Relationship between Diversification, Institutional Environment and Growth: A Study of Agricultural Companies in China

Abstract

China’s economic growth has been heavily driven by its agriculture-related industries. This growth has been attributed to several factors, including government policies, technological advancements, and changing consumer preferences. This research aims to shed light on the underlying mechanisms that have enabled agriculture-related listed companies to thrive in China’s rapidly evolving economic landscape. Specifically, this study examines the role of the institutional environment, diversification strategy, and Confucian culture in the growth of these companies. Based on the institutional basic view theory, transaction cost theory, principal–agent theory, brand theory, and enterprise growth theory, this study uses empirical research to analyse the diversification strategy and growth of 204 agriculture-related listed companies from 2010 to 2019. The study selected companies listed as Class A in agriculture, forestry, animal husbandry, and fishery with complete data and more than three years of history. The selection also covered those whose income from agriculture accounted for more than 50% of the total in 2021, or those with the highest income related to agriculture accounting for over 30% of the total income. The collected data is analysed with SPSS and Stata. The results showed that the formal institutional environment significantly suppresses the diversification strategy of agriculture-related listed companies, while the Confucian culture in the informal institutional environment significantly promotes it. The interaction between the formal and informal institutional environments has a complementary effect on the choice of diversification strategy. The study also finds that diversification strategy significantly suppresses the growth of agriculture-related listed companies, and the formal institutional environment has a more significant inhibitory effect on the diversification strategy of agriculture-related listed companies. In the informal institutional environment, Confucian culture plays a more significant role in promoting the diversification strategy of agriculture-related listed companies. The output of this study is an empirical investigation of the level of institutional setting and diversity production, which could aid in sustainable development and revitalisation of rural communities. Applying the results of this study could help managers anticipate future policy changes and adjust their strategies accordingly, while also assisting companies in developing new products and services that meet changing demand.

1. Introduction

China’s rapid economic growth over the past few decades has been driven by the development of its agriculture-related industries [1,2]. As China is vast, and its economic growth is still in transition, the outcomes of its agricultural development have worldwide repercussions [3]. In the view of academics, China’s fast development and unique transition paths constitute a growing phenomenon that defies explanation [4]. This growth has been attributed to various factors, including government policies, technological advancements, and changing consumer preferences [5,6]. With a population of over 1.4 billion people, China is the world’s most populous country, and it has a rapidly growing middle class demanding higher-quality food products [7].

According to He et al. [1], the institutional environment is one of the critical variables that has influenced the sustainable development of agricultural listed companies in China. The institutional environment refers to the regulatory and legal frameworks, government policies, and other formal and informal rules that shape economic behaviour [8]. Document No.1 of the Central Committee first proposed “Supporting qualified agriculture-related enterprises to go public”. In July 2010, “Opinions on Promoting Innovation of Rural Financial Products and Services”, jointly issued by the “Three Meetings of the People’s Bank of China”, clearly expressed support for the listing of qualified agricultural enterprises and further broadened the financing channels of agricultural enterprises [9]. In response to policies, local governments have screened, drafted, and promoted the listing of agriculture-related enterprises.

There have been indications of a rising tendency toward increased variety in China’s manufacturing sector, though few academics [3,10,11,12,13] have sought to quantify the diversification associated with structural change, and even fewer have studied the degree and patterns of diversification across regions. Diversification strategy refers to the decision of companies to expand their operations into new sectors or markets to reduce risk and increase profits [14]. In China, many agriculture-related companies have diversified their operations to include multiple product lines or services [15], enabling them to adapt to changing market conditions and reduce dependence on a single product or market [2].

Fan et al. (2020) [5] suggested that cultural factors, such as Confucianism, influence the growth and success of agriculture-related listed companies in China. The Confucian culture emphasises values such as harmony, hierarchy, and respect for authority [16]. These values influence how agriculture-related companies interact with other stakeholders, such as government officials, customers and suppliers. For example, companies should prioritise building relationships with these stakeholders to maintain harmony and avoid conflict [17].

Overall, the growth of agriculture-related listed companies in China is a complex phenomenon influenced by various factors. By examining the role of the institutional environment, diversification strategy, and Confucian culture, this research aims to shed light on the underlying mechanisms that have enabled these companies to thrive in China’s rapidly evolving economic landscape.

Prior research has focused mainly on the diversification of technologies, market motives, and approaches for risk avoidance [3,9,13,18]. China’s agricultural industry has transformed significantly, driven by changing consumer preferences, policy reforms emphasising environmental protection, and food security concerns. Studying China’s structural change and development is essential for effective policy formation, whereas Li and Chen [2] argued for the need to measure the level of diversification in rural economies and its impact on rural development in China. Li et al. [19] also examined the relationship between institutional quality and rural development in China, and Zhou and Li [20] investigated the role of cultural diversity in promoting rural tourism development in China.

However, quantifiable measurement of diversification has yet to be performed to the extent of agricultural growth using Confucianism culture and organisational structure. By identifying the factors that have contributed to the success of these companies, managers can make informed decisions about investment, resource allocation, and strategic planning; for example, understanding the impact of government policies on the growth of agriculture-related companies can help managers anticipate future policy changes and adjust their strategies accordingly. Similarly, knowledge about consumer preferences and technological advancements can help companies develop new products and services that meet changing demands.

To address this, a study is needed to investigate the institutional setting and national diversity level for sustainable development and revitalisation of rural communities. This study empirically investigates the level of the institutional setting and diversity production.

2. Theoretical Analysis

This study is based on several well-established theoretical frameworks pertinent to the institutional environment, diversification strategy, and Confucian culture of Chinese agriculture-related listed firms.

According to the institutional basis theory, an organisation’s strategic conduct is impacted by its institutional context. This theory posits that institutions, including formal and informal rules and regulations, influence economic behaviour and outcomes [21]. This theory particularly applies to examining rising market economies, such as China, which are undertaking substantial institutional transformations and are defined by distinctive institutional arrangements.

Coase’s 1937 transaction cost theory applies to evaluating diversification techniques. According to this idea, when market transaction costs are high, corporations must internalise certain activities to cut costs and improve efficiency [22]. Due to information asymmetry, regulatory hurdles, and cultural differences, transaction costs are significant for agriculture-related listed companies in China.

The 1970s-born principal–agent theory applies to investigating agency relationships within businesses. This theory asserts that principals (such as shareholders) assign decision-making authority to agents (such as managers) and that conflicts of interest consequently occur [23]. In the context of agriculture-related listed firms in China, cultural variables, such as the importance of personal ties and the influence of Confucian principles, intensify principal–agent disputes.

The 1937 branding theory created by Lorenz applies to examining cultural elements that impact strategic action, suggesting that individuals have long-term reactions to specific environments that can influence their behaviour and decision-making [24]. In the context of agriculture-related listed firms in China, cultural variables such as the influence of Confucianism influences managers’ and other stakeholders’ behaviour and decision-making.

The enterprise growth theory, which incorporates both neoclassical and new institutional perspectives, applies to analysing factors that influence the growth of agriculture-related listed firms in China. This theory indicates that growth is driven by both internal and external factors, including firm-specific resources and market circumstances, and institutional arrangements, respectively [25]. In China’s transitional market economy, institutional variables such as regulatory changes and market-oriented policies play a crucial role in determining the growth of agriculture-related publicly traded enterprises.

This study intends to contribute to the literature on the institutional motivation of diverse strategic conduct and its economic repercussions in the setting of agriculture-related Chinese listed firms. Using these theoretical frameworks and China’s specific institutional and cultural context, this study intends to shed light on the factors influencing strategic behaviour and growth results in this crucial sector.

2.1. Construction of Theoretical Research Framework

Firstly, this paper analyses the influence of Confucian culture in the formal institutional environment and informal institutional environment, as well as their interaction on the diversification degree of listed agriculture companies, the mechanism of Confucian culture on diversification strategy choice, and the influence of Confucian culture in the formal institutional environment and informal institutional environment on diversification types of listed companies involved in agriculture. It tests the heterogeneity based on the nature of property rights.

Secondly, it examines the effects of diversification strategy and type on the growth of agriculture-related listed companies, and conducts a heterogeneity test based on property rights. Thirdly, it examines how Confucian culture in formal and informal institutional environments affects agriculture-related listed companies’ growth, and conducts a heterogeneity test based on property rights. Finally, the paper examines how Confucian culture in formal and informal institutional environments influences diversified strategic choices and agriculture-related listed company growth. It also examines whether diversification types help Confucian culture in formal and informal institutions grow agriculture-related listed companies (see Figure 1).

2.2. Hypothesis Development

2.2.1. Formal Institutional Environment and Diversification Strategy of Agriculture-Related Listed Companies

China has a vast territory, and each region’s natural geographical environment and economic base differ. The unsynchronised pace of regional market-oriented reform and regional development paths has widened this gap.

This has caused enterprises in different formal institutional environments to pursue different ways to acquire and utilise resources affecting enterprises, in order to implement different business strategies [26]. When the transaction cost of the external market is higher than the internal operation cost of the enterprise, and the enterprise has specific surplus resources [27], it will tend to choose a diversification strategy and compensate for the deficiency of the external market by digesting the internal resources, to alleviate the risks encountered by the enterprise’s specialised operation. Based on the above theoretical analysis, the following research hypothesis is put forward:

Hypothesis 1 (H1):

The more perfect the formal institutional environment is, the lower the diversification degree of agriculture-related listed companies will be.

