The Impact of Intellectual Capital Efficiency on Corporate Sustainable Growth-Evidence from Smart Agriculture in China

: In this paper, we expand the value-added intellectual coefficient (VAIC) model by constructing a comprehensive financial capital (FC) component. Human capital efficiency is subdivided into executive (EHCE) and nonexecutive human capital efficiency (NHCE). We have sampled listed agriculture companies (LAC) in China’s Shanghai and Shenzhen A-share markets from 2009 to 2018 and categorized them as high-tech (HTAC) and non-high-tech agriculture companies (NHTAC). We find that capital employed efficiency (CEE) and EHCE have a significant positive effect on corporate sustainable growth (CSG) of HTAC but no significant effect on CSG of NHTAC, while FC has a significant positive effect on both. These results suggest that companies, especially HTAC, should invest in human capital, and their executives and policymakers should develop effective knowledge management tools and begin accumulating the necessary intellectual capital to allow adaptation to their changing environment. In the spirit of the intellectual agriculture concept, we present some new ways to study the performance of agricultural companies using intellectual capital and offer suggestions that can help to modernize the industry.


Introduction
The population in China has increased, and available resources have become scarce [1]. The inefficiencies associated with traditional agriculture have hindered the optimization of human and land resources and contributed to ecological deterioration and the low recycling rate of water resources [2,3]. Due to the deepening financial crisis, the appearance of new domestic and foreign competitors, and relatively low profitability, China's agriculture has become unstable [3]. Some listed agriculture companies (LAC) have converted themselves to nonagricultural business or operated illegally. The proportion of LAC in the capital markets has decreased, and operating performance has declined [4]. The average net profit margin of LAC remains at only 3%, and total revenue is lower than in other industries [5]. The traditional research approach is to select financial indicators to analyze performance [6][7][8][9] and to focus on influencing factors like internal business operations and government policies [10][11][12]. As this approach is subject to the limitations of GAAP (generally accepted accounting principles), all of the performance and influencing factors cannot be presented in financial statements. Research on business performance has confirmed that the quantity of tangible assets is not the only key factor in maintaining a competitive advantage. Intellectual capital (IC), including developed a comprehensive indicator. In the VAIC model, most scholars use salary as a proxy indicator of human capital efficiency (HCE); however, the contributions of executive officers are different from those of ordinary employees. Executive officers play a decisive role in company operations and have more impact than nonexecutive employees [54,55]. The analysis of HCE needs to differentiate between executive and nonexecutive salaries.
To fill the gaps concerned above, first, this paper divides human capital efficiency (HCE) into executive human capital efficiency (EHCE) and nonexecutive human capital efficiency (NHCE) and divides agriculture companies into high-tech and traditional types and investigates the impact of IC on each. Second, this paper extends the VAIC model and uses factor analysis to calculate FC. Finally, this paper analyzes the impacts of IC on corporate sustainable growth and investigates the impacts of knowledge, intellectual capacity, and nonfinancial factors on the operations of LAC. The rest of this paper includes a literature review (Section 2), research design, hypotheses, and model development (Section 3), results, analysis and discussion (Section 4), and conclusions, suggestions for the industry, and suggestions for follow-up studies (Section 5).

