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Abstract

With the rapid growth of the digital economy and the increasing importance of sustainable development, digital orientation (DO) plays a vital role, as does green absorptive capacity (GAC) and sustainable business model innovation (SBMI). This study is the first to explore the internal mechanism of DO affecting the sustainable performance of agricultural enterprises through GAC and SBMI. Using data collected from 267 agricultural enterprises in China, the validity of the conceptual model was tested using hierarchical regression analysis. The research results show that DO has a positive impact on the sustainable performance of agricultural enterprises. Furthermore, DO indirectly affects the sustainable performance of agricultural enterprises through the mediating and chain mediating effects of GAC and SBMI. This study contributes to the existing research on the sustainable performance of agricultural enterprises by highlighting the significance of DO and validating the crucial mediating roles played by GAC and SBMI. The findings of this study offer valuable theoretical insights into promoting sustainable development of agricultural enterprises and addressing the environmental challenges associated with their growth.

JEL codes : M1; M13; M14; Q56

Plain language summary

Research on the impact of digital orientation on sustainable performance of agricultural enterprises

This study investigates how digital orientation (DO), green absorptive capacity (GAC), and sustainable business model innovation (SBMI) influence the sustainable performance of agricultural enterprises in China.We collected data from 267 agricultural enterprises and used hierarchical regression analysis to test our conceptual model. Our results show that DO positively impacts the sustainable performance of agricultural enterprises. Additionally, DO indirectly affects sustainable performance through the mediating and chain mediating effects of GAC and SBMI. This research sheds light on the importance of DO and validates the significant mediating roles of GAC and SBMI in promoting sustainable performance. These findings provide valuable theoretical insights for enhancing the sustainable development of agricultural enterprises and addressing associated environmental challenges.

Keywords

digital orientation green absorptive capacity sustainable business model innovation enterprise sustainable performance agricultural enterprise

Introduction

In the face of increasingly severe challenges posed by resource depletion, environmental pollution, and social inequality, the importance of sustainable development has become increasingly prominent. In September 2020, China set clear goals for achieving “peak carbon dioxide emissions” by 2030 and “carbon neutrality” by 2060, known as the “dual carbon goals”. Agriculture serves as the foundation of the national economy, and agricultural enterprises play a pivotal role in building a robust agricultural nation and achieving the “dual carbon goals.” Accelerating the low-carbon transformation and upgrading of agricultural enterprises and achieving sustainable development are not only necessary to promote the high-quality development of agriculture in China but also the only means to achieve the “dual carbon goals” (Zhang & Li, 2021). In recent years, with the rapid growth of the digital economy and the increasing importance of sustainable development, digitalization has emerged as a crucial tool for enterprises to accelerate the adoption of green business practices (Savage, 2018). This has given rise to a new strategic orientation known as the digital orientation (DO) (Zhang et al., 2023).

DO serves as the guiding principle for enterprises to pursue digital technology opportunities and gain a competitive advantage (Kindermann et al., 2021). It is regarded as dynamic vigilance that enables enterprises to adapt to the external environment and fulfill their social responsibilities (Zhang et al., 2023). The natural resource-based view (NRBV) suggests that enterprises should fully consider environmental factors and develop key competitive resources and capabilities while formulating strategies (Hart, 2009). These resources and capabilities can provide enterprises with competitive advantages, such as a good reputation and legitimacy, ultimately improving their competitive advantage (Bataineh et al., 2023). DO can be seen as a strategic resource that resides between the core organizational culture and dynamic capabilities (Bendig et al., 2023). This cultivates enterprises’ ability to perceive and seize opportunities, leading to sustainable development (Chavez et al., 2023). By adopting a DO, enterprises can simultaneously consider the “triple bottom line” of the economy, society, and the environment. This study views DO as the dynamic capability of enterprises to enhance their sustainable performance and respond to the external natural environment, as suggested by scholars at the foundation of NRBV.

However, previous studies on DO have primarily focused on concept definition and theoretical framework construction (Dantsoho et al., 2020; Quinton et al., 2018), with relatively few empirical studies conducted at the enterprise level. Moreover, existing research on the relationship between DO and enterprise performance has predominantly focused on the manufacturing (Yang et al., 2021) and financial industries (Niemand et al., 2021). Compared with other industries, agriculture has a more significant environmental impact because of its close connection with the natural environment (Dias et al., 2021; Qi et al., 2022). It is crucial for agricultural enterprises to shift their development models toward sustainability. However, there is a lack of research on DO in agricultural enterprises. Unlike the manufacturing and financial industries, agricultural enterprises often face financial constraints and operate on low-profit margins (Sher et al., 2019). Therefore, it is crucial to explore the pathways and mechanisms through which agricultural enterprises can transform DO into a sustainable performance.

According to the dynamic capability view (DCV), firm performance relies on an effective combination of resources and capabilities, particularly the resilient ability to build and reorganize internal and external resources to gain a competitive advantage in response to rapid market changes (Teece, 2007). Resources and capabilities that are valuable, scarce, difficult to replicate, and irreplaceable serve as the foundation for sustainable competitive advantage (Barney, 1991). Green absorptive capacity (GAC) and sustainable business model innovation (SBMI) have strategic capabilities with these characteristics, enabling companies to establish sustainable competitive advantages. GAC is considered a dynamic capability that enables companies to develop the knowledge and skills necessary to support environmental initiatives (Makhloufi et al., 2022). This involves restructuring business processes, fostering new skills and learning, and enhancing training programs to meet environmental standards (Pacheco et al., 2018). Additionally, when implementing DO, companies must transform their organizational processes for sustainable value creation, delivery, and acquisition (Shakeel et al., 2020). Embracing the SBMI has emerged as the most viable approach to achieving these objectives (Bashir et al., 2022). Moreover, the development of GAC helps companies overcome the influence of green cognitive bias and outdated organizational practices (Makhloufi et al., 2022). By integrating green knowledge and resources through innovation, companies can reshape their sustainable business models to meet the evolving market demands (Gauthier & Zhang, 2020). It is evident that a connection exists among DO, GAC, SBMI, and sustainable enterprise performance. However, few studies have explored the mechanisms of GAC and SBMI in the relationship between DO and sustainable performance of agricultural enterprises. Furthermore, the existence of a causal relationship and mutual cooperation between GAC and SBMI requires further examination and discussion.

