business philosophy, operational model, and internal structure. These documents provide insights into a firm’s strategic characteristics and future development direction. As a systemic transformation that deeply integrates digital technology with corporate strategy, digital strategic change is more likely to be reflected in the summary and guiding content of annual reports.

This study adopted a text analysis approach based on listed firms’ annual reports. It identified keywords related to digital strategic change, calculated their frequencies, and applied a logarithmic transformation to the total frequency to construct a proxy variable for digital strategic change. The process of constructing the digital strategic change index involved the following steps.

First, we reviewed major national policy documents, including Report to the 20th National Congress of the Communist Party of China and Digital Economy Development Plan of the 14th Five-Year Plan (2021–2025), the Development Guidelines for the Big Data Industry (2016–2020), and the Strategic Outline for Digital Economy Development. From these documents, we compiled a dictionary of feature terms related to digitalization, transformation, upgrading, and change. Second, drawing on key studies in the literature [50], we identified 85 specific keywords (as shown in

Table 1. Summary of key words for digital strategic change.

Categories

Keyword Thesaurus

Digital strategy

Digitalization, digital transformation, information, intelligence, intelligent manufacturing, internalization, Industrial Internet, Industry 4.0, green manufacturing, mobile Internet, mobile Internet, Internet medical, e-commerce, mobile payment, third-party payment, NFC payment, smart energy, B2B, B2C, C2B, C2C, 020, smart wear, intelligent transportation, intelligence Neng Medical, intelligent customer service, intelligent home, intelligent investment advisory, intelligent cultural travel, intelligent environmental protection, smart grid, intelligent marketing, digital marketing, unmanned retail, Internet finance, network connection, Fintech, quantitative finance, open bank, information technology, artificial intelligence, big data, blockchain, digital finance, Internet of Things, Smart Internet, cloud computing, 5G, business intelligence, intelligent data analysis, image understanding, investment decision aid systems, intelligent robotics, machine learning, deep learning, semantic search, biometrics, face recognition, voice recognition, identity verification, autonomous driving, natural language processing, digital currency, distributed computing, differential privacy technology, intelligent financial contracts, stream computing, graph computing, memory computing, multi-party security computing, brain-like computing, green computing, cognitive computing, fusion architecture, 100 million level concurrency, EB level storage, information physics system, data mining, text mining, data visualization, heterogeneous data, credit information, augmented reality, mixed reality, virtual reality

) that capture various aspects of digital strategic change. These terms reflect not only the strategic direction, long-term focus, and consistency of firms’ digital strategies, but also the specific technologies and digital tools adopted during the transformation process.

2.

Independent Variable: manager cognitive style

Most existing studies on the measurement of executive cognition apply case studies, scales, experiments, textual analysis, and other methods of measurement. Compared with scales and experiments, textual analysis overcomes the limitations of the former in terms of the possible cognitive bias and the limitations of artificially simulated environments, enhances the universality and robustness of the study in question, and can more realistically reflect the cognitive characteristics of the executives and the dynamic changes in the actual operation. Referring to the study of Deng [51], a textual analysis of managerial discussion and analysis (MD&A) in annual reports of listed companies was conducted. The management discussion and analysis (MD&A) in the annual reports of listed companies is agreed or written by key managers and made public, including management’s discussion and analysis of the operating period as well as management’s outlook for the future, and thus can be an essentially objective response to managers’ cognition. The keywords were grouped into five dimensions, namely, external environment perception, rapid response, change and innovation, integration and reconfiguration of resources and capabilities, and organizational learning.

Cognitive Flexibility (CF). Cognitive flexibility is the ability of managers to capture changes in the external environment and promptly adjust their cognitive strategies, so the external environment perception dimension reflects the cognitive flexibility of managers. Referring to Deng’s study [51], the word frequency of external environment perception appearing in MD&A was counted using Python, and the total word frequency was logarithmized to obtain the proxy variable of cognitive flexibility.

Cognitive Complexity (CC). Cognitive complexity reflects the cognitive inclusiveness and integration ability of managers regarding effectively processing diverse information and can be characterized by the breadth of managers’ attention allocation. The Herfindahl–Hirschman Index (HHI Index) was used to measure the breadth of managers’ attention allocation in the five dimensions of external environment perception, rapid response, change and innovation, integration and reconfiguration of resources and capabilities, and organizational learning. The formula is as follows:

$$\mathrm{CC} = 1-{\sum }_{\mathrm{i}=1}^5\left({\mathrm{P}}_{\mathrm{i}}\right)2$$

where CC is the manager’s breadth of attention allocation and P_i indicates the total frequencies of keywords in category i of the five dimensions relative to the total frequencies of keywords in the five categories. The larger CC is, the higher the manager’s cognitive complexity is.

3.

Mediating variable: absorptive capacity (AC)

This paper measured absorption capacity in the following two dimensions: ① R&D expenditure intensity. R&D intensity reflects the level of resource investment in R&D activities, where the higher the R&D expenditure, the stronger the company’s foundational conditions for acquiring, understanding, and assimilating external knowledge [52], thereby enhancing its absorption capacity. Therefore, this paper adopted R&D intensity intensity—the ratio of annual R&D expenditure to operating revenue—to measure a company’s absorption capacity. ② Patent citations. Patent citations reflect a company’s ability to absorb and integrate external knowledge during the innovation process. By citing others’ patents, a company demonstrates its understanding, conversion, and application of external technical knowledge [53]. Therefore, this paper measured the absorption capacity by adding 1 to its patent citations and taking the logarithm. Combining these two indicators provides a more comprehensive reflection of the entire process of knowledge integration and conversion.

4.

Moderating variable: environmental dynamism (ED)

Environmental dynamism reflects the degree of uncertainty of the changes in the external environment faced by enterprises [54]. These changes involve technological change, customer demand, and market competition. For example, technological change drives the rapid evolution of products and services toward digitization and intelligence, forcing companies to make frequent adjustments to their organizational processes, business models, and resource allocation, which increases operational instability and leads to fluctuations in sales revenue [55]. Consequently, these external environmental changes ultimately result in fluctuations in business activities, which in turn lead to fluctuations in a company’s sales revenue. Drawing on the study of Ghosh and Olsen [56], this study measured environmental dynamism by calculating the coefficient of variation of a firm’s operating revenue over the past five years. Specifically, for year t, the firm’s operating revenues in years t, t-1, t-2, t-3, and t-4 were regressed on the values 5, 4, 3, 2, and 1, respectively, and the standard error of the regression coefficients of the obtained model was divided by the mean value of the company’s main business income in 5 years, which yielded the result for the environmental dynamism indicator. The larger the indicator, the higher the environmental dynamism faced by the company.

5.