2.2.2. Informal Institutional Environment and Diversification Strategy of Agriculture-Related Listed Companies

For the Chinese market in a transition economy, besides the formal institutional environment, some alternative mechanisms of the informal institutional environment also affect the business decision-making behaviour and survival, and development of enterprises [28]. China has 5000 years of history and civilisation, and its long-standing cultural thoughts are deeply rooted in the people’s hearts. In a sense, the informal cultural system is the root of entrepreneurs’ decision-making in China [29]. Confucianism’s idea of “risk aversion” is consistent with conservatism, which measures people’s attitudes toward unknown risks [30]. Based on the above theoretical analysis, the following research hypothesis is put forward:

Hypothesis 2a (H2a):

The greater the influence of Confucian culture in the informal institutional environment, the higher the diversification degree of agriculture-related listed companies.

The Confucian values of “loyalty and faith”, “self-cultivation and self-restraint”, and “valuing righteousness over profit” are conducive to alleviating the agency conflicts of enterprises, reducing the blind diversification strategic choices of listed companies involved in agriculture, and restraining the degree of diversification [31]. In business management, when the principal is absent and lacks necessary supervision, the self-disciplined agent should also have a solid binding force on himself. Based on the above theoretical analysis, the following research hypothesis is put forward:

Hypothesis 2b (H2b):

The greater the influence of Confucian culture in the informal institutional environment, the lower the diversification degree of agriculture-related listed companies.

2.2.3. Interaction between Formal and Informal Institutional Environment and Diversification Strategy of Agriculture-Related Listed Companies

As two essential aspects of the institutional system, the formal and informal institutional environments will significantly impact the decision-making behaviour of individuals or organisations. They are interdependent and complementary or competitive substitutes [32]. With the continuous improvement of the formal institutional environment, the positive influence of the Confucian culture in the informal institutional environment on the diversification of listed companies involved in agriculture will be strengthened; with the strengthening of the influence of Confucian culture in an informal institutional environment, the negative influence of formal institutional environment on the diversification of listed companies related to agriculture will be strengthened. Based on the above theoretical analysis, the following research hypothesis is put forward:

Hypothesis 3a (H3a):

The influence of Confucian culture in formal and informal institutional environments on the diversification degree of agriculture-related listed companies has complementary effects.

Under the imperfect construction of the formal institutional environment, the influence of the informal institutional environment makes up for the deficiency of the formal institutional environment and plays an alternative governance function, thus ensuring the smooth operation of economic activities [33,34]; on the contrary, when the formal institutional environment is perfect, the informal institutional environment rarely needs to play a role. In countries or regions where the formal institutional environment is imperfect, the informal institutional environment will play a positive role in substitution [35]. Based on the above theoretical analysis, the following research hypothesis is put forward:

Hypothesis 3b (H3b):

The influence of Confucian culture in formal and informal institutional environments on the diversification degree of agriculture-related listed companies has a substitution effect.

2.2.4. The Influence of Diversification Strategy on the Growth of Listed Companies Related to Agriculture

Enterprises’ diversification strategies have positive and negative effects on their economic consequences [36,37,38]. Although some scholars believe that the diversification strategy of agriculture-related listed companies has a more significant positive impact on enterprise development [10,39,40], most scholars have found that the diversification strategy of listed companies involved in agriculture has a more significant negative impact on the development of enterprises [4,10,40,41,42,43]. Furthermore, the system construction in China is relatively backward, and the listed companies’ supervision mechanisms still imperfect. Finally, as the primary industry of the national economy, agriculture-related policies are frequently adjusted, and the policy risks faced by listed companies related to agriculture will affect enterprises’ sustained and stable growth. Based on the above theoretical analysis, this paper puts forward the following research hypothesis:

Hypothesis 4 (H4):

The higher the degree of diversification, the lower the growth of agriculture-related listed companies.

2.2.5. Formal Institutional Environment and the Growth of Agriculture-Related Listed Companies

Since the reform and opening-up, China’s market-oriented reform has made remarkable achievements, and the construction of a formal institutional environment has been gradually improved. However, due to the different resource endowments in different regions and the speed of promoting national policies, the formal institutional environment construction in different regions is quite different, resulting in different development patterns in the east, the west and the east. Based on the above theoretical analysis, the following research hypothesis is put forward:

Hypothesis 5 (H5):

The more perfect the formal institutional environment is, the higher the growth of agriculture-related listed companies will be.

2.2.6. Informal Institutional Environment and the Growth of Agriculture-Related Listed Companies

The western mainstream theory holds that an excellent formal institutional environment, a sound market mechanism, and a perfect legal system are necessary for economic growth. However, China has achieved sustained and rapid economic growth without the necessary institutional environment conditions proposed by the mainstream theory [4,44,45]. The idea of honesty advocated by Confucian culture will encourage managers to form an honest management style and employees to form moral values [4], which is conducive to healthy and sustainable growth. “Innovation” is a critical concept advocated by Confucian culture. The Book of Rites University mentions, “if you are new, every day is new, and every day is new”, which advocates that people should have the innovative spirit of “innovating the old and innovating the new”. The Book of Changes points out that “Tian Xingjian, a gentleman, should strive for self-improvement”. It also points out that “being rich means great cause, and being new means being virtuous”, which advocates that people should follow the example of heaven and earth and strive for self-improvement and innovation. Relevant scholars’ research shows that Confucian culture can significantly promote enterprise innovation [20]. The greater the investment in innovation, the better the growth of enterprises [46]. Based on the above theoretical analysis, the hypothesis of promoting effect is put forward:

Hypothesis 6a (H6a):

The greater the influence of Confucian culture in the informal institutional environment, the higher the growth of agriculture-related listed companies.

The “golden mean” and “hierarchical concept” emphasised by Confucianism will hinder the growth of listed companies involved in agriculture. Lew et al. (2011) [47] pointed out that “the rationalism of Confucianism is to adapt to the world and fear any changes rationally”, and they believed that the golden mean advocated by Confucianism did not have the rationalism needed by the development of modern capitalism [48]. The critical feature of this high power distance culture is the absolute obedience of subordinates to superiors. People in the high power distance cultural atmosphere often mix compliments and speculations when transmitting information, and even filter out helpful information, which will reduce the efficiency and quality of information transmission [20], and hinder the improvement of enterprise growth. Based on the above theoretical analysis, the shackles effect hypothesis is put forward:

Hypothesis 6b (H6b):

The greater the influence of Confucian culture in the informal institutional environment, the lower the growth of listed companies related to agriculture.

2.2.7. Interaction between Formal and Informal Institutional Environment and the Growth of Agriculture-Related Listed Companies

According to the theory of new institutional economics, informal institutions, such as culture and customs, are at the first level, while formal institutions, such as market regulation, law, and property rights, are at the second level. There are differences in institutional evolution mechanisms and processes at different levels, both mutually restricted and interrelated [49,50]. On the contrary, when the formal system construction is sound and can play a better role, the role of Confucian culture in promoting the growth of agriculture-related listed companies will be weakened. Based on the above theoretical analysis, the following research hypothesis is put forward:

Hypothesis 7a (H7a):

The influence of Confucian culture in formal and informal institutional environments on the growth of agriculture-related listed companies has a substitution effect.

Although there are significant differences between the formal and informal institutional environments, they have a close relationship. Even in the United States, where the formal institutional environment is developed, the informal institutional environment also plays an important role [47,49,50]. With the improvement of the formal institutional environment, the role of Confucian culture in an informal institutional environment in promoting the growth of agriculture-related listed companies will be strengthened [51]; with the strengthening of the influence of Confucian culture in an informal institutional environment, the promotion of formal institutional environment on the growth of agriculture-related listed companies will be strengthened. Based on the above theoretical analysis, the following research hypothesis is put forward:

Hypothesis 7b (H7b):

The influence of Confucian culture in formal and informal institutional environments on the growth of agriculture-related listed companies has complementary effects.

2.2.8. The Intermediary Effect of Diversification Strategy in the Relationship between Institutional Environment and the Growth of Agriculture-Related Listed Companies

In the process of institutional transformation of comprehensively deepening reform, the question of how enterprises make correct strategic decisions is related to whether enterprises can survive and develop better [52]. Formal and informal institutional environments are essential factors that influence enterprises’ strategic choices and management results [18,53]. Confucian value norms, such as “risk aversion”, and ethical constraints, such as “faithfulness”, will affect the diversified strategic choices of enterprises by influencing the risk awareness level of the management of listed companies related to agriculture and the self-interest motivation of agents. When managers make diversified strategic decisions, they try to consider the interests of all parties, thus affecting the decision-making efficiency, and consequently affecting the growth promotion of listed companies related to agriculture. Based on the above theoretical analysis, this paper puts forward the following research hypotheses:

Hypothesis 8a (H8a):

A diversification strategy is a practical path for a formal institutional environment to influence the growth of listed companies related to agriculture.

Hypothesis 8b (H8b):

Diversification strategy is a practical path for Confucian culture in the informal institutional environment to influence the growth of agriculture-related listed companies.

3. Research Design

3.1. Sample Selection of Sample Agriculture-Related Listed Companies

The 2001 and 2012 China Securities Regulatory Commission Guidelines for Industry Classification of Listed Companies are used to classify listed companies in China. The list also shows the relationship between the Statistical Classification of Agriculture and Related Industries (2020) and the National Economic Industry Classification, including their scope and description [54].

