Frontiers in Business, Economics and Management ISSN: 2766-824X | Vol. 16, No. 3, 2024 117 Research on the Evaluation of Competitiveness of Steel Industry Enterprises Xiaoqing Guo 1, Huan Huang 2 1 School of managemengt, Sichuan University of Science & Engineering, Zi Gong 643000, China 2 School of managemengt, Chengdu University of Technology, Cheng Du 610000, China Abstract: The steel industry, as a fundamental industry in the organic development framework of China's economy, has been facing the long-standing problems of production exceeding consumption, high asset liability ratio, severe product homogenization competition, and high pollution and carbon emissions in the production process in recent times. At the same time, due to the impact of the sluggish demand caused by the COVID-19, the rapid rise in the cost of raw materials such as iron ore, and the gradual disappearance of a series of policy dividends such as supply side reform, the financial situation of ste el enterprises is becoming more and more pessimistic. How to cope with the increasing financial risks and gain competitive advantages through strengthening financial competitiveness in the fierce market competition has become a major issue that China's steel enterprises urgently need to solve. This article selects steel industry enterprises as the research object for analysis. By reviewing relevant literature on enterprise competitiveness, the relevant concepts of enterprise competitiveness are extracted, and the research results and evaluation system are organized. Based on factor analysis, 16 targeted indicators are selected based on scale, efficiency, growth, operational capacity, debt paying capacity, and research and development capacity. An analysis model is constructed and analysis conclusions are drawn. In the analysis results, problems are identified regarding the poor competitiveness of steel industry enterprises. The following research results were obtained: 1) The overall competitiveness score of enterprises in the industry is not high, all less than 1. Among them, operational capability has the greatest impact on the competitiveness of steel industry enterprises, followed by scale, debt paying ability, efficiency, research and development ability, and growth. 2) The worst scoring indicators among the six enterprise competitiveness indicators are the scale indicator and the growth indicator. There is a significant gap in industry scale, with 12 companies scoring positive in the scale factor, accou nting for 34.29% of the industry; The scale factor score of 23 enterprises is negative, with an industry proportion of 65.71%. In addition, there are growth indicators, with 12 companies in the industry having positive growth factor scores and 23 companies having negative growth factor scores, indicating a significant head effect on overall growth capacity. Keywords: Steel Industry Enterprises, Enterprise competitiveness. 1. Introduction If a company wants to achieve long-term survival and development, not be eliminated in fierce market competition, and have its own long-term competitive advantage, it must have its own corporate competitiveness. Therefore, enterprises urgently need to know how to identify and evaluate their competitiveness in order to enhance their own competitiveness. After the reform and opening up, especially during the period of rapid economic growth and high-speed development, China's steel enterprises have achieved remarkable achievements that have attracted worldwide attention. According to statistics, the production capacity of China's steel enterprises has approached 40% of the world's total, making it a major steel country. However, the development of China's steel enterprises is not optimistic, especially under the influence of the 2008 economic crisis, the development of steel enterprises has entered a difficult situation. At present, the contradiction of oversupply in the production of steel enterprises nationwide has become prominent, and the competition between enterprises is becoming increasingly fierce and brutal. How to survive has become a major challenge for steel enterprises in the future, and whether they can stand out in the current fierce competition has also become an opportunity for enterprises, which largely depends on their competitiveness. From the current development status of China's steel enterprises, it can be seen that their production is high, but their demand is relatively low, leading to an increase in inventory; At present, most steel companies are experiencing a continuous decrease in sales revenue, an increase in expenses, and a general decline in sales profit margins. Loss making companies account for more than 50% of their losses, and the amount of losses is relatively large. The price of products is low, but the decrease in procurement costs is significantly lower than the decrease in product prices, resulting in a decrease in profit margins. The proportion of funds recovered by companies is low, and the difficulty of recovery is also relatively high. At present, steel enterprises are facing so many difficulties and problems that urgently need to be solved. In order for steel enterprises to smoothly overcome these difficulties, their development is entering a new era where improving their competitiveness is the main focus. The strength of the competitiveness of various steel enterprises has become a new focus of attention for the entire steel industry. Therefore, this article uses quantitative indicators to visually describe the competitiveness of enterprises, establishes a comprehensive and scientific competitiveness evaluation index system, obtains objective and direct indicators and weights that can reflect the strength of enterprise competitiveness, evaluates the competitiveness of steel enterprises, and makes a correct analysis. The analysis results are used as information and basis for the continued development of enterprises, in order to establish a theoretical basis for enterprises to refer to and achieve long-term development. Establishing an evaluation index system for the 118 competitiveness of steel enterprises and evaluating their comprehensive competitiveness has theoretical and practical significance. 