The Role of Digital Transformation in the Economic Benefits of the Sports Industry and Optimization Paths
Pubblicato online: 22 set 2025
Ricevuto: 14 gen 2025
Accettato: 01 mag 2025
DOI: https://doi.org/10.2478/amns-2025-0971
Parole chiave
© 2025 Yanqiu Liu, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
At present, China is in a new round of scientific and technological revolution and industrial change breakthrough burst of the historical convergence period [1–2]. With the digital economy as the representative of innovation in multiple fields, group accelerated breakthroughs, the real economy using the digital economy, the breadth and depth of the expanding, new models, new business models continue to emerge, industrial organization and the shape of the real economy continue to reshape the digital economy leading kinetic advantage is obvious, is accelerating to lead the depth of industrial integration [3–6].
In today’s new era of digital economy, the innovation of science and technology, industry, mode and talent caused by digital technology is profoundly affecting the development of sports industry, and injecting innovative vitality into the transformation and upgrading of the traditional sports industry structure, and improving quality and efficiency [7–8]. In this sense, the high-quality development of the sports industry needs to be driven by the implementation of key elements of production such as industrial digitization, data valorization and digital industrialization. As an emerging core element, digital has long been empowered in many fields such as economy, medical care, health, sports, culture, etc., presenting powerful creativity, innovation and driving force of digital elements, and thus has an iterative advantage for the transformation and upgrading of the traditional sports industry [9–10]. Through the digital transformation and upgrading of the entire elements of the sports industry chain and the reengineering of industrial processes, the allocation of resources is optimized, thus enhancing the development efficiency of the sports industry. It is because the efficiency improvement of the sports industry as a traditional industry has become an important basis for measuring the high-quality development of the sports industry [11–14].
Relevant departments of China’s sports industry management for the new development stage of the sports industry to further point out the direction of high-quality development, and issued a series of policies to accelerate the promotion of the digital transformation of various industries, for the development of the digital economy to plan the layout, steering direction [15–18]. The relevant policy documents put forward “to promote the digital transformation of the sports industry, promote the data to empower the whole industry chain to collaborate in the transformation”, “the implementation of the sports industry digital strategy” and other initiatives to promote the digital transformation and upgrading of the sports industry [19–20].
China’s sports industry is at a critical stage of transition from initial transformation to in-depth development due to the increasing digital audience, the gathering of head enterprises, the gradual standardization of market development and the gradual habit of digital payment. In the face of the arduous and heavy industrial change, how to accelerate the digital transformation of sports industry, constantly meet people’s new demand for quality, high-end and fashionable sports consumption, and cultivate new forms and modes of sports industry [21–23]. Therefore, in-depth analysis of the driving factors of the digital transformation of the sports industry in the era of digital economy, reviewing the existing challenges faced in the process of transformation, and proposing optimization strategies, with a view to providing theoretical support and decision-making reference for the digital transformation of China’s sports industry [24–26]. Accelerating the construction of new digital infrastructure and consolidating the basic support of new sports industry is an effective strategy to further enhance the economic benefits of sports industry. This paper selects the listed sports enterprises of Shanghai and Shenzhen A-shares in the Cathay Pacific database from 2012 to 2023 as the research sample, and explores the specific impact of digital transformation on the economic efficiency of the sports industry using multiple linear stepwise regression. The impact of digital transformation on the operational efficiency and operational cost of sports enterprises is analyzed through the test of the mechanism of action, and the robustness test of the benchmark regression is carried out by combining the lagged term of the level of digital transformation with the method of replacing the explanatory variables. In addition, the specific impact of the level of digital transformation is investigated for the economic efficiency of sports enterprises under different sizes and different industries. Based on the analysis results, the optimization and improvement path of the economic efficiency of the sports industry is proposed in terms of market demand and industrial layout.
Realizing the high-quality development of the sports industry requires making full use of the advantages of digital technology and realizing the efficient use of resources through the deep integration with data technology. The sports industry is at a critical stage of digital economic transformation, and there is still the problem of insufficient depth of transformation in the process of specific practice. Therefore, in the process of sports industry development, it is necessary to create a policy environment for digital development, improve the market regulatory system, increase the investment in advanced digital technology, strengthen the training of digital talents, improve the production efficiency of the sports industry, and promote the digital transformation of the traditional sports industry.
