Frontiers in Business, Economics and Management ISSN: 2766-824X | Vol. 6, No. 2, 2022 197 An Empirical Study of Applying SPSS Technology to Analyze the Relationship between Enterprise Performance and Its Stakeholders ‐‐ Based on Henan listed company Hui Zhi1, 2, a 1Nanchang Institute of Technology Nanchang, China 2Philippine Christian University Center for International, Manila, Philippines a173223411@qq.com Abstract: This paper uses SPSS technology to extract the operating performance indicator F from the 2012-2014 panel data of listed companies in Henan Province from the factors of corporate profitability, operating capacity and solvency. Combined with the relationship with the government, employees, shareholders, customers, creditors and suppliers and other stakeholders, it conducts factor analysis and multiple regression analysis on the impact of comprehensive corporate performance indicators. Keywords: Enterprise performance, Comprehensive indicators, Stakeholders. 1. Introduction In recent years, with my country's reform and opening up, how to effectively promote enterprise development and how to improve operating performance has become a top priority for enterprises. Therefore, the overall performance of the company is evaluated according to the relationship between the company and its stakeholders. This article uses SPASS technology to analyze the three major capabilities of the enterprise, abandons the traditional direct measurement indicators, and uses factor analysis to measure the six major relationships between the enterprise and its stakeholders (with the government, employees, shareholders, customers, creditors, and suppliers). Comprehensive performance indicators of performance, through the use of multiple regression analysis, combine the results and draw corresponding recommendations. This paper, based on the three major capabilities of enterprises, extract the comprehensive indicators of financial performance rather than simply using the return on net assets, taking into account the interpretation of the comprehensive. 2. The Design of Empirical Research A. Research assumptions This paper uses the solvency, operational capacity and development ability to refine the comprehensive performance indicators of enterprises, and then analyse their impact on the stakeholders. It mainly selects three variables for explanation. First, the explanatory variable: F value, the comprehensive indicators of enterprise performance is used as a measure; the second is the interpretation variable which refers to the relationship with the stakeholders, including the relationship with the Government, the staff, shareholders, customers, creditors and suppliers. The third is the control variables: the year of listing, the nature of enterprises, business scale and industry.[1] 1) the relationship with the government. Government as the top of the enterprise regulatory authorities, not only guides enterprises through its policies, but also will give some support in the development of enterprises, so the relationship with the government have a great relationship in improving the performance of enterprises. Hypothesis 1: There is a positive correlation between enterprise performance and government relations. 2) the relationship with the employees. Employees as the main members to provide services to the enterprise, have an impact on the output, supply and sales of products; while enterprise performance is often dependent on the level of production and supply chain, so the enthusiasm of the production staff and good internal relations is helpful to maintain a health cycle of business performance. Hypothesis 2: There is a positive correlation between enterprise performance and the relationship with employees. 3) the relationship with the shareholders. The financing channels of enterprises are mainly divided into equity financing and debt financing. The issuance of shares is the main way of equity financing. The holders of shares i.e. shareholders have important influence on the working capital of enterprises, and they ultimately affect the performance of enterprises and long-term development by changing the enterprise governance through changes in the company's equity. Hypothesis 3: There is a positive correlation between enterprise performance and the relationship with shareholder. 4) the relationship with customers. The presence of the customers can explain whether the product is selling, whether it meets the needs of the public, whether the company achieves the greatest return at the lowest cost. Customers’ confidence and loyalty in the product will affect the performance of enterprises through sales. Hypothesis 4: There is a positive correlation between enterprise performance and the relationship with customers. 5) the relationship with creditors. Creditors are connected to enterprises based on the debt financing channels. On one hand they can ease the company's financial pressure and reduce financial risk, on the other hand they can produce the tax shield effect which leads to legitimate tax avoidance.