Enterprises analyze resources, technology, information and other factors in the external environment, and make decisions on whether to adopt strategic adjustment and change accordingly. Therefore, Beal et al. (2000) [18] summarized that external factors to some extent determine whether an enterprise changes its business strategy. However, the existing literature has not yet formed a unified conclusion on the mechanism of dynamic environment impacting strategic change. Li Shu et al. (2014) [19] thought that in the face of strong dynamic environment, enterprise is hard to get all the information that making decisions require from the environment, so companies will be based on the limited information and perceptual cognition to seek satisfaction solution, due to unpredictable environmental changes speed and direction, companies tend to keep the original strategy as a satisfactory solution, rather than the implementation of strategic change. After analyzing the previous literature, He Zheng et al. (2006) [20] concluded that when the dynamics of the external environment significantly reduces, the external environment is more suitable for the survival and growth of enterprises, and enterprises are more willing to take the initiative to change the existing strategy. Tan et al. (1994) [21] studied the relationship between environmental dynamics and enterprise strategy in the context of China’s economic transformation and believed that the stronger the environmental dynamics in the transformation process, the more likely the enterprise is to adopt defensive strategy rather than actively seek change.

At the same time, many scholars hold the opposite view. They emphasized that the dynamic environment has a promoting effect on the enterprise strategic change. Wu Lidong et al. (2012) [22] believed that only by keeping consistent with changes in the external environment can an enterprise not be eliminated and develop. Haveman (1992) [23] pointed out that enterprises will actively implement strategic changes to adapt to changes in the environment. Based on contingency theory, F. Luthans (1976) [24] proposed that strategic management should be timely integrated to match with the external dynamic environment. It should always pay attention to the matching degree between the fluctuation of the environment and the existing strategy. Once it was found that the inherent strategy no longer adapted to the changes of the environment, it should quickly change the strategy to ensure the adaptability of enterprise strategy to the environment. Based on the resource-based view, Wernerfelt (1984) [25] believed that heterogeneous resources are the basis for enterprises to build competitiveness. In the fierce competition in the external environment, only by actively adjusting the internal allocation of key strategic resources and forming non-replicable strategic resources can enterprises gain stronger competitiveness and profitability.

A high degree of environmental dynamics is accompanied by the high frequency of use of resources in the environment, the high change speed of customer preferences, and the high update speed of technology, only by optimizing the allocation of strategic resources, and actively making strategic changes to effectively seize the opportunity and improve the adaptability and matching degree of the enterprise in the external environment. Combined with practical cases, we find that many enterprises actively implement strategic changes in a dynamic environment and are able to stand out. Therefore, this paper believes that the positive promotion mechanism is more realistic. In conclusion, hypothesis 1 is proposed in this paper.

Hypothesis 1: With the enhancement of environmental dynamics, the degree of strategic change implemented by manufacturing enterprises is greater.

Graham et al. (2009) [26] regarded financial flexibility is the ability of an enterprise to timely acquire or utilize financial resources in order to cope with the dynamic changing environment, grasp investment opportunities and maximize enterprise value. Most literatures discuss the construction of enterprise financial flexibility from three dimensions: 1) high cash holdings. For example, Soenen (2003) [27] thought sufficient cash reserves can bring enterprises more choices and development opportunities, and it is easier to enhance financial flexibility; 2) low level of financial leverage. For example, Modigliani et al. (1963) [28] thought it is easier for enterprises to seize investment opportunities for financing and obtain high financial flexibility by keeping low financial leverage ratio; 3) appropriate payment policies. For example, Oded (2009) [29] pointed out that compared with cash dividends, stock repurchase plans retain the option of repurchase, which makes enterprises have stronger financial flexibility. In conclusion, enterprises should comprehensively consider cash holdings, financial leverage and payment policies to obtain strong financial flexibility, so as to meet the capital demand caused by unexpected events. Zeng Aimin et al. (2013) [30] proved through empirical data that when faced with major unexpected changes, enterprises with strong financial flexibility can timely raise and mobilize financial resources and respond more effectively to turbulent external environment or take advantage of favorable investment opportunities generated by random events.

In the highly dynamic environment, if the enterprise carries on the strategic transformation in time, it can maintain the adaptability to the environment and not be eliminated. Financial flexibility provides necessary resources for the implementation of strategic change and can reduce the loss caused by the failure of change. O’Brien (2003) [31] drew the conclusion that financial flexibility provides enterprises with necessary resources for the research and development of new products and investment in new fields, so that they can still maintain their competitive advantages in a dynamic environment. Marchica et al. (2010) [32] believed that enterprises with financial flexibility do not have to worry about capital chain rupture, so they are more willing to promote projects with high uncertainty. Combined with the above viewpoints, it can be seen that when an enterprise has strong financial flexibility, it will have favorable conditions for implementing strategic change in a dynamic environment and be able to promote strategic change more conveniently. In conclusion, hypothesis 2 is proposed in this paper.