2. Industry Overview The steel industry is a symbol of China's comprehensive national strength and an important foundational industry of the national economy. It was a key focus of development in China for about 30 years after the founding of the People's Republic of China. At that time, the foundation of China's steel industry was very weak. In that era, the Central Committee of the Communist Party of China put forward the development guidance policy of "taking steel as the key". After laying such a foundation, the steel industry in China has entered a stage of steady and then rapid development since the 1980s. However, environmental issues, especially climate change, have become increasingly a global concern in recent years. If the consequences of climate change are not controlled, they will be devastating. Countries around the world are calling for "green", "environmental protection", and "low-carbon", and have introduced relevant policies to address the impact of climate change. Many developed countries have even achieved carbon peak in the 1990s. Under the dual pressure of developed countries' emission reduction achievements and China's "dual carbon" strategy, energy conservation and carbon reduction work is still a long way to go. From the perspective of energy consumption, the steel industry has always had the characteristic of "high input, low output", which has led to a huge overall energy consumption and belongs to a high energy consumption and high pollution industry. The energy loss of the entire industry has accounted for as much as 10% of the national energy loss. From the perspective of energy consumption types within the industry, the energy consumption structure of China's steel industry since 2002 is roughly as follows: coal accounts for 72.19%, electricity accounts for 23.52%, fuel oil accounts for 1.90%, and natural gas accounts for 0.31%. In addition, from the perspective of pollution, as a typical long process manufacturing industry, the steel industry has many pollution generating links and a large amount of pollutant emissions. At the same time, as a resource and energy intensive industry, it requires a large amount of resource and energy consumption in the production process, and 75% of the energy comes directly or indirectly from coal. The energy structure is significantly high in carbonization, and there is a lack of alternative clean energy use, which will lead to the generation of a large amount of industrial solid waste, causing great pressure on resources, energy, and the ecological environment. Based on this, China has implemented relevant policies and released the "14th Five Year Plan for Industrial Green Development" in 2021, proposing to reduce the emission intensity of major pollutants in the industry by 10%, deepen the ultra-low emission transformation of the steel industry, implement technology and equipment transformation such as coke oven gas desulfurization, high proportion pellet smelting, coking negative pressure distillation, and coking full process optimization, and strive to complete the ultra-low emission transformation of 80% of enterprises and 530 million tons of steel production capacity by 2025. This is an important measure to help win the battle to protect the blue sky. In 2023, relevant ministries and commissions of the country successively issued regulations and policies such as the "Guiding Opinions on Coordinating Energy Conservation, Carbon Reduction, and Recycling to Accelerate the Upgrading and Transformation of Key Product Equipment" and the "Notice on Doing a Good Job in the Management of Greenhouse Gas Emission Reports for Power Generation Enterprises from 2023 to 2025", mainly focusing on environmental protection governance, energy conservation and carbon reduction, and orderly promoting the green and low-carbon development of the steel industry. However, in recent years, the proportion of sulfur dioxide, nitrogen oxides, and particulate matter emissions in the steel industry to the national industrial source emissions has not shown a significant downward trend or even increased. The reason for this is that according to the environmental protection investment situation in the steel industry, there are significant differences in the level of environmental protection investment among enterprises in the industry. Although a small number of enterprises insist on taking environmental protection as their own responsibility, they are influenced by objective factors such as enterprise size and subjective environmental awareness, and most enterprises have not taken on environmental protection responsibilities, resulting in a low overall green level in the industry and a high proportion of industry pollutant emissions. Based on the above analysis, it can be seen that although the steel industry has achieved certain results in environmental protection in recent years, due to the fact that the steel industry already has more pollution emissions and pollution links than other industries, the current achievements are not enough to cover the amount of pollution emissions. Therefore, the steel industry still needs to continue to strengthen investment, update technological processes, and the