Therefore, based on the combination of NRBV and DCV, this study explores the internal mechanism by which DO affects the sustainable performance of agricultural enterprises through GAC and SBMI. This study contributes to the literature in two ways: on the one hand, this study promotes research on DO and enterprises’ sustainable performance. Previous research has mainly unilaterally examined the role of DO in enhancing enterprises financial or environmental performance, but little attention has been paid to its influence on sustainable performance. In addition, given the urgency of implementing digital strategies in agricultural enterprises under the backdrop of the rural revitalization strategy, it is crucial to shift the focus of DO research toward the agricultural field. Thus, this study provides valuable theoretical support for the implementation of DO in agricultural enterprises and extends the conclusions of previous studies on the factors affecting sustainable performance. On the other hand, drawing on the perspectives of the NRBV and DCV, the study delves into the internal driving mechanism of sustainable performance growth in digital-oriented agricultural enterprises. Building upon the “strategy-behavior-performance” S-SCP strategic analysis framework, this study specifically explores the mediating and chain mediating effects of GAC and SBMI. In doing so, it not only provides an explanation for the mechanism through which DO influences enterprises’ sustainable performance but also offers empirical evidence to uncover the “black box” between these variables. Moreover, it expands the research scope of GAC and SBMI, and enriches and enhances relevant research within the NRBV and DCV frameworks.

Theoretical and Hypotheses

Digital Orientation (DO)

DO is a relatively new concept that has emerged in the digital era (Kindermann et al., 2021). Currently, research on DO is still in its early stages and there is no consensus on its precise definition (Quinton et al., 2018; Dantsoho et al., 2020). Existing studies have proposed different dimensions for defining the DO. The first relates to the strategic aspects of DO. Quinton et al. (2018) suggest that DO involves the strategic positioning of enterprises by leveraging the opportunities presented by digital technologies. Arias-Pérez and Vélez-Jaramillo (2021) defined DO as the deliberate strategic positioning of enterprises to create value through the utilization of emerging digital technologies. The second perspective focuses on the implementation of digitalization. Ardito et al. (2021) view DO as a strategic decision made by enterprises to adopt digital strategies. The third perspective emphasizes the application and utility of digital technology. Dantsoho et al. (2020) describe DO as the trend of enterprises adapting to the digital ecosystem by employing new technologies to develop innovative business models, streamline operations, and enhance customer experience. Khin and Ho (2019) conceptualized DO as an enterprises’ commitment to leveraging digital technologies to provide customers with innovative products, services, and solutions. Kindermann et al. (2021) defined DO as the guiding principle of an organization that focuses on leveraging digital technology to gain a competitive edge in the market. Although there is no consensus on a precise definition of DO, these perspectives collectively underscore the significance of digital technology and reflect a proper understanding of its potential applications and value. Therefore, based on the perspective proposed by Kindermann et al. (2021). This study considers DO as a strategic approach for enterprises to pursue digital technology opportunities, gain a competitive advantage, and achieve sustainable development.

Natural Resource-based View (NRBV) and Dynamic Capability View (DCV)

This study was supported by the combination of NRBV and DCV. Both theories emphasize the significance of resources and capabilities in attaining competitive advantage and driving sustainable development, with different focuses on these resources and capabilities. The integration of NRBV and DCV offers a comprehensive perspective for exploring the mechanisms by which DO affects the sustainable performance of agricultural enterprises. It addresses the limitations of the RBV and provides a valuable complement to it (Liu et al., 2020). The NRBV posits that the competitiveness of enterprises is a result of the environmental resources and capabilities dedicated to pollution and waste elimination, product and service management, and the overall sustainability of the organization (Hart, 2009). Building on the NRBV, this study argues that the process of organizations utilizing DO to enhance sustainable enterprise performance can be seen as a strategic resource that contributes to improving business competitiveness.

DCV is a suitable perspective for testing the utilization of business analysis (Chen et al., 2015). It refers to an organization’s capability to integrate, build, and reconfigure both internal and external resources, enabling the enterprise to respond effectively to changes in the external environment and gain an innovative advantage (Teece, 2007). The underlying logic of DCV lies in an enterprise’s capacity to absorb knowledge from both internal and external sources, enabling the integration of new resources with existing ones (Hurtado-Palomino et al., 2009). This logic is also applicable to the key challenges that GAC aims to address (Makhloufi et al., 2022). As dynamic capabilities enable organizations to adapt and respond to changes in the external environment, GAC focuses on the organization’s capability to absorb and leverage environmental knowledge and resources (Pacheco et al., 2018). Furthermore, when GAC is aligned with consumer needs, a company can effectively integrate its resources (Ma & Li, 2022), including innovating sustainable business models. SBMI serves as a natural outcome of dynamic capabilities, enabling firms to sustain long-term profitability in the long run by continuously sensing and seizing new opportunities (Bashir et al., 2022). This is achieved through the transformation of corporate strategies and organizational routines (Teece, 2018). According to Eden and Ackermann (2000), as the dynamic capabilities, business models align a company’s distinctive competencies with its organizational aspirations and outcomes. Consequently, the DCV provides a theoretical perspective for appropriately examining the SBMI. Therefore, based on the limitations of the application of NRBV and DCV, this study introduces NRBV and DCV into DO research, exploring the internal mechanism of DO affecting the sustainable performance of agricultural enterprises through GAC and SBMI. This combination provides a comprehensive framework for strategic management, proves the applicability of the NRBV and DCV in environmental management, and expands the research on NRBV and DCV. This study offers valuable theoretical insights into the importance of leveraging DO, GAC, and SBMI to drive the sustainable performance of agricultural enterprises.

DO and Enterprise Sustainable Performance

The concept of sustainable performance in agricultural enterprises emphasizes the importance of considering environmental and social benefits alongside economic profits (Mousa & Othman, 2020). Currently, the “triple bottom line” principle serves as a means to assess the sustainable performance of enterprises by evaluating their environmental and social performance, in addition to conventional financial performance (Elkington, 1998). Environmental performance is intricately linked to the efficient utilization of resources as well as the sustainable development and preservation efforts undertaken by enterprises (Zhu et al., 2017). On the other hand, social performance pertains to a company’s contribution to society and its endeavors to enhance the well-being of employees and other stakeholders (Mousa & Othman, 2020).

DO encompasses a wide array of digital technologies, capabilities, collaborations within the digital ecosystem, and configurations of the digital architecture (Kindermann et al., 2021). These elements collectively enable agricultural enterprises to attain a consistent competitive edge, thereby securing a strong market position and fostering sustainable development (AlNuaimi et al., 2022). First, digitally oriented agricultural enterprises demonstrate more proactive adoption of digital strategies and initiatives than their competitors (Arias-Pérez & Vélez-Jaramillo, 2022). By implementing disruptive digital technologies, these enterprises can achieve intelligent and precise agricultural production, optimize agricultural product supply and service chains, enhance resource allocation efficiency and reduce reliance on human resources (Nayal et al., 2022). These forward-thinking actions enable agricultural enterprises to gain a competitive advantage and establish a virtuous cycle that improves economic performance and fosters sustainable development. Second, DO encourages agricultural enterprises to actively engage in the design and development of digital agricultural products and intelligent agricultural service models, as well as to establish new sales channels and digital agricultural ecosystems (Yang et al., 2021). This will facilitate cost reduction, efficiency improvement, optimized resource allocation, social collaboration, and enhanced value creation (Chavez et al., 2023), ultimately enhancing the sustainable performance of agricultural enterprises. Lastly, incorporating DO into strategic planning allows agricultural enterprises to play a vital role in promoting high-quality agricultural development and bridging the urban-rural gap. Demonstrating a strong sense of social responsibility enhances a company’s reputation, facilitates market entry and strengthens stakeholder connections (Bataineh et al., 2023). This not only positively impacts the financial performance of agricultural enterprises, but also aligns with their development goals. Based on the aforementioned analysis, DO significantly contributes to the sustainable performance of agricultural enterprises in terms of economic, environmental, and social benefits. Therefore, we propose the following hypothesis:

H1. DO has a positive impact on sustainable performance of agricultural enterprises.