Control variables

In accordance with the existing literature [57,58], this paper selected enterprise size (Size), which is the natural logarithm of the total assets of the enterprise; enterprise age (Age), which is the natural logarithm of the number of years of the enterprise’s establishment; gearing ratio (Lev), which is the total liabilities at the end of the year divided by the total assets at the end of the year; profitability (ROE), which is the enterprise’s return on net assets; number of executives (TMT), which is the total number of the senior management team; board size (Board), i.e., the number of directors on the board takes the natural logarithm; board independence (Indep), i.e., the ratio of independent directors to the total number of directors on the board; two positions in one (Dual), i.e., whether the chairman and the general manager are the same person (1 = yes, 0 = no); shareholding concentration (Top1), i.e., the proportion of the first-largest shareholder’s shareholding; nature of property rights (SOE), a dummy variable (state-owned enterprises = 1, otherwise = 0); and MD&A word count (MDA), i.e., the number of words (in thousands) in the MD&A section of the annual reports of the firms. In addition, this paper controlled for year (Year) and industry (Ind) dummy variables to eliminate the effects of time and industry on digital strategy change. The variable definitions are shown in

Table 2. Variable definitions.

Variable Type	Variable Symbol	Variable Name	Variable Definition
Dependent Variable	DSC	Digital Strategic Change	Natural logarithm of 1 + total frequency of digital strategic change keywords in annual reports
Independent Variable	CF	Cognitive Flexibility	Natural logarithm of 1 + total frequency of external environment perception keywords in annual reports
	CC	Cognitive Complexity	Breadth of attention allocation across five dimensions: external environment perception, rapid response, innovation and change, integration and reconfiguration of resources and capabilities, and organizational learning
Mediating Variable	AC	Absorptive Capacity	R&D expenditure intensity, patent citations
Moderating Variable	ED	Environmental Dynamism	Ratio of the standard deviation to the mean of the firm’s operating income over the past five years
Control Variables	Size	Firm Size	Logarithm of total assets at the end of the period
	Age	Firm Age	Difference between the observation year and the year of establishment
	Lev	Liability	Total assets/total liabilities
	ROE	Return on net assets	Net profit/shareholders’ equity balance
	TMT	Top Management Team Size	Total number of top executives
	Board	Board Size	Natural logarithm of the number of board members
	Indep	Board Independence	Ratio of independent directors to total number of board members
	Dual	CEO Duality	Dummy variable: 1 if the chairman also serves as CEO, 0 otherwise
	Top1	Top1 Shareholding	Ratio of the number of shares held by the largest shareholder of the enterprise to the total number of shares of the enterprise
	SOE	State Ownership	Dummy variable: 1 if the firm is state-owned, 0 otherwise
	MDA	MD&A Word Count	Total word count of the MD&A section in annual reports
	Year	Year	Year fixed-effects dummy variables
	Ind	Industry	Industry fixed-effects dummy variables

.

3.3. Model Setting

In order to test the impact of executive cognitive flexibility and cognitive complexity on enterprise digital strategy change, the following model was constructed:

$$DS{C}_{i,t}={\alpha }_0+{\alpha }_{1}C{F}_{i,t}+Control{s}_{i,t}+Year+Ind+{\epsilon }_{i,t}$$

(1)

$$DS{C}_{i,t}={\beta }_0+{\beta }_{1}C{C}_{i,t}+Control{s}_{i,t}+Year+Ind+{\epsilon }_{i,t}$$

(2)

where DSC denotes digital strategic change, CF is executive cognitive flexibility, CC is executive cognitive complexity, Controls is a control variable, Year is a year dummy variable, Ind is an industry dummy variable, i denotes a sample firm, t denotes a year, α and β are coefficients of variables, and ε is a random error term. If the coefficients α1 and β1 are significantly positive, this means that executive cognition makes a significant positive contribution to digital strategic change.

To test the mediating effect of absorptive capacity, this paper constructed the following model:

$$A{C}_{i,t}={\delta }_0+{\delta }_{1}C{F}_{i,t}+Control{s}_{i,t}+Year+Ind+{\epsilon }_{i,t}$$

(3)

$$A{C}_{i,t}={\gamma }_0+{\gamma }_{1}C{C}_{i,t}+Control{s}_{i,t}+Year+Ind+{\epsilon }_{i,t}$$

(4)

$$DS{C}_{i,t}={\sigma }_0+{\sigma }_{1}C{F}_{i,t}+{\sigma }_{2}A{C}_{i,t}+Control{s}_{i,t}+Year+Ind+{\epsilon }_{i,t}$$

(5)

$$DS{C}_{i,t}={\eta }_0+{\eta }_{1}C{C}_{i,t}+{\eta }_{2}A{C}_{i,t}+Control{s}_{i,t}+Year+Ind+{\epsilon }_{i,t}$$

(6)

Equations (3) and (4), respectively, test the effects of cognitive flexibility and cognitive complexity on absorptive capacity, while Equations (5) and (6) test whether absorptive capacity plays a mediating role in the effects of executive cognitive flexibility and complexity on digital strategy change.

To examine the moderating effect of environmental dynamism, the following model with interaction terms was set up:

$$DS{C}_{i,t}={\theta }_0+{\theta }_{1}C{C}_{i,t}+{\theta }_{2}E{D}_{i,t}+{\theta }_{3}C{C}_{i,t}\times E{D}_{i,t}+Control{s}_{i,t}+Year+Ind+{\epsilon }_{i,t}$$

(7)

$$DS{C}_{i,t}={\mu }_0+{\mu }_{1}C{F}_{i,t}+{\mu }_{2}E{D}_{i,t}+{\mu }_{3}C{F}_{i,t}\times E{D}_{i,t}+Control{s}_{i,t}+Year+Ind+{\epsilon }_{i,t}$$

(8)

Equations (7) and (8), respectively, test the moderating role played by environmental dynamism in the impact of cognitive flexibility and cognitive complexity on digital strategic change.

4. Empirical Analysis

4.1. Descriptive Statistical Analysis

Table 3. Descriptive statistical results.

Variables	Sample Size	Mean Value	Standard Deviation	Minimum	Maximum
DSC	14,165	2.852	1.268	0	5.924
CF	14,165	3.568	0.498	2.079	4.905
CC	14,165	0.384	0.135	0	0.685
AC	14,165	0.0493	0.0504	−0.0233	0.413
ED	14,165	0.0462	0.0402	−0.0285	0.293
Size	14,165	22.73	1.292	20.10	26.75
Age	14,165	2.985	0.289	1.946	3.611
Lev	14,165	0.431	0.186	0.0575	0.886
ROE	14,165	0.0530	0.128	−1.087	0.363
TMT	14,165	6.549	2.376	2	15
Board	14,165	2.129	0.193	1.609	2.708
Indep	14,165	37.62	5.475	30	60
Dual	14,165	0.252	0.434	0	1
Top1	14,165	32.14	14.51	6.528	74.30
SOE	14,165	0.388	0.487	0	1
MDA	14,165	20.78	11.58	1.191	273.9

