Abstract
In the era of the digital economy, digital transformation is becoming essential for microeconomic entities to achieve high-quality development. This study utilizes A-share listed agribusinesses from 2007 to 2022, constructing a micro-level digital transformation index through textual analysis. It empirically examines the impact and mechanism of digital transformation on total factor productivity. Our findings indicate a positive effect of digital transformation on total factor productivity, considering endogeneity and other issues. The impact mechanism reveals that digital transformation enhances risk-taking ability, alleviates information asymmetry, and decreases financing constraints, thereby promoting total factor productivity (TFP). Heterogeneity analysis demonstrates varied impacts on ownership, industry, size, and region. This study provides an empirical basis for the high-quality development of agribusinesses and informs policy formulation from the perspective of agribusiness digital transformation.
1. Introduction
Under the background of rural revitalization in China, it is necessary to improve the level of agricultural industrialization, and agricultural enterprises have played an important role as market players. The raw materials and finished products of agricultural enterprises are mostly fresh and perishable products, which need to ensure freshness and food safety in a limited time, which makes it more important to encourage agricultural enterprises to improve production efficiency.
In the current era of the digital economy, digitalization is the general trend. In the 14th Five-Year Plan for Digital Economy issued by the State Council in 2022, it was put forward that “digital technology should be widely and deeply infiltrated into all fields of economic, social and industrial development … and a development pattern should be formed in which technological development promotes total factor productivity and field application drives technological progress”. In recent years, the digital economy has shown a trend of rapid development. In 2020, the added value of core industries in China’s digital economy will account for 7.8% of the GDP. As the micro-foundation of economic operation, enterprises also conform to this trend and steadily promote digital transformation with the help of digital technology (Peng et al., 2022).
In recent years, agricultural enterprises have begun to use automatic and digital food processing technology, preservation technology, and other digital technologies in the process of production and operation, which is conducive to the reduction of costs. However, owing to the high threshold, high cost, and imitatability of digital technology (Firk et al., 2021), how to overcome the paradox of digital transformation and obtain economic benefits in the digital economy has become a problem that agricultural enterprises need to study in depth. Therefore, this study puts forward the following questions: Does digital transformation help to improve the total factor productivity of agricultural enterprises? What path did agricultural enterprises follow? It is of great significance to analyze these problems for the high-quality development of agricultural enterprises in China.
Research has revealed that digital transformation improves companies’ economic performance, including their financial performance (Karimi et al., 2015), market value (Tao et al., 2023a,b), and innovation performance (Svahn et al., 2017; Galindo-Martín et al., 2019), and contributes to their non-economic performance, such as the specialization of the workforce (Yuan, 2021), sustainability (Li, 2023; Wang, 2022), and the performance of enterprises in the environment, society, and enterprise governance (ESG) (Hu et al., 2023). However, digital transformation has high failure rates (Loonam et al., 2018). Businesses are subject to operational restructuring (Weick et al., 1999) and may face challenges such as employee resistance (Hanelt et al., 2019). Additionally, they may fall into the digital transformation paradox (Danneels et al., 2022). While digital transition is crucial, limited studies on its impact on businesses and operational mechanisms exist.
This study takes the agricultural enterprises in the A-share market as the research object, constructs the degree of digital transformation employing text analysis, measures the development level of enterprises by using TFP, and investigates the influence of digital transformation of agricultural enterprises on TFP and its mechanism. Empirical research shows that digital transformation can promote the improvement of the TFP of agricultural enterprises, which is mainly achieved by improving risk-taking ability, alleviating information asymmetry, and reducing financing constraints. Heterogeneity analysis shows that digital transformation has a more obvious improvement on the TFP of state-owned agricultural enterprises, primary producers, small-scale agricultural enterprises and enterprises in the western region.
This study makes several contributions. On the one hand, the existing literature analyzes the influence of digital transformation on the economic performance and non-economic performance of manufacturing enterprises, while this study investigates the influence of digital transformation on the total factor productivity of agricultural enterprises, broadening the research object in this field. On the other hand, considering the ongoing development of the digital economy, understanding how digital transition affects agricultural enterprises becomes crucial. This research aims to analyze the effects of digital transition on TFP enhancement in agricultural enterprises, focusing on risk-carrying ability, asymmetric information, and funding restrictions. It offers a fresh perspective on integrating and developing the digital economy in practical business contexts.