The Mediating Role of GAC

GAC refers to an enterprise’s ability to integrate and restructure both internal and external green knowledge, encompassing the acquisition, internalization, sharing, and application of such knowledge (Gluch et al., 2009). Unlike general absorptive capacity, GAC places greater emphasis on sustainable development and is considered crucial for the innovative development of services and green products that align with policies and regulations (Pacheco et al., 2018). According to NRBV, green knowledge is an intangible asset that plays a vital role in the effective functioning of enterprises, transcending the physical realm (Hart, 2009). In recent years, the construction of GAC has garnered significant attention in academic circles. GAC has been demonstrated to enhance customer satisfaction, foster innovation and competitiveness, and improve the sustainable performance of enterprises (Abbas & Khan, 2023; Gauthier & Zhang, 2020). With the emergence of big data and the application of digital technologies, the connection between GAC and digitalization has become even more intertwined. Ardito et al. (2021) conducted an empirical study of 369 digitally oriented SMEs in North America and found that DO enables SMEs to access a broader range of green knowledge sources, thereby enhancing their GAC and sustainable competitiveness. Therefore, this study posits that DO may influence GAC and subsequently impact the sustainable performance of agricultural enterprises.

First, agricultural enterprises that prioritize DO tend to be more inclusive of digital technologies, such as the Internet of Things, big data, and artificial intelligence (Khin & Ho, 2019). This encourages the utilization of digital technology to automate and optimize the green absorption process, enabling the automatic identification and measurement of tacit knowledge, meta knowledge, and differentiated green knowledge (Ma & Li, 2022). Consequently, traditional green absorption processes are transformed and GAC is enhanced. By promoting GAC, agricultural enterprises can acquire, integrate, innovate, and apply knowledge pertaining to sustainable agriculture, thereby establishing a foundation to improve sustainable performance (Wang, Zhang, et al., 2023). Second, by focusing on DO, agricultural enterprises can overcome traditional technical limitations and physical constraints associated with green knowledge management (Ma & Li, 2022). This has led to the development of digital and modular green knowledge modules, as well as cross-organizational links for green knowledge (Bendig et al., 2023). Previous research has demonstrated that digitally oriented enterprises drive the creation of green knowledge, construction of green knowledge networks, and storage and application of digital green knowledge (Tajudeen et al., 2022). These factors are crucial for enhancing the innovation capabilities and improving the sustainable performance of enterprises. Finally, the green and sustainable development of agricultural enterprises is an ongoing process that involves the deconstruction, reconstruction, and integration of relevant green knowledge into a new green knowledge system (Wang, Zhang, et al., 2023). Big data analysis technology, as a facilitator of GAC, enables the processing, capture, and sharing of vast amounts of structured and unstructured data (Kastelli et al., 2022). Consequently, it helps enterprises uncover hidden green knowledge and generate new green knowledge (Chen et al., 2012). This technology addresses issues related to information scarcity and limited green awareness within agricultural enterprises (Nayal et al., 2022), enabling them to gain competitive advantages and achieve superior sustainable performance compared with their competitors. Thus, we propose the following hypothesis:

H2. GAC mediates the relationship between DO and the sustainable performance of agricultural enterprises.

The Mediating Role of SBMI

SBMI involves enterprises adopting a new approach to seek innovation in sustainable value propositions, sustainable value creation and delivery, and sustainable value acquisition (Bashir et al., 2022). Compared to traditional business model innovation, SBMI places greater emphasis on sustainable value creation, multi-stakeholder management, and long-term perspectives (Geissdoerfer et al., 2018). In the era of the digital economy, data has emerged as a crucial driver of economic development, and digital technology continues to reshape the way enterprises create value and operate within business ecosystems (Fischer et al., 2020). As a guiding principle, DO aims to leverage digital technology opportunities to gain competitive advantage (Kindermann et al., 2021). Highly digitally oriented agricultural enterprises can leverage artificial intelligence and big data analysis technology to accurately identify consumer preferences and gain a better understanding of changes in consumer demand (Chen & Chen, 2022). This enables them to develop sustainable products and services and drive sustainable value proposition innovation (Arias-Pérez & Vélez-Jaramillo, 2022). In addition, digitally oriented agricultural enterprises can enhance the scale of network effects through digital transformation and the establishment of digital platforms (Nasiri et al., 2022). This allows them to provide sustainable new insights into business decisions, such as pricing, trading methods, and sales channels for agricultural products (Yang et al., 2021). Consequently, they promote sustainable value creation and innovation.

Numerous studies have highlighted the significance of the SBMI as a crucial driver of sustainable competitive advantage and a means to enhance sustainable enterprise performance (Bashir et al., 2022; Geissdoerfer et al., 2018). First, a successful SBMI can enable agricultural enterprises to effectively and sustainably utilize their resources and capabilities, which are not easily replicated or replaced by competitors, to meet market demands (Bashir et al., 2022). Moreover, SBM results in a noticeable increase in sustainable business value (Yang et al., 2017), thereby enhancing the overall sustainable performance of agricultural enterprises. Second, through SBMI, agricultural enterprises can foster meaningful engagement with consumers, allowing them to better understand and respond to evolving customer needs. This enables the repositioning of agricultural product brands in a way that aligns with sustainability principles and customer expectations (Hacklin et al., 2009). Finally, SBMI prioritizes the development of sustainable and responsible value propositions (Mignon & Bankel, 2023). This ensures ethical and transparent management of multiple interests. Moreover, the SBMI incorporates concepts such as caring for ethics and technology for good (Geissdoerfer et al., 2018). Considering the moral sentiments and value demands of all stakeholders fosters a sustainable brand image, promotes the growth of sustainable business value, and ultimately improves sustainable performance (Massa et al., 2017). Therefore, we propose the following hypothesis:

H3. SBMI mediates the relationship between DO and the sustainable performance of agricultural enterprises.

The Chain Mediating Effect of GAC and SBMI

DO can effectively steer the green and sustainable development of agricultural enterprises. However, strategic orientation alone is insufficient for companies to enhance sustainability. Given the ever-changing, intricate, uncertain, and competitive external environment, enterprises must possess the capacity to adapt and evolve, which is known as a dynamic ability (Primc & Čater, 2016). Agricultural enterprises with high DO levels are more inclined to prioritize the development of GAC and exhibit sustainable management and innovative practices in their business models (Wang, Nie, et al., 2023). The stronger the GAC, the better equipped agricultural enterprises seek green knowledge and quickly respond to environmental changes (Pacheco et al., 2018). This enables them to identify green business opportunities arising from environmental challenges, which in turn drives innovation in sustainable business models (Shakeel et al., 2020).