environmental protection task is still a long way to go. 3. Construction of Enterprise Competitiveness Evaluation System 3.1. Data Sources This article selects steel industry companies listed and traded on the Shanghai Stock Exchange and Shenzhen Stock Exchange as research samples. After excluding companies listed after 2015 and those with ST and * ST, data from 35 steel companies from 2015 to 2023 remain as the research sample. The specific companies are shown in the table 3-1: 119 Table 3-1. Research Samples Enterprise code Enterprise Name Enterprise code Enterprise Name 000629.SZ Vanadium Titanium 600010.SH Baosteel Group 000655.SZ Jinling Mining 600019.SH Baosteel 000708.SZ CITIC Special Steel 600022.SH Shandong Iron and Steel 000709.SZ Hebei Iron and Steel 600126.SH Hanggang 000717.SZ Zhongnan Corporation 600231.SH Linggang Corporation 000761.SZ Ben Steel Plate 600282.SH Nangang Corporation 000778.SZ Emerging cast pipes 600295.SH Erdos 000825.SZ TISCO Stainless Steel 600307.SH Jiugang Hongxing 000898.SZ Ansteel Corporation 600399.SH Fushun Special Steel 000923.SZ Hebei Iron and Steel Resources 600507.SH Fangda Special Steel 000932.SZ Hualing Steel 600569.SH Anyang Iron and Steel 000959.SZ Shougang Corporation 600581.SH Bayi Iron and Steel 002075.SZ Shagang 600782.SH New Steel Corporation 002110.SZ San Gang Min Guang 600808.SH Ma Steel 002318.SZ Jiulite Material 601003.SH Liugang Corporation 002443.SZ Jinzhou Pipeline 601005.SH Chongqing Iron and Steel 002478.SZ Changbao 601969.SH Hainan Mining 603878.SH Wujin Stainless Steel 3.2. Indicator Selection The differences in corporate competitiveness are the result of various factors, and scholars at home and abroad have different views on the measurement standards of corporate competitiveness. Therefore, the selection of the evaluation index system in this article is based on the following considerations: firstly, it should objectively reflect the actual situation of the enterprise while ensuring comprehensiveness as much as possible, reflecting both financial data and the ability to generate profits and repay debts, as well as the growth and turnover of the enterprise, reflecting its operational and development capabilities, and not neglecting its innovation capabilities in technology. Based on this, drawing on the approach of Jinbei [] to calculate the score of enterprise competitiveness, and referring to the research of relevant scholars in the industry, while considering the three aspects of scale, growth, and efficiency, debt paying ability, operational ability, and research and development ability have been added. In the end, this article designed and selected a total of 16 indicators to evaluate the competitiveness level of enterprises. By using factor analysis, common factors were extracted to replace the evaluation indicators of enterprise competitiveness, reducing the dimensions of enterprise competitiveness indicators and constructing comprehensive evaluation indicators of enterprise competitiveness (as shown in Table 3-2). As one of the evaluation methods for enterprise competitiveness, factor analysis has many advantages. Firstly, factor analysis determines weights based on the variance contribution rate of each common factor, which is more objective; Secondly, standardization processing eliminates the correlation between financial performance indicators and the differences caused by indicator values, improving the quality of financial performance evaluation. Table 3-2. Evaluation Index System for Enterprise Competitiveness type name data sources scale sales revenue Financial statement data net assets Net profit increase Revenue growth rate Current year's operating income/Last year's operating income net profit growth rate Total assets of the year/Total assets of last year efficiency Net asset profit margin Net profit/net assets Total asset contribution rate Net profit/total assets Overall labor efficiency Revenue/Total number of employees operation capacity Inventory turnover rate Operating costs/average inventory balance Net asset turnover ratio Operating revenue/average net assets Total Asset turnover Operating revenue/total assets solvency quick ratio (Current Assets - Inventory)/Current Liabilities current ratio Current assets/current liabilities x 100% Asset liability ratio Total liabilities/total assets x 100% R & D capabilities Proportion of R&D personnel Number of R&D personnel/total number of employees Proportion of R&D investment R&D investment/operating income 3.3. Construction of Evaluation System 3.3.1. Data standardization The measurement units of the indicators we selected are different, and factor analysis is used to obtain different covariance or correlation matrices. The dimension of competitiveness indicators has a significant impact on the process of factor analysis. In order to make the analysis results more accurate and scientific, the data must be 120 standardized before factor analysis. There are many methods for standardization, and the commonly used method in this article is to standardize the raw data. The calculation of this process can be directly completed through SPSS software. 