[2] Hypothesis 5: There is a positive correlation between 198 enterprise performance and the relationship with creditors. 6) the relationship with suppliers. Suppliers as upstream enterprises providing raw materials for downstream enterprises, they need to set up a reasonable order price, do not make price adjustment without agreement, and establish good upstream-downstream relationship. Hypothesis 6: There is a positive correlation between enterprise performance and the relationship with suppliers. B. Index selection and variable definition Table 1. Variable definition table Variable Type English abbreviation Variable name Variable definition Explained Variable F comprehensive performance Explained variable factor Profitability total assets net profit margin net profit / total assets Net profit after tax net profit after tax / net assets Sales profit margin sales profit / business income Operating Capacity Accounts Receivable Turnover Business income / Accounts Receivable Total Assets Turnover Business income / total assets Inventory turnover rate operating costs / inventory Accounts Payable Turnover Cost of Sales / Accounts Payable Solvency Current ratio Current Assets / Current Liabilities Quick ratio quick current assets / current liabilities Cash ratio currency funds / current liabilities Liabilities to assets ratio total assets / total liabilities Explaining variable GCSR The relationship with the Government Total taxes / business income Tax return / business income ECSR The relationship with employees Cash paid to employees and paid for employees / business income DCSR The relationship with shareholders Tax Profit / Total Share Capita CCSR The relationship with customers Operating cost / business income LCSR The relationship with creditors Total Assets / Total liabilities SCSR The relationship with suppliers Operating Costs / Accounts Payable PCSR The relationship with charities Outgoing Donation/ business income Control variable AGE Year listed listing anniversary CHARACTER nature of enterprise 0 non-state-owned 1state-owned SIZE Scale of enterprise registered capital INDUSTRY Industry 1 Manufacturing 2 Service 3 Agriculture, Forestry, Animal Husbandry and Fishery 4 Nonferrous Metals C. Data sources and sample descriptions The data from this paper are from www.eastmoney.com and empirical analysis is carried out based on the panel data of A-share listed companies in Henan Province from 2012 to 2014. Screening the sample data during the study according to the following principles: 1.exclude ST and *ST type companies; 2.exclude listed companies that are missing data; 3.exclude listed companies with abnormal value of indicators so as not to affect the objectivity of the study, The According to the above principles after filtering, we get 70 sample business that meet the requirements. Data Analysis and Model Construction is conducted using SPSS17.0.[3] 3. Data Analysis and Model Building A. Frequency statistics analysis In this paper, the panel data of 70 listed companies in Henan Province for the years 2012-2014 are selected. Descriptive statistics are used to analyze the explanatory variables. The overall number of samples is judged from the values of maximum, minimum, mean and standard deviation, to aid the study later in this paper. The results of the descriptive statistics are shown in Table 2. From Table 2, it can be seen that the standard deviation of total assets, net assets, business income, current assets, current liabilities, sales cost and accounts receivable turnover ratio of enterprises is above 1000, which shows that the difference among enterprises is huge. This is due to the strength disparity of enterprise in different regions and with different sizes in the same industry.[4] B. Factor analysis This paper chooses the comprehensive index as the explanatory variable, according to the previous analysis of the total asset net profit margin, net asset net profit margin after tax and sales profit margin under profitability; accounts receivable turnover ratio, total asset turnover ratio, inventory turnover ratio and accounts payable turnover ratio of operating capacity; current ratio, quick ratio, cash ratio and liabilities under liquidity, a total of 11 indicators for using for factor analysis. 