shows the results of the descriptive statistical analysis of the research variables. It can be seen that the mean value of digital strategic change is 2.852, the standard deviation is 1.268, the minimum value is 0, and the maximum value is 5.924, which indicates that there are large differences in the digital strategic change of different enterprises. The mean value of executive cognitive flexibility is 3.568, with a standard deviation of 0.498, and the minimum and maximum values differ from the mean by one unit up or down, indicating that the distribution of cognitive flexibility among senior managers in the sample companies is relatively concentrated and that there is not much difference in the level of cognitive flexibility among the executive teams of most companies. The mean value of cognitive complexity is 0.384, the standard deviation is 0.135, the minimum value is 0, and the maximum value is only 0.685, indicating that the fluctuation in the level of cognitive complexity is even smaller, and the cognitive complexity of the executive team of most enterprises is close to the mean value. The descriptive statistics of the rest of the variables are basically consistent with the existing studies, and there are no outliers, which to a certain extent indicates that the research sample of this paper does not have the problem of possible bias. In addition, this paper also tested the Person correlation coefficients between the variables, none of which exceeded the critical value of 0.7, indicating that the regression analysis does not have the problem of multicollinearity. This paper further calculated the variance inflation factors (VIFs) of the variables and found that the maximum value for VIF was 1.87, the minimum value was 1.02, and the mean value was 1.34, which were all much smaller than the critical value of 10, indicating that there was no serious multicollinearity problem in the regression.

4.2. Analysis of Empirical Results

4.2.1. Benchmark Regression

Table 4. Main-effect regression results on cognitive style of top executives and digital strategic change.

	(1)	(2)	(3)
	DSC	DSC	DSC
CF		0.329 ***
		(0.019)
CC			0.982 ***
			(0.057)
Size	0.119 ***	0.119 ***	0.120 ***
	(0.009)	(0.008)	(0.008)
Age	−0.047	−0.048	−0.052 *
	(0.031)	(0.031)	(0.031)
Lev	0.015	0.037	0.043
	(0.053)	(0.052)	(0.052)
ROE	0.239 ***	0.259 ***	0.244 ***
	(0.063)	(0.063)	(0.063)
TMT	0.016 ***	0.018 ***	0.015 ***
	(0.003)	(0.003)	(0.003)
Board	0.065	0.084	0.050
	(0.051)	(0.051)	(0.051)
Indep	0.004 **	0.005 ***	0.004 **
	(0.002)	(0.002)	(0.002)
Dual	0.078 ***	0.076 ***	0.075 ***
	(0.018)	(0.018)	(0.018)
Top1	−0.001 **	−0.001 **	−0.001 **
	(0.001)	(0.001)	(0.001)
SOE	−0.081 ***	−0.076 ***	−0.123 ***
	(0.019)	(0.019)	(0.019)
MDA	0.020 ***	0.015 ***	0.019 ***
	(0.001)	(0.001)	(0.001)
Year	YES	YES	YES
Ind	YES	YES	YES
_cons	−0.492 **	−1.650 ***	−0.789 ***
	(0.230)	(0.237)	(0.228)
N	14,165	14,165	14,165
R2	0.522	0.531	0.532
F	135.162	150.464	151.039

Note: ***, **, and * mean significant at 1%, 5%, and 10% levels, respectively; standard errors in parentheses.

presents the regression results for the main effects of executive cognitive style on digital strategic change. Column (1) reports the benchmark model, in which the dependent variable is regressed solely on the control variables. In this model, firm size and profitability exhibit significantly positive coefficients at the 1% level, while firm age and ownership concentration show significantly negative coefficients, consistent with findings in the prior literature.

Column (2) reports the effect of executive cognitive flexibility on digital strategic change. The coefficient of cognitive flexibility (CF) is 0.329 (p < 0.01), indicating that higher levels of cognitive flexibility among executives are associated with a greater propensity for firms to implement digital strategic change, which supports Hypothesis 1a. Column (3) shows the results of the test of the impact of the cognitive complexity of executives on the digital strategic change of enterprises. It can be seen that the regression coefficient of CC is 0.982 (p < 0.01), indicating that the higher the degree of cognitive complexity, the more inclined enterprises are to carry out digital strategic change, which supports Hypothesis 1b.

4.2.2. Mediating-Effect Test

To verify Hypotheses 3a and 3b, this paper used absorptive capacity as the mechanism variable and further explored the influence mechanism of executive cognitive style on digital strategic change based on the steps of the mediation-effect test.

Table 5. Test results for the mediating effect of absorptive capacity (R&D intensity).

	(1)	(2)	(3)	(4)
	AC1	AC1	DSC	DSC
CF	0.004 ***		0.325 ***
	(0.001)		(0.019)
CC		0.007 ***		0.974 ***
		(0.002)		(0.057)
AC1			1.189 ***	1.234 ***
			(0.194)	(0.194)
Size	−0.002 ***	−0.002 ***	0.121 ***	0.122 ***
	(0.000)	(0.000)	(0.008)	(0.008)
Age	−0.007 ***	−0.007 ***	−0.040	−0.043
	(0.001)	(0.001)	(0.031)	(0.031)
Lev	−0.045 ***	−0.045 ***	0.091 *	0.099 *
	(0.002)	(0.002)	(0.053)	(0.053)
ROE	−0.031 ***	−0.031 ***	0.296 ***	0.283 ***
	(0.003)	(0.003)	(0.063)	(0.063)
TMT	0.002 ***	0.002 ***	0.016 ***	0.014 ***
	(0.000)	(0.000)	(0.003)	(0.003)
Board	−0.000	−0.001	0.084 *	0.051
	(0.002)	(0.002)	(0.051)	(0.051)
Indep	0.000 ***	0.000 ***	0.005 ***	0.004 **
	(0.000)	(0.000)	(0.002)	(0.002)
Dual	0.002 ***	0.002 ***	0.073 ***	0.072 ***
	(0.001)	(0.001)	(0.018)	(0.018)
Top1	−0.000 ***	−0.000 ***	−0.001 *	−0.001 **
	(0.000)	(0.000)	(0.001)	(0.001)
SOE	−0.003 ***	−0.003 ***	−0.072 ***	−0.119 ***
	(0.001)	(0.001)	(0.019)	(0.019)
MDA	0.001 ***	0.001 ***	0.014 ***	0.018 ***
	(0.000)	(0.000)	(0.001)	(0.001)
Year	YES	YES	YES	YES
Ind	YES	YES	YES	YES
_cons	0.094 ***	0.105 ***	−1.762 ***	−0.919 ***
	(0.010)	(0.010)	(0.238)	(0.228)
N	14,165	14,165	14,165	14,165
R2	0.441	0.440	0.533	0.533
F	121.507	142.149	120.392	142.937

Note: ***, **, and * mean significant at 1%, 5%, and 10% levels, respectively; standard errors in parentheses.

shows the results of exploring the mediating effect of absorption capacity measured by R&D intensity. Column (1) show that the cognitive flexibility of executives can promote the absorptive capacity of enterprises (δ₁ = 004, p < 0.01), which supports Hypothesis 2a. Similarly, Column (2) indicates that the cognitive complexity of executives can also promote the absorptive capacity of enterprises (λ₁ = 0.007, p < 0.01) and support Hypothesis 2b; columns (3) and (4) further show that the capacity of enterprises to absorb external knowledge and resources is the key driving factor of digital transformation, and both cognitive flexibility and cognitive complexity promote digital strategic change by enhancing the absorptive capacity of enterprises. Specifically, the coefficient of cognitive flexibility decreased from 0.329 (p < 0.01) in the benchmark model of Table 4 to 0.328 (p < 0.01). This indicates that the absorptive capacity plays a partially mediating role in this equation, which supports Hypothesis 3a. The cognitive complexity coefficient decreased from 0.982 (p < 0.01) in the benchmark model to 0.974 (p < 0.01), indicating that the absorption capacity also played a partially mediating role. Hypothesis 3b was verified.