The paper uses a detailed selection method to choose listed companies related to agriculture in China. The authors start by selecting Class A listed companies in agriculture, forestry, animal husbandry, and fishery from the Industry Classification Results of Listed Companies in the 4th Quarter of 2019 issued by the China Securities Regulatory Commission. Then, they select companies engaged in specific industries that correspond to the categories of the Statistical Classification of Agriculture and Related Industries (2020), and the National Economic Industry Classification (GB/T 4754-2017) [55]. The authors also eliminate samples with missing or undisclosed data, special treatment such as ST, and less than three years of observed values. Finally, they select samples with a proportion of main business income related to agriculture greater than 50% in the 2021 year, or the highest main business income related to agriculture among all businesses and accounting for more than 30% of the total business income of the enterprise when the proportion of all business income is less than 50%. In total, 204 listed companies related to agriculture were selected.

The sample study period was from 2010 till 2019. A total of 1660 valid sample observations were collected from 36 listed companies, which mainly engaged in agriculture, forestry, animal husbandry and fishery. 40 listed companies engaged in agricultural and side-line food processing; 36 listed companies engaged in food manufacturing; 36 listed companies engaged in wine, beverage, and refined tea manufacturing companies specialise in pesticides and fertilisers; 5 in veterinary drugs; 2 in natural rubber raw materials and agricultural plastic products; 4 in agricultural machinery manufacturing; 3 in wholesale; and 5 in retail.

After the author team revised the report in 2022, the general market index and formal institutional environment sub-index were published in China Provincial Market Index Database. All indexes are calculated using 2001 as the base year, making them comparable across years.

3.2. Evaluating the Growth of Agriculture-Related

The total growth score of sampled agriculture-related listed companies in each year from 2010 to 2019 was calculated using SPSS 26.0 statistical software and factor analysis. Calculation table output is omitted due to large data output. According to the principle that the original characteristic value is greater than one, five common factors are extracted yearly. The cumulative contribution rate is above 80%, accurately reflecting the evaluation index. The regression method calculates five common factors’ score coefficient matrix, score functions and growth score values.

The weight coefficient is the quotient of the five-factor variables’ cumulative contribution rate of variance and the final cumulative contribution rate. The factor-weighted total score calculated by the linear weighted sum formula measures enterprise growth.

Manually sorting and calculating the proportion of industries and products in an enterprise’s operating income in WIND and CSMAR databases, and consulting listed company annual reports for missing or incomplete data, yields the data needed to calculate diversification strategy.

WIND, CSMAR, and Juchao Information Network provide other financial data [56]. Quantile truncating all continuous variables by less than 1 percent and more than 99 percent removes extreme values from the research conclusion. Stata 17.0 processes data.

3.3. Control Variables

Referring to the relevant literature, the control variables in this study are mainly selected;

Table 1. Definition of Main Variables.

Key Variable	Variable Symbol	Variable Definition
Formal institutional environment	MD	Total Marketization Index in China Provincial Marketization Index Database (2022)
Confucian culture in an informal institutional environment	CONF200	Number of Jinshi in Ming and Qing dynasties within the radius of 200 km of the registered place of the company (standardized by dividing by 1000)
Degree of diversification	TEI	${\displaystyle \sum }_{i=1}^{n}P\mathrm{iln}\left(\frac{1}{\mathrm{i}}\right)$ i = the proportion of the industry i’s revenue to operating revenue, n = the number of industries calculated by three-digit industry codes.
Scale	Size	The natural logarithm of total assets at the end of the period.
Enterprise age	Age	The natural logarithm of the years of establishment of an enterprise
trading on equity	Lev	Total liabilities/total assets
Transferability degree of assets	Tran	Net fixed assets/total assets
Industry profit rate	IndRoa	The annual industry median of Roa of the enterprise
market prospect	IndTobinq	Enterprise Tobinq’s annual industry median
Industry diversification	IndHHI	Annual industry median of diversified HHI
provincial characteristics	Area	Take 1 when the enterprise is located in the eastern region, otherwise take 0.
Management shareholding	Msh	The ratio of the number of shares held by management to the total share capital
Combination of two jobs	Dual	Is the chairman concurrently the CEO?
The largest shareholder holds shares.	Shr1	The shareholding ratio of the largest shareholder
Political connection	Pc	If the chairman or general manager of the enterprise has served as a government official, take 1, otherwise, take 0.
Proportion of independent directors	Indir	The proportion of independent directors on the board of directors
Financing cost	Exp	Net interest expense/total profit of the enterprise
Enterprise growth	Growth	Comprehensive growth score calculated by factor analysis

elaborates on specific definitions of each variable.

3.4. Model Construction

This paper establishes a model to test the influence of the institutional environment on the diversified strategic behaviour of agriculture-related listed companies [57,58].

Div_it = β₀ + β₁MD_it + Controls_it + ∑Year + ∑Ind + ε_it

(1)

Div_it = β₀ + β₁CONF200_it + Controls_it + ∑Year + ∑Ind + ε_it

(2a)

Div_it = β₀ + β₁CONF200_it + β₂MD_it + Controls_it + ∑Year + ∑Ind + ε_it

(2b)

Div_it = β₀ + β₁MD_it + β₂CONF200_it + β₃MD_it*CONF200_it + Controls_it + ∑Year + ∑Ind + ε_it

(3)

Growth_it = β₀ + β₁ Div_it + Controls_it + ∑Year + ∑Ind + ε_it

(4)

(1) To test the influence of the institutional environment on the growth of listed companies involved in agriculture, the following models are established:

Growth_it = β₀ + β₁MD_it + Controls_it + ∑Year + ∑Ind + ε_it

(5)

Growth_it = β₀ + β₁CONF200_it + Controls_it + ∑Year + ∑Ind + ε_it

(6a)

Growth_it = β₀ + β₁CONF200_it + β₂MD_it + Controls_it + ∑Year + ∑Ind + ε_it

(6b)

Growth_it = β₀ + β₁MD_it + β₂CONF200_it + β₃MD_it*CONF200_it + Controls_it + ∑Year + ∑Ind + ε_it

(7a)

(2) In order to test the path of the institutional environment’s effect on the growth of agriculture-related listed companies, an empirical analysis is conducted using the intermediary effect test, which has been widely used in the field of corporate finance in recent years. Using the method of mediation, the following mediation effect test model is constructed:

Growth_it = α₀ + α₁Institution_it + Controls_it + ∑Year + ∑Ind + ε_it

(7b)

Div_it = β₀ + β₁Institution_it + Controls_it + ∑Year + ∑Ind + ε_it

(8a)

Growth_it = γ₀ + γ₁ Institution_it + γ₂ Div_it + Controls_it + ∑Year + ∑Ind + ε_it

(8b)

Among them, i represents the ith listed company related to agriculture, t represents the t year, growth is the proxy variable of enterprise growth, Div is the proxy variable of a diversification strategy, Institution is the proxy variable of the institutional environment (MD, CONF 200, respectively), Controls represents all the control variables, ∑Year represents the fixed effect of year, ∑Ind represents the fixed effect of industry, and ε is the residual error of regression model.

4. Empirical Results and Analysis

4.1. Descriptive Statistics and Correlation Analysis of Main Variables

As can be seen from

Table 2. Descriptive Statistics of Main Variables.

Variable	Sample Size	Average/Mean Value	Standard Deviation	Median	Minimum Value	Maximum
MD	1660	8.670	1.825	8.908	3.371	11.494
CONF200	1660	2.644	2.840	1.590	0.000	10.990
TEI	1660	0.447	0.413	0.368	0.000	1.431
Size	1660	21.932	0.985	21.801	19.868	24.691
Age	1660	2.814	0.340	2.890	1.609	3.367
Lev	1660	0.372	0.182	0.352	0.045	0.848
Area	1660	0.552	0.497	1.000	0.000	1.000
Pc	1660	0.452	0.498	0.000	0.000	1.000
Tran	1660	0.269	0.138	0.247	0.022	0.632
IndRoa	1660	0.095	0.059	0.081	−0.018	0.245
IndTobinq	1660	2.856	1.303	2.612	0.887	6.427
IndHHI	1660	0.137	0.092	0.117	0.015	0.465
Msh	1660	0.127	0.205	0.001	0.000	0.768
Dual	1660	0.269	0.443	0.000	0.000	1.000
Shr1	1660	36.436	14.211	35.609	4.078	79.658
Growth	1660	0.238	0.081	0.236	0.045	0.435
Exp	1660	0.249	0.852	0.031	−1.981	5.135
Indir	1660	0.376	0.062	0.333	0.182	0.8

, the minimum value of formal institutional environment MD is 3.371, the maximum value is 11.494, and the average value is 8.670, which shows that China, as a newly emerging transitional market economy, has significant differences in the development of marketisation among different regions. The minimum value of Confucian culture CONF200 is 0, the maximum value is 10.990 and the average value is 2.644, which shows noticeable differences in the influence of Confucian culture on different agriculture-related listed companies. The minimum TEI of diversification degree is 0, the maximum TEI is 1.431, and the average TEI is 0.447, which shows that the diversification degree of listed companies involved in agriculture is quite different. The distribution of other control variables is roughly the same as that of the existing research.