199 Table 2. Descriptive statistics values N Minimum Maximum Mean Standard deviation variance Year of listing 70 1 23 10.43 6.401 40.973 Nature of the enterprise 70 0 1 0.43 0.498 0.248 Scale of the enterprise 70 0.80 43.40 8.4617 8.23342 67.789 Industry 70 1 4 2.06 1.361 1.852 Net Profit 210 -35.00 40.40 2.0940 6.27597 39.388 Total Assets 210 1.46 511.00 63.7818 83.74109 7012.570 Total Assets profit margin 210 -0.17 0.28 0.0429 0.06326 0.004 Net profit after tax 210 -35.00 40.40 2.0940 6.27597 39.388 Net assets 210 -0.40 164.00 29.3180 33.47487 1120.567 Net assets profit margin 210 -3.12 2.47 0.05 0.30665 0.094 Business profit 210 -35.30 51.10 2.3012 7.54800 56.972 Business income 210 0.64 457.00 48.1462 76.84063 5904.483 Net profit margin 210 -3.62% 1.31% 8.25% 1.67% 279.413 Current assets 210 1.06 172.00 29.8102 34.49685 1190.033 Current liabilities 210 0.26 311.00 26.7026 44.55656 1985.287 current flow ration 210 0.41 22.83 2.3116 2.69601 7.268 Quick assets 210 0.47 161.20 22.2988 27.64440 764.213 Quick ratio 210 0.19 20.81 1.8016 2.36906 5.612 Money funds 210 0.18 93.30 9.8646 14.16711 200.707 Cash ratio 210 0.03 13.46 0.8473 1.36767 1.871 Total assets turnover(times) 210 0.03 4.20 0.7148 0.58266 0.339 Cost of sales 210 0.39 366.00 40.2670 66.09567 4368.637 Inventories 210 0.06 73.30 7.6123 10.55762 111.463 Inventory turnover rate 210 0.57 70.94 6.3815 9.65109 93.143 Accounts receivable 210 0.01 85.80 5.9230 10.46092 109.431 Accounts receivable turnover rate 210 0.35 1200.00 25.1321 97.70239 9545.756 Total taxes and fees 210 0.06 36.90 2.9984 6.00153 36.018 GCSR 210 0.00 0.48 0.0729 0.05921 0.004 Tax returns 210 0.00 298.00 3.0544 25.48907 649.693 Tax returns to business income ratio 210 0.00 0.15 0.0114 0.02256 0.001 Wage cash benefit 210 0.07 79.10 4.9766 10.78166 116.244 ECSR 210 0.01 0.49 0.1178 0.07580 0.006 Total share capital 210 0.67 168.87 8.4859 20.63872 425.957 DCSR 210 -15 15 0.38 1.750 3.064 CCSR 210 0.25 16.31 0.8741 1.26990 1.613 Total liabilities 210 0.46 407.00 35.1520 57.99241 3363.120 LCSR 210 0.20 28.13 3.1714 3.11330 9.693 Accounts payable 210 0.07 63.60 6.8618 11.12293 123.720 SCSR 210 0.91 99.75 9.4136 13.53178 183.109 Valid N (as listed) 70 Table 3. KMO and Bartlett inspection Samples of sufficient measures Kaiser-Meyer-Olkin 0.625 Bartlett’s sphericity test Similar chi-square 1536.863 df 36 Sig. 0.000 Table 4. Total variance of the explained variable Ingredient Initial Eigenvalue Extract square and input Total % of variance Accumulated % Total % of variance Accumulated % 1 3.414 37.936 37.936 3.414 37.936 37.936 2 1.638 18.196 56.132 1.638 18.196 56.132 3 1.194 13.272 69.404 1.194 13.272 69.404 4 0.930 10.337 79.740 5 0.777 8.632 88.372 6 0.731 8.126 96.499 7 0.224 2.488 98.987 8 0.083 0.918 99.905 9 0.009 0.095 100.000 200 The cumulative value of the variance of the factor after the extraction is higher, indicating that the extracted factor can describe these nine indexes well. According to the analysis of the variance contribution rate of Table 4, we can see that the variance contribution rate of Factor 1 is 37.936%, the characteristic value of which is 3.414, the variance contribution rate of Factor 2 is 18.196%, the characteristic value is 1.638, the variance contribution rate of Factor 3 is 13.272%, and the characteristic value is 1.194. These three initial eigenvalues are greater than 1 which explains the variance of 69.404%. Respectively, the Factors 1, 2, and 3 are named F1, F2 and F3. Table 5. Composition Matrixa Ingredients 1 2 3 Total assets profit margin 0.477 0.750 -0.147 Net assets net profit 0.230 0.562 -0.401 Net sales margin 0.579 0.407 -0.282 Flow ration 0.947 -0.141 0.224 Quick ratio 0.948 -0.155 0.233 Cash ratio 0.911 -0.173 0.264 Total asset turnover (times) -0.262 0.596 0.532 Inventory turnover -0.263 0.355 0.386 Accounts receivable turnover Rate -0.186 0.193 0.572 Table 6. Composition Score Coefficient Matrix ingredients 1 2 3 Total assets profit margin 0.140 0.458 -0.123 Net 0.067 0.343 -0.335 Net sales margin 0.170 0.249 -0.236 Flow ratio 0.277 -0.086 0.187 Quick ratio 0.278 -0.094 0.195 Cash ration 0.267 -0.106 0.221 Total assets turnover (times) -0.077 0.364 0.445 Inventory turnover -0.077 0.217 0.323 Accounts receivable turnover rate -0.055 0.118 0.479 The variance contribution rate (37.936%, 18.196%, and 13.272%) of the three principal components is normalized, and the variance contribution rate of each principal component is the weight, obtains the expression of the comprehensive performance index F as: F = 37.936%* F1 + 18.196% * F2 + 13.272%* F3 C. Regression analysis Before the linear regression analysis, the variance expansion factor coefficient test is carried out to clarify whether there are serious multiple collinearity problems between the model variables. The test results are shown in the following table: Table 7. Factor coefficient test Variable Collinearity statistics Tolerance VIF Year of listing 0.772 1.295 Nature of Enterprise 0.672 1.487 Size of Enterprise 0.863 1.159 Industry 0.751 1.332 GCSR 0.331 3.021 Tax return to business income ratio 0.834 1.199 ECSR 0.653 1.532 DCSR 0.849 1.177 CCSR 0.356 2.809 LCSR 0.737 1.357 SCSR 0.494 2.023 In general, the tolerance range is between 0 and 1, and the closer to 1 indicates the weaker the multiple collinearity. The variance coefficient of expansion factor (VIF) is between 0 and 10, indicating that there is no multiple collinearity between variables. In this paper, the tolerance of the variables is close to 1, VIF is about 3, indicating that there is no multiple collinearity between the explanatory variables, the model design is reasonable, so as to ensure the reliability of the linear regression analysis.