Table 6. Test results for the mediating effect of absorptive capacity (patent citations).

	(1)	(2)	(3)	(4)
	AC2	AC2	DSC	DSC
CF	0.058 **		0.324 ***
	(0.029)		(0.019)
CC		0.498 ***		0.939 ***
		(0.086)		(0.057)
AC2			0.089 ***	0.087 ***
			(0.006)	(0.006)
Size	0.795 ***	0.795 ***	0.048 ***	0.051 ***
	(0.013)	(0.013)	(0.009)	(0.009)
Age	−0.183 ***	−0.186 ***	−0.031	−0.036
	(0.046)	(0.046)	(0.030)	(0.030)
Lev	−0.501 ***	−0.491 ***	0.082	0.085 *
	(0.078)	(0.078)	(0.052)	(0.052)
ROE	0.360 ***	0.359 ***	0.227 ***	0.213 ***
	(0.094)	(0.094)	(0.062)	(0.062)
TMT	0.022 ***	0.021 ***	0.016 ***	0.014 ***
	(0.005)	(0.005)	(0.003)	(0.003)
Board	0.205 ***	0.194 **	0.065	0.033
	(0.077)	(0.076)	(0.051)	(0.051)
Indep	0.007 ***	0.006 **	0.005 ***	0.004 **
	(0.003)	(0.003)	(0.002)	(0.002)
Dual	0.124 ***	0.123 ***	0.065 ***	0.065 ***
	(0.027)	(0.027)	(0.018)	(0.018)
Top1	−0.001	−0.001	−0.001 **	−0.001 **
	(0.001)	(0.001)	(0.001)	(0.001)
SOE	0.255 ***	0.232 ***	−0.099 ***	−0.143 ***
	(0.029)	(0.029)	(0.019)	(0.019)
MDA	0.001	0.002	0.014 ***	0.018 ***
	(0.001)	(0.001)	(0.001)	(0.001)
Year	YES	YES	YES	YES
Ind	YES	YES	YES	YES
_cons	−13.837 ***	−13.782 ***	−0.412 *	0.404 *
	(0.357)	(0.342)	(0.247)	(0.239)
N	14,165	14,165	14,165	14,165
R2	0.452	0.453	0.540	0.540
F	556.003	559.637	161.354	160.396

Note: ***, **, and * mean significant at 1%, 5%, and 10% levels, respectively; standard errors in parentheses.

shows the results of exploring the mediating effect of absorption capacity as measured by patent citation volume. Similarly, the results in Table 6 also validate the mediating role of absorption capacity in the impact of cognitive flexibility and cognitive complexity on digital strategy transformation. H2a, H2b, H3a, and H3b are all supported.

4.2.3. Moderating-Effect Test

Table 7. Testing the moderating effects of environmental dynamism.

	(1)	(2)
	DSC	DSC
CF	0.328 ***
	(0.019)
CC		0.993 ***
		(0.057)
CF_ED_c	−0.697 *
	(0.386)
CC_ED_c		3.217 **
		(1.335)
ED	−0.428 **	−0.684 ***
	(0.191)	(0.193)
Size	0.118 ***	0.119 ***
	(0.008)	(0.008)
Age	−0.048	−0.053 *
	(0.031)	(0.031)
Lev	0.038	0.049
	(0.052)	(0.052)
ROE	0.251 ***	0.232 ***
	(0.063)	(0.063)
TMT	0.018 ***	0.015 ***
	(0.003)	(0.003)
Board	0.080	0.049
	(0.051)	(0.051)
Indep	0.005 ***	0.004 **
	(0.002)	(0.002)
Dual	0.077 ***	0.076 ***
	(0.018)	(0.018)
Top1	−0.001 **	−0.001 **
	(0.001)	(0.001)
SOE	−0.078 ***	−0.128 ***
	(0.019)	(0.019)
MDA	0.015 ***	0.019 ***
	(0.001)	(0.001)
Year	Yes	Yes
Ind	Yes	Yes
_cons	−1.610 ***	−0.748 ***
	(0.238)	(0.229)
N	14,165	14,165
R2	0.532	0.532
F	129.564	130.730

Note: ***, **, and * mean significant at 1%, 5%, and 10% levels, respectively; standard errors in parentheses.

presents the test results for the moderating effect of environmental dynamism on the cognitive style of executives and the digital strategy change of enterprises. To avoid multicollinearity, cognitive flexibility, cognitive complexity, and environmental dynamism were regenerated into interactive terms after being centralized. As shown in Table 7, the coefficient of interaction terms between cognitive flexibility and environmental dynamism is −0.697 (p < 0.1), indicating that environmental dynamism negatively moderates the relationship between cognitive flexibility and enterprise digital strategic change, that is, environmental dynamism will weaken the promoting effect of cognitive flexibility on enterprise strategic change, that is, compared with a high-dynamic environment, cognitive flexibility is more conducive to digital strategic change in a low-dynamic environment, so H4a is not supported. Similarly, the interaction term coefficient of cognitive complexity and environmental dynamism is 3.217 (p < 0.05), indicating that environmental dynamism positively regulates the relationship between cognitive complexity and corporate digital strategic change, that is, when environmental dynamism is high, the positive impact of cognitive complexity on corporate digital strategic change is strengthened, so Hypothesis 4b is supported. By observing the interaction term coefficients of the two, we hypothesized that the reason why H4a is not supported is that in a highly dynamic environment, cognitive complexity may play a greater role than cognitive flexibility. Cognitive complexity helps managers comprehensively assess the complex changes in the environment from multiple dimensions and formulate long-term strategies, while cognitive flexibility focuses more on short-term flexible adjustments and rapid resource restructuring, offering a distinct advantage during the initial stages of transformation. Since digital strategic change typically involves deep technical applications and organizational restructuring, such long-term, systematic strategic adjustments rely more on cognitive complexity than on cognitive flexibility alone. Therefore, highly dynamic environments may amplify the role of cognitive complexity and weaken the role of cognitive flexibility in digital strategic change.