The structure of this paper is as follows: Section 2 presents a summary of the literature and theory analyses, while Section 3 outlines the model design, variable selection, and data sources. Section 4 includes the underlying model regression, endogenous testing, an analysis of the impact of digital transition on agricultural TFP, and an examination of heterogeneity. Finally, Section 5 concludes the paper and provides recommendations.
2. Literature review
In the definition of digital transformation, digital transition serves as a strategic response to technological developments and disruptions (Vial, 2019), as well as significant societal and industry changes driven by the use of ICTs (Agarwal et al., 2010). It constitutes a vital element of digital transformation (Agarwal et al., 2010). Building on this foundation, Schallmo et al. (2017) and Selimovic et al. (2021) defined enterprise digital transformation as the application of IT in operations, business model innovation, or digitization strategy to create value for companies.
The influence of digital transformation on enterprises can be divided into positive and negative aspects. In the positive aspect, digital transition is an essential strategy for incumbent firms to retain and preserve their competitiveness in conventional sectors (Ferreira et al., 2019). Hence, the digitized conversion can improve business efficiency. First, the Internet and digital technologies have changed businesses’ traditional operating patterns, increasing their financial capacity (Karimi et al., 2015). Second, the use of digital technologies allows established companies to transcend institutional architecture and re-engineer their product development with a systemic integrated view, thereby enhancing their innovative capacity (Svahn, 2017). Concurrently, higher levels of ICT assist companies to transform their data and enhance their productivity and profit margins (Feng et al., 2023). Third, the digital transition offers entrepreneurs opportunities to explore new markets and reach a broader audience, thereby promoting global trade, competitiveness, and the creation of new businesses (Galindo-Martín et al., 2019). Moreover, the digital transition contributes to improving the economic performance of enterprises. It can reduce enterprises’ internal and external transaction costs, significantly improving the specialized division of labor (Yuan et al., 2021). Li (2023) reveals that in times of minimal disturbance in the market, a higher level of digital transformation leads to an improved environment. Wang et al. (2022) analyze the internal mechanism of the Internet’s influence on the green economy, demonstrating that Internet development can improve the industry structure, accelerate business innovation, and indirectly promote the green economy. Conversely, Hu et al. (2023), in a comprehensive study of the relationship between digital transition and the non-economic performance of enterprises, reveal that digital transition can improve environmental, social, and governance (ESG) performance in three ways: innovative techniques, transparent internal business information, and efficient decision-making and operations.
In the negative aspect, the complex and uncertain nature of the digital transition can also result in an organization’s digital transition paradox (Danneels et al., 2022). First, the digital transition is often a complicated and precarious long process involving large investment costs, which can be challenging to compensate for in the short term, resulting in various defaults (Loonam et al., 2018). Concurrently, the digital transition requires the rebalancing of completely new business models and organizational structures with modifications to existing ones (Vial, 2019). This can lead to a downward spiral of costs but not a higher return, and possibly even a backlash from an employee (Hanelt et al., 2019). Conversely, digital transformation creates several continuous paradigm shifts, portraying firms as dynamic systems constantly undergoing disruption and adaptation (Weick et al., 1999). However, conventional companies are often dependent on advanced sources and are not easy to restructure. Therefore, the digital transition puts companies in a difficult situation where they have to strike the right balance between organizational stability and flexibility. Overall, no scientific agreement on the effect of digital transition on companies exists. Moreover, while most of the existing research is targeted at production companies, limited emphasis is placed on the digital transition in agriculture.
While existing literature has analyzed the influence of digital transformation on enterprises from many angles, research gaps remain. First, most existing studies focus on manufacturing enterprises. Given the context of rural revitalization in China, agricultural enterprises significantly contribute to the modernization of agriculture and rural areas, necessitating improved production efficiency. This study examines the impact of digital transformation on the TFP of agricultural enterprises in China. Second, existing studies mainly focus on the impact of digital transformation on specific aspects of enterprise performance, such as financial capacity (Karimi et al., 2015), innovative capacity (Svahn, 2017), and profit margins (Feng et al., 2023). This study calculates the TFP of agricultural enterprises and analyzes the impact of digital transformation on agricultural enterprises. Third, the study explores the mechanisms through which digital transformation improves the total factor productivity of agricultural enterprises from three perspectives: risk-taking ability, information asymmetry, and financing constraints, thereby contributing to the existing research in this field.