Building on the “strategy-behavior-performance” S-SCP strategic analysis framework, we further propose that DO plays a crucial role in driving GAC and fostering SBMI. Companies with a DO are known for their ability to leverage advanced digital technologies across all areas of their business and implement digital initiatives with a positive mindset and strong dedication (Wang, Nie, et al., 2023). By embracing digital technologies, these firms can effectively integrate green knowledge elements and business management (Chen & Chen, 2022), leading to innovation in their sustainable business models. Furthermore, GAC helps agricultural enterprises overcome organizational inertia and adapt their sustainable business models to volatile business environments (Khin & Ho, 2019). Innovative sustainable business models allow agricultural enterprises to meet the market demand for green products or services, create and enhance sustainable value for various stakeholders, improve economic efficiency, and maintain a competitive edge, ultimately leading to sustainable development (Bashir et al., 2022). Therefore, we propose the following hypothesis:

H4. GAC and SBMI chains mediate the relationship between DO and the sustainable performance of agricultural enterprises.

The conceptual model of this study is shown in Figure 1.

Figure 1.

Conceptual model.

Research Methods

Sample Characteristics and Data Collection

Our research object is agricultural enterprises in China. There are several reasons for selecting Chinese agricultural enterprises as the focus of this study: First, the implementation of the rural revitalization strategy in China has led to rapid development of the rural economy. Entrepreneurship in the agricultural sector flourished. However, the previous extensive economic development model, which prioritized profits at the expense of the environment and ecology, resulted in damage to the rural ecological environment. Therefore, it is necessary to shift the development mode of agricultural enterprises toward sustainability and high quality. Second, Chinese agricultural enterprises have played a crucial role in accelerating the development of the agricultural industry chain and constructing a modern agricultural system. Their contributions have been indispensable in expediting Chinese-style modernization. Third, as a representative developing country, China’s experiences and practices in promoting green agriculture can provide valuable insights and reference for other developing nations. The research area encompasses provinces with well-developed agriculture in China, namely Jilin, Sichuan, Heilongjiang, Hebei, Henan, and Shandong. By collecting samples from various economic zones across China, we minimized the potential for regional bias in our study (Zhang et al., 2023).

During the questionnaire design process, the study prioritized minimizing any potential risks to participants. To safeguard their privacy and confidentiality, an anonymous online questionnaire method was employed, ensuring that no identifying information was collected. Prior to the formal survey, scholars and experts with management backgrounds in the agricultural field were invited to evaluate and enhance the reliability of the questionnaire. Additionally, a small-scale preliminary survey was conducted with 20 agricultural enterprises in Jilin Province to refine and modify the questionnaire based on the survey results. This ensured the accuracy and relevance of the content prior to formal research. This study targeted middle and above-management personnel, including chairpersons, general managers, and marketing directors of agricultural enterprises. The data were collected through Wenjuanxing (Questionnaire Star) platform. Previous studies have established several instances in which such measures have been used, and have shown a positive correlation between perceived corporate data and objective data (Dias et al., 2021; Kong et al., 2020). Additionally, the study’s purpose, the voluntary nature of participation, and the assurance of anonymity were clearly communicated before the questionnaire began. Participants were informed that they could withdraw from the study at any stage without facing any consequences. By proceeding with the questionnaire, participants indicated their consent to take part in the study. Consequently, utilization of the questionnaire in this study was deemed appropriate and acceptable. The methodology employed in this study was designed according to Beatty (2008), who suggested a staged survey approach to ensure reliable responses. The formal survey was conducted from June 10, 2022, to December 30, 2022. In order to weaken the problem of endogeneity in the measurement of variables, we sent the questionnaire in two installments. A total of 510 questionnaires were distributed, and 267 valid questionnaires were collected, accounting for 52.4% of the total. This is normal for such studies, as the quality of respondents is more important than the response rate (Zhang et al., 2023).

The statistical characteristics of the samples are listed in Table 1. 26.2% of enterprises are in the initial stage, 22.1% of agricultural enterprises are in the transition from the initial stage to the growth stage, and more than half of enterprises (51.7%) are in the rapid growth stage. Small enterprises accounted for 39.3%, medium-sized enterprises accounted for 48.7%, and large enterprises accounted for 12%. Non-state-owned enterprises account for more than 80%. In terms of business scope, the sample of this study covers Scale farming (12.7%), Scale breeding (16.9%), Ventures in agroindustry (22.1%), Modern Agricultural Service (22.5%), Facilities Horticultural Industry (11.2%) and Leisure Agriculture (11.2%). On the whole, this sample is representative.

Table 1.

Composition of Sample Distribution.

Firm characteristics	Frequency	Percentage
Firm age
Less than 1 year	10	3.7
1–3 years	60	22.5
3–5 years	59	22.1
More than 5 years	138	51.7
Firm size
Less than 50 employees	19	7.1
50–100 employees	86	32.2
100–200 employees	77	28.8
200–300 employees	53	19.9
More than 300 employees	32	12.0
Ownership
State-owned/collective firms	30	11.2
Private-owned firms	215	80.5
Foreign-funded/joint firms	4	1.5
Other types	18	6.7
Business scope
Scale farming	34	12.7
Scale breeding	45	16.9
Ventures in agroindustry	59	22.1
Modern agricultural service	60	22.5
Facilities horticulture industry	30	11.2
Leisure agriculture	39	14.6

Measures

To enhance measurement effectiveness, the scales used to assess variables in this study were derived from a widely utilized foreign maturity scale. These scales were suitably adjusted and modified to align with the research objectives and Chinese context. The measurement was conducted using a 7-point Likert scale, where respondents provided objective evaluations based on the actual circumstances of their respective enterprises. The scale ranges from 1 (completely inconsistent) to 7 (completely consistent). This approach allows for a comprehensive and nuanced assessment of the variables under investigation.

DO in this study was measured using a 5-item scale adopted from Arias-Pérez & Vélez-Jaramillo (2022) and Crespo et al. (2023). Sustainable performance of agricultural enterprises was measured using a 10-item scale adopted from Bhattarai et al. (2019) and Mousa et al. (2020). For the mediating variable of GAC, a 5-item scale was adopted from the Chen et al. (2015). For the mediating variable of SBMI, a 10-item scale was adopted from the research conducted by Bashir et al. (2022). For further details regarding the structure and project, please refer to the Table A1.