Hypothesis 2: financial flexibility plays a positive role in regulating the relationship between environmental dynamics and enterprise strategic change.

The above two assumptions are summarized in this paper, and the theoretical model of this paper is shown in Figure 1.

1) Strategic Change

This study refers to existing studies and measures strategic changes with financial data. The specific measurement method is combined with the research of Zhang et al. (2010) [1] , strategic change value is measured by the strategic resource allocation between annual volatility. If the strategic resource that the enterprise allocates between year has bigger difference, the enterprise can be judged to have carried out a greater degree of strategic change, on the other hand, the degree of strategic change is smaller. The specific measurement steps are as follows: Step 1, get six dimensions of indexes of enterprise’s strategic resources: advertising rate (advertising promotional cost/operating income), R & d investment rate (R & D expenses/operating income), renewal rate of fixed assets (net value of fixed assets/original value of fixed assets), indirect rate ((management cost + sales cost, financial cost)/operating income), inventory levels (inventory/operating income), and financial leverage (incur debts/total assets). Step 2, calculate the difference between the values of the six intensities in year t and year t − 1, respectively. For example, “R & D intensity = |R & D intensity t − R & D intensity t − 1|”. Step 3 is to standardize the difference. Step 4 is to sum up the six standardized values and divide them by six to get the mean, namely the strategic change value.

2) Environmental Dynamics

Dess et al. (1984) [13] pointed out that environmental dynamics is the degree of change of events in the environment and the predictability of changes. The dynamics of the environment arise from the external environment, and the business activities of the enterprise are adjusted around the fluctuations of the external environment and the sales revenue of the enterprise changes accordingly. Therefore, Ghosh et al. (2009) [33] thought it is feasible to measure environment dynamics by the performance changes of the enterprise. Dess et al. and Niu Jianbo et al. measured the environmental dynamics based on the sales revenue of the enterprise. However, this article fails to obtain sufficient data for sales revenue in the database, so to replace sales revenue with operating income, and calculated the value of the environmental dynamics through the existing methods. In conclusion, based on the operating income to measure the degree of dynamic business environment, the concrete measure method refers to the research of Niu Jianbo et al. (2012) [34] with the variation coefficient of business income in five years to measure the dynamics of the environment, namely the enterprise standard deviation divided by the expected value of revenues for five years. The size of the variation corresponds to the environmental dynamics of the enterprise.

3) Financial Flexibility

Enterprise financial flexibility can be measured by three indexes: cash holding level, debt financing flexibility and equity financing flexibility. Due to the unique institutional environment in China, the state strictly monitors the equity refinancing process of listed companies, making it difficult for companies to have equity financing flexibility. Therefore, this index is not taken into account in the actual measurement. Zeng Aimin et al. (2013) [30] summed up three ways to define financial flexibility: a) single index judgment method. Compare whether a single financial index is higher than a certain standard to judge the strength of financial flexibility; b) multi-index combination method. Judge financial flexibility level by combining cash holdings and financial leverage ratio. If an enterprise holds a high amount of cash and has a low financial leverage ratio, it indicates that it has a high financial flexibility. On the contrary, if the cash holdings and financial leverage ratio of an enterprise are both at a low level, it reflects its low financial flexibility; c) multi-index synthesis method. After comprehensive analysis of the two types of financial indicators, assign specific weights to each category of financial indicators and sum up to get the final score, determine the level of financial flexibility according to the summation score. The following refers to the multi-index synthesis method, and follows the measurement logic of Wang Aiqun et al. (2017) [17] , the measurement formula: financial flexibility = cash flexibility + debt financing flexibility, where, cash flexibility = enterprise cash holding rate − industry average cash holding rate, debt financing flexibility = Max (0, industry average debt ratio − enterprise debt ratio).

This paper establishes and tests the regression model to verify the above two hypotheses. Model 1 is used to test hypothesis 1, model 2 is used to test hypothesis 2, and the product of Dyna_i,t and Ff_i,t is used to study the adjustment effect of financial flexibility. The model to be tested is as follows:

Ch i , t = β 0 + β 1 Dyna i , t + β 2 Age i , t + β 3 Esize i , t + β 4 Csize i , t + ε i , t (1)