Intellectual Capital: Definition and Methods
IC is not a static asset. In a sense, it is a series of "intellectual activities" rather than just "intellect as pure intellect" [56,57]. Stewart [58] defined IC as "something that cannot be touched, although it slowly makes you rich". Edvinsson and Malone [59] described it as a group of qualities that provide a competitive advantage. They include knowledge, practical experience, customer relations, technologies, and professional skills. The Chartered Institute of Management Accountants [60] defines IC as "the possession of knowledge and experience, professional knowledge and skill, good relationships, and technological capacities, which, when applied, will give organizations a competitive advantage." IC is a main strategic intangible asset, which can help to produce sustainable competitive advantage and achieve excellent financial performance [61]. The growing gap between market and book value has triggered research interest in this invisible value. Research on the IC concept is at an early stage. Most measurements still require more empirical support before the concept can be confirmed as a theory. Sullivan [62] mentioned that "(the) knowledge capital management movement is believed to have taken off from three distinctly different origins". As for the management of organizations, Sullivan proposed a logical explanation for the dependence on the knowledge and innovation of employees as key factors of business growth rather than the traditional production function [63].
Pulic [64] believed that physical capital efficiency was the only indicator used to measure corporate performance during the last two centuries. He established the VAIC model to assess three types of corporate efficiency: (1) Capital Employed Efficiency (CEE), the efficiency of physical capital and financial capital, (2) Human Capital Efficiency (HCE), the efficiency of employees and, (3) Structural Capital Efficiency (SCE), the efficiency of corporate systems and procedures. The integration of these components generates the value for VAIC. Higher VAIC values indicate more capability to create value. Pulic [50] analyzed 30 (UK) FTSE 250 enterprises between 1992 and 1998. He proved that there is a strong connection between the average value of VAIC and market value.
Firer and Stainbank [65] used the VAIC model to analyze the relationship between IC and profitability, productivity, market value, and other traditional measurements of business performance. Although a moderate positive correlation between a company's profitability and the value-added (VA) efficiency of its structural capital was confirmed, the correlation between profitability and the VA efficiency of the most important corporate resource was not confirmed. However, a significant correlation between productivity and the VA efficiency of human Agriculture 2020, 10, x; doi: FOR PEER REVIEW www.mdpi.com/journal/agriculture capital did exist. In general, the studies of Firer and Stainbank [65] indicate that in South Africa, physical capital is still the most important resource contributing to business performance. Chen et al. [66] completed an empirical study on the data of Taiwanese listed companies and used the VAIC model to explore the relationship between IC, market value, and corporate performance. Their results showed that investors preferred companies with higher IC efficiency. These results highlight the importance of IC for business and national economic growth.
Barathi Kamath [67] used the VAIC model to analyze the value-based performance of Indian private business banks from 2002 to 2007. This study ranked them based on indicators of IC efficiency and concluded that differences in human capital contributed to differences in performance.
Ghosh and Mondal [68] randomly selected 80 Indian software and pharmaceutical companies and studied the relationship between IC and traditional corporate performance. They used the VAIC model to analyze the impacts of IC (human capital and structural capital) and physical capital on performance and found that there was a connection between IC and corporate profitability, but no obvious correlation between IC and productivity or market value.
Mondal and Ghosh [69] studied 65 Indian banks and measured their value-based performance using the VAIC model. Their IC was critical to building and maintaining a competitive advantage, but there were different relationships between IC and profitability and productivity.
Tripathy et al. [70] analyzed the relationship between IC efficiency and corporate market value in India and found that the explanatory power of the individual elements of IC efficiency (physical capital efficiency, human capital efficiency, and structural capital efficiency) was much better than that of the integrated composite evaluation. Their results also showed that investment in innovation capital and relationship capital can positively affect corporate value.
Meles et al. [71] believed that IC can positively affect the performance of American banks and that HCE generates greater impacts on performance than other components of IC efficiency. Executives and policymakers can realize their goals by developing effective tools for knowledge management and accumulating IC.
These literature reviews underline the relationship between IC and corporate value creation and give solid reasons to develop a strategy of knowledge resource management. However, only a few studies have focused on the application of IC in agriculture in China. In this paper, we examine the relationship between IC and corporate performance in the agriculture industry.

The Impact of IC on Corporate Sustainable Growth (CSG)
Several studies have confirmed that IC has a significant impact on CSG [17,18,29,[72][73][74]. To operate in a global environment, companies need to build and maintain a competitive advantage, so they must depend on both physical capital and IC. Hitt et al. [75] proved that intangible capital played a more important role than tangible capital. Other studies have confirmed that IC contributed more to the sustainable growth of corporate efficiency, effectiveness, productivity, and innovation than physical capital [66]. Pulic and Bornemann [76] showed that companies can achieve added value through the application of IC and confirmed that it is a critical resource. Other research has also confirmed it as a value creator, and that IC drives corporate performance improvement, and is the most critical element in building and maintaining competitive advantage [77]. Pena [78] proved that IC management contributes to CSG and that it was usually in place at the planning stage. Mehralian et al. [79] examined the relationships between IC and profitability, productivity, and market value, and found that there was a significant positive correlation between human capital and CSG in low as well as in high knowledge-based companies.