Additionally, different types of digital strategies may further influence the significance of moderating effects. Under high environmental dynamism, exploratory strategies emphasize the introduction of cutting-edge technologies and the development of new business models, requiring managers to integrate cross-domain knowledge and allocate resources systematically, which aligns more closely with the characteristics of cognitive complexity. In contrast, exploitative strategies focus on leveraging existing resources and improving efficiency, better leveraging the advantages of cognitive flexibility. Therefore, when firms lean toward exploratory strategies, high environmental dynamism may reinforce the dominant role of cognitive complexity, thereby weakening the marginal contribution of cognitive flexibility to digital strategy change. This finding suggests that cognitive flexibility and cognitive complexity may exhibit differentiated effects under different environmental conditions and strategic types, warranting further refinement in future research.

4.3. Robustness Test

4.3.1. Adjusting for Fixed Effects

The previous regression model controls for the possible impact of year fixed effects and industry fixed effects on the results, but over time the system structure of the industry itself and its external macro-environment will change dynamically, leading to differences in the development of different industries in the time series, thus affecting the strategic changes of enterprises. In order to reduce the impact of this factor on the conclusions, this paper added Industry–Year (Ind × Year) interaction fixed effects to the model and re-ran the regression analysis, and the regression results are shown in

Table 8. Robustness test of adjusted fixed effects.

	(1)	(2)
	DSC	DSC
CF	0.341 ***
	(0.020)
CC		0.954 ***
		(0.058)
Size	0.118 ***	0.118 ***
	(0.009)	(0.009)
Age	−0.048	−0.051 *
	(0.031)	(0.031)
Lev	0.042	0.041
	(0.053)	(0.053)
ROE	0.244 ***	0.231 ***
	(0.065)	(0.065)
TMT	0.018 ***	0.016 ***
	(0.003)	(0.003)
Board	0.089 *	0.056
	(0.052)	(0.052)
Indep	0.006 ***	0.005 ***
	(0.002)	(0.002)
Dual	0.068 ***	0.069 ***
	(0.018)	(0.018)
Top1	−0.001 **	−0.002 ***
	(0.001)	(0.001)
SOE	−0.077 ***	−0.123 ***
	(0.019)	(0.020)
MDA	0.015 ***	0.019 ***
	(0.001)	(0.001)
Year	YES	YES
Ind	YES	YES
Ind×Year	YES	YES
_cons	−1.694 ***	−0.783 ***
	(0.240)	(0.231)
N	14,165	14,165
R2	0.547	0.546
F	147.931	145.103

Note: ***, **, and * mean significant at 1%, 5%, and 10% levels, respectively; standard errors in parentheses.

, which shows that the significant levels and the direction of influence between the variables are consistent with the previous results, such that the research hypothesis is still supported.

4.3.2. Replacing Measures of Core Variables

Referring to the research of Zhen et al. [59], this paper used the Digital Transformation Index for Chinese listed companies, developed by CSMAR, as an alternative measure of the dependent variable. The index constructs an index system to quantify the digital transformation of enterprises in six dimensions: enterprise strategic leadership, technology drive, organization empowerment, environmental support, digital achievements, and digital application. To reduce the distributional bias of the original index, we applied logarithmic processing. The regression results are reported in

Table 9. Robustness test of substituting core variables.

	(1)	(2)
	DSC1	DSC1
CF	0.035 ***
	(0.004)
CC		0.147 ***
		(0.012)
Size	0.035 ***	0.035 ***
	(0.002)	(0.002)
Age	−0.020 ***	−0.021 ***
	(0.006)	(0.006)
Lev	−0.020 *	−0.018 *
	(0.011)	(0.011)
ROE	−0.014	−0.016
	(0.013)	(0.013)
TMT	0.003 ***	0.003 ***
	(0.001)	(0.001)
Board	0.011	0.007
	(0.011)	(0.010)
Indep	0.001 ***	0.001 ***
	(0.000)	(0.000)
Dual	0.026 ***	0.026 ***
	(0.004)	(0.004)
Top1	−0.000 ***	−0.001 ***
	(0.000)	(0.000)
SOE	−0.022 ***	−0.029 ***
	(0.004)	(0.004)
MDA	0.002 ***	0.002 ***
	(0.000)	(0.000)
Year	YES	YES
Ind	YES	YES
_cons	2.712 ***	2.792 ***
	(0.049)	(0.047)
N	14,165	14,165
R2	0.567	0.569
F	100.543	107.556

Note: ***, and * mean significant at 1%, and 10% levels, respectively; standard errors in parentheses.

. The coefficients of cognitive flexibility and cognitive complexity are, respectively, 0.035 (p < 0.01) and 0.147 (p < 0.01). These results indicate that after replacing the proxy variable for the dependent variable, the positive and significant impact of executive cognitive style on digital strategic change remains robust.

4.3.3. Lagged and Lead Variables

To alleviate potential reverse causality between independent and dependent variables, and considering that executive cognition may exhibit a lagged effect on digital strategic change, we conducted robustness tests by lagging the independent variables by one period and leading the dependent variable by one period. The regression results are shown in

Table 10. Robustness test of variable lag and pre-processing.

	(1)	(2)	(3)	(4)
	DSC	DSC	F.DSC	F.DSC
L.CF	0.292 ***
	(0.020)
L.CC		0.813 ***
		(0.061)
CF			0.298 ***
			(0.021)
CC				0.830 ***
				(0.062)
Size	0.119 ***	0.120 ***	0.118 ***	0.120 ***
	(0.009)	(0.009)	(0.009)	(0.009)
Age	−0.006	−0.012	−0.011	−0.014
	(0.034)	(0.034)	(0.033)	(0.033)
Lev	0.067	0.064	0.002	−0.003
	(0.056)	(0.057)	(0.057)	(0.057)
ROE	0.175 ***	0.186 ***	0.232 ***	0.222 ***
	(0.066)	(0.066)	(0.070)	(0.070)
TMT	0.020 ***	0.017 ***	0.021 ***	0.018 ***
	(0.004)	(0.004)	(0.004)	(0.004)
Board	0.133 **	0.099 *	0.117 **	0.090
	(0.055)	(0.055)	(0.056)	(0.056)
Indep	0.005 ***	0.004 **	0.005 ***	0.004 **
	(0.002)	(0.002)	(0.002)	(0.002)
Dual	0.075 ***	0.075 ***	0.086 ***	0.085 ***
	(0.019)	(0.019)	(0.020)	(0.020)
Top1	−0.001 *	−0.001 **	−0.001	−0.001
	(0.001)	(0.001)	(0.001)	(0.001)
SOE	−0.079 ***	−0.112 ***	−0.073 ***	−0.108 ***
	(0.021)	(0.021)	(0.021)	(0.021)
MDA	0.015 ***	0.018 ***	0.013 ***	0.017 ***
	(0.001)	(0.001)	(0.001)	(0.001)
Year	YES	YES	YES	YES
Ind	YES	YES	YES	YES
_cons	−1.693 ***	−0.911 ***	−1.561 ***	−0.826 ***
	(0.256)	(0.247)	(0.259)	(0.250)
N	12,056	12,056	12,056	12,056
R2	0.523	0.522	0.508	0.507
F	126.877	124.278	108.533	106.711

Note: ***, **, and * mean significant at 1%, 5%, and 10% levels, respectively; standard errors in parentheses.