3. Theoretical analysis
Enterprise theory suggests that the goal of an enterprise is to maximize profits and shareholder equity (Friedman, 1970). Moreover, the value of enterprise digital transformation is reflected in the improvement of enterprise performance (Hu et al., 2023). Enterprise digital transformation promotes the deep integration of digital technologies with production and operational activities, thereby improving operational efficiency. In the procurement process, digital supply chains, generated by digital technology, enable agribusinesses to understand the supply situation of suppliers, realize automated process allocation, and reduce procurement costs. In the production chain, the digital transformation of agribusinesses can improve the production process, promote the research and development of new products, and enhance product quality. Simultaneously, the use of digital technologies can boost production efficiency and safety and reduce production costs. This enables agribusinesses to maintain their competitive advantages under fierce market competition and improve corporate performance. In the management link, the large volume of data generated during enterprise operations is summarized and refined through big data analysis, making enterprise decision-making more timely, scientific, and accurate. Intelligent packaging systems, embedded with IoT technology, can automatically monitor the status of food items and respond instantly through autonomous decision-making, ensuring that unexpected food safety problems are promptly addressed (Chen et al., 2020). Simultaneously, enterprise digital transformation can compel management to strengthen the learning of advanced knowledge and technology, improve the cognitive structure and values of management, and enhance the decision-making capabilities of management personnel. These changes will affect the development strategy of the entire enterprise. In marketing, agribusinesses use digital technology to promptly understand market dynamics, better communicate with customers through Internet platforms, and accurately assess customer demand (Günther et al., 2017), to achieve optimal matching of supply and demand, consequently improving the TFP of agribusinesses. Therefore, we propose H1:
H1: Digital transformation of agribusinesses increases TFP.
First, digital transformation in agribusinesses is marked by technological and product innovations, often accompanied by risk and uncertainty. Therefore, digital transformation is a highly uncertain and risky investment that can directly improve corporate risk-taking capacity by stabilizing the supply chain, thereby improving business performance. This is primarily reflected in the ability to prepare for potential risks, respond swiftly when they occur, and subsequently recover and rebuild. Big data and advanced analytics enable agribusinesses to predict future demand trends (Wu et al., 2016). Additionally, the digital transformation of agribusinesses also has an obvious information spillover effect (Feng et al., 2023). Scenario simulation analysis can predict the impact of extreme events, aiding agribusinesses in preparing for risk warnings and prevention in advance. Secondly, consumers can interact with agribusinesses using various channels, such as mobile terminals and social platforms, and provide feedback on their needs, thus overcoming information transmission distortions. This enables agribusinesses to deliver timely, customized, and diversified products and professional services based on customer feedback (Shan et al., 2014), enhancing the ability of supply and demand docking and improving market competitiveness. The use of digital technologies also accelerates the storage, diffusion, and dissemination of information. This helps agribusinesses reach diverse upstream and downstream enterprises, making key contract elements, such as product price, process, and quality, transparent and comparable. Finally, it reduces enterprises’ search and negotiation costs (Xiao et al., 2013). From the perspective of internal control costs, the embedding of IoT technology allows for the extraction of extensive data, enriching the information available for enterprise decision-making. Under IoT technology, information flow, capital flow, and logistics data within the supply chain can be recorded accurately in real-time. Analyzing massive operational data and characteristic information through digital technology enables banks and other financial institutions to accurately assess the credit and risk status of agribusinesses. This subsequently helps overcome spatial and temporal limitations in lending, broadens indirect financing channels for agribusinesses, reduces financing costs, and improves financing availability and accessibility (Liu and Chiu, 2021). Therefore, we propose H2:
H2: The digital transformation of agribusinesses improves corporate risk-taking, reduces information asymmetry, and reduces financing constraints, thus contributing to TFP.
4. Empirical models
4.1 Model setting
To study the impact of digital transformation on the TFP of agribusinesses, we set the baseline estimation model as follows.
TFPit = β0 + β1 Digitalit + β3Xit + δi + μt + zit + εit
where TFPit is the TFP of agribusiness t in year i. The core explanatory variable Digitalit indicates firm-level digital transformation indicators. Xit represents various control variables, including firm size (size), two jobs (both), gearing ratio (debt_asset), board size (board), percentage of independent directors (inde_ration), and percentage of shares held by the first largest shareholder (Largerate). δi, μt and zit are individual-fixed effects, time-fixed effects, and provincial fixed effects, respectively, and εit is a random error term.