Dependent Variables
The deeper meaning of corporate sustainable growth (CSG) is the continuous creation of value. Only sustainable growth with the continuous growth of value can represent true sustainable growth. Therefore, CSG should prevent financial risks and capital flows from being broken from the perspective of operating capital management, establish a link between value and growth from the value-added perspective, and make the growth serve the value [43]. At present, two types of CSG models are available: accounting-based and cash-flow-based sustainable growth models. We examine the growth of agriculture companies from the perspective of IC. Therefore, the cash-flow-based sustainable growth model is used in this paper. This type of model also includes the Rappaport model [54] and the Colley model [80].
To make the results more reliable, we use these two models to calculate the CSG of agriculture companies in China.
Colley's sustainable growth model is based on the following hypotheses: (1) The assetliability and dividend payout ratios remain unchanged, (2) pre-tax profits, current assets, current liabilities, fixed assets, and other assets increase in proportion to sales growth, and (3) depreciation can be used for fixed asset reinvestments. The formula is as follows: where, SGR1 represents the financially sustainable growth rate of agriculture companies; EBIT represents the earnings before interest and taxes; I represents the interest expenses on debt; t represents the income tax rate; DER represents the debt-to-equity ratio; DPO represents the dividend payout ratio; NA0 represents the net assets at the beginning of the period. Rappaport's sustainable growth model is based on the following hypotheses: (1) New shares will not be raised, (2) the gross operating profit margin, the investment growth corresponding to the sales growth per yuan, the target asset-liability and the target dividend distribution ratios remain unchanged, and (3) depreciation is used for maintenance expenses. The formula is as follows: where, SGR2 represents the financially sustainable growth rate of agriculture companies; represents net profit; 0 represents sales revenue at the beginning of the period; represents debt; represents equity; represents dividend payment rate; represents capital expenditure; represents working capital.
Human capital is divided into executive salaries and nonexecutive salaries. Therefore, HCE is calculated as follows.
where, EHCE and NHCE are, respectively, executive and nonexecutive human capital efficiency; EHC and NHC are, respectively, executive and nonexecutive salaries. Structural capital efficiency (SCE) In addition, SC is the structural capital. Capital Employed Efficiency (CEE) where, CA: physical capital and book value of net assets. Financial capital (FC): earnings per share, operating profit ratio, net assets per share, current ratio, asset-liability ratio, equity ratio, and quick ratio.

Control Variables
Size of company (TA) and the growth rate of the consumer price index (GCPI) are used as control variables. TA: natural logarithm of total assets; GCPI: development rate of CPI.

Proposed Hypotheses
Lots of existing literature have dealt with the relationship between IC and CSG, but none have focused on agriculture. We use the VAIC model proposed by previous scholars to evaluate IC. First, we extend the VAIC model and use factor analysis to calculate a comprehensive FC that considers all financial indicators that influence performance. Second, we divide human capital efficiency (HCE) into executive (EHCE) and nonexecutive human capital efficiency (NHCE) and study their impacts on performance. Last, to minimize external impacts, we use the natural logarithm of macroeconomic indicators and total assets as the control variable. Our preliminary model follows.
We use SGR1 and SGR2 as indicators of CSG, and for FC indicators, earnings per share, operating profit ratio, net assets per share, current ratio, asset-liability ratio, equity ratio, and quick ratio, and we use factor analysis to build a comprehensive FC. For VAIC, we use CEE, HCE, SCE to measure IC. We divide human capital into two dimensions, NHCE and EHCE, and we use the natural logarithm of the growth rate of the consumer price index (GCPI) and total assets as the control variable.
Based on previous research results and our analyses, we propose the following hypotheses.
H1: Capital employed efficiency (CEE) has a significant positive impact on CSG of hightech and non-high-tech agriculture companies.
H2: Nonexecutive human capital efficiency (NHCE) has a significant positive impact on CSG of high-tech and non-high-tech agriculture companies.
H3: Executive human capital efficiency (EHCE) has a significant positive impact on CSG of high-tech and non-high-tech agriculture companies.
H4: Structural capital efficiency (SCE) has a significant positive impact on CSG of hightech and non-high-tech agriculture companies.
H5: Financial capital (FC) has a significant positive impact on CSG of high-tech and nonhigh-tech agriculture companies.