[5] 201 Table 8. Model summary Model R R square AdjustedR square Estimated standard error Change in statistics Durbin- Watson Change in R square Change in F df1 df2 Change in Sig. F 1 0.854a 0.729 0.678 2.07076 0.729 14.187 11 58 0.000 1.880 Variables for forecast: (constant), year of listing, nature of the enterprise, enterprise size, industry, GCSR, ECSR, DCSR, CCSR, LCSR, SCSR, tax return to business income ratio. Dependent variable: F Table 9. Variance analysis Model Sum of squares df Mean square F Sig. 1 Regression 669.170 11 60.834 14.187 0.000a Residual 248.707 58 4.288 Total 917.876 69 Variables for forecast: (constant), Accounts payables turnover, year of listing, tax return to business income ratio, DCSR, enterprise size, asset-liability ratio, ECSR, industry, nature of enterprise, CCSR, GCSR. Dependent variable: F Table 10. Coefficienta Model Non-normalised coefficient Normalised Coefficient t Sig. B Standard error Trial version 1 (Constant) -2.186 0.876 -2.494 0.015 Year of listing 0.092 0.044 0.162 2.078 0.042 Nature of enterprise -0.159 0.61 -0.022 -0.261 0.795 Size of enterprise 0.054 0.033 0.122 1.664 0.102 Industry -0.251 0.211 -0.094 -1.189 0.239 GCSR 11.758 5.947 0.235 1.977 0.053 Tax return to business income ratio -6.099 11.434 -0.04 -0.533 0.596 ECSR -5.777 3.587 -0.136 -1.611 0.113 DCSR 0.458 0.184 0.185 2.488 0.016 CCSR -0.064 0.193 -0.038 -0.332 0.741 LCSR 0.687 0.083 0.659 8.283 0.000 SCSR 0.049 0.035 0.136 1.395 0.168 Dependent variable: F As shown in Table 7, the R value is 0.854, the R square is 0.729, the adjusted R square is 0.678, which means that the explanatory variable has a strong explanatory effect on the variable explained and the overall model fit is high. In general, when Durbin-Watson is between 1.5 and 2, it indicates that there is no autocorrelation problem between variables. The DW value of this model is 1.88, which satisfies the requirement. There is no autocorrelation problem in this paper. As shown in Table 8, the F test value is 14.187 and the Sig value is 0.00, less than 0.01, indicating that the regression effect is significant at the 0.01 level.[6] The 9 coefficient table shows: 1) The correlation coefficient between year of listing and F was 0.092, which indicated that the year of listing had a positive correlation with F. The P value of the bilateral test was 0.042 which was less than 0.05, and the significance test shows that the positive correlation between year of listing and F was significant. 2) The correlation coefficient between the nature of the enterprise and F was -0.159, which indicated that the nature of the firm was negatively correlated with F. The P value of the bilateral test was 0.795 which was greater than 0.05, and the significance test was not passed, which indicated that negative correlation between the nature of the enterprise and F was insignificant. 3) The correlation coefficient between enterprise size and F was 0.054, which indicated that the size of enterprise was positively correlated with F; the P value of bilateral test was 0.102 which was greater than 0.05, and the significance test was not passed, indicating the positive correlation between size of enterprise and F was insignificant. 4) The correlation coefficient between industry and F is - 0.251, indicating that the industry has a negative correlation with F; bilateral test P value is 0.239 which was greater than 0.05; it did not pass the significance test, indicating that the negative correlation between the industry and F was insignificant. 5) The correlation coefficient between GCSR and F was 11.758, which indicated that GCSR had a positive correlation with F; bilateral test P value was 0.053 which was less than 0.1, significant correlation was found in 90% confidence interval, GCSR was positively correlated with F significantly, which was consistent with Hypothesis 1. 6) The correlation coefficient between tax return to business income ratio and F is -6.099, indicating that the tax return to business income ratio is negatively correlated with F, and the bilateral test P value is 0.596 which is greater than 0.05, and the significance test is not passed. There was an insignificant negative correlation between tax return to business income and F, which was not consistent with Hypothesis 1. 202 7) The correlation coefficient between ECSR and F was - 5.777, which indicated that ECSR had a negative correlation with F; the bilateral test P value was 0.113 which was greater than 0.05, and the significance test was not passed showing that ECSR had an insignificant negative correlation with F which was inconsistent with Hypothesis 2. 