To minimize the impact of firm-level variables on the sustainable performance of agricultural enterprises, we incorporated control variables such as firm size, firm age, and ownership type (Makhloufi et al., 2022; Mousa & Othman, 2020). This approach was adopted to mitigate the influence of these variables on research outcomes. Firm size was assessed based on the number of employees, while firm age was determined by the number of years the company had been in operation (Makhloufi et al., 2022). Ownership type was categorized into state-owned and non-state-owned firms (Zhang et al., 2023), with state-owned firms assigned a value of one and non-state-owned firms assigned a value of zero.

Analyses and Results

Common Method Variance (CMV) and Multicollinearity

In survey research, non-response bias and CMV are two commonly encountered issues (Kong et al., 2020). To assess non-response bias, we categorized the collected samples based on the time of questionnaire administration and conducted a t-test to compare the age, scale, and industry type between the top 10% and bottom 10% of the samples (Swink & Nair, 2007). The results revealed no significant differences between these two groups (p > .05, two-tailed), suggesting that non-response bias is not a major concern in our study, and that the data can be considered reliable.

To address the potential threat of CMV, we employ the approach proposed by Podsakoff et al. (2003). First, we implemented strategies such as anonymous questionnaire completion, reducing item predictability, and balancing item order to proactively control the CMV. Second, we conducted exploratory factor analysis using Harman’s single-factor test. The results indicated that all items loaded onto four factors with eigenvalues greater than one, and the cumulative variance contribution rate was 66.753%. The first factor accounted for 38.292% of the total item variation, which is below the threshold of 40%. We performed confirmatory factor analysis (CFA) to further examine the presence of CMV (see Table 2). We evaluated a model that connected all items associated with dependent and independent factors to a single factor. However, our analysis revealed that this alternative model did not adequately align with the observed data (χ²/pf = 7.323, RMSEA = 0.154, TLI = 0.495, CFI = 0.530, and TLI = 0.532). Finally, we employed the marker variable technique, selecting a specific measurement item to assess the respondents’ recognition of their interpersonal communication style (I sometimes get irritated by people who plead with me) as the marked variable (Chan et al., 2022). The marked variable exhibited a mean absolute correlation of only 0.02 with all the other variables examined in the study. Subsequently, a T-test was conducted, which does not reveal any significant difference. Consequently, it can be concluded that CMV did not exert a significant influence on this study. Furthermore, the variance inflation factor (VIF) was found to be less than 2, and the tolerance between variables exceeded the critical threshold of 0.6. Therefore, multicollinearity can be disregarded.

Table 2.

Results of Confirmatory Factor Analysis.

Model	χ²	df	χ²/df	RMSEA	TLI	CFI	IFI
Four-factor model	726.519	399	1.821	0.056	0.934	0.940	0.940
Three-factor model	1269.004	402	3.157	0.090	0.828	0.841	0.842
Two-factor model	1924.485	404	4.761	0.119	0.699	0.721	0.722
Single-factor model	2965.667	405	7.323	0.154	0.495	0.530	0.532

Notes. The theoretical model includes DO, GAC, SBMI, and enterprise sustainable performance. Three-factor model: combine DO and GAC into one factor; Two-factor model: combine DO and GAC and SBMI into one factor; Single factor model: All items are grouped into one factor.

Reliability and Validity

We employed SPSS and AMOS to test the reliability and validity of the scale and to ensure measurement accuracy. Based on the data presented in Table 3, it can be observed that the minimum factor loading for DO, GAC, SBMI, and enterprise sustainable performance is 0.650, while the minimum value of Cronbach’s α is .895. These values remain stable even if any individual item is removed, indicating a good overall scale reliability (Zhang et al., 2023). In addition, this study examined the validity of the variable scale. The average variance extracted (AVE) values for all variables exceeded the standard threshold of 0.5, and the composite reliability (CR) values exceeded the standard threshold of 0.7, suggesting strong convergent validity for the scale. Furthermore, as shown in Table 4, discriminant validity was assessed by comparing the square root of each construct’s AVE with the correlations between that construct and the others. The square root of the AVE for each construct was found greater than the correlation with the other constructs, indicating satisfactory discriminant validity.

Table 3.

Validity Assessment.

Variables	Factor loading (Min)	Cronbach’s α	AVE	CR
DO	0.773	.901	0.647	0.901
GAC	0.723	.895	0.633	0.896
SBMI	0.725	.929	0.569	0.930
ESP	0.650	.942	0.622	0.942

Notes: AVE = average variance extracted; CR = construct reliability.

DO = digital orientation; GAC = green absorptive capacity; SBMI = sustainable business model innovation; ESP = enterprise sustainable performance.

Table 4.

Means, Standard Deviations, and Correlation.

Variables	Mean	SD	1	2	3	4	5	6	7
1. Firm age	3.220	0.921
2. Firm size	2.970	1.135	0.261**
3. Ownership	0.112	0.316	0.006	0.238**
4. DO	3.666	1.255	0.005	0.229**	0.074	0.804
5. GAC	4.667	1.114	0.076	0.151*	−0.085	0.431**	0.795
6. SBMI	4.176	0.995	−0.048	0.072	−0.008	0.359**	0.427**	0.755
7. ESP	5.211	1.002	0.091	0.126*	−0.034	0.390**	0.413**	0.452**	0.789

Notes: Bolded diagonal elements are the square root of the average variance extracted (AVE).

N = 267; *p < .05. **p < .01.

In this study, AMOS 21.0 was utilized to perform confirmatory factor analysis on the collected data. The results of this analysis are presented in Table 2. All the fitting indexes met the established criteria (Pan et al., 2022), indicating a high level of model fit and strong discrimination validity. Specifically, the fitting indices demonstrated favorable values (χ²/pf = 1.821, RMSEA = 0.056, TLI = 0.934, CFI = 0.940, and TLI = 0.940). Based on these analyses, the scale utilized in this study demonstrated high levels of convergent and discriminant validity as well as satisfactory reliability.

Hypotheses Testing

The effectiveness of hierarchical regression analysis in the management field has been extensively validated in previous studies (Wang, Zhang, et al., 2023; Zhang et al., 2023). This analytical approach is well suited for investigating the relationships between variables in a model and understanding their influence mechanisms. Hence, this study employs a hierarchical regression method to examine this hypothesis.

The present study aimed to examine the relationship between DO, GAC, SBMI, and sustainable performance of agricultural enterprises. To achieve this, a descriptive statistical analysis of the data was conducted, and the results are presented in Table 4. The findings reveal a significant correlation between enterprise size (β = .229, p < .01) and DO, indicating that larger agricultural enterprises tend to exhibit a stronger inclination toward digitalization. Similarly, a significant correlation was observed between enterprise size (β = .151, p < .05) and GAC, suggesting that larger enterprises have greater capacity to absorb green practices. Furthermore, the analysis revealed a significant correlation between enterprise size (β = .126, p < .05) and the sustainable performance of agricultural enterprises, indicating that larger enterprises tend to achieve better sustainable performance outcomes. Additionally, the results indicate that DO (β = .390, p < .01), GAC (β = .413, p < .01), and SBMI (β = .452, p < .01) were significantly related to the sustainable performance of agricultural enterprises.