4.2 Selection of variables
Independent variables. The study employs Python to construct a digital transformation index for agricultural enterprises through text analysis. Initially, annual report data spanning from 2007 to 2022 are compiled and transformed into text format. Subsequently, a terminology dictionary for digital transformation is established, encompassing terms such as “digital platform,” “information technology,” “business intelligence” and “finance technology” to assess the extent of digital change among agricultural enterprises. Finally, the study quantifies the frequency of digital transformation-related words extracted from the financial reports of these enterprises. Given that the MD&A section typically details business conditions and developmental initiatives, this research uses MD&A narratives from annual reports to gauge the level of digital transformation in agricultural enterprises in the baseline regression. Furthermore, it conducts an endogenous test by analyzing the language used in the MD&A section of the annual reports.
Dependent variables. In this study, TFP is used to measure the efficiency of agricultural enterprises’ production processes (O’Donnell, 2018). Acknowledging the simultaneous equations bias inherent in least squares estimation (Huang et al., 2023), this study adopts the Olley and Pakes (OP) method (Olley and Pakes, 1996). This method uses investment as a proxy variable for unobservable productivity, which addresses the endogenous problem of production factors and productivity. Yasar et al. (2008) detail the operational steps of the OP method using Stata. Among them, the output factor is the total output of the enterprise, expressed by the logarithm ( y) of the main income of the enterprise. Input factors include enterprise age (age), capital (k), investment (i), price of intermediate inputs (m) and labor force (l).
We calculate the TFP of agricultural enterprises in China from 2007 to 2022 using the OP method. We also calculate the TFP of agricultural enterprises in the eastern, middle, and western regions, and the TFP of primary producers and processors, according to the types of agricultural enterprises. The results are shown in Figure 1. Overall, the TFP of agricultural enterprises in China exhibited an upward trend from 2007 to 2022. Concerning regions, in 2007, the TFP of agricultural enterprises in the western region was the lowest; however, it experienced the fastest growth rate, narrowing the gap between the three regions. Regarding enterprise types, the TFP of processors has always been greater than that of primary producers, from 2007 to 2022. However, the TFP of primary producers is growing rapidly, with the gap between them first expanding and then narrowing.
TFP of agricultural enterprises in China from 2007 to 2022.
Citation: International Food and Agribusiness Management Review 27, 5 (2024) ; 10.22434/ifamr1064
Ackerberg et al. (2015) identified collinearity issues in production function estimation and proposed the ACF method, which integrates labor input into the intermediate input function to alleviate these issues and enhance estimation accuracy. Consequently, this study employs the OP method for benchmark regression and the ACF method for robustness tests to estimate TFP for agricultural enterprises. In the ACF method, the added value of agricultural enterprises (add) is used as the output factor.
Control variables. Considering that other factors may affect the robustness of the empirical results, we incorporated several control variables, including enterprise size (size), two positions (both, assigned 0 if the chairman of the board of directors and general manager are the same individuals, and assigned 1 if not the same person), gearing ratio (debt_asset), size of the board of directors (board), the proportion of independent directors (inde_ration), and proportion of shares held by the largest shareholder (largerate).
4.3 Data sources
In this study, we selected agricultural enterprises listed on the A stock exchange between 2007 and 2022. The selected sectors are agricultural, forest, livestock, and fishing (A01–A05); agricultural and food processing (C13); food production (C14); and liquor, drinks, and refined tea (SEC). We excluded a sample of TT firms. Descriptive statistical data for key variables are presented in Table 1. The annual company data required for the business digital conversion index were derived from the Wind Data Base. Additionally, supplementary enterprise-level information is sourced from China Stock Market and Accounting Research Database (CSMAR) and (Chinese Research Data Services Platform (CNRDS).
Descriptive statistics of variables
Citation: International Food and Agribusiness Management Review 27, 5 (2024) ; 10.22434/ifamr1064
5. Empirical results
5.1 Benchmark regression results
Base regression analysis is presented in Table 2. Column (1) contains only key explanatory variables, whereas Column (2) contains individual fixed effects, time-fixed effects, and provincial fixed effects. In Column (3), the enterprise-level variables of concern are further controlled. Numerical conversion coefficients (digital_report) are all positive at 1%. In comparison with the previous two columns, the model settings in (3) are stricter, leading to a comparatively low estimation factor, so this research concentrates on the analysis of the coefficient in Column (3). Considering the economic significance of the results in Column (3), the TPF of agricultural enterprises rises by 11.6746% for each additional standard deviation of the level of digital conversion. These results show that digital conversion plays a significant role in improving the quality of TFP. Digital conversion of agriculture can expedite the incorporation of digitized techniques in the manufacturing and operating processes of agricultural companies, thus increasing the productivity and operating efficiency of agricultural enterprises. This, in turn, contributes to the rise of TFP. Thus, Hypothesis 1 is validated.