8) The correlation coefficient between DCSR and F is 0.458, which indicates that there is a positive correlation between DCSR and F; the bilateral test P value is 0.016 which is less than 0.05, and the significant correlation between DCSR and F is obtained by the significance test, which is consistent with Hypothesis 3. 9) The correlation coefficient between CCSR and F is - 0.064, indicating that CCSR and F showed a negative correlation; the bilateral test P value is 0.741 which is greater than 0.05, it did not pass the significance test, CCSR and F has an insignificant negative correlation, which is inconsistent with Hypothesis 4. 10) The correlation coefficient between LCSR and F correlation is 0.687, indicating that LCSR and F were positively correlated with bilateral test P value of 0.00 which is less than 0.01, through the significance test, LCSR and F were significantly positive correlation, which is consistent with Hypothesis 5. 11) The correlation coefficient between SCSR and F was 0.049, which indicated that SCSR had a positive correlation with F, the P value was 0.168 which was greater than 0.05, and the significance test was not passed, SCSR had an insignificant positive correlation with F, which was not consistent with Hypothesis 6. 12) the regression equation obtained is as follows: F = -2.186 + 11.758 * GCSR + 0.687 * LCSR + 0.458 * DCSR + 0.092 * YEAR It can be seen from the regression equation that among the positive correlations, the government has the most significant impact on the performance of the enterprise. The influence coefficient is 11.758, followed by the relationship with the creditor, the relationship with the shareholder, and finally the year of listing of the enterprise.[7] 4. Conclusions and Recommendations A. Conclusion Based on the review of previous studies and the combination of normative research and empirical research, this paper first elaborates the present situation of enterprise performance, puts forward the research hypothesis, selects 70 listed companies in Henan as the research samples, and collects data of nine indicators to explain the comprehensive performance of enterprises, conducts descriptive statistical analysis, factor analysis and multivariate linear regression analysis using the SPSS17.0 software. The conclusions are as follows: The listed year has a significant positive correlation with the performance of the enterprise, indicating that in the sample data the earlier the year of listing, the better the development is, and the company's performance will also increase. And the relationship with the government was positively correlated, and the coefficient of influence was 11.758. It was also positively correlated with the shareholders. The coefficient was 0.458. And there was a significant positive relationship with the creditors. The coefficient was 0.687. There is an insignificant negative correlation between enterprise performance and industry, employees, customers and nature of the enterprise. The negative impact of these variables on enterprise performance is not significant. There is an insignificant positive correlation between enterprise performance and size of enterprise and suppliers. There is a positive correlation with little influence. B. Recommendations In this paper, the government, employees, shareholders, customers, creditors and suppliers as business stakeholders are used as a foothold to explore the relationship with the stakeholders to the impact on enterprise performance. Conduct reasonable plans with focus to improve enterprise performance according to the results of the model analysis. 1) enterprises should strengthen the relationship with the government, and actively fulfil their social responsibility. Take the initiative to carry out compliance with the law, take the initiative to fulfil their tax obligations defined by the government, pay taxes on time, although this will increase the short-term costs of enterprises, they can get support from government, so that they can utilise government’s preferential policies for enterprises and conduct a good external environment to the business. 2) Take the initiative to maintain the relationship with the shareholders. On one hand focus on the distribution of benefits on the dividend, consolidate the actual control status of shareholders; on the other hand, improve the relationship between agents and shareholders, reduce agency costs, form a good mutual relationship between managers and shareholders, enhance the overall performance of enterprises, and achieve harmony and sustainable development. 3) Reasonable optimization of the capital structure of enterprises, focus on the relationship with the creditors. As part of the improvement in enterprise performance comes from the creditor's credit investment, debt financing will ease financial difficulties and get more "benefits" to create better performance. 4) The stakeholders having impact on business performance should be treated differently. 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