. The findings demonstrate that whether executive cognition is lagged or the dependent variable is led, cognitive flexibility and complexity consistently exert a significant positive influence on digital strategic change at the 1% level, which is consistent with the main conclusions of the study.

4.4. Heterogeneity Test

4.4.1. Heterogeneity Test Based on Digital Economy Development Level

The improvement in the level of the regional digital economy can provide enterprises with better digital infrastructure and communication platforms, thereby reducing transformation costs and enabling more effective resource sharing and integration [60]. Considering the evident differences in digital economy development across Chinese provinces, this paper adopted the comprehensive digital economy index constructed by Liu and Chen [61], dividing the sample into high-level and low-level digital economy groups based on the mean values. Regression results are presented in

Table 11. Heterogeneity test based on the level of development of the digital economy.

	Digital Economy Development Level
	(1) High-Level	(2) Low-Level	(3) High-Level	(4) Low-Level
	DSC	DSC	DSC	DSC
CF	0.467 ***	0.042
	(0.023)	(0.041)
CC			1.227 ***	0.187 *
			(0.069)	(0.110)
Size	0.116 ***	0.109 ***	0.123 ***	0.109 ***
	(0.010)	(0.015)	(0.010)	(0.015)
Age	0.004	−0.103 *	−0.024	−0.103 *
	(0.036)	(0.057)	(0.037)	(0.057)
Lev	0.009	0.141	−0.029	0.148
	(0.063)	(0.090)	(0.064)	(0.090)
ROE	0.183 **	0.385 ***	0.171 **	0.384 ***
	(0.076)	(0.108)	(0.076)	(0.108)
TMT	0.020 ***	0.015 ***	0.016 ***	0.015 **
	(0.004)	(0.006)	(0.004)	(0.006)
Board	0.026	0.133	0.018	0.120
	(0.064)	(0.081)	(0.065)	(0.082)
Indep	0.002	0.008 ***	0.002	0.008 ***
	(0.002)	(0.003)	(0.002)	(0.003)
Dual	0.066 ***	0.051	0.080 ***	0.047
	(0.022)	(0.032)	(0.022)	(0.032)
Top1	−0.002 ***	−0.001	−0.002 **	−0.001
	(0.001)	(0.001)	(0.001)	(0.001)
SOE	−0.029	−0.112 ***	−0.122 ***	−0.118 ***
	(0.024)	(0.031)	(0.025)	(0.031)
MDA	0.014 ***	0.012 ***	0.020 ***	0.012 ***
	(0.001)	(0.001)	(0.001)	(0.001)
Year	YES	YES	YES	YES
Ind	YES	YES	YES	YES
_cons	−1.826 ***	−0.702 *	−0.773 ***	−0.580
	(0.289)	(0.423)	(0.282)	(0.384)
N	9512	4653	9512	4653
R2	0.569	0.433	0.565	0.433
F	129.019	28.247	120.547	28.415

Note: ***, **, and * mean significant at 1%, 5%, and 10% levels, respectively; standard errors in parentheses.

.

The regression results show that in regions with higher digital economy development, executive cognitive flexibility significantly promotes digital strategic change, while its effect is insignificant in low-level regions. This may be attributed to the favorable environment in high-level regions, where digital infrastructure and technology acceptance allow for strategic agility. In contrast, at low levels, due to resource, institutional, or infrastructure constraints, even executives with cognitive flexibility may struggle to convert their capabilities into strategic change.

Cognitive complexity is only significant in high-level regions, suggesting that executives with complex cognition are more capable of integrating information and identifying strategic opportunities, thus driving digital transformation.

In terms of cognitive complexity, the regression analysis results indicate that in regions with a higher level of digital economy, cognitive complexity has a significant impact on digital strategy transformation at the 1% level, while in regions with a lower level of digital economy, cognitive complexity has a significant impact on digital strategy transformation at the 10% level. This can be interpreted as follows: In high-level regions, well-developed digital infrastructure, high technology acceptance, and supportive institutional conditions provide executives with ample complex cognition resources and support to effectively integrate information and identify strategic opportunities, thereby strongly promoting digital strategic change. In low-level regions, although overall resources and institutional support are limited, executives with high cognitive complexity can still leverage limited information and multidimensional perspectives to make innovative decisions, moderately facilitating digital strategic change; however, due to environmental constraints, the effect is weaker and only significant at the 10% level.

4.4.2. Heterogeneity Test Based on Firm Life Cycle

According to life cycle theory, firms, as economic entities, undergo different stages such as growth, maturity, and decline, during which their strategic goals, resource allocation, and decision-making models differ. These differences may affect how executive cognition influences digital strategic change. Drawing on prior research [62], this study classified firms into growth, maturity, and decline stages based on sales growth rate, retained earnings ratio, capital expenditure ratio, and firm age. The regression results are presented in

Table 12. Heterogeneity test based on firm life cycle.

	(1) Growth	(2) Maturity	(3) Decline	(4) Growth	(5) Maturity	(6) Decline
	DSC	DSC	DSC	DSC	DSC	DSC
CF	0.596 ***	0.194 ***	0.026
	(0.030)	(0.030)	(0.044)
CC				0.812 ***	1.237 ***	0.067
				(0.089)	(0.085)	(0.141)
Size	0.083 ***	0.124 ***	0.124 ***	0.084 ***	0.125 ***	0.124 ***
	(0.012)	(0.013)	(0.021)	(0.013)	(0.013)	(0.021)
Age	−0.021	−0.053	−0.094	−0.020	−0.062	−0.094
	(0.044)	(0.049)	(0.074)	(0.045)	(0.048)	(0.074)
Lev	−0.144 *	−0.020	0.036	−0.198 **	0.001	0.038
	(0.082)	(0.080)	(0.116)	(0.084)	(0.078)	(0.116)
ROE	0.119	0.218 **	0.078	0.110	0.214 **	0.077
	(0.101)	(0.100)	(0.122)	(0.104)	(0.098)	(0.123)
TMT	0.017 ***	0.017 ***	0.014 *	0.013 **	0.016 ***	0.014 *
	(0.005)	(0.005)	(0.008)	(0.005)	(0.005)	(0.008)
Board	0.037	0.042	0.277 **	0.028	0.012	0.271 **
	(0.077)	(0.076)	(0.124)	(0.079)	(0.075)	(0.124)
Indep	0.004	0.007 ***	0.005	0.003	0.006 **	0.005
	(0.003)	(0.003)	(0.004)	(0.003)	(0.003)	(0.004)
Dual	0.054 **	0.061 **	0.079 *	0.060 **	0.061 **	0.078 *
	(0.026)	(0.028)	(0.043)	(0.027)	(0.028)	(0.043)
Top1	−0.001	−0.002 **	0.002	−0.001	−0.002 **	0.002
	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)
SOE	−0.098 ***	0.004	−0.139 ***	−0.144 ***	−0.052 *	−0.142 ***
	(0.029)	(0.029)	(0.044)	(0.030)	(0.029)	(0.045)
MDA	0.013 ***	0.013 ***	0.013 ***	0.020 ***	0.014 ***	0.014 ***
	(0.001)	(0.001)	(0.002)	(0.001)	(0.001)	(0.002)
_cons	−1.377 ***	−1.234 ***	−1.446 **	0.420	−0.950 ***	−1.367 **
	(0.351)	(0.366)	(0.592)	(0.345)	(0.346)	(0.567)
N	6063	5756	2312	6063	5756	2312
R2	0.602	0.511	0.423	0.581	0.525	0.423
F	88.881	40.760	14.888	59.080	55.981	14.877