Impact of digital transformation on TFP of agribusinesses
Citation: International Food and Agribusiness Management Review 27, 5 (2024) ; 10.22434/ifamr1064
5.2 Endogeneity issues
In this study, the baseline regression may be influenced by endogenous issues. Specifically, the TFP of agricultural enterprises may be enhanced by digital transition, and a higher TFP may tend to undergo digital transformation. Furthermore, accurately measuring digital conversion is challenging, and any metric used can be prone to significant errors. Therefore, this study adopts the following approach to address possible reverse causality and measurement error problems to significantly mitigate potential endogenous effects.
First, considering that the development of TFP in agriculture is an accumulation process and that the degree of digitization can presently influence TFP, this study introduces a delayed one-cycle of key variables into the regression model.
Second, this study describes the external policy impact of “Broadband China” as an instrumental factor. Particularly, the State Council launched the “Broadband China” Strategic Implementation Program in 2013. Subsequently, from 2014 to 2016, the NRC designated 120 “Broadband China” model cities nationwide to facilitate the development of broadband and other infrastructure. In 2014, 2015, and 2016, 120 “Broadband China” model cities were established to accelerate the development of broadband infrastructure. The policy has significantly bolstered regional digital economic growth and laid a foundation for the implementation of digital activities by companies. Notably, given that the policy is not influenced by individual companies, it qualifies as exogenous. Therefore, if an agricultural enterprise is situated in a “Broadband China” model town established during or after 2014, an instrumental variable is assigned a value of 1. The results of the estimates are given in Table 3, Column 2. At a rate of 1%, the influence factor of the main explanatory variable digital _ message is substantial. This suggests that the key results of this research are still valid even when a tool is introduced in Broadband China to alleviate endogenous problems such as inverse causality.
Endogeneity test and robustness test
Citation: International Food and Agribusiness Management Review 27, 5 (2024) ; 10.22434/ifamr1064
5.3 Robustness test
First, the key explanation variables are revised. Considering the possible differences between the various DTI measurements and the fact that the MD&A is used as an example, this study makes a textual analysis of MD&A. The estimates are presented in Column (3) of Table 3. Moreover, a significant positive factor of the re-measured DPR was found to be a significant positive factor at 1%, thus confirming the underlying findings as reliable and reliable.
Second, reevaluate the explained variables. To increase the robustness of the benchmark regression results, the study re-estimates the total factor productivity of agricultural enterprises following Ackerberg et al. (2015). The results are presented in Table 3, Column (4). The regression result remains significantly positive after using the ACF method to calculate the total factor productivity of agricultural enterprises.
5.4 Mechanism test
Based on the foregoing analysis, we have achieved a reliable and accurate key conclusion employing standard model regression and endogenous approach. They argue that the digital conversion of agriculture helps to improve TFP. However, it is merely an empirical test of causality and not a mechanical one. According to Selimovic et al.’s (2021) definition, an enterprise’s digital transformation indicates that a company creates value by applying IT to its manufacturing and operating procedures. Thus, in this study, we use the intermediary effect model to investigate the transfer mechanism by which digital transition influences TFP in agricultural enterprises.
Improvement of risk-taking capacity. The digital transformation of agribusinesses is uncertain and can directly promote the improvement of enterprises’ risk-bearing capacity. Concurrently, because of the application of digital technology, agribusinesses benefit by enhancing their capabilities in early risk warning and prevention and improving their ability to prepare before a risk occurs, respond quickly when it occurs, and subsequently recover and rebuild. In this study, we use the standard deviation of annual individual stock returns of agribusinesses (groupsd) to measure risk-taking ability; the estimates are presented in Table 4. The influence factor of digital_report in Column (1) is positive, which shows that the TFP of enterprises is improved and that the additive effect is present. At 10%, the influence factor of the digital_report in Column (2) is remarkable, which shows that digital transformation can decrease the fluctuation of agricultural shares. In Column (3), the group’s coefficient is −0.3598. The ratio of the main explanatory variable to digital_report continues to be substantially positive, indicating that the impact of digital change on agricultural TFP is partially achieved through greater risk-taking capabilities.