Table 3. Univariate difference test between groups.

Variable		Low Group			Higher Group			Mean Difference Test	Median Difference Test
		Sample Number	Average/Mean Value	Mid-Value	Sample Number	Average/Mean Value	Mid-Value
Panel A: group according to MD median value.	TEI	793	0.477	0.417	867	0.42	0.329	0.0570 ***	4.889 **
Panel B: group according to that value in CONF200	TEI	828	0.432	0.342	832	0.462	0.38	−0.0289 *	0.964
Univariate difference test between groups	Growth	828	0.25	0.248	832	0.226	0.22	0.0240 ***	39.480 ***
Panel C: group according to MD median value.	Growth	793	0.199	0.197	867	0.274	0.27	−0.0743 ***	261.341 ***
Panel D: group according to the values in CONF200	Growth	828	0.218	0.217	832	0.258	0.254	−0.0394 ***	52.781 ***

Note: Panel A and Panel B, in the table, the mean difference between groups is tested by t test, and the median difference between groups is Wilcoxon rank sum test. *, ** and *** indicate the significance level of 10%, 5% and 1%, respectively.

reports the test of univariate differences between groups of samples used for the study. The samples are divided into two groups, according to the perfect degree of the formal institutional environment construction. The group with lower and higher perfection degrees is included in the analysis.

The average diversification degree of agriculture-related listed companies in areas with low perfection of formal institutional environment is 0.477, which is more significant than those with high perfection. The difference between them is significant at 1%. Results are consistent with the expectation of Hypothesis 1. First, the samples are grouped according to the intensity of the influence of Confucian culture, and then the univariate difference test between groups is carried out.

Panel A shows that the average diversification degree of agricultural listed companies in the group with strong influence of Confucian culture is 0.462, which is more significant than those with low influence. Panel B reports the test results grouped according to the values in CONF200. The difference between them is significant at 10%.

The average growth of listed companies in agriculture with a low diversification degree is 0.250, while the difference between the two is significant at 1%. It shows that the higher the degree of diversification, the lower the growth of agriculture-related listed companies. This is consistent with the expectation of Hypothesis 3.

The more perfect the formal institutional environment is, the higher the growth of listed companies related to agriculture will be, consistent with the expectation of Hypothesis 4. Panel C in Table 3 reports the test results, grouped by MD median. The average growth of agriculture-related listed companies in areas with low formal institutional construction is 0.199, and the difference between them is 1%.

Confucian culture influences the growth of listed companies related to agriculture. The stronger the influence, the higher the growth rate of agri-related companies. This is consistent with the expectation of Hypothesis 7a from panel D on the Confucian phenomenon in China.

Table 4. Analysis of Correlation Coefficient of Main Variables.

	Growth	TEI	Size	Age	Tran	Exp	IndTobinq	IndHHI	Msh
Growth	1
TEI	−0.151 ***	1
Size	0.122 ***	−0.044 *	1
Age	0.123 ***	0.080 ***	0.169 ***	1
Tran	−0.106 ***	0.049 **	0.038	0.048 **	1
Exp	−0.142 ***	0.105 ***	0.006	−0.013	0.112 ***	1
IndTobinq	0.006	−0.148 ***	0.061 **	0.156 ***	0.008	−0.091 ***	1
IndHHI	−0.132 ***	0.071 ***	−0.109 ***	−0.066 ***	−0.042 *	0.060 **	0.007	1
Msh	0.134 ***	−0.025	−0.136 ***	−0.256 ***	−0.003	−0.065 ***	−0.051 **	−0.048 *	1
Indir	0.070 ***	−0.008	0.056 **	0.040 *	0.009	−0.022	−0.02	0.084 ***	0.028
Dual	0.153 ***	−0.026	−0.043 *	−0.139 ***	−0.054 **	−0.04	−0.025	−0.101 ***	0.222 ***
Area	0.434 ***	−0.024	−0.044 *	−0.042 *	0.032	−0.027	−0.03	0.007	0.140 ***
MD	0.536 ***	-	0.082 ***	0.167 ***	0.070 ***	−0.023	−0.028	−0.120 ***	0.116 ***
CONF200	0.353 ***	-	−0.034	0.079 ***	0.049 **	−0.015	−0.058 **	−0.128 ***	0.021
Lev		-			0.178 ***		−0.149 ***	−0.049 **	−0.158 ***
Pc	−0.126 ***	-	0.015	−0.157 ***	0.02	0.060 **	−0.071 ***	0.069 ***	0.113 ***
IndRoa	0.115 ***	-	0.161 ***	0.147 ***		−0.142 ***	0.654 ***	0.654 ***	−0.108 ***
Shr1	0.151 ***	−0.180 ***	0.133 ***	−0.102 ***	−0.034	−0.051 **	0.061 **	0.061 **	−0.057 **
	Indir	Dual	Area	MD	CONF200	Lev	Pc	IndRoa	Shr1
Indir	1
Dual	0.039	1
Area	−0.012	0.114 ***	1
MD	-	0.153 ***	0.669 ***	1
CONF200	-	0.108 ***	0.423 ***	0.521 ***	1
Lev	-	−0.037	−0.147 ***	−0.089 ***	−0.012	1
Pc	-	0.070 ***	−0.054 **	−0.076 ***	−0.095 ***	0.101 ***	1
IndRoa	-	−0.029	−0.033	−0.033	−0.047 *	−0.146 ***	−0.051 **	1
Shr1	-	0.026	0.092 ***	0.055 **	0.083 ***	−0.062 **	0.005	0.063 ***	1

Note: The lower triangle is Pearson correlation coefficient test. *, **, *** are 10%, 5% and 1% significance levels, respectively.

reports the correlation coefficients of the main variables. The correlation coefficient between the formal institutional environment and diversification degree is −0.091, which is significant at the 1% level. The formal institutional environment negatively correlates with the diversification degree of agriculture-related listed companies, without considering other influencing factors. This result supports the expectation of Hypothesis 1. A significant positive correlation exists between Confucian culture in an informal institutional environment and the diversification degree of agriculture-related listed companies, without considering other influencing factors.

This further supports the expectation of Hypothesis 2a. The correlation coefficient for this study is 0.032, which is significant at 10%. The correlation coefficient between the diversification degree and the growth of agriculture-related listed companies is −0.151, which is significant at 1%. This result shows that diversification degree is negatively correlated with the growth in agriculture-based companies without considering other influencing factors. The results support the expectation of Hypothesis 3, which suggests that diversification degrees are associated with economic growth.

The correlation coefficient between the formal institutional environment and the growth of agriculture-related listed companies is 0.536, which is significant at 1%. This result shows a significant positive correlation between the agri-industrial sector’s structure and the agricultural industry growth rate. The results support the expectation of Hypothesis 5. The correlation coefficient between Confucian culture and the growth of agriculture-related listed companies is 0.353, which is significant at 1%. This result shows a significant positive correlation between Confucian culture in the informal institutional environment and agricultural sector growth. The results support the expectation of Hypothesis 6a. However, because other control variables are highly correlated with the growth of enterprises’ degree of diversification, multiple regression analysis is needed to control other influencing factors to obtain more reliable conclusions.

4.2. Benchmark Regression Analysis

A mixed multiple regression method is adopted for the empirical test, and the test results are shown in

Table 5. Institutional environment and diversification degree of listed companies related to agriculture.