Modeling
Our models follow. Model 1: Model 2: In Formulas (9) and (10), i refers to the company i; t refers to the year t; means the coefficient of the variable; C is the control variable; , is the error term of the model.

Statistical Methods
In this paper, many methods are employed to explore the relationship between intellectual capital efficiency and corporate sustainable growth. First, we adopt the descriptive and

Empirical Analysis and Discussion
This paper studies the impact of intellectual capital efficiency on corporate sustainable growth. We refer to the contents of "National economic industry classification", "Industry classification guidelines of listed companies", and "National key leading enterprises of agricultural industrialization". From the perspective of industry classification, agricultural companies include not only companies engaged in planting, forestry, animal husbandry, and aquaculture, but also companies closely related to agriculture, including agrarian sideline products processing industry and food manufacturing industry. From the perspective of the industrial chain, most companies cover the links of agricultural production and postnatal production.
We sampled 50 agriculture listed companies in Shanghai and Shenzhen A-share markets from 2009 to 2018 and divided them into one group of 29 non-high-tech companies, and another group of 21 high-tech companies. Using their annual reports and the Wind Website, we sorted and calculated them according to the VAIC model, and processed the data with Excel, Eviews, and Stata. Table 1 contains the descriptive analysis of variables, and Table 2 and  Table 3, the correlation analyses of variables of the two groups. The time span (T = 8) is relatively short, and its effect is not considered.    Table 4. For the high-tech group, Model (1) and (2) reject the null hypotheses, so the fixed effect model should be chosen. For the non-high-tech group, Model (1) and (2) also reject the null hypotheses, so the fixed effect model should be chosen for them as well. The results of the Hausman test are shown in Table 5. In the high-tech group, Model (1) rejects the null hypothesis (Prob. = 0.0000) and indicates that the fixed effect model should be chosen. An endogeneity problem will occur if we use the random effect model. Model (2) does not reject the null hypothesis, indicating that both the fixed effect and the random effect models are consistent, but the value of Prob. = 0.9869 implies that the random effect model is more appropriate. In the non-high-tech group, Model (1) does not reject the null hypothesis, indicating that both the fixed effect and the random effect models are consistent, but the value of Prob. = 0.0984 implies that the random effect model is more appropriate. Model (2) rejects the null hypothesis with Prob. = 0.0523, indicating that the fixed effect model should be chosen.  According to the results in Table 6, in the HTAC group, Model (1) rejects the null hypothesis (Prob. = 0.000), so there is in-group heteroscedasticity. In the NHTAC group, Model (2) rejects the null hypothesis (Prob. = 0.0000), and there is also in-group heteroscedasticity. According to the autocorrelation test results in Table 7, in the HTAC group, Model (1) rejects the null hypothesis (Prob. = 0.0004), and there is no first-order sequence autocorrelation. Model (2) does not reject the null hypothesis (Prob. = 0.6816), and there is a first-order sequence autocorrelation. In the NHTAC group, Model (1) rejects the original model (Prob. = 0.0025), and there is no first-order sequence autocorrelation. Model (2) rejects the original (Prob. = 0.0146), and there is also no first-order sequence autocorrelation. The final regression results are in Table 8. In the HTAC group, the coefficient of CEE in Model (1) is 36.8592, and the P-value is less than 0.01, while the coefficient of CEE in Model (2) is 80.0755, and the P-value is less than 0.01. Therefore, CEE has a significant positive correlation with both SGR1 and SGR2. H1 is confirmed. As one dimension of IC, CEE can be a good reflection of the positive impacts of IC. The coefficient of NHCE in Model (1) is 0.0023, and the P-value is greater than 0.1. The coefficient of NHCE in Model (2) is 0.0002, and the P-value is greater than 0.1. Therefore, NHCE has no significant correlation with SGR1 and SGR2. H2 does not hold. The coefficient of EHCE in Model (1) is 0.0001, and the P-value is less than 0.1, and in Model (2), it is 0.0099 with a P-value of less than 0.05. Therefore, EHCE has a significant positive correlation with SGR1 and SGR2. H3 is confirmed. The coefficient of SCE in Model (1) is 0.0217, and the Pvalue is less than 0.1, and in Model (2), it is 0.0385, and the P-value is greater than 0.1. Therefore, SCE has no significant correlation with SGR1 and SGR2. H4 does not hold. The coefficient of FC in Model (1) is 1.6087, and the P-value is less than 0.01, and in Model (2), it is 1.2776, and the P-value is less than 0.01. Therefore, FC has a significant positive correlation with SGR1 and SGR2. H5 is confirmed.
In the NHTAC group, the coefficient of CEE in Model (1) is 0.3017, and the P-value is greater than 0.1, and in Model (2), it is 1.8197, and the P-value is less than 0.01. Therefore, CEE has a significant positive correlation with SGR2 but has no correlation with SGR1. H1 is partially confirmed. The coefficient of NHCE in Model (1) is 0.0009, and the P-value is greater than 0.1, and in Model (2), it is 0.0181, and the P-value is less than 0.1. Therefore, NHCE has a significant positive correlation with SGR2 but has no correlation with SGR1. H2 is partially confirmed. The coefficient of EHCE in Model (1) is 0.0005, with a P-value greater than 0.1, and in Model (2), it is 0.0003, with a P-value greater than 0.1. Therefore, EHCE has no significant correlation with SGR1 and SGR2. H3 does not hold. The coefficient of SCE in Model (1) is -0.0024, and the P-value is greater than 0.1, and in Model (2), it is -0.0409, and the P-value is greater than 0.1. Therefore, SCE has no significant correlation with SGR1 and SGR2. H4 does not hold. The coefficient of FC in Model (1) is 0.0491, and the P-value is less than 0.05, and in Model (2), it is 0.0654, and the P-value is less than 0.01. Therefore, FC has a significant positive correlation with SGR1 and SGR2. H5 is confirmed. Table 9 shows a comparison between null hypotheses and empirical results, where "/" indicates that the hypothesis is partially confirmed, and "N" indicates that the hypothesis does not hold. We compare our results with the results of Lee and Mohammed [82], where they explored the impact of intellectual capital on agricultural firm performance. They also examined whether firm size and corporate governance characteristics as control variables influence performance. Their results indicated that intellectual capital has a positive impact on financial and productivity performances. However, the relationship between intellectual capital and economic performance is insignificant. The results also revealed that the capital employed and structural capital are major determinants of financial and productivity performances. Different from the study of Lee and Mohammed [82], this paper aimed to explore the relationship between intellectual capital and corporate sustainable growth. We used the variables proposed by Colley and Rappaport to represent corporate sustainable growth. Therefore, the results show that intellectual capital has a significant positive impact on corporate sustainable growth. In addition, we also divide human capital efficiency into two components to further explore the relationship between human capital and corporate sustainable growth, which is the main contribution of our study.