3.3.2. KMO and Bartlett's test Before conducting factor analysis, we usually need to perform KMO and Bartlett sphericity tests to test whether the conditions for conducting factor analysis are met, that is, whether there is a high correlation between various indicators. Validate the raw data using SPSS software. The conclusion obtained through KMO and Bartlett's test is that the KMO measurement value is 0.620, which is greater than 0.5 and meets the feasibility criteria of factor analysis. The significance level of Bartlett's sphericity test is 0.000, which is less than the significance level of 0.05. This fully indicates that the selected variables are highly correlated and suitable for factor analysis methods. The test results are shown below. Table 3-3. KMO and Bartlett sphericity test KMO and Bartlett inspection KMO sampling suitability quantity. .620 Bartlett sphericity test Approximate chi square 4201.181 freedom 120 significance .000 3.3.3. Analysis of Variable Commonality Variable communality refers to the variance of common factors, which represents the proportion of variance explained by common factors. It reflects the degree to which the original information contained in each variable has been extracted. An extracted value greater than 0.6 is considered to have strong explanatory power. If the communality of most variables is higher than 0.8, it indicates that only a small amount of information is lost and the factor analysis effect is good. Using SPSS software to extract common factors, the results are shown in the following table. Table 3-4. variance results of common factors communalities initial extract Zscore (sales revenue) 1.000 .958 Zscore (net assets) 1.000 .952 Zscore (Net profit) 1.000 .854 Zscore (Revenue growth rate) 1.000 .760 Zscore (net profit growth rate) 1.000 .752 Zscore (Net asset profit margin) 1.000 .678 Zscore (Total asset contribution rate) 1.000 .797 Zscore (Overall labor efficiency) 1.000 .768 Zscore (Inventory turnover rate) 1.000 .801 Zscore (current asset turnover) 1.000 .804 Zscore (Total Asset turnover) 1.000 .828 Zscore (quick ratio) 1.000 .934 Zscore (current ratio) 1.000 .950 Zscore (Asset liability ratio) 1.000 .737 Zscore (The proportion of researchers to the total number of personnel) 1.000 .770 Zscore (Research and development as a percentage of operating revenue 1.000 .795 Through SPSS analysis, the overall extraction rate of 15 indicators reached over 70%, and 8 indicators reached over 80%, indicating that the extraction degree of each indicator was relatively good, and the extracted common factors explained the degree of the original variables relatively high. 3.4. Determine Common Factors The variance contribution rate and cumulative variance contribution rate of each factor were obtained through spass analysis to determine the number of factors. The results are shown in the table below. Table 3-5. Explanation of Total Variance initial eigenvalue Extract the sum of squared loads Sum of squared rotational loads total Variance percentage Accumulated% total Variance percentage Accumulated% total Variance percentage Accumulated% 1 3.866 24.166 24.166 3.866 24.166 24.166 2.839 17.745 17.745 2 2.968 18.549 42.714 2.968 18.549 42.714 2.763 17.271 35.017 3 2.168 13.552 56.266 2.168 13.552 56.266 2.746 17.164 52.181 4 1.645 10.283 66.549 1.645 10.283 66.549 1.701 10.631 62.811 5 1.365 8.531 75.080 1.365 8.531 75.080 1.547 9.667 72.478 6 1.125 7.029 82.109 1.125 7.029 82.109 1.541 9.632 82.109 7 .610 3.812 85.921 8 .492 3.074 88.995 9 .465 2.904 91.899 10 .401 2.506 94.405 11 .277 1.733 96.138 12 .267 1.669 97.807 13 .184 1.150 98.956 14 .132 .825 99.781 15 .028 .178 99.959 16 .007 .041 100.000 From the table, it can be seen that there are 6 factors with eigenvalues greater than 1, and the variance contribution rates after rotation are 17.745%, 17.271%, 17.164%, 10.631%, 9.667%, and 9.632%, respectively. The total variance explanation is 82.109%, indicating that if the original 16 variables are not used to evaluate the competitiveness level of enterprises, these 6 factors can represent the information of the original 16 variables. Therefore, 6 principal components are extracted as common factors. In addition, the importance of these six common factors can 121 be more intuitively displayed through the gravel plot, as shown in the figure below. The horizontal axis of the gravel plot represents the factor number, and the vertical axis represents the characteristic value. From the graph, it can be seen that the characteristic values of the first six factors are all greater than and located on steep slopes, while the other factors tend to be gentle slopes and have relatively small characteristic values. Through variance contribution rate and scree plot, it can be confirmed that selecting the top 6 principal components for factor analysis is effective. Figure 3-1. Gravel Diagram Table 3-6. Rotated component matrixa component 1 2 3 4 5 6 Zscore (Inventory turnover rate) .870 .029 .173 -.059 -.090 -.045 Zscore (Total Asset turnover) .834 -.012 -.091 .333 .107 -.005 Zscore (current asset turnover) .829 .042 -.334 .051 .018 -.007 Zscore (Overall labor efficiency) .726 .438 -.081 -.019 .202 .037 Zscore (net assets) -.010 .971 -.082 -.048 .012 -.018 Zscore (operating revenue) .175 .943 -.178 -.049 .065 -.025 Zscore (Net profit) .091 .832 .047 .369 .021 .121 Zscore (quick ratio) -.123 -.112 .952 .011 .008 .010 Zscore (current ratio) -.160 -.130 .947 .064 .075 .015 Zscore (Asset liability ratio) -.072 -.013 -.816 -.250 -.042 -.044 Zscore (Net asset profit margin) .028 .054 .052 .819 .013 .013 Zscore (Total asset contribution rate) .154 .064 .257 .817 .055 .184 Zscore (Research and development as a percentage of operating revenue) -.103 .070 .008 .067 .872 -.115 Zscore (The proportion of researchers to the total number of personnel .225 .018 .100 -.003 .838 .083 Zscore (Revenue growth rate) -.012 -.013 .022 -.016 -.074 .868 Zscore (net profit growth rate) -.018 .059 .029 .183 .043 .844 3.5. Factor Rotation and Naming After determining the number of factors, select the maximum variance method that comes with SPSS, rotate and sort the factor loading matrix to better classify the raw data represented by each factor, and facilitate factor interpretation of the original variables. The