Mechanism test: increasing risk-taking capacity
Citation: International Food and Agribusiness Management Review 27, 5 (2024) ; 10.22434/ifamr1064
Mitigating information asymmetry. To measure the effect of asymmetric information, we use the market index of the state in which the enterprise is located. The results from the estimates are presented in Table 5. The digital_report Factor in Column (1) is markedly positive, which means that there is an overall impact. Based on the forecast results of Column (2), the regional market index has been significantly improved by the digital conversion of agricultural enterprises. The main reason for this is that, from a supply-side perspective, the development of digital technologies will speed up the gathering, storing, and spreading of information, which will allow companies to access more information. This makes it more transparent and comparable in terms of the main components of a contract, for example, price, process, and quality. In addition, it makes it much easier for companies to communicate and cooperate and to cut down on finding and negotiating costs. Conversely, from the demand perspective, it is possible to precisely reflect the customer’s preferences and behavior. Customers will be able to respond in time to their requirements, which will improve the capacity of farmers to match supply and demand, lower trade costs, and mitigate asymmetric information. In Column (3), the experimental results of the intermediate effect are reported, and a significantly positive estimation factor for the market index is found. Digital conversion is still important, which shows that the TFP is enhanced through the mitigation of informational asymmetry, and the intermediate effect is 15.15%. Thus, Hypothesis 3 is valid.
Mechanism test: mitigating information asymmetry
Citation: International Food and Agribusiness Management Review 27, 5 (2024) ; 10.22434/ifamr1064
Reducing financing constraints. To test the financing effect of digital transformation, this study measures the degree of financing constraints of agribusinesses by using the SA index, following Charles et al. (2010), which is calculated as SA=−0.737*lnsize+0.043*lnsize^2−0.040*age. lnsize denotes the total asset size of an enterprise’s natural logarithm, and age denotes the firm’s operating year. The SA index is negatively related to the degree of financing constraints. Table 6 reports the estimation results of financing constraints. Column (2) indicates that digital conversion has an obvious positive impact on SA, which means that the financial constraints of agricultural enterprises can be alleviated by digitization. According to the outcome of the intermediary effect test described in Column (3), the TFP of agricultural enterprises is enhanced through easing funding restrictions. This can be attributed to the following reasons: (i) agribusinesses undergoing digital transformation release positive signals regarding their production and operations to the outside world, improving market confidence and simultaneously increasing the possibility of obtaining financing; (ii) with the application of information technology, such as the Internet, relevant information about business operations can be accurately and promptly recorded and further analyzed. This aids investment institutions in accurately portraying the credit and risk profiles of agribusinesses. Simultaneously, digital finance can overcome time and space constraints and broaden agribusinesses’ financing channels; (iii) digital transformation can reduce production costs, increase own funds, and realize capital accumulation. This allows enterprises to use their remaining funds for R&D investment and alleviate financing constraints, thus helping increase agribusiness TFP. Thus, H2 is verified.
Mechanism test: reducing financing constraints
Citation: International Food and Agribusiness Management Review 27, 5 (2024) ; 10.22434/ifamr1064
5.5 Heterogeneity test
The results of the empirical analysis of the whole sample indicate that the TFP of agricultural enterprises can be improved through digital conversion. However, considering the micro features of the sample companies, some differences in the level of digitalization exist. The study analyzes the impact of the digital transition on TFP based on the features of ownership, sector, size and region.
Heterogeneity analysis of agribusiness ownership. This research categorizes agricultural enterprises into two groups according to their ownership and returns to the group. The results of the regression are illustrated in Table 7, Columns (1) and (2). The digital transition significantly affects both state and non-state agricultural enterprises, with a stronger impact on state-owned farms. This may be attributed to the ample manpower and physical and financial resources that state agricultural enterprises possess. Additionally, owing to government ownership and control, SOEs bear greater social responsibility (Hu et al., 2023). In the digital economy era, state-owned farms are more likely to support and facilitate the digital transition, resulting in a positive impact on the TFP of state-owned agricultural enterprises.
Heterogeneity test
Citation: International Food and Agribusiness Management Review 27, 5 (2024) ; 10.22434/ifamr1064
Heterogeneity analysis of agribusiness industries.