Note: ***, **, and * mean significant at 1%, 5%, and 10% levels, respectively; standard errors in parentheses.

.

The results show that executive cognition exhibits stage-specific impacts. In the growth stage, both cognitive flexibility and complexity significantly promote digital strategic change, with complexity demonstrating a stronger role in strategic integration. In the maturity stage, both remain influential, with cognitive complexity being particularly critical for strategic planning. However, in the decline stage, constrained by limited resources and capabilities, the effects of executive cognition are insignificant. This suggests that digital strategies should be tailored to match cognitive characteristics and strategic priorities at different life cycle stages to foster adaptive and forward-looking transformation.

4.4.3. Heterogeneity Test Based on Factor Intensity

Different industries vary in their reliance on and allocation strategies for various production factors such as human capital and technical support. Therefore, the impact of executive cognitive styles on corporate digital strategy change may differ depending on the factor intensity. This study draws on existing research to categorize corporate samples into technology–capital-intensive and labor-intensive industries for regression analysis [63]. The regression results in

Table 13. Heterogeneity test based on factor intensity.

	(1) Technology– Capital-Intensive	(2) Labor-Intensive	(3) Technology– Capital-Intensive	(4) Labor-Intensive
	DSC	DSC	DSC	DSC
CF	0.428 ***	0.028
	(0.023)	(0.036)
CC			1.212 ***	0.130
			(0.068)	(0.104)
Size	0.120 ***	0.101 ***	0.119 ***	0.102 ***
	(0.010)	(0.015)	(0.010)	(0.015)
Age	0.007	−0.235 ***	0.009	−0.237 ***
	(0.036)	(0.057)	(0.036)	(0.057)
Lev	0.128 **	−0.218 **	0.144 **	−0.220 **
	(0.060)	(0.097)	(0.060)	(0.096)
ROE	0.188 **	0.435 ***	0.205 ***	0.427 ***
	(0.074)	(0.109)	(0.074)	(0.109)
TMT	0.021 ***	0.006	0.018 ***	0.006
	(0.004)	(0.006)	(0.004)	(0.006)
Board	0.066	0.144 *	0.037	0.140
	(0.061)	(0.087)	(0.061)	(0.087)
Indep	0.003	0.010 ***	0.001	0.010 ***
	(0.002)	(0.003)	(0.002)	(0.003)
Dual	0.065 ***	0.084 **	0.063 ***	0.083 **
	(0.021)	(0.034)	(0.021)	(0.034)
Top1	−0.001	−0.003 ***	−0.001 *	−0.003 ***
	(0.001)	(0.001)	(0.001)	(0.001)
SOE	−0.102 ***	0.004	−0.163 ***	−0.001
	(0.023)	(0.034)	(0.023)	(0.034)
MDA	0.016***	0.009 ***	0.022 ***	0.009 ***
	(0.001)	(0.001)	(0.001)	(0.001)
_cons	−2.072 ***	0.104	−0.901 ***	0.147
	(0.278)	(0.426)	(0.267)	(0.410)
N	10,247	3918	10,247	3918
R2	0.573	0.449	0.572	0.449
F	141.472	20.706	139.026	20.791

Note: ***, **, and * mean significant at 1%, 5%, and 10% levels, respectively; standard errors in parentheses.

show that cognitive flexibility has a significant positive effect on digital strategic change in technology–capital-intensive companies, but not in labor-intensive companies; cognitive complexity also has a significant positive effect in technology–capital-intensive companies, but not in labor-intensive companies. This suggests that technology–capital-intensive enterprises, leveraging their higher technical resources and information processing capabilities, are better able to translate executives’ cognitive abilities into strategic innovation and adjustment, while labor-intensive enterprises, constrained by technological and resource conditions, exhibit a relatively limited influence of executives’ cognitive abilities on digital strategic transformation.

5. Research Conclusions and Recommendations

5.1. Research Conclusions

In an era of accelerating technological innovation and global interconnectedness, digital strategic change has emerged as a critical pathway for enhancing enterprise resilience and long-term competitiveness. Enterprise digital strategy change is embedded in a complex and changing digital ecosystem, where firms must not only reconfigure internal cognitive resources and dynamic capabilities but also adapt to rapidly changing external environments. Against this backdrop, and taking Chinese A-share listed firms from 2015 to 2023 as the research sample, this study develops a theoretical framework based on the “cognition–capability–strategy” paradigm, focusing on how executive cognition influences absorptive capacity and strategic behavior under conditions of environmental dynamism. The key conclusions are as follows:

(a) Executive cognitive style significantly influences digital strategic change. Cognitive flexibility enables firms to flexibly adjust strategies and overcome organizational inertia, thereby reducing resistance to change and facilitating strategic adjustment. Cognitive complexity enhances the breadth and depth of digital strategies by integrating multidimensional information, breaking path dependencies, and formulating diversified response strategies. Executive cognition promotes the deepening and implementation of firm digital strategy change by enhancing the ability of firms to conduct continuous strategic exploration, value reconstruction, and model innovation in digital transformation.

(b) Absorptive capacity operates as a mediating mechanism linking the cognitive and strategic subsystems. It enables the transformation of external knowledge into internal value by coordinating resource assimilation, integration, and application, thereby sustaining strategic renewal in the system and thereby further promoting digital strategic transformation.

(c) Environmental dynamism, acting as an exogenous perturbation, exerts a differential moderating effect on the relationship between executive cognitive style and digital strategic change. Specifically, environmental dynamism weakens the positive effect of cognitive flexibility while amplifying the role of cognitive complexity. In highly dynamic environments, although cognitive flexibility helps executives respond adaptively to changes, its effectiveness may be constrained by limited resources and increased uncertainty. Moreover, compared with cognitive complexity, cognitive flexibility is more suited to tactical adjustments rather than deep strategic transformation, which may explain its limited impact in dynamic contexts.