rotated component matrix is shown in the table below: After rotating the component matrix, the original data was reorganized. As shown in the table above, the first common factor has a high load on inventory turnover rate (X9), total asset turnover rate (X11), current asset turnover rate (X10), and overall labor efficiency (X8). The indicators reflect more in terms of operational capability. Therefore, the first common factor F1 is named the operational capability factor; The second factor shows a high load on net assets (X2), operating income (X1), and net profit (X3), all of which are indicators reflecting the size of the enterprise. Therefore, it is named scale factor F2; The third common factor has a high load on quick ratio (X12), current ratio (X13), and asset liability ratio (X14), so it is named debt paying ability factor F3; The fourth common factor has a high load on net asset profit margin (X6) and total asset contribution rate (X7), hence it is named F4 efficiency factor; The fifth factor has a high load on the proportion of R&D investment (X16) and the proportion of researchers to the total number of people (X15), so it is named F5 R&D capability factor; The sixth factor has a high load on revenue growth rate (X4) and net profit growth rate (X5), hence it is named F6 growth factor. 3.6. Factor Score Situation The factor scores are shown in the table: 122 Table 3-7. Component score coefficient matrix component 1 2 3 4 5 6 Zscore (sales revenue) -.003 .355 .011 -.090 -.006 -.024 Zscore (net assets) -.068 .388 .042 -.085 -.037 -.026 Zscore (Net profit) -.053 .303 .029 .169 -.047 .014 Zscore (Revenue growth rate) .017 -.018 -.002 -.131 -.020 .594 Zscore (net profit growth rate) -.018 -.007 -.029 .003 .047 .552 Zscore (Net asset profit margin) -.077 -.033 -.094 .560 -.029 -.107 Zscore (Total asset contribution rate) -.012 -.028 -.003 .488 -.014 .010 Zscore (Overall labor efficiency) .247 .113 .044 -.123 .077 .038 Zscore (Inventory turnover rate) .370 -.022 .154 -.153 -.121 -.016 Zscore (current asset turnover) .297 -.065 -.083 -.007 -.023 .007 Zscore (Total Asset turnover) .285 -.093 -.031 .156 .017 -.031 Zscore (quick ratio) .028 .030 .379 -.113 -.031 -.011 Zscore (current ratio) .005 .018 .362 -.073 .016 -.013 Zscore (Asset liability ratio) -.061 -.039 -.309 -.039 .027 .017 Zscore (The proportion of researchers to the total number of personnel) .049 -.046 .015 -.086 .550 .087 Zscore (Research and development as a percentage of operating revenue -.105 -.013 -.052 .035 .585 -.062 Calculate the scores of each factor and the overall score of the enterprise from the component score coefficient matrix, as follows: F1=-0.003X1-0.068X2-0.053X3+0.017X4-0.018X5- 0.077X6- 0.012X7+0.247X8+0.370X9+0.297X10+0.285X11+0.028X 12+0.005X13-0.061X14+0.049X15-0.105X16 F2=0.355X1+0.388X2+0.303X3-0.018X4-0.007X5- 0.033X6-.028X7+0.113X8-0.022X9-0.065X10- 0.093X11+0.030X12+0.018X13-0.309X14-0.046X15- 0.013X16 F3=0.011X1+0.042X2+0.029X3-0.002X4-0.029X5- 0.094X6-0.003X7+0.044X8+0.154X9-0.083X10- 0.031X11+0.379X12+0.362X13-0.309X14+0.015X15- 0.052X16 F4=-0.090X1-0.085X2+0.169X3- 0.131X4+0.003X5+0.560X6+0.488X7-0.123X8-0.153X9- 0.007X10+0.156X11-0.113X12-0.073X13-0.039X14- 0.086X15+0.035X16 F5=-0.006X1-0.037X2-0.047X3-0.020X4+0.047X5- 0.029X6-0.014X7+0.077X8-0.121X9-0.023X10+0.017X11- 0.031X12+0.016X13+0.027X14+0.550X15+0.585X16 F6=-0.024X1-0.026X2+0.014X3+0.594X4+0.552X5- 0.107X6+0.010X7+0.038X8-0.016X9+0.007X10- 0.031X11-0.011X12-0.013X13+0.017X14+0.087X15- 0.062X16 The overall score is: F=(17.745F1+17.271F2+17.164F3+10.631F4+9.667F5+9.6 32F6)/82.109 4. Evaluation of Enterprise Competitiveness 4.1. Evaluation of Enterprise Scale Table 4-1. Average Score and Ranking of Scale Factors for Steel Enterprises from 2015 to 2023 Enterprise Name score ranking Enterprise Name score ranking Baosteel 4.8855 1 Changbao -0.1708 19 Ansteel Corporation 0.8059 2 Wujin Stainless Steel -0.1919 20 Hebei Iron and Steel 0.7454 3 San Gang Min Guang -0.2344 21 Hualing Steel 0.5395 4 Shagang -0.2692 22 Baosteel Group 0.4051 5 Jiulite Material -0.2820 23 Hanggang 0.3365 6 Jinzhou Pipeline -0.2968 24 Jinling Mining 0.2792 7 Vanadium Titanium -0.3035 25 Shougang Corporation 0.2668 8 Liugang Corporation -0.3371 26 Ma Steel 0.1461 9 Ben Steel Plate -0.3717 27 TISCO Stainless Steel 0.1459 10 Chongqing Iron and Steel -0.3745 28 Shandong Iron and Steel 0.0633 11 Fangda Special Steel -0.4151 29 CITIC Special Steel 0.0571 12 Linggang Corporation -0.6264 30 Emerging cast pipes -0.0045 13 Fushun Special Steel -0.7397 31 Hebei Iron and Steel Resources -0.0559 14 Jiugang Hongxing -0.7645 32 Nangang Corporation -0.0941 15 Anyang Iron and Steel -0.7910 33 New Steel Corporation -0.1045 16 Zhongnan Corporation -0.8919 34 erdos -0.1411 17 Bayi Iron and Steel -1.0485 35 Hainan Mining -0.1673 18 From the table, it can be seen that the overall score of the steel industry is not high, with a significant gap in scale. There 123 are 12 enterprises with positive scale factor scores, accounting for 34.29% of the industry; The scale factor score of 23 enterprises is negative, with an industry proportion of 65.71%. Baosteel Group's scale factor score is 4.8855, ranking first in the industry, while the second ranked Angang Steel's score is only 0.8059, which is 4.0796 higher than the second ranked company, indicating that Baosteel Group has a significant gap in scale compared to other steel enterprises. The scale indicator accounts for 21.03% of the comprehensive evaluation of enterprise competitiveness, and is the second largest influencing factor of enterprise competitiveness, providing good support for the overall competitiveness level of enterprises. 