Conclusions
We have divided listed agricultural companies into high-tech (HTAC) and non-high-tech (NHTAC) groups and compared them using VAIC. We explain the impacts of IC on each group and demonstrate the effects of IC on CSG. Through the comparison, we have developed the following conclusions.  Companies should optimize their financial indicators (liquidity ratio, asset-liability ratio, equity ratio, quick ratio, earnings per share, operating profit ratio, and net assets per share). HTAC can improve economic efficiency through the development of intellectual agriculture and produce significant impacts on financial indicators.
The most critical part of IC is human capital (executive professional quality, ability to acquire knowledge, work experience, leadership strategy, and dynamic learning capacity). Therefore, investment in human capital should be included in long term plans. Although our results show an insignificant correlation between structural capital and CSG, many studies indicate that structural capital does have positive impacts on CSG. Structural capital can influence how human capital is applied to increase CSG. Considering the impacts of human capital, structural capital, and financial capital on CSG and the mutual interaction between these dimensions of IC, it is important to balance and coordinate the development of IC.
Because of the short-listing period of some companies, we have only selected companies listed from 2009 to 2018. In addition, we have focused on the Shanghai and Shenzhen stock markets. Hong Kong, Macao, and Taiwan's agriculture companies are not within the scope of this study. Scholars who are interested in this subject should expand their scope by introducing samples from other regions.
Author Contributions: The research is designed and performed by X.L.X. The data was collected by X.L.X. and H.H.C. Analysis of data was performed by X.L.X. and R.R.Z. Finally, the paper is written by X.L.X. and H.H.C. All authors have read and agreed to the published version of the manuscript.