Ch i , t = β 0 + β 1 Dyna i , t + β 2 Ff i , t + β 3 Dyna i , t × Ff i , t + β 4 Age i , t + β 5 Esize i , t + β 6 Csize i , t + ε i , t (2)

where, the dependent variable Ch_i,t represents the strategic change of year t of enterprise i, the explanatory variable Dyna_i,t represents the environmental dynamics of year t of enterprise i. the regulatory variable Ff_i,t represents enterprise financial resource flexibility, Dyna_i,t × Ff_i,t is the cross term, Age_i,t, Csize_i,t and Esize_i,t are the natural logarithms of enterprise age, total assets and total employees respectively. This paper mainly discusses the estimation of the regression coefficient of the independent variable β₁ in model 1 and the regression coefficient of the product term β₃ in model 2. If the regression coefficient of the independent variable β₁ in model 1 and model 2 is positive, the dynamic environment will positively affect the enterprise’s strategic change behavior. If, in model 2, the product term regression coefficient β₃ and the regression coefficient of the independent variable β₁ are opposite signs, it indicates that financial flexibility reduces the impact of environmental dynamics on strategic change. Otherwise, it shows that financial flexibility enhances the effect of environmental dynamics on strategic change. In addition, in order to eliminate the interference of other relevant factors on enterprise strategic change, the model controls the enterprise age, total employee size and total asset size. Detailed descriptions of each variable are shown in Table 1.

In this paper, manufacturing enterprises listed on the Shanghai and Shenzhen stock exchanges from 2011 to 2017 and listed in the industry classification standard of CSRC (SIC level 1 code) were taken as the basic sample database, and ST or *ST and enterprises with serious data loss are excluded, these excluded data was totally ignored. The data was from the WIND database. Finally, 1277 samples (a total of 8939 observations) were obtained. Excel2013 and Stata15.0 were used to process and analyze data in this paper.

The descriptive statistical results of each variable in this paper are shown in Table 2, including the basic descriptive statistical values of each variable. As shown in Table 2, the mean, standard deviation and range of the strategic change of the sample companies are 0, 0.5552 and 8.4202 respectively, which reflects the great difference in the strategic change among Chinese manufacturing enterprises.

The mean value of environmental dynamics is 0.2860, and the standard deviation and range are 0.2218 and 1.8621, respectively, indicating that the environment faced by manufacturing enterprises presents a certain dynamic nature, and there is a great difference among them. The average value of financial flexibility is 0.0792, the standard deviation is 1.4993, and the range difference is 22.5202, indicating that the level of financial flexibility reserve of China’s manufacturing enterprises is generally low, and different enterprises have different financial flexibility.

By analyzing the correlation of various variables in the model, we can preliminarily grasp the relationship among environmental dynamics, financial flexibility and enterprise strategic change. In the following paragraphs, Pearson correlation coefficient method is used to complete the correlation analysis of each variable, and STATA operation results are sorted out in Table 3.

The enterprise age has a small and insignificant correlation with the strategic change, and the total employee size, total assets size and the strategic change are significantly correlated. There is a significant positive correlation between the environmental dynamics and strategic change, indicating that dynamic environment can promote manufacturing enterprises to adopt strategic change behavior. There is a significant positive correlation between financial flexibility and enterprise strategic change, which reflects that manufacturing enterprises with financial flexibility are more inclined to adopt strategic change.

In this study, the samples selected balanced panel data for regression analysis. According to Hausman test results, the calculation results of fixed effect model are more effective. The data in this paper are panel data, which may have heteroscedasticity, serial correlation and other problems. The standard error will be underestimated by using the common panel data estimation method, resulting in biased model estimation results. Only the “Heteroscedasticity―sequence correlation” robust standard deviation can be unbiased, consistent and effective. Therefore, in the subsequent panel data model estimation, this paper mainly adopts “Heteroscedasticity―sequence correlation” robust standard deviation method for estimation. The time fixed effect is added into the model, and the time dummy variable is used to realize it, and the first dummy variable is removed to avoid full collinearity. The empirical results are shown in Table 4.

The dependent variable of the three models shown in Table 4 is strategic change, the benchmark model is the control variable regression analysis model, you can see from the benchmark model, the total employee scale of regression

Significance level: ***p < 0.01, **p < 0.05, *p < 0.1, N = 1277.

Significance level: ***p < 0.01, **p < 0.05, *p < 0.1, N = 1277. The values in parentheses are measured as p. The above model is the result of adjusting the “Heteroscedasticity sequence correlation” robust standard deviation.

coefficient is negative and the significance level is less than 5%, namely, it is negatively correlated with strategic change. However, the regression coefficients of the enterprise age and the total asset size of the enterprise are positive, so they are positively correlated with the strategic change. Adding environmental dynamics variables into the benchmark model to form model 1 to test hypothesis 1, the regression coefficient of environmental dynamics is 0.443, and the significance level is below 1%, indicating that the higher the environmental dynamics of manufacturing enterprises, the more willing they are to adopt strategic changes, and hypothesis 1 is supported. Model 2 is on the basis of model 1 to join the product of environment dynamics and flexible financial, flexible financial variables, the results show that the regression coefficient of the product of environmental dynamics and financial flexibility is 0.221, staying below 1% significance level, environmental dynamics regression coefficient is positive, the product term is the same sign as the regression coefficient of environmental dynamics ,it shows that flexible financial reserve can positively adjust the relationship between dynamic environment and strategic change of manufacturing enterprises, in line with the content of the hypothesis 2.