4.2. Evaluation of Enterprise Growth Table 4-2. Average scores and rankings of growth factors for steel enterprises from 2015 to 2023 Enterprise Name score ranking Enterprise Name score ranking Hebei Iron and Steel Resources 1.5843 1 Chongqing Iron and Steel -0.0400 19 Bayi Iron and Steel 0.3810 2 Ben Steel Plate -0.0431 20 CITIC Special Steel 0.2914 3 San Gang Min Guang -0.0456 21 New Steel Corporation 0.2196 4 Liugang Corporation -0.0478 22 Hainan Mining 0.0619 5 Shandong Iron and Steel -0.0610 23 Shougang Corporation 0.0463 6 Vanadium Titanium -0.0618 24 Nangang Corporation 0.0393 7 Fangda Special Steel -0.0714 25 erdos 0.0358 8 Hebei Iron and Steel -0.1032 26 Zhongnan Corporation 0.0195 9 Emerging cast pipes -0.1067 27 TISCO Stainless Steel 0.0049 10 Shagang -0.1097 28 Baosteel Group 0.0024 11 Hanggang -0.1158 29 Changbao 0.0006 12 Ansteel Corporation -0.1215 30 Jiulite Material -0.0069 13 Anyang Iron and Steel -0.1237 31 Hualing Steel -0.0084 14 Fushun Special Steel -0.1352 32 Ma Steel -0.0181 15 Baosteel -0.1385 33 Linggang Corporation -0.0207 16 Jinling Mining -0.4002 34 Wujin Stainless Steel -0.0290 17 Jiugang Hongxing -0.8412 35 Jinzhou Pipeline -0.0376 18 The impact of growth capacity factor on the comprehensive evaluation index F of enterprise competitiveness is 11.72%, ranking last among the six factors, indicating that the influence of growth capacity on enterprise competitiveness evaluation is relatively weak. In the steel industry, 12 companies have positive growth factor scores and 23 companies have negative growth factor scores, indicating a significant head effect on the overall growth capacity of the steel industry. However, most companies do not have very high growth factor scores. Among the top 12 companies, only Hegang Resources scored above 1, while the remaining 11 were between 0 and 0.5. 4.3. Enterprise Efficiency Evaluation Table 4-3. Average scores and rankings of efficiency factors for steel enterprises from 2015 to 2023 Enterprise Name score ranking Enterprise Name score ranking Fangda Special Steel 1.2143 1 Ansteel Corporation -0.0665 19 Shagang 0.7324 2 Ma Steel -0.1052 20 Fushun Special Steel 0.6583 3 Baosteel -0.1638 21 Jiulite Material 0.6335 4 Anyang Iron and Steel -0.1750 22 erdos 0.5223 5 Ben Steel Plate -0.2137 23 Jinzhou Pipeline 0.4597 6 TISCO Stainless Steel -0.2208 24 CITIC Special Steel 0.4396 7 Hainan Mining -0.2292 25 San Gang Min Guang 0.4010 8 Hebei Iron and Steel -0.2409 26 Wujin Stainless Steel 0.4003 9 Baosteel Group -0.2470 27 Liugang Corporation 0.3179 10 Hebei Iron and Steel Resources -0.2613 28 Changbao 0.3047 11 Jiugang Hongxing -0.3032 29 Zhongnan Corporation 0.2946 12 Shandong Iron and Steel -0.3636 30 Nangang Corporation 0.1810 13 Shougang Corporation -0.4327 31 Hualing Steel 0.1663 14 Hanggang -0.7060 32 New Steel Corporation 0.0406 15 Jinling Mining -0.9102 33 Linggang Corporation 0.0259 16 Chongqing Iron and Steel -0.9775 34 Vanadium Titanium 0.0209 17 Bayi Iron and Steel -1.1883 35 Emerging cast pipes -0.0086 18 The efficiency factor is the fourth factor in the comprehensive evaluation of enterprise competitiveness, and its impact on enterprise competitiveness is 12.95%, which is relatively small. From the above table, it can be seen that the efficiency factor scores in the steel industry are also in a two- tier differentiation state, with 17 companies scoring above 0 124 and 18 companies scoring below 0. The top five Chinese special steel companies occupy a leading position in the industry, with a score of 1.2143. This indicates that the enterprise has created a gap in efficiency compared to other enterprises, contributing to its competitiveness. Next are Shagang Group, Fushun Special Steel, and Jiulite Materials. The difference in efficiency factor scores between them is not very large. In addition, the last five companies are Shougang Group, Hanggang Group, Jinling Mining, Chongqing Iron and Steel, and Bayi Iron and Steel. Among them, the score of Bayi Iron and Steel is the worst in the industry, with a score of -1.1883. The gap between the first place in the industry is very large. Therefore, Bayi Iron and Steel adopts effective measures to reduce the efficiency gap with other enterprises and enhance the competitiveness of the enterprise. 4.4. Evaluation of Enterprise Operational Capability Table 4-4. Average scores and rankings of operational capability factors for steel enterprises from 2015 to 2023 Enterprise Name score ranking Enterprise Name score ranking Zhongnan Corporation 1.9543 1 Ma Steel 0.0272 19 Hanggang 1.7871 2 Shagang 0.0031 20 Shandong Iron and Steel 1.2331 3 Jinzhou Pipeline -0.1073 21 San Gang Min Guang 0.9717 4 Emerging cast pipes -0.1759 22 New Steel Corporation 0.9690 5 Jinling Mining -0.3221 23 TISCO Stainless Steel 0.6747 6 Ben Steel Plate -0.4405 24 Linggang Corporation 0.6449 7 Baosteel -0.4508 25 Liugang Corporation 0.5467 8 Anyang Iron and Steel -0.6519 26 Fangda Special Steel 0.4601 9 Changbao -0.7954 27 Hualing Steel 0.4589 10 Hebei Iron and Steel -0.9109 28 Vanadium Titanium 0.4413 11 erdos -0.9308 29 Bayi Iron and Steel 0.3501 12 Jiulite Material -0.9933 30 Ansteel Corporation 0.3242 13 Hainan Mining -1.0097 31 Jiugang Hongxing 0.2963 14 Wujin Stainless Steel -1.0321 32 CITIC Special Steel 0.2575 15 Hebei Iron and Steel Resources -1.2713 33 Nangang Corporation 0.1559 16 Baosteel Group -1.2964 34 Chongqing Iron and Steel 0.1175 17 Fushun Special Steel -1.3841 35 Shougang Corporation 0.0987 18 Through the analysis of the overall evaluation, we can see that operational capability has the greatest impact on Baosteel Group's competitiveness. From the above table, it can be seen that there are 20 enterprises in the steel industry with positive operating capacity and 15 enterprises with negative operating capacity. The top five companies, Zhongnan Shares, Hanggang Shares, and Shandong Iron and Steel, all scored above 1, occupying a leading position in the industry. The scores of the last five companies, Hainan Mining, Wujin Stainless Steel, Hegang Resources, Baosteel Group, and Fushun Special Steel, are all less than -1. It can be seen that there is a significant gap in the operational capabilities of steel industry enterprises. In order to enhance their competitiveness and gain a competitive advantage, these enterprises need to strive to be the first in terms of operational capabilities and narrow the gap with other steel enterprises. 