	1	2	3	4	5	6	7	8	9	10	11
	TEI	TEI	TEI	TEI	Growth	Growth	Growth	Growth	Growth	Growth	Growth
MD	−0.050 ***		−0.061 ***	−0.043 ***				0.015 ***		0.013 ***	0.016 ***
	(−6.381)		(−7.569)	(−4.066)				−14.709		−12.272	−11.607
TEI					−0.029 ***	−0.025 ***	−0.034 ***
					(−6.038)	(−5.834)	(−8.725)
CONF200		0.009 **	0.018 ***	0.005				0.005 ***	0.003 ***	0.002 **
		−2.294	−4.386	−0.847				−9.199	−5.871	−2.165
MD×CONF200				0.008 ***						0.001 ***
				−2.949						−3.209
Size	−0.027 ***	−0.031 ***	−0.022 **	−0.019 **		0.010 ***	0.008 ***	0.005 ***	0.007 ***	0.006 ***	0.006 ***
	(−2.760)	(−3.152)	(−2.226)	(−1.967)		−5.592	−5.043	−3.256	−4.582	−3.741	−3.913
Age	0.152 ***	0.114 ***	0.139 ***	0.130 ***		0.040 ***	0.013 ***	0.003	0.006	0.001	−0.001
	−4.57	−3.254	−4.147	−3.851		−7.487	−2.668	−0.655	−1.131	−0.112	(−0.135)
Lev	0.560 ***	0.566 ***	0.541 ***	0.533 ***
	−9.429	−9.517	−9.073	−9.067
Area	0.140 ***	0.003	0.125 ***	0.113 ***		0.068 ***	0.067 ***	0.028 ***	0.051 ***	0.025 ***	0.023 ***
	−5.365	−0.141	−4.778	−4.277		−20.743	−24.139	−8.03	−17.874	−7.195	−6.59
Pc	−0.01	−0.012	−0.004	−0.004				−0.016 ***	−0.013 ***	−0.015 ***	−0.015 ***
	(−0.521)	(−0.630)	(−0.237)	(−0.217)				(−5.405)	(−4.504)	(−5.168)	(−5.165)
Tran	−0.139 **	−0.160 **	−0.140 **	−0.145 **		−0.068 ***	−0.078 ***
	(−1.987)	(−2.218)	(−1.994)	(−2.063)		(−5.598)	(−6.638)
IndRoa	−0.31	−0.229	−0.277	−0.274				0.203 ***	0.200 ***	0.210 ***	0.211 ***
	(−0.920)	(−0.656)	(−0.835)	(−0.816)				−3.473	−3.177	−3.586	−3.586
IndTobinq	0.001	0.001	0	0		−0.002	0.004 **	0.001	0	0	0
	−0.089	−0.058	(−0.017)	(−0.008)		(−1.469)	−2.574	−0.359	−0.105	−0.219	−0.224
IndHHI	−0.403 *	−0.444 *	−0.425 *	−0.400 *		−0.081 ***	0.028	−0.004	−0.003	−0.008	−0.005
	(−1.659)	(−1.799)	(−1.783)	(−1.657)		(−4.548)	−0.686	(−0.113)	(−0.080)	(−0.220)	(−0.127)
Msh	−0.105 **	−0.113 **	−0.093 *	−0.096 **		0.038 ***	0.015 *	0.024 ***	0.032 ***	0.027 ***	0.027 ***
	(−2.211)	(−2.333)	(−1.952)	(−2.031)		−4.528	−1.942	−3.261	−4.274	−3.679	−3.641
Dual	−0.026	−0.043 **	−0.033	−0.028		0.016 ***	0.007 **	0.007 **	0.008 **	0.006 *	0.006 *
	(−1.238)	(−2.084)	(−1.592)	(−1.381)		−4.133	−2.012	−2.154	−2.374	−1.742	−1.945
Shr1	−0.003 ***	−0.003 ***	−0.003 ***	−0.004 ***				0.001 ***	0.001 ***	0.001 ***	0.001 ***
	(−5.054)	(−5.274)	(−5.478)	(−5.669)				−7.014	−6.172	−6.567	−6.413
Exp						−0.008 ***	−0.007 ***	−0.008 ***	−0.008 ***	−0.008 ***	−0.008 ***
						(−4.695)	(−4.648)	(−5.197)	(−5.019)	(−5.104)	(−5.108)
Indir						0.079 ***	0.032
						−2.927	−1.378
_cons	0.950 ***	0.853 ***	0.950 ***	0.774 ***	0.251 ***	−0.113 ***	−0.132 ***	−0.160 ***	−0.130 ***	−0.157 ***	−0.181 ***
	−4.208	−3.714	−4.213	−3.28	−91.798	(−2.886)	(−3.533)	(−4.204)	(−3.373)	(−4.133)	(−4.660)
F	30.087	28.145	29.594	28.433	36.455	72.184	68.194	71.722	64.528	72.961	72.401
adj. R2	0.269	0.251	0.278	0.282	0.022	0.3	0.524	0.556	0.532	0.565	0.567

Note: The heteroscedasticity adjusted T value is in brackets, and *, ** and *** indicate the significant level of 10%, 5% and 1%, respectively.

. Results show that Confucian culture impacts the diversification degree of listed companies related to agriculture, while diversification strategy affects the growth of agriculture-related listed companies, and the corresponding research hypothesis.

The results in Table 5 show that the institutional environment significantly impacts the diversification and growth of listed companies related to agriculture.

The dependent variable (TEI) is expressed differently based on the model’s specification. The independent variables include financial variables (MD, CONF200), firm-specific variables (Size, Age, Lev, Area), and industry-specific variables (Pc, Tran, IndRoa, IndTobinq, IndHHI).

The coefficients are represented by numbers with asterisks (***, **, *) indicating their significance levels. The asterisks indicate that the coefficients are statistically significant at 10% (), 5% (), and 1% () levels, respectively. The numbers in parentheses are t-statistics for each coefficient, which indicate how many standard deviations away from zero the coefficient is estimated to be. The t-statistics assess the hypothesis that each coefficient equals to zero (i.e., the null hypothesis). A larger t-statistic implies more robust evidence against the null hypothesis.

Based on the results, some variables have a statistically significant impact on TEI. Such as, MD has a negative and significant effect on TEI, while Lev has a positive and significant effect on TEI. The interaction term between MD and CONF200 also positively and significantly affects TEI. On the other hand, some variables do not significantly impact TEI, such as IndTobinq, which refers to the verification of hypotheses no-1, 2a, 3a, 4, 5, 6a and 7b.

4.3. Analysis of Empirical Results Based on the Intermediary Effect of Diversification Strategy

Table 6. Formal institutional environment, diversification degree and growth of agriculture-related listed companies.

	1	2	3	4	5	6
	Growth	TEI	Growth	Growth	TEI	Growth
MD	0.015 ***	−0.053 ***	0.014 ***
	−13.381	(−6.873)	−12.277
TEI			−0.023 ***			−0.033 ***
			(−6.389)			(−9.180)
CONF200				0.005 ***	0.011 ***	0.006 ***
				−9.313	−2.935	−10.191
Size	0.005 ***	0.01	0.005 ***	0.007 ***	0.007	0.008 ***
	−3.541	−0.992	−3.741	−4.957	−0.669	−5.235
Age	0.003	0.146 ***	0.007	0.006	0.103 ***	0.009 *
	−0.623	−4.307	−1.306	−1.155	−3.02	−1.867
Lev
Area	0.028 ***	0.124 ***	0.031 ***	0.051 ***	−0.025	0.051 ***
	−7.187	−4.776	−7.977	−16.391	(−1.181)	−16.53
Pc	−0.016 ***	0.003	−0.015 ***	−0.013 ***	0.001	−0.013 ***
	(−5.534)	−0.169	(−5.574)	(−4.565)	−0.059	(−4.667)
IndRoa	0.203 ***	−0.343	0.195 ***	0.200 ***	−0.249	0.192 ***
	−3.881	(−0.971)	−3.774	−3.722	(−0.699)	−3.657
IndTobinq	0.001	0.003	0.001	0	0.002	0
	−0.347	−0.203	−0.383	−0.102	−0.154	−0.14
IndHHI	−0.004	−0.440 *	−0.014	−0.003	−0.488 *	−0.019
	(−0.113)	(−1.767)	(−0.394)	(−0.082)	(−1.936)	(−0.524)
Msh	0.024 ***	−0.171 ***	0.020 ***	0.032 ***	−0.178 ***	0.026 ***
	−3.32	(−3.465)	−2.801	−4.289	(−3.568)	−3.571
Dual	0.007 **	−0.022	0.006 **	0.008 **	−0.041 *	0.006 **
	−2.133	(−1.014)	−1.997	−2.338	(−1.859)	−1.972
Shr1	0.001 ***	−0.004 ***	0.001 ***	0.001 ***	−0.004 ***	0.000 ***
	−6.599	(−5.649)	−5.727	−5.995	(−5.910)	−4.749
Exp	−0.008 ***	0.023 **	−0.007 ***	−0.008 ***	0.024 **	−0.007 ***
	(−4.736)	−2.116	(−4.452)	(−4.582)	−2.16	(−4.200)
_cons	−0.160 ***	0.424 *	−0.151 ***	−0.130 ***	0.313	−0.120 ***
	(−4.636)	−1.815	(−4.400)	(−3.672)	−1.329	(−3.460)
F	58.688	14.565	59.606	53.372	13.186	56.872
adj. R2	0.556	0.227	0.567	0.532	0.209	0.555
Sobel Z	4.679 ***			−2.796 ***
p-value	0			0.005

Note: The heteroscedasticity adjusted T value is in brackets, and *, ** and *** indicate the significant level of 10%, 5% and 1%, respectively.

shows the intermediary influence of diversification strategy and Confucian culture on agriculture-related listed enterprises in the informal institutional environment. The regression coefficient α1 between the formal institutional environment and the growth of agriculture-related listed firms is 0.015, which is significant at 1%, indicating that the formal institutional environment has dramatically improved the growth of these companies. The regression coefficient β1 between the formal institutional environment and diversification degree is −0.053, which is significant at 1%, demonstrating that improving the formal institutional environment considerably reduces the diversity degree of listed agricultural enterprises; column (3) shows that the regression coefficient between the formal institutional environment and agricultural listed company growth is 0.014, which is significant at 1%. Diversification has a −0.023 regression coefficient on agricultural listed company growth, which is significant at 1%. In addition, the indirect effect β1 and the direct effect 1 have the same sign, and the regression coefficient 1 (0.014) is smaller than the regression coefficient α1 (0.015), indicating that the diversification strategy is “1” in the formal institutional environment affecting the growth of listed agricultural companies.

Sobel Z value is highly positive at a 1% level, confirming the “partial intermediary effect”. Thus, a formal institutional environment can influence the growth of listed agricultural enterprises through a diversification strategy, confirming research hypothesis 8a.