(d) The heterogeneity tests reveal that the impact of executive cognitive styles on firm digital strategic change varies significantly across regional digital economy development levels, the firm life cycle, and industry factor intensities. In regions with more advanced digital economies, executive cognitive styles more effectively facilitate digital strategic transformation. Similarly, during the growth and maturity stages of the firm life cycle, cognitive flexibility and complexity enable firms to overcome path dependence, integrate internal and external resources, and build long-term, robust digital strategic system. Furthermore, industry-level heterogeneity results show that cognitive flexibility and complexity significantly promote digital strategic change in technology–capital-intensive firms, whereas in labor-intensive firms, these effects are weak or not significant, indicating that firms with richer technological and resource endowments are better able to leverage executive cognition for strategic innovation and adjustment.

5.2. Suggestions

This study provides several implications for both enterprise management practices and governmental efforts to accelerate digital development:

First, firms should optimize executive team structures and prioritize the selection and development of top executives with strong cognitive abilities to enhance strategic decision-making. Since executive cognitive styles significantly influence digital strategic change, companies should incorporate cognitive capacity into executive recruitment—using scientific assessment tools to identify individuals with flexible thinking and systems-level understanding. In addition, firms can organize training, industry exchanges, and other activities to expose executives to diverse perspectives, prompting them to be able to perceive changes in the external environment more keenly, which will not only help firms to improve their strategies but also enhance their ability to cope with the challenges of digitalization.

Second, firms should focus on building dynamic capabilities and systematically improve their absorption capacity. Companies should implement improvements in absorption capacity through specific processes, achieving enhanced efficiency in knowledge acquisition and reuse by strengthening three key pathways: information search, resource allocation, and routine breakthrough. First, improve information search mechanisms. Companies can establish an “information technology-sharing platform” to collect real-time market and technological frontier information, guiding management to proactively obtain external data and cutting-edge information to enhance knowledge acquisition capabilities. Second, optimize resource allocation methods. Companies can build an AI-based data management and decision support platforms to integrate internal resources and business data into a dynamic resource pool, enabling the visualization and intelligent allocation of resources. Through technical laboratories, companies can continuously optimize allocation strategies to improve the efficiency and decision-making capabilities of digital transformation. Third, promote breakthroughs and innovations in organizational practices. Companies should leverage the effectiveness of organizational practices, actively screen and update practices and norms that align with the current organizational context, and promptly adjust their structures to improve and reengineer business operations and standard processes. By advancing these three aspects in tandem, companies can achieve continuous optimization of their absorptive capacity, enhancing their adaptability and competitiveness in digital transformation.

Third, firms should dynamically align digital strategy according to the level of perturbation of the external system. Since environmental dynamism has a complex moderating effect on the relationship between executive cognition and digital strategy, firms should optimize their strategic direction according to the dynamic characteristics of the environment they are in, implement diversified executive team configurations, and bring cognitive flexibility and complexity into play in a complementary manner so as to improve the adaptability and foresight of strategy formulation. In highly dynamic environments, firms should pay more attention to the role of cognitive complexity, accurately assess environmental changes through a multidimensional strategic perspective, and formulate more adaptive digital strategy paths, while using cognitive flexibility for short-term tactical adjustments, forming an organic combination of strategy and tactics. In stable environments, firms should avoid excessive flexibility that may lead to resource waste or strategic inconsistency and instead focus on continuity and gradual strategic change. Companies should also diversify their executive teams to complement each other’s cognitive strengths and improve the adaptability and foresight of their strategic planning.

Fourth, when formulating a digital strategy, firms should comprehensively consider the systemic structure in which they operate, including their stage of development, the level of digital economic development, and industry factor intensity in order to design a personalized digital strategy that aligns with their cognitive structure. For example, firms located in regions with a higher level of digital economy and those in technology–capital-intensive industries should take the lead in deepening strategic transformation, leveraging digital capabilities and technological advantages to pursue exploratory innovation and pioneering projects. Firms in the growth and maturity stages should capitalize on the advantages of their life cycle phases. Growth-stage firms, characterized by organizational flexibility and strong innovation momentum, should establish cross-departmental collaboration mechanisms and innovation pilot platforms to accelerate the adoption of new technologies and business models. Mature firms, benefiting from accumulated resources and well-developed management systems, should rely on digital resource management platforms and strategic planning capabilities to drive business process reengineering and resource optimization. In contrast, firms located in regions with lower levels of digital economy, those in the decline stage, or labor-intensive enterprises need to rely more heavily on external resources and policy support. Such firms may introduce technical support teams or establish cross-departmental knowledge-sharing mechanisms to gradually achieve strategic adjustment and capability upgrading. At the regional level, industrial alliances or resource-sharing platforms can be leveraged to facilitate the diffusion of experience and technology among firms, thereby enhancing the overall level of digital transformation.

Fifth, for government departments, it is essential to establish and improve a public training system for enhancing executives’ digital cognitive capabilities. By collaborating with universities and research institutions, systematic training can be provided to help managers strengthen their digital understanding and strategic thinking. At the same time, government data governance should be reinforced by improving public data platforms and information disclosure mechanisms, thereby reducing firms’ information search costs and enhancing their capacity to absorb new knowledge and technologies. Moreover, continuous investment in digital infrastructure is required to narrow regional gaps in digital development and create a favorable external environment for firms’ digital strategic transformation. For example, targeted initiatives such as digital training, technical consulting, and pilot project guidance can be offered to labor-intensive enterprises, facilitating regional digital upgrading and the coordinated development of firm strategies.

5.3. Limitations and Future Research

Although this study provides new theoretical and empirical evidence for understanding the mechanism of action between executive cognitive style, absorptive capacity, and digital strategic change, the generalizability of the results of this study may be limited due to certain limitations associated with the research objects. First, the sample of this study is made up of Chinese A-share listed companies with certain specificities in their institutional environments, market characteristics, and digital transformation paths. For example, China’s state-owned enterprises (SOEs) are deeply influenced by government policies in terms of resource acquisition, strategy formulation, and execution, leading to significant differences in the pace and strategic direction of policy-driven digital transformation compared to market-driven economies. These institutional differences may affect the applicability of this study’s findings in other economies and give rise to differences in external validity. Future research could test the mechanism of the role of cognition and capabilities in digital strategic change in different countries and institutional contexts to further validate the generalizability of the findings.

Furthermore, this paper explores how executive cognition influences absorptive capacity through mechanisms such as information search, resource allocation, and routine breakthrough. Future research could further examine the differences in knowledge preferences among various cognitive styles from an interdisciplinary perspective combining cognitive psychology and knowledge management. Based on this, subsequent research could delve into how such knowledge preferences influence the four stages of absorptive capacity—acquisition, digestion, transformation, and utilization. Such research not only helps reveal the more nuanced mechanisms linking executive cognitive styles to absorptive capacity but also provides a stronger psychological foundation for expanding the theory of dynamic capabilities.