4.5. Evaluation of Corporate Debt Repayment Ability Table 4-5. Average scores and rankings of debt repayment ability factors for steel enterprises from 2015 to 2023 Enterprise Name score ranking Enterprise Name score ranking Jinling Mining 3.4039 1 Ansteel Corporation -0.2067 19 Hebei Iron and Steel Resources 1.7026 2 Chongqing Iron and Steel -0.2781 20 Hanggang 0.9686 3 Linggang Corporation -0.3000 21 Wujin Stainless Steel 0.9360 4 Hualing Steel -0.4017 22 Jinzhou Pipeline 0.8436 5 TISCO Stainless Steel -0.4214 23 Changbao 0.6176 6 Nangang Corporation -0.4581 24 Vanadium Titanium 0.4953 7 Ma Steel -0.4655 25 Hainan Mining 0.4923 8 erdos -0.5136 26 Jiulite Material 0.4083 9 Liugang Corporation -0.6033 27 Baosteel Co., Ltd 0.2735 10 Ben Steel Plate -0.6348 28 Shagang Co., Ltd 0.1186 11 Baosteel Group -0.6588 29 Fangda Special Steel 0.1152 12 Shougang Corporation -0.6958 30 San Gang Min Guang 0.0948 13 Hebei Iron and Steel -0.7787 31 New Steel Corporation 0.0059 14 Zhongnan Corporation -0.8004 32 CITIC Special Steel -0.0944 15 Jiugang Hongxing -0.8875 33 Emerging cast pipes -0.1050 16 Anyang Iron and Steel -0.9173 34 Fushun Special Steel -0.1148 17 Bayi Iron and Steel -0.9750 35 Shandong Iron and Steel -0.1652 18 125 The impact of debt paying ability factor on the comprehensive evaluation index F of enterprise competitiveness is 20.9%, indicating that this ability has a certain influence on enterprise competitiveness. From the above table, it can be seen that the debt paying ability of enterprises in the steel industry is funnel-shaped, with a clear polarization. Baosteel Group, which ranks first in industry market value, ranks 10th in this ranking with a value of 0.2735. Although the ranking is still relatively high, it is still far behind the top ranked Jinling Mining, indicating that in an environment where the head effect of debt paying ability in the steel industry is obvious, Baosteel Group's value is not high despite ranking relatively high. Its debt paying ability performance is average, and it should learn more from top enterprises to improve its debt paying ability. 4.6. Evaluation of R&D Capability Table 4-6. Average scores and rankings of R&D capability factors for steel enterprises from 2015 to 2023 Enterprise Name score ranking Enterprise Name score ranking Fushun Special Steel 1.3512 1 Ma Steel 0.0779 19 Jiulite Material 0.9996 2 Baosteel Group -0.0642 20 CITIC Special Steel 0.9595 3 Baosteel -0.0972 21 Wujin Stainless Steel 0.9517 4 Hebei Iron and Steel Resources -0.1659 22 Hualing Steel 0.9003 5 Emerging cast pipes -0.3876 23 TISCO Stainless Steel 0.8955 6 Bayi Iron and Steel -0.4531 24 New Steel Corporation 0.8187 7 Hanggang -0.5347 25 Changbao 0.7805 8 Ansteel Corporation -0.6058 26 Chongqing Iron and Steel 0.7589 9 Linggang Corporation -0.6299 27 Nangang Corporation 0.6549 10 Jiugang Hongxing -0.7395 28 Jinzhou Pipeline 0.5987 11 Hebei Iron and Steel -0.7487 29 Ben Steel Plate 0.5666 12 erdos -1.0695 30 San Gang Min Guang 0.4964 13 Hainan Mining -1.1105 31 Vanadium Titanium 0.4855 14 Liugang Corporation -1.1146 32 Shougang Corporation 0.2985 15 Shandong Iron and Steel -1.1707 33 Zhongnan Corporation 0.2142 16 Jinling Mining -1.3153 34 Shagang 0.1106 17 Fangda Special Steel -1.8066 35 Anyang Iron and Steel 0.0944 18 It can be clearly seen from the table that more than half of the 35 companies have R&D capability scores above 0, indicating a strong overall awareness of technological innovation in the industry. However, the average score of Baosteel Group's R&D capability factor in the past nine years is not high, ranking 21st in the industry. However, through analysis of the R&D factor scores of the steel industry in each year, it was found that Baosteel Group's R&D factor score has been continuously increasing, from -0.78.34 in 2015 to 2.2582 in 2023, ranking first in the industry, indicating that Baosteel Group is increasingly valuing innovation. 4.7. Comprehensive Evaluation of Enterprise Competitiveness Table 4-7. Average scores and rankings of comprehensive factors for steel enterprises from 2015 to 2023 Enterprise Name score ranking Enterprise Name score ranking Baosteel 0.9385 1 Jiulite Material 0.0103 19 Hanggang 0.4915 2 Fangda Special Steel -0.0276 20 Jinling Mining 0.3810 3 Ma Steel -0.0673 21 New Steel Corporation 0.3161 4 Shougang Corporation -0.0834 22 San Gang Min Guang 0.2855 5 Emerging cast pipes -0.1202 23 Hualing Steel 0.2552 6 Linggang Corporation -0.1283 24 CITIC Special Steel 0.2520 7 Chongqing Iron and Steel -0.1534 25 Jinzhou Pipeline 0.2163 8 Liugang Corporation -0.1746 26 Hebei Iron and Steel Resources 0.2019 9 Fushun Special Steel -0.2503 27 Vanadium Titanium 0.1877 10 Ben Steel Plate -0.2721 28 TISCO Stainless Steel 0.1658 11 Hainan Mining -0.3036 29 Zhongnan Corporation 0.1331 12 Hebei Iron and Steel -0.3343 30 Ansteel Corporation 0.1022 13 Baosteel Group -0.3719 31 Wujin Stainless Steel 0.0927 14 erdos -0.3923 32 Shagang 0.0638 15 Jiugang Hongxing -0.5073 33 Shandong Iron and Steel 0.0532 16 Bayi Iron and Steel -0.5112 34 Changbao 0.0527 17 Anyang Iron and Steel -0.5251 35 Nangang Corporation 0.0233 18 From the table, it can be seen that the overall competitiveness score of enterprises in the industry is not high, all of which are less than 1. From the distribution of scores, there are 19 companies with scores greater than 0 and 16 126 companies with scores less than 0. Baosteel Group's comprehensive score is 0.9385, ranking first in the industry. Through the formula F for the comprehensive score of factors, it can be seen that the three aspects of operational ability, debt paying ability, and scale largely determine the competitiveness of steel industry enterprises. Among them, the scale factor score is in an absolute leading position in the industry, and the score has been ranked first in the industry from 2015 to 2023. This is also the main reason why the comprehensive competitiveness score of the enterprise is leading compared to other enterprises in the industry. However, the performance in terms of operational ability and debt paying ability is not good, especially in terms of operational ability, whose average score during the research period is in the bottom few of the industry, which is the main factor leading to the weakening of Baosteel's competitive advantage position and needs to be highly valued by the management. 5. Summary On the basis of elaborating on the current status of research on the competitiveness evaluation of Chinese steel enterprises, the theory of enterprise competitiveness, and a review of domestic and foreign literature, this article proposes to construct an evaluation index system for the competitiveness of steel enterprises. Based on the principles of constructing the index system, it attempts to construct an evaluation index system for the competitiveness of Chinese steel enterprises from six aspects: scale, efficiency, growth, debt repayment ability, research and development ability, and operational ability. Factor analysis is used to evaluate and analyze the competitiveness of 35 selected steel enterprises listed on the Shanghai and Shenzhen stock markets. Through this study, the following conclusions are drawn: (1) Literature review. In the literature review section, this article sequentially discusses the concept of enterprise competitiveness, competitiveness theory, and national competitiveness Review the research on the evaluation system of internal and external enterprise competitiveness. Domestic and foreign institutions and scholars have different levels and perspectives on the concept of enterprise competitiveness and the establishment of theoretical evaluation systems. Different evaluation models and indicator systems have emerged based on the research of research institutions and numerous scholars. The construction of theoretical and evaluation indicator systems for enterprise competitiveness is also constantly developing and improving. (2) Establish an evaluation index system for the competitiveness of steel enterprises. This article introduces the evaluation indicators for the competitiveness of steel enterprises On the basis of the selected principles, a competitiveness evaluation index system for steel enterprises was constructed by summarizing and sorting out relevant theoretical research of predecessors, consisting of six elements and 16 indicators: scale, efficiency, growth, debt paying ability, research and development ability, and operational ability. Each indicator was explained and analyzed. (3) Application of evaluation index system. The article selected 35 steel companies from the Shanghai and Shenzhen stock markets as research objects for factor analysis, which not only obtained the ranking of each company's sub capabilities, but also the ranking order of their comprehensive competitiveness. It can be concluded that the development of various capabilities in most enterprises is unbalanced, with each having its own competitive advantages and disadvantages. The comprehensive competitiveness is influenced by the sub capability scores, with Baosteel Group, Hangzhou Iron and Steel Group, Jinling Mining Group, New Steel Group, and Sansteel Minguang ranking in the top five, while Baosteel Group, Ordos, Jiuquan Iron and Steel Hongxing, Bayi Iron and Steel, and Anyang Iron and Steel rank in the bottom five, opening up the level of comprehensive competitiveness. The article analyzes and evaluates the competitiveness of Chinese steel enterprises by constructing an evaluation index system for their competitiveness. The aim is to provide reference for steel enterprises to enhance their competitiveness, and to offer suggestions and countermeasures for government regulatory departments, enterprise investment decisions, and managers. However, due to the limited ability and level of the author, the article has the following shortcomings: the construction of the indicator system is not comprehensive enough, and qualitative indicators could have been added, but due to the difficulty in quantifying and quantifying qualitative indicators, the data is not true and the corporate culture and brand influence have been deleted; The lack of in-depth research on steel enterprises in the article leads to a lack of steel enterprise characteristics in the constructed indicator system, and the indicators are too general; More scientific and rigorous methods can be adopted for analysis in evaluation methods. I hope scholars interested in corporate competitiveness can conduct further analysis and research in these areas, so that the theory and evaluation index system of corporate competitiveness can become increasingly perfect References [1] BARNEY.J. 2002. Gaining and Sustaining Competitive Advantage [J]. Prentice Hall. 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