To examine the diversity of farming activities across different sectors, this study classifies the farms in the sample as primary producers and processors of agricultural products based on the 2012 Sectoral Classification Standard published by the Stock and Exchange Commission. Primary producers comprise farming, forestry, livestock farming, and fishing, whereas processors include the production of food products, foodstuffs, drinks, and brewing. The estimates presented in Columns 3 and 4 of Table 7 reveal that the digital transition significantly impacts TFP for agricultural enterprises in both sectors. The growth of agricultural TFP is higher in the primary producers than in the processing industry. According to the theory of diminishing marginal returns, the increase in TFP in agricultural enterprises diminishes with the development of digitalization. Given that primary producers are predominantly involved in farming, livestock farming, and harvesting and demonstrate less digitization than processors, the TFP is more evident with the adoption of digital technologies.
Heterogeneity analysis of agribusiness size. Given that each enterprise has its resources and strategies, this study conducts group regression according to the size of the assets, utilizing both small and large firms. As evident from columns (5) and (6) in Table 7, the positive impact of digital transformation on small farms’ TFP is more significant, while for large enterprises, the impact on TFP is positive but not substantial. Large companies, having higher start-up costs, hold greater market expectations and output, leading to increased primary productive elements such as machinery and labor. Relative to small firms, the higher cost of digital conversion for larger firms does not exert a significant impact on TFP.
Heterogeneity analysis of region. To verify the heterogeneous performance of agricultural enterprises in different regions, the samples are divided into three groups based on their locations: eastern, central, and western. Regression analyses are conducted separately for each group, and the results are shown in Table 7, columns (7), (8) and (9). Generally, digital transformation positively impacts the TFP of agricultural enterprises in the three regions. Among them, the western region has the greatest impact, followed by the eastern region, with the central region experiencing the least impact. Agriculture in western China is relatively developed, which enhances western agricultural enterprises with sufficient enthusiasm to perform digital transformation. Additionally, the implementation of the “rural revitalization” strategy promotes the development of agriculture-related industries and provides policy support for the digital transformation of agricultural enterprises in the western region.
6. Conclusions and recommendations
In recent years, with the development of the digital economy, the digital transformation of agricultural enterprises has become more and more important. In this study, we select A-share listed agricultural enterprises from 2007 to 2022 as our sample to analyze the effect of digital transition on agricultural TFP. The results indicate that: (1) Digital conversion significantly promotes the improvement of agriculture TFP, and (2) Regarding the influence mechanism, digital transformation elevates agriculture TFP by enhancing risk-taking capacity, mitigating information asymmetry, and decreasing funding restrictions. (3) Regarding heterogeneous analysis, digital transition exerts a more significant influence on TFP of state-owned agricultural enterprises, primary producers, small-scale agricultural enterprises and enterprises in the western region. Therefore, the study proposes the following policy recommendations.
First, creating a supportive social environment for the digital transformation of agricultural enterprises necessitates ongoing improvements in our policies, laws, and regulations, particularly bolstering intellectual property. Second, the government should increase support for agricultural enterprises, intensify the promotion of digitization, and raise awareness among these enterprises about the implementation of digital transition. This approach facilitates easier access to credit and other funding sources for the digitalization of agriculture. Third, agricultural enterprises prioritize the TFP’s positive impact and expedite digital transition efforts. This involves investing in the research and development of digital technologies, utilizing smart manufacturing facilities, and implementing essential techniques to enhance agricultural productivity. Furthermore, it is crucial to focus on staff and company management enhancements to augment knowledge structure and value. They should also utilize network platforms for effective communication with customers to achieve high quality.
7. Future prospects
This study analyzes the influence of digital transformation on the total factor productivity of agricultural enterprises. The results indicate that digital transformation can promote the improvement of TFP in agricultural enterprises. However, this study focuses on all listed agricultural enterprises in China’s A-share market. Owing to data limitation, the samples are categorized into primary producers and processors in the heterogeneity test without further subdivisions based on the position of agricultural enterprises in the value chain. Future research will explore the heterogeneity of digital transformation in agricultural enterprises and analyze how to promote digital transformation across different types of agricultural enterprises from an entire value chain perspective.
Acknowledgements
This work was supported by the China Postdoctoral Science Foundation (Grant No. 2021M701673); the Post-Funded Project of Jiangsu Social Science Foundation (Grant No. 22HQB35); and the Jiangsu University High Level Talent Initiation Fund (Grant No. 21JDG004).
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