Direct Effect Test

Based on the control for age, scale, and ownership type of the enterprise, this study employed multiple regression analysis to examine the relationship between the key variables discussed in this study. The results of the analysis are listed in Table 5. Model 3 of the regression analysis incorporates control variables such as firm size, age, and ownership type to assess their influence on the sustainable performance of agricultural enterprises. Model 4 builds on Model 3 by including the independent variable DO to examine its main effect. According to the findings of Model 4, the impact of DO on the sustainable performance of agricultural enterprises was significant (β = .387, p < .001). This suggests that DO has a substantial positive influence on the sustainable performance of agricultural enterprises, thus confirming Hypothesis 1.

Table 5.

Regression Analysis Results.

Variables	GAC		ESP
Variables	Model1	Model 2	Model 3	Model 4	Model 5	Model 6
Firm age	0.032	0.056	0.059	0.081	0.046	0.065
Firm size	0.173	0.071	0.126	0.033	0.057	0.012
Ownership	−0.127	−0.134	−0.064	−0.071	−0.013	−0.032
DO		0.424***		0.387***		0.289***
GAC					0.399***	0.265***
R ²	0.039	0.209	0.023	0.165	0.177	0.231
△R ²	0.028	0.197	0.012	0.152	0.164	0.216
F	3.599	17.339***	2.102	12.944***	14.060***	15.692***

Notes: N = 267. ***p < .001.

Mediating Effect Test

This study employs a hierarchical regression method to examine the mediating role of GAC and SBMI in the relationship between DO and sustainable performance of agricultural enterprises. The results demonstrated that DO had a significant positive effect on GAC (β = .424, p < .001), as indicated by Model 2. Model 5 reveals that GAC has a positive and significant effect on the sustainable performance of agricultural enterprises (β = .399, p < .001). This study combines DO and GAC to regress the dependent variable of the sustainable performance of agricultural enterprises. The results depicted in Model 6 indicate that DO significantly influence the sustainable performance of agricultural enterprises (β = .289, p < .001). Additionally, GAC significantly affects the sustainable performance of agricultural enterprises (β = .265, p < .001), with a lower regression coefficient than the previous model (0.399 > 0.265). These findings support the partial mediating role of GAC between DO and the sustainable performance of agricultural enterprises, thereby confirming Hypothesis 2. The specific analysis results are presented in Table 5.

The results from Model 8 indicated a positive impact of DO on SBMI (β = .359, p < .001). The results from Model 9 show that the SBMI has a significant impact on the sustainable performance of agricultural enterprises (β = .450, p < .001). Furthermore, this study combines DO and SBMI to regress the dependent variable and sustainable performance of agricultural enterprises. The results, as presented in Model 10, indicate that DO has a significant impact on the sustainable performance of agricultural enterprises (β = .361, p < .001). Additionally, SBMI has a significant impact on the sustainable performance of agricultural enterprises (β = .257, p < .001), with a lower regression coefficient than the previous model (0.450 > 0.257). These findings suggest that SBMI partially mediates the relationship between DO and the sustainable performance of agricultural enterprises, thereby confirming Hypothesis 3. The specific analysis results are listed Table 6.

Table 6.

Regression Analysis Results.

Variables	SBMI		ESP
Variables	Model7	Model 8	Model 3	Model 4	Model 9	Model 10
Firm age	−0.073	−0.052	0.059	0.081	0.091	0.100
Firm size	0.098	0.012	0.126	0.033	0.082	0.029
Ownership	−0.031	−0.037	−0.064	−0.071	−0.050	−0.057
DO		0.359***		0.387***		0.361***
SBMI					0.450***	0.257***
R ²	0.011	0.133	0.023	0.165	0.223	0.278
△R ²	−0.001	0.119	0.012	0.152	0.212	0.264
F	0.952	10.011***	2.102	12.944***	18.846***	20.129***

Notes. N = 267; ***p < .001.

Chain Mediating Effect Test

In this study, the bootstrap method was employed to examine the role of chain mediation in this hypothesis (Wang, Zhang, et al., 2023). Based on the confidence intervals in Table 7, it can be observed that DO has a significant indirect impact on the sustainable performance of agricultural enterprises (β = .150). Specifically, when considering GAC as the intermediary variable, the effect was 0.063 (0.017, 0.114), and when considering SBMI as the intermediary variable, the effect was 0.052 (0.016, 0.095). Moreover, when both GAC and SBMI were considered chain intermediary variables, the effect was 0.035 (0.018, 0.059), excluding 0. These findings indicate that GAC and SBMI play a chain mediating role in the relationship between DO and sustainable performance of agricultural enterprises, thereby supporting hypothesis 4. The detailed analysis results can be found in Table 7.

Table 7.

Bootstrap Analysis.

Path	Effect	Boot SE	Boot LLCI	Boot ULCI
DO → ESP	0.150	0.032	0.093	0.216
DO → GAC → ESP	0.063	0.025	0.017	0.114
DO → SBMI → ESP	0.052	0.020	0.016	0.095
DO → GAC → SBMI → ESP	0.035	0.011	0.018	0.059

Endogeneity Analyses

We employ two endogeneity tests to examine the presence of endogeneity. First, we included regional economic level as a control variable to mitigate any observation errors arising from missing variables (Mostafiz et al., 2020; Yin et al., 2020). Upon incorporating the regional economic level as a control variable, we found that the significance of all the model results remained unchanged. Second, we used the instrumental variables approach to address reverse causation. We choose the mean value of DO for firms in the sample region as the instrumental variable. As can be seen from Table 8, in the first stage, the coefficient of IV is significantly positive at the 1% level. In the second stage, the coefficient of DO is 0.213, which is significantly positive at the 10% level. Therefore, based on the above findings, we confirm that endogeneity does not pose a challenge to our study.

Table 8.

Endogeneity Analyses.

	Adding control variables	instrumental variable approach
	ESP	DO	ESP
Variables	Model1 11	Stage 1	Stage 2
Firm age	0.105	−0.152	0.083
Firm size	−0.037	0.333***	0.004
Ownership	0.05	−0.104	0.049
Gdp	0.011
DO	0.310***		0.213*
IV		0.978***
R ²	0.159
△R ²	0.143	0.184	0.144
F	9.847***

Notes: N = 267; *p < .05. **p < .01. ***p < .001.