Column (4) shows that the regression coefficient α1 between Confucian culture and the growth of agriculture-related listed firms is 0.005, which is significant at the 1% level, demonstrating that Confucian culture has greatly enhanced agriculture-related listed company growth. In column (5), the regression coefficient β1 between Confucian culture and diversification degree is 0.011, which is significant at 1%, indicating that Confucian culture has greatly increased the diversification degree of agriculture-related listed firms. Column (6) shows that Confucian culture and agriculture-related listed company growth have a regression coefficient of 0.006, which is significant at 1%. Diversification has a −0.033 regression coefficient on agricultural listed company growth, which is significant at 1%. However, the symbols of indirect impact β1 and direct effect 1 are different signs, and the regression coefficient 1(0.006) is greater than that of α1(0.005), indicating that the diversification strategy is “Confucian” in influencing agriculture-related listed businesses growth. The diversification method helps Confucian culture promote agriculture-related listed enterprises, proving research hypothesis 8b.

4.4. Expansive Analysis: Mechanism Test of Institutional Environment Influencing Diversified Strategic Decision-Making

The theoretical analysis shows that the formal institutional environment mainly affects the diversification strategy choice of listed companies related to agriculture by influencing the market transaction costs. The results of columns (1) and (2) in

Table 7. Regression results of mechanism test.

	Low Transaction Cost	High Transaction Cost	The Risk Level is Low	The Risk Level is High
	(1)	(2)	(3)	(4)
	TEI	TEI	TEI	TEI
MD	−0.073 ***	−0.021 **
	(−6.408)	(−2.049)
CONF200			0	0.015 ***
			−0.057	−2.872
Size	0.005	−0.048 ***	−0.013	−0.046 ***
	−0.303	(−3.869)	(−0.779)	(−4.133)
Age	0.161 ***	0.146 ***	0.089	0.132 ***
	−3.404	−3.149	−1.611	−2.92
Lev	0.522 ***	0.624 ***	0.461 ***	0.301 ***
	−6.42	−7.259	−4.249	−2.883
Area	0.206 ***	0.077 **	0.001	0.015
	−4.878	−2.359	−0.017	−0.588
Pc	−0.049 *	0.038	−0.001	−0.008
	(−1.759)	−1.495	(−0.028)	(−0.346)
Tran	−0.026	−0.209 **	−0.16	−0.189 *
	(−0.247)	(−2.241)	(−1.437)	(−1.963)
IndRoa	−0.297	−0.609	−0.038	−0.701
	(−0.635)	(−1.174)	(−0.065)	(−1.612)
IndTobinq	0.03	−0.008	0.008	0.008
	−1.519	(−0.489)	−0.424	−0.529
IndHHI	−0.366	−0.779 *	−0.374	−0.536 *
	(−1.154)	(−1.869)	(−0.923)	(−1.709)
Msh	−0.049	−0.145 *	−0.121	−0.144 **
	(−0.751)	(−1.948)	(−1.640)	(−2.163)
Dual	−0.083 ***	0.034	−0.058 *	−0.022
	(−2.677)	−1.261	(−1.772)	(−0.854)
Shr1	−0.007 ***	−0.001	−0.004 ***	−0.001
	(−6.347)	(−1.446)	(−4.080)	(−1.420)
_cons	0.486	1.190 ***	0.699 *	1.026 ***
	−1.462	−3.751	−1.788	−3.783
adj. R2	0.249	0.33	0.164	0.31
Coefficient difference between groups	−0.053 ***		−0.015 **
p-value	0		0.027

*, ** and *** indicate the significant level of 10%, 5% and 1%, respectively.

show that the test of inter-group coefficient difference is significant at 1%, indicating that the influence of the formal institutional environment on diversification strategy choice will be different because of the market transaction cost.

The results of columns (3) and (4) in Table 7 show that the test of the coefficient difference between groups is significant at 1%, indicating that the influence of Confucian culture on diversification strategy choice will be different because of the level of business risk. To a certain extent, this shows that risk aversion is an effective way for Confucian culture to influence diversified strategic choices.

4.5. Heterogeneity Test

State-owned enterprises and private enterprises often have different motivations for adopting diversification strategies. State-owned enterprises have more competitive advantages than private enterprises in obtaining loans, policy information and government subsidies [59], but it is also assumed that state-owned enterprises should achieve multiple goals of the government, such as improving the level of economic development, solving the employment, and stabilising the society [60].

Table 8. Heterogeneity Test.

	Institutional Environment				Diversification Strategy		The Intermediary Effect of Diversification Strategy
	Private Enterprise	State-Owned Enterprise	Private Enterprise	State-Owned Enterprise	Private Enterprise	State-Owned Enterprise	Private Enterprise	State-Owned Enterprise	Private Enterprise	State-Owned Enterprise
	(1)	(2)	(3)	(4)	(1)	(2)	(1)	(2)	(3)	(4)
	TEI	TEI	TEI	TEI	Growth	Growth	Growth	Growth	Growth	Growth
MD	−0.062 ***	−0.033 **					0.017 ***	0.014 ***
	(−5.874)	(−2.569)					−11.649	−7.985
CONF200			0.013 ***	−0.002					0.004 ***	0.006 ***
			−2.875	(−0.211)					−5.971	−5.231
TEI					−0.038 ***	−0.033 ***
					(−7.976)	(−4.875)
Size	−0.037 **	−0.011	−0.038 ***	−0.021	0.012 ***	0.004 *	0.009 ***	0.003	0.011 ***	0.006 **
	(−2.558)	(−0.703)	(−2.625)	(−1.440)	−5.039	−1.67	−3.809	−1.187	−4.345	−2.503
Age	0.109 ***	0.316 ***	0.075 **	0.252 ***	0.014 **	0.011	0.009	−0.013	0.009	0.003
	−3.122	−3.287	−2.02	−2.72	−2.482	−0.91	−1.557	(−1.137)	−1.558	−0.235
Lev	0.574 ***	0.556 ***	0.553 ***	0.593 ***
	−7.458	−5.957	−7.25	−6.386
Area	0.150 ***	0.171 ***	−0.009	0.089 **			0.020 ***	0.030 ***	0.048 ***	0.051 ***
	−4.469	−3.62	(−0.363)	−2.33			−4.296	−4.986	−13.03	−9.174
Pc	−0.036	0.084 ***	−0.03	0.074 **			−0.012 ***	−0.018 ***	−0.013 ***	−0.011 **
	(−1.543)	−2.707	(−1.281)	−2.306			(−3.341)	(−3.731)	(−3.504)	(−2.250)
Tran	−0.246 ***	0.033	−0.255 ***	0.006	−0.089 ***	−0.086 ***
	(−2.755)	−0.284	(−2.756)	−0.049	(−5.547)	(−5.016)
IndRoa	−0.709 *	0.661	−0.528	0.647			0.231 ***	0.152 *	0.219 ***	0.159 *
	(−1.664)	−1.241	(−1.158)	−1.237			−3.103	−1.765	−2.699	−1.742
IndTobinq	0.011	−0.019	0.005	−0.017	0.004 **	0.002	−0.001	0.001	0	0
	−0.658	(−1.076)	−0.291	(−0.946)	−2.025	−0.849	(−0.281)	−0.336	(−0.154)	−0.064
IndHHI	−0.156	−1.116 ***	−0.266	−1.069 ***	0.091 *	−0.180 ***	0.032	−0.139 **	0.043	−0.158 ***
	(−0.538)	(−2.790)	(−0.881)	(−2.704)	−1.791	(−3.181)	−0.716	(−2.519)	−0.876	(−2.688)
Msh	0.018	−1.302 ***	0.003	−1.291 **	0.029 ***	−0.116	0.028 ***	0.04	0.035 ***	0.123
	−0.344	(−2.680)	−0.066	(−2.458)	−3.506	(−1.064)	−3.479	−0.321	−4.241	−0.892
Dual	−0.041 *	0.034	−0.057 **	0.023	0.013 ***	0.008	0.014 ***	0	0.014 ***	0
	(−1.735)	−0.977	(−2.367)	−0.649	−3.328	−1.244	−3.775	−0.077	−3.775	−0.022
Shr1	−0.003 ***	−0.003 ***	−0.004 ***	−0.003 ***	0.058 ***	0.073 ***	0.001 ***	0.000 ***	0.001 ***	0.000 *
	(−4.171)	(−3.150)	(−4.967)	(−2.926)	−15.993	−14.972	−5.868	−2.891	−5.988	−1.942
_cons	1.150 ***	0.231	0.907 ***	0.418	−0.215 ***	−0.014	−0.262 ***	−0.027	−0.203 ***	−0.051
	−3.683	−0.499	−2.874	−0.937	(−3.928)	(−0.214)	(−5.121)	(−0.404)	(−3.783)	(−0.757)
Exp					−0.006 ***	−0.006 **	−0.009 ***	−0.003	−0.008 ***	−0.004 *
					(−2.831)	(−2.359)	(−4.843)	(−1.410)	(−4.364)	(−1.728)
Indir					−0.005	0.089 ***
					(−0.160)	−3.104
N	1042	618	1042	618	1042	618	1042	618	1042	618
F	13.164	36.247	13.498	35.738	37.763	49.299	41.659	50.217	34.264	46.505
adj. R2	0.193	0.433	0.201	0.425	0.49	0.647	0.528	0.649	0.489	0.631
Coefficient difference between groups	−0.029 **		0.015 **		−0.005		0.003 *		−0.002
p-value	0.036		0.033		0.248		0.097		0.104

*, ** and *** indicate the significant level of 10%, 5% and 1%, respectively.

shows how formal and informal institutional environments affect agriculture-related listed firms’ diversification strategies under different property rights. The heterogeneity test table is used to determine if there are differences in the results between subgroups within the sample. In this case, the subgroups are defined by the institutional environment (private enterprise vs. state-owned enterprise) and the diversification strategy (TEI, growth). The table reports the regression model’s coefficients and significance levels for each independent variable.

The first column shows the results for the private enterprise institutional environment, and the second column shows the results for the state-owned enterprise institutional environment. The third and fourth columns show the results for the diversification strategy of TEI and growth, respectively.

For example, the coefficient for the variable “MD” in the first row is −0.062. It is significant at the 1% level (***) in the first column, indicating that a unit increase in “MD” in private enterprise institutions leads to a decrease in the dependent variable by 0.062 units. On the other hand, in the second column, the coefficient for “MD” is not significant.

In the second row, the coefficient for the variable “CONF200” is significant at the 1% level (***) in the fourth column for the diversification strategy of growth in state-owned enterprises, indicating that in these enterprises, a unit increase in “CONF200” leads to an increase in the dependent variable by 0.013 units. Overall, the table shows the differences in the effect of the independent variables on the dependent variable for different subgroups defined by institutional environment and diversification strategy.

4.6. Treatment of Endogenous Problems

The institutional environment, an external environmental component that affects listed agricultural enterprises’ diversification plan choice, mitigates reverse causality’s internal difficulties. This research first employs topographic relief [61] to tackle endogenous difficulties between the formal institutional environment and the growth of agriculture-related listed firms. Second, employ the chastity memorial archway as a tool variable to solve any endogenous difficulties between Confucian culture and the expansion of agriculture-related listed firms. Thus, chastity archways are an excellent tool variable.

The results of the tool variable regression analysis in

Table 9. Regression Results of Tool Variables.

	Formal Environment and Diversification Degree		Confucian Culture and the Degree of Pluralism		Diversification Degree of Lagging Phase I and Growth			Diversification Degree and Growth
	(1)	(2)	(3)	(4)	(1)	(2)	(3)	(1)	(2)	(3)
	MD	TEI	CONF200	TEI	Growth	Growth	Growth	Growth	Growth	Growth
L.TEI					−0.030 ***	−0.022 ***	−0.033 ***
					(−5.931)	(−4.980)	(−8.052)
TEI								−0.020 **	−0.014 *	−0.013 *
								(−2.512)	(−1.900)	(−1.702)
Pai200			0.046 ***
			−168.372
Terrain	−0.501 ***
	(−15.639)
CONF200				0.009 **
				−2.468
MD		−0.109 ***
		(−5.212)
Size	0.124 ***	−0.021 **	0.006	−0.031 ***		0.009 ***	0.009 ***	−0.008	−0.018 ***	−0.018 ***
	−3.93	(−1.960)	−0.347	(−2.970)		−5.414	−5.586	(−1.490)	(−3.600)	(−3.604)
Age	0.423 ***	0.186 ***	0.286 ***	0.114 ***		0.023 ***	0.016 ***	0.091 ***	−0.03	−0.031
	−4.167	−5.305	−5.525	−3.448		−4.018	−2.978	−6.239	(−1.252)	(−1.248)
Lev	−0.298 *	0.544 ***	0.234 ***	0.566 ***
	(−1.697)	−9.443	−2.601	−9.931
Area	1.808 ***	0.276 ***	0.310 ***	0.002	0.070 ***	0.068 ***	−0.110 ***	−0.131 ***	−0.147 ***
	−27.349	−5.315	−9.866	−0.096	−20.936	−23.021	(−11.968)	(−14.222)	(−7.095)
Pc	0.045	−0.005	−0.001	−0.012
	−0.78	(−0.251)	(−0.022)	(−0.633)
Tran	0.35	−0.117	0.266 **	−0.160 **	−0.049 ***	−0.059 ***	−0.141 ***	−0.140 ***	−0.137 ***
	−1.596	(−1.631)	−2.374	(−2.256)	(−4.039)	(−4.833)	(−6.286)	(−7.702)	(−7.616)
IndRoa	−0.899	−0.378	0.355	−0.228
	(−0.849)	(−1.094)	−0.657	(−0.664)
IndTobinq	−0.006	0.001	0.008	0.001	0.003 **	0.004 **	−0.008 ***	0.004 **	0.004 ***
	(−0.163)	−0.063	−0.436	−0.054	−2.027	−2.474	(−6.015)	−2.321	−2.642
IndHHI	0.343	−0.371	−0.366	−0.445 *	−0.070 ***	0.054	−0.025	0.079 *	0.086
	−0.458	(−1.520)	(−0.956)	(−1.834)	(−3.842)	−1.165	(−0.555)	−1.906	−1.634
Msh	0.269 *	−0.090 *	0.339 ***	−0.113 **	0.026 ***	0.007	0.009	−0.028 *	−0.028 *
	−1.8	(−1.838)	−4.432	(−2.328)	−2.946	−0.848	−0.511	(−1.826)	(−1.729)
Dual	0.182 ***	−0.011	0.004	−0.044 **	0.011 ***	0.005	0.010 **	0.008 **	0.008 **
	−2.805	(−0.512)	−0.115	(−2.065)	−2.747	−1.366	−2.085	−2.075	−2.167
Indir						0.077 ***	0.033	0.013	−0.003	−0.002
						−2.83	−1.356	−0.326	(−0.085)	(−0.059)
_cons					0.255 ***	−0.079 **	−0.062	0.282 ***	0.702 ***	0.688 ***
					−93.336	(−1.964)	(−1.566)	−2.758	−5.73	−5.398
Exp					−0.006 ***	−0.006 ***	−0.002	−0.001	−0.001
					(−3.677)	(−3.601)	(−1.268)	(−0.976)	(−0.940)
Shr1	0.001	−0.003 ***	0.002 **	−0.003 ***
	−0.356	(−4.787)	−2.362	(−5.230)
Constant	2.583 ***	1.052 ***	−0.985 ***	0.852 ***
	−3.575	−4.43	(−2.676)	−3.653
F	244.591		28349		35.176	63.08	53.532
adj. R2	0.643	0.241	0.962	0.251	0.025	0.294	0.48	0.138	0.53	0.532

*, ** and *** indicate the significant level of 10%, 5% and 1%, respectively.

indicate the following:

The negative association between terrain and the formal institutional environment is confirmed after correcting for endogenous issues. The positive correlation between the formal institutional environment and the growth of agriculture-related listed businesses is confirmed, with a regression coefficient of 1%. The positive correlation between chastity memorial archways and Confucian culture is confirmed, with a regression coefficient of 1%. The positive association between Confucian culture and the growth of agriculture-related listed businesses is confirmed, with a regression coefficient of 1%. The negative association between diversification and the growth of agriculture-related listed businesses is confirmed, with a negative regression coefficient of 1%. The negative relationship between diversification and agriculture-related listed company growth remains negative after controlling for other variables, including individual, year, and industry fixed effects. The instrumental variable test supports the conclusion of the paper.

4.7. Robustness Test

(1)

Replacing the formal and informal institutional environment

Table 10. Replacing the formal and informal institutional environment.

	(1) TEI	(2) TEI	(3) TEI	(4) TEI
TEMP200		1.962 **	4.995 ***	6.346 ***
		−2.049	−5.202	−6.307
MD × TEMP200				2.750 ***
				−4.483
City_js		0.014 ***	0.017 ***	0.010 ***
		−5.527	−6.889	−2.687
MD × City_js				0.005 **
				−2.207
CONF250		0.006 *	0.013 ***	0.004
		−1.754	−4.018	−0.853
MD × CONF250				0.007 ***
				−3.237
MD	−0.050 ***		−0.063 ***	−0.049 ***
	(−6.381)		(−7.820)	(−4.581)
CONF150		0.018 ***	0.031 ***	0.015 *
		−3.218	−5.331	−1.743
MD × CONF150				0.010 **
				−2.178
LAW	−0.013 ***		−0.021 ***	−0.017 ***
	(−2.681)		(−3.958)	(−3.120)
CONF200		0.009 **	0.016 ***	0.006
		−2.294	−3.727	−1.238
LAW × CONF200				0.004 ***
				−2.732
GOV	−0.029 ***		−0.036 ***	−0.023 ***
	(−4.031)		(−4.916)	(−2.892)
CONF200		0.009 **	0.015 ***	0.003
		−2.294	−3.625	−0.668
GOV × CONF200				0.009 ***
				−3.895
F	28.891	28.145	28.18	27.806
adj. R2	0.257	0.251	0.264	0.27

*, ** and *** indicate the significant level of 10%, 5% and 1%, respectively.

reports the effect of institutional environment on the diversification degree of listed companies related to agriculture when variables are used as the proxy variable of formal and informal institutional environments. It can be seen from the table that the regression coefficient of GOV, LAW, CONF150, City_js, and TEI is statistically correlated at 1%; CONF250 and TEI are significantly positively correlated at 10%; MD and City_js are significantly positively correlated at 5%; MD and CONF150 are significantly positively correlated at 5%; TEM200 and TEI are at 5%; MD and TEM200 are correlated at 1%; and the regression coefficient of GOV and CONF200 interaction is positively correlated at 1% level. The above results are consistent with the benchmark regression results, which verifies the research conclusion of this paper.

(2)

Replacing the degree of diversification

Table 11. Replace the degree of diversification.