Discussion and Conclusion

Conclusion

Based on the combination of NRBV and DCV, this study is the first to explore the internal mechanism by which DO affects the sustainability performance of agricultural enterprises through GAC and SBMI. Hierarchical regression analysis was employed to examine the validity of the conceptual model using data gathered from 267 agricultural enterprises in China. Through our analysis, we found that DO positively influences the sustainable performance of agricultural enterprises. This finding is consistent with our research hypothesis H1, thus verifying the correctness of H1. Specifically, enterprises with high DO pay more attention to long-term interests, environmental protection and social responsibility, and can achieve better sustainable performance. When discussing the influence path of DO on the sustainable performance of agricultural enterprises, we found that GAC and SBMI played a partial intermediary role in the relationship between DO and the sustainable performance of agricultural enterprises, and H2 and H3 were supported. GAC and SBMI, as intermediary variables, explain to some extent how DO is transformed into the sustainable performance of enterprises. When the GAC of agricultural enterprises is stronger, they are more likely to integrate the concept of environmental protection and sustainable development into the decision-making and operation of enterprises, thus promoting the improvement of sustainable performance. Similarly, enterprises with digital orientation can effectively improve their sustainable performance by implementing SBMI, such as adopting new agricultural models such as circular agriculture and ecological agriculture. Furthermore, DO indirectly affected the sustainable performance of agricultural enterprises through the chain mediating effects of GAC and SBMI. This discovery provides strong evidence for our research hypothesis H4, that is, DO not only directly affects sustainable performance, but also indirectly promotes the promotion of sustainable performance through a series of chain reactions of intermediary variables. That is to say, the promotion of GAC will also stimulate enterprises to pursue SBMI. The innovation of this business model will not only help enterprises to realize the sustained growth of economic benefits, but also better meet consumers’ demand for environmentally friendly and healthy agricultural products, thus further enhancing the sustainable performance of enterprises. The findings of this study offer valuable theoretical insights into promoting the sustainable development of agricultural enterprises and addressing the environmental challenges associated with their growth.

Theoretical Contribution

This study makes significant theoretical contributions to the existing literature in several respects. First, it focuses on agricultural enterprises and confirms the significant impact of DO on sustainable performance. It highlights how DO enables agricultural enterprises to efficiently create sustainable products and services that meet market needs (AlNuaimi et al., 2022), while also facilitating communication and cooperation with partners through the establishment of digital ecosystems (Kindermann et al., 2021). Previous research has mainly unilaterally examined the role of DO in enhancing enterprises’ financial, organizational, and environmental performance (Bendig et al., 2023; Khin & Ho, 2019; Quinton et al., 2018), but little attention has been given to its influence on sustainable performance. Thus, this study provides valuable theoretical support for the implementation of DO in agricultural enterprises and extends the conclusions of previous studies on the factors that affect sustainable performance.

Second, drawing on the perspectives of NRBV and DCV, this study delves into the internal driving mechanism of sustainable performance growth in digitally oriented agricultural enterprises. Building upon the “strategy-behavior-performance” S-SCP strategic analysis framework, this study specifically explores the mediating roles of GAC and SBMI. Although GAC and SBMI provide enterprises with an effective approach to consider the “triple bottom line” of the economy, environment, and society (Geissdoerfer et al., 2018; Gluch et al., 2009), previous research on these topics has certain limitations. Existing studies primarily focus on examining the benefits that enterprises can derive from GAC and SBMI (Abbas & Khan, 2023; Bashir et al., 2022), but do not adequately address the driving factors behind the adoption of GAC and SBMI. This study fills this gap by empirically verifying the mediating effects of GAC and SBMI. In doing so, it not only provides an explanation for the mechanism through which DO influences enterprises’ sustainable performance but also offers empirical evidence to uncover the “black box” between these variables. Moreover, it expands the research scope of GAC and SBMI and enriches and enhances relevant research within the NRBV and DCV frameworks.

Third, the study analyzed the chain mediation role played by the combination of GAC and SBMI. This analysis provides a more comprehensive understanding of the internal reasons behind the relationship between DO and sustainable performance of agricultural enterprises. It offers insights and new research perspectives on how agricultural enterprises can create competitive advantages and improve their sustainability. Furthermore, this study’s selection of agricultural enterprises as the research object helps fill gaps and improve theoretical research on DO in agricultural enterprises. Given the urgency of implementing digital strategies in agricultural enterprises under the backdrop of the rural revitalization strategy, it is crucial to shift the focus of DO research toward the agricultural field. This study contributes to clarifying the impact mechanism of DO on the sustainable performance of agricultural enterprises, thus addressing a pressing research need in this context.

Management Implications

The management enlightenment derived from this study can be summarized into three key aspects. First, the sustainable development of enterprises has always been a major concern for managers, yet they often struggle with “how to achieve it.” The empirical findings of this study demonstrate that adopting a DO can offer a pathway for enhancing the sustainable performance of agricultural enterprises. Therefore, in the era of the digital economy, it is crucial for agricultural enterprises to foster willingness and motivation to implement DO strategies, and allocate sufficient attention and resources to develop the digital capabilities of the organization to align with current developmental trends. By doing so, not only can the financial and environmental performance of agricultural enterprises be enhanced but their social performance can also be improved.

Second, GAC plays a crucial role in driving environmental initiatives in agricultural enterprises. Business managers should recognize that developing GAC can transform market failures into future growth opportunities. These capabilities enable companies to effectively identify market prospects and enhance their sustainable performance. Therefore, agricultural enterprises must establish and enhance their GAC digital systems. This includes componentizing and enabling intelligent green absorption at the content level, building knowledge scenes driven by user demand and knowledge services at the application level, and prioritizing the application of new digital technologies such as knowledge graphs, componentized content, and semantic search at the technical level. Furthermore, agricultural enterprises should focus on integrating digital resources and strengthening digital management. For instance, they can establish a dedicated knowledge management department within a company. They can also create incentives to encourage knowledge creation, sharing, and application. Additionally, constructing a knowledge network that facilitates knowledge exchange and sharing both within and outside an enterprise is crucial.

Third, this study provides valuable insights for managers by highlighting the significance of SBMI in driving environmentally friendly growth of agricultural enterprises. By implementing reforms and fostering innovation in sustainable value propositions, value creation, and value acquisition mechanisms, digitally oriented agricultural enterprises can effectively address green demands, tackle environmental challenges, enhance individuals’ well-being, and enhance their sustainable performance. The adoption of digital business strategies, such as digital product development, digital process development, and digital management relies heavily on SBMI. Consequently, enterprises are encouraged to integrate environmental management practices into their sustainable business models, fulfill corporate social responsibilities, and ultimately achieve sustainable economic, environmental, and social development.

Limitations and Future Research

Although this study empirically tested the impact of DO on the sustainable performance of agricultural enterprises, it is important to acknowledge existing limitations that should be addressed in future research. First, it focuses on provinces in China with well-developed agriculture and ensured a certain breadth of data sources. However, there were instances in which the number of questionnaires collected in some regions was relatively small. To enhance the generalizability of the research findings, future studies should expand the investigation to include a wider range of regions. Additionally, the survey data in this study were obtained through questionnaires, which may have been subject to respondents’ subjective judgments, potentially influencing the final results. To improve the accuracy of this study, future studies could incorporate more objective methods or adopt mixed research methods. Furthermore, based on NRBV and DCV, this study examined the relationship between DO and the sustainable performance of agricultural enterprises by considering the mediating roles of GAC and SBMI. However, these mediating factors may not fully explain all of the paths between DO and sustainable performance. Future studies could explore additional theories and perspectives to uncover the potential “black box” between the two, thus providing a clearer understanding of the mechanisms at play. Finally, this study focused solely on Chinese agricultural enterprises, limiting its practical significance to these organizations. To validate the external validity of this study, future research could extend the scope to include other industries, allowing for a more comprehensive understanding of the phenomena under investigation.