	(1)	(2)	(3)	(4)	(1)	(2)	(3)	(4)	(1)	(2)	(3)	(4)
	HHI	HHI	HHI	HHI	DSN	DSN	DSN	DSN	TEI	TEI	TEI	TEI
MD	−0.023 ***		−0.028 ***	−0.018 ***	−0.146 ***		−0.177 ***	−0.129 ***	−0.033 ***		−0.044 ***	−0.020 *
	(−5.304)		(−6.279)	(−3.131)	(−7.104)		(−8.388)	(−4.814)	(−3.766)		(−4.898)	(−1.786)
CONF200		0.004 *	0.008 ***	0.001		0.024 ***	0.050 ***	0.017		0.010 **	0.016 ***	0
		−1.839	−3.496	−0.421		−2.615	−5.168	−1.187		−2.477	−3.835	(−0.023)
MD×CONF200				0.005 ***				0.022 ***				0.011 ***
				−2.788				−2.961				−3.835

*, ** and *** indicate the significant level of 10%, 5% and 1%, respectively.

reports the influence of the institutional environment on the diversification degree of listed companies related to agriculture as a proxy variable of diversification degree. It can be seen from the table that the regression coefficients of MD and HHI are negatively correlated at a 1% level, MD and DSN are negatively correlated at a 1% level, CONF200 and DSN are positively correlated at a 1% level, MD and CONF200 interactions are positively correlated at a 1% level, the regression coefficients of CONF200 and HHI are positively correlated at a 10% level, coefficient of MD and TEI is negatively correlated at a 1% level, that of CONF200 and TEI is positively correlated at a 5% level, and that of MD and CONF200 interaction is positively correlated at a 1% level. The regression coefficients of MD and CONF200 interaction terms are positively correlated at a 1% level. The above results are consistent with the benchmark regression results, which verifies the research conclusion of this paper.

5. Research Conclusion and Prospect

5.1. Research Enlightenment

The research discussed in this paper aims to understand the diversification strategies and growth of listed companies in China’s agriculture sector. By analysing the essential characteristics of diversification strategy, enterprise growth, as well as the influence of formal and informal institutional environments on the decision-making behaviour of agriculture-related listed companies, the study aims to provide a complete understanding of the internal logical relationship between institutional environment, diversification strategy, and the growth of agriculture-related listed companies.

The study is based on system-based theory, transaction cost theory, principal–agent theory, branding theory and enterprise growth theory, and is influenced by China’s particular transitional economic system and Confucian culture’s unique informal system background. The study also considers the characteristics of the agricultural industry and agriculture-related enterprises.

The study is conducted using data from 204 agriculture-related listed companies selected from the perspective of the whole industry chain, collected from the years 2010–2019. The study’s findings indicate that the number of agriculture-related listed companies in China has been increasing yearly, with the number of agriculture-related listed companies in the middle reaches increasing slowly and the number of agriculture-related listed companies in the lower reaches increasing rapidly. The eastern region has the most significant number of listed companies related to agriculture, and the total number of listed companies related to agriculture in the western and central regions is similar. Additionally, the study found that the proportion of agriculture-related listed companies implementing diversification strategies has increased over time, from 58.9% in 2010 to 65.7% in 2019.

The overall diversification degree of listed companies involved in agriculture is relatively high, and the related diversification degree is higher than the unrelated diversification degree. The middle reaches of listed companies have the highest degree of diversification, while the lower reaches of agriculture-related listed companies have the lowest. The upstream agriculture-related listed companies have the highest degree of diversification, while the downstream agriculture-related listed companies have the lowest degree of diversification. The middle reaches agriculture-related listed companies have the highest degree of unrelated diversification, while the upstream agriculture-related listed companies have the lowest degree of unrelated diversification. The growth average of listed companies in the lower reaches of agriculture is the highest, and that of listed companies in the middle reaches is relatively low.

5.2. Implication of the Study

Based on the particular transitional economic background of China and the sample of listed companies related to agriculture from the perspective of the whole industrial chain, the results of this analysis can indicate an internal logical relationship between the institutional environment, diversification strategy, and the growth of listed companies related to agriculture from both theoretical and empirical aspects, which has significant theoretical value and practical significance.

5.2.1. Theoretical Implications

Theoretically, the study has significant implications for enterprise diversification strategy, institutional environment, and growth.

Firstly, the study provides new insights into the research of enterprise diversification strategy. It enriches the theoretical achievements of diversification strategy research and perfects the theory of strategic choice motivation based on the system-based view. By considering the influence of informal institutional environments such as traditional culture, this study provides a new interpretation of the diversification strategy choice of enterprises, especially agriculture-related listed companies. This is a significant contribution to the literature in this area and provides a more comprehensive and nuanced understanding of the motivation for diversification.

Secondly, the study deepens the theoretical analysis paradigm of “system-behaviour-performance (ICP)” under the dual characteristic situation of “transition + emerging” in China. It introduces the informal institutional environment of Confucian culture into the research framework. It explores the relationship between the formal and informal institutional environments and their impact on the diversification strategy choice of agriculture-related listed companies. This contributes to a more comprehensive and nuanced understanding of the influence of the institutional environment on enterprise behaviour and performance.

Finally, the study improves the theory of enterprise growth. By constructing a growth evaluation model and index system for agriculture-related listed companies and examining the influence of Confucian culture in both formal and informal institutional environments on the growth of agriculture-related listed companies, this study provides valuable insights into the impact of the institutional environment on enterprise growth. This contribution is particularly relevant for scholars and practitioners interested in understanding enterprises’ growth in the emerging market economy.

In conclusion, the study provides new theoretical insights into enterprise diversification strategy, institutional environment and enterprise growth. It advances the understanding of the interplay between these three variables in the context of China’s agriculture-related listed companies.

5.2.2. Managerial Implications

The study findings have several managerial implications for agriculture-related listed companies in China. Firstly, the study indicates that the formal institutional environment significantly inhibits the strategic diversification behaviour of listed companies involved in agriculture. Therefore, companies should be aware of the regulatory environment when making diversification decisions.

Secondly, the study highlights that Confucian culture in the informal institutional environment significantly promotes the diversified strategic behaviour of listed companies involved in agriculture. Thus, companies should consider cultural background when making diversification decisions. Thirdly, the study shows that a diversification strategy significantly inhibits the growth of agriculture-related listed companies in the long term. Therefore, companies should carefully evaluate the costs and benefits of the diversification strategy before implementing it.

Fourthly, there are significant growth of institutional environment of listed companies related to agriculture. Therefore, companies should be aware of the regulatory and cultural environment when making business decisions to ensure growth. Finally, the study indicates that unrelated diversification strategy has a more significant inhibitory effect on the growth of listed companies related to agriculture than related diversification strategy. Therefore, companies should be cautious about unrelated diversification and focus on related diversification to achieve growth.

5.3. Limitations and Future Research Directions

This study has some limitations that should be acknowledged. Firstly, there are some things that could be improved in the measurement and definition of diversification strategy. Due to the imperfections in the information statistics and disclosure of diversified industries and products in the annual reports of listed companies related to agriculture, some companies’ diversification strategies could not be fully captured. Additionally, the study was limited by a need for regional data on the products and industries of listed companies involved in agriculture. Therefore, it only measured the strategic diversification choices of listed companies involved in agriculture from the degree of business diversification without considering the degree of regional diversification. Secondly, there are limitations in the research samples. The study only used agriculture-related listed companies from the perspective of the whole industry chain as samples for relevant research. The research conclusions and insights are of greater guiding significance to agriculture-related listed companies. Therefore, further research is needed to understand the diversification strategies and growth of other listed or non-listed companies.

In further research, researchers can list the samples with relatively complete information disclosure of agricultural listed companies separately, and discuss the interaction among institutional environment, regional diversification strategy, and the growth of agricultural listed companies. In future research, the sample companies can be expanded to all listed companies to study the interaction among institutional environment, diversification strategy, and enterprise growth. This would make the research’s conclusions and insights more universal.

5.4. Conclusions

This study provides significant contributions to the literature on the growth and diversification strategies of agriculture-related listed companies in China. The findings emphasise the importance of understanding both formal and informal institutional factors in shaping the decision-making behaviour of these companies. The study highlights the need for companies to focus on their core business while optimising their diversified strategic behaviour, and for policymakers to improve the construction of formal institutional environments to create a fairer and more orderly business environment for listed companies involved in agriculture.

For managers in the agricultural industry, this study provides valuable insights into anticipating future policy changes and adjusting strategies accordingly. By understanding the factors that influence the growth and diversification of agriculture-related listed companies, managers can develop new products and services that meet changing demands and contribute to the sustainable development and revitalisation of rural communities. Furthermore, the study underscores the importance of Confucian culture in promoting the diversification strategy of agriculture-related listed companies, offering guidance for managers in navigating the cultural landscape of China.

For policymakers, the findings of this study could inform the formulation of policies that support the growth and development of agriculture-related listed companies, contributing to the broader goal of achieving sustainable and inclusive economic development. By creating a more favourable institutional environment for these companies, policymakers can promote innovation, entrepreneurship, and investment in the agricultural sector, unlocking new opportunities for growth and development.

Overall, this study makes a valuable contribution to agricultural economics and business studies, with important implications for real-life applications in the industry. By understanding the institutional environment and cultural landscape of China, managers and policymakers can work together to promote the sustainable growth and development of agriculture-related listed companies, contributing to the broader goal of achieving sustainable and inclusive economic development.