Footnotes

Appendix

Table A1.

Appendix-Measurement Items.

Constructs	Label	Measurement items	Sources
DO	DO1	We have a climate that encourages the exploration of new ways to use digital technologies	Arias-Pérez & Vélez-Jaramillo (2022) and Crespo et al. (2023)
	DO2	We are always looking for new ways to improve the effectiveness of digital technology use
	DO3	We stay up-to-date with the latest digital technology innovations
	DO4	We are able to experiment with new digital technologies as needed
	DO5	Using digitalization in our internal processes is a significant aspect of our business
GAC	GAC1	The firm is able to communicate green knowledge across its divisions	Chen et al. (2015)
	GAC2	The firm is able to effectively apply new external green knowledge on commercial purposes
	GAC3	The firm is able to identify, obtain, and value external green knowledge which is crucial to its operations
	GAC4	The firm is able to integrate existing green knowledge with new obtained and incorporated green knowledge
	GAC5	The firm’s organizational structure facilitates the development of the ability to analyze, comprehend, and deduce information from external green knowledge
SBMI	SBMI 1	These are our focus has shifted toward customers who seek sustainability	Bashir et al. (2022)
	SBMI 2	Our products and service offerings have become sustainable over the years
	SBMI 3	We have positioned ourselves to be sustainable
	SBMI 4	We make regular efforts to make our core competencies and resources more sustainable
	SBMI5	We make regular efforts to convert internal value creation activities to be more sustainable
	SBMI6	We make regular efforts to partner with firms that focus on sustainability
	SBMI7	We make regular efforts to make our distribution channels sustainable
	SBMI8	We regularly try to replace short-term sources of revenues with sustainable (long-term) recurring revenue models (e.g., leasing)
	SBMI9	Our profit margins have increased by offering sustainable products
	SBMI10	We make regular efforts to reduce manufacturing costs by incorporating sustainable practices
ESP	ESP1	The profit margin of our enterprise is growing	Bhattarai et al. (2019) and Mousa et al. (2020)
	ESP2	The sales volume of our enterprise is growing
	ESP3	The market share of our enterprise is growing
	ESP4	The overall economic performance of our enterprise is satisfactory
	ESP5	Our enterprise can reduce pollution
	ESP6	Our enterprise can reduce energy and material consumption
	ESP7	Our enterprise can reduce the consumption of hazardous, harmful or toxic materials
	ESP8	Customers have more and more trust in our enterprise’s products
	ESP9	The public believes that our enterprise has broad prospects
	ESP10	Stakeholders have a high evaluation of our enterprise

Notes: DO, digital orientation; GAC, green absorptive capacity; SBMI, sustainable business model innovation; ESP, enterprise sustainable performance.

Acknowledgements

We would like to thank the National Social Science Fund of China (20BGL059) for supporting this research.

ORCID iDs

Xiu-e Zhang

Wei Wang

Liu Yang

Yongbo Yu

Taochan Zhu

Xinyu Teng

Ethical Considerations

This study did not involve animal subjects but focused on human participants through a questionnaire. It was designed with a strong emphasis on minimizing any risks to participants. The study employed an anonymous online questionnaire method, ensuring that no identifying information was collected, thus safeguarding privacy and confidentiality. The questions were non-intrusive, avoiding sensitive or personally identifiable information, and participants were given the option to skip any questions they felt uncomfortable with.

Moreover, the potential social and personal benefits of the study clearly outweighed any possible risks. The findings will offer valuable insights into digital orientation, which can support relevant policy development and deepen understanding in this field. Participants indirectly benefited from contributing to this knowledge.

Given the low-risk nature of this study—limited to an anonymous questionnaire with no sensitive questions—the benefits far outweigh the risks. Informed consent was obtained via a consent form on the online platform. The purpose of the study, the voluntary nature of participation, and the assurance of anonymity were clearly communicated before participants began the questionnaire. Participants were also free to withdraw from the study at any point without facing any repercussions. By continuing to complete the questionnaire, participants indicated their consent to participate in the study.

Informed Consent Statement

Informed consent was obtained from all individual participants included in the study. Participants were provided with clear and concise information about the nature of the study, the potential risks, and their rights.

Author Contributions

Xiu-e Zhang: 1. Have made substantial contributions to conception and design; 2. Been involved in revising it critically for important intellectual content. 3. Participation in the revision of papers

Wei Wang: 1. Have made substantial contributions to conception and design, and acquisition of data, and analysis and interpretation of data; 2. Been involved in drafting the manuscript or revising it critically for important intellectual content. 3. Participation in the revision of papers

Liu Yang: 1. Contributed to the conceptualization of the paper; 2. Have made contributions to acquisition of data, and analysis and interpretation of data; 3. Been involved in proofreading this article.

Yongbo Yu: 1. Contributed to the conceptualization of the paper; 2.Participated in data collection; 3.Performed data analysis and interpretation; 4.Conducted additional data analysis during revisions; 5.Proofread the manuscript.

Taochan Zhu: 1. Contributed to the conceptualization of the paper; 2.Assisted with data collection; 3.Conducted grammatical revisions; 4.Revised sections of the paper during revisions; 5.Proofread the manuscript.

Xinyu Teng: 1. Have made contributions to acquisition of data, and analysis and interpretation of data; 2. Been involved in proofreading this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the National Social Science Fund of China (grant 20BGL059).

Declaration of Conflicting Interests

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

Data Availability

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

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Does Digital Orientation Improve the Sustainable Performance of Agricultural Enterprises in China?

Abstract

Plain language summary

Keywords

Introduction

Theoretical and Hypotheses

Digital Orientation (DO)

Natural Resource-based View (NRBV) and Dynamic Capability View (DCV)

DO and Enterprise Sustainable Performance

The Mediating Role of GAC

The Mediating Role of SBMI

The Chain Mediating Effect of GAC and SBMI

Research Methods

Sample Characteristics and Data Collection

Measures

Analyses and Results

Common Method Variance (CMV) and Multicollinearity

Reliability and Validity

Hypotheses Testing

Direct Effect Test

Mediating Effect Test

Chain Mediating Effect Test

Endogeneity Analyses

Discussion and Conclusion

Conclusion

Theoretical Contribution

Management Implications

Limitations and Future Research

Footnotes

Appendix

Acknowledgements

ORCID iDs

Ethical Considerations

Informed Consent Statement

Author Contributions

Funding

Declaration of Conflicting Interests

Data Availability

References