Based on the emerging elements of “means of labor” represented by big data, Internet of Things, AI, 3D, 5G, cloud computing and other new digital technologies, the economic efficiency of the sports industry is strongly enhanced under the drive of science and technology innovation. “High quality” is embodied in the innovation of sports industry management and consumption mode resulting from the structural upgrading and transformation of sports industry, sports industry innovation and new business forms, consumption demand of the public, sports industry management and sports services. Its theoretical logic structure is shown in Figure 1, which mainly lies in the profound influence and transformative effect of the structural transformation of the sports industry, sports industry innovation and sports industry model innovation on the high-quality development of the sports industry [27].
The impact of digital transformation on the economic efficiency of the sports industry The study found that digital transformation can promote the improvement of enterprise performance level by reducing costs and improving efficiency, and this promotion effect is more significant in private enterprises. The digital transformation of enterprises can improve enterprise performance by improving internal control capabilities, and then attracting investment institutions to hold shares and empowering innovation and other mechanisms. Moreover, digital transformation strategies and organizational innovations play a positive mediating role, making digital technologies enhance corporate performance. For SMEs, focusing on investing in digital technologies, employee digital skills and digital transformation strategies are key factors that facilitate digital transformation, thus helping to improve performance and maintain its sustainability. Mechanism of digital transformation on the economic benefits of sports industry At present, the rapid development of digital society makes the sports consumption of the public expand from offline physical consumption to “online + offline” experience and perception. However, the sports industry is also facing a lot of practical development problems such as imperfect policy regulation, unsound industrial mechanism, insufficient optimization of industrial structure, market scale still needs to be expanded, lack of high-end industrial talents, insufficient supply of industrial resources, etc. This requires the use of digital technology to drive the sports industry. It is necessary to leverage digital technology to drive the high-quality development of sports industry. The reason is that digital technology, as a core element of the new quality productivity, has its own unique advantages in enhancing the operational efficiency and cost of sports industry, innovating the development mode of sports service industry, promoting the integration of sports industry and other industries, meeting the demand of social people for personalized sports products (services), and breaking the limitation of time and space in the industry. For example, through intelligent manufacturing and big data, the manufacturing process of sporting goods can be more accurate and efficient, while enhancing the technological content and market competitiveness of products. Providing digital communication innovations such as fitness services, event broadcasting, and online training through Internet sports platforms not only enhances the traditional sports consumer perception experience, but also creates new consumption scenarios and market demand [28]. Heterogeneity of digital transformation on the economic benefits of the sports industry Relying on digital transformation to realize the high-quality development of the sports industry is the consensus of the existing research, for different industries and enterprises of different sizes, there are certain differences in the effect of digital transformation on the enhancement of their economic benefits. Based on the industry’s own characteristics, needs and differences in economic level, the marketing effect of digital transformation on economic benefits is slightly different.
Digital drive sports industry high quality development
Based on the relevant theoretical analysis of digital transformation and economic benefits of sports industry in section 2.1.1, this paper proposes the following research hypotheses:
H1: Digital transformation has a significant positive effect on the economic efficiency of the sports industry. H2: Digital transformation can increase the economic efficiency of sports enterprises by improving operational efficiency. H3: Digital transformation can increase the economic efficiency of sports enterprises by reducing operational costs. H4: There is heterogeneity in the effect of digital transformation on the economic efficiency of different sports enterprises.
Considering the data availability and meeting the scope of sample data, this paper selects the annual data of all A-share listed sports enterprises in Shanghai and Shenzhen during the period from 2012 to 2023 as samples. And the data were screened and processed as follows:
The samples of ST and *ST listed companies are excluded. Companies with serious missing financial data were removed. Shrinking adjustments were made within the level of 1%~99%.
In this paper, all the Shanghai and Shenzhen A-share listed sports companies from 2012 to 2023 were selected as research samples from the Cathay Pacific database (CSMAR). And the initial data are processed by deleting missing values, shrinking the tail of continuous variables, and removing ST companies, etc., and finally 829 listed companies totaling 18,594 sets of data are obtained. In addition, because of the enterprise’s digital transformation related data may be partially missing, this paper mainly adopts the interpolation method to make up for the fact that some enterprises did not carry out digital transformation before 2015, then do “0” value processing, as the enterprise’s digital transformation has not yet begun.
Explained variables. Referring to the existing relevant studies, this paper chooses the financial performance of sports enterprises (ROA) as the economic efficiency of sports industry, and the return on net assets of sports enterprises as its measurement index.
Explanatory variables. This paper chooses the level of digital transformation (DT) as an explanatory variable. At present, there is no uniform standard in the academic world to measure the degree of digitization or the use of technology in enterprises, and the existing research methods are divided into intangible assets method, word frequency method and questionnaire survey method. Digital transformation, as a major strategy for high-quality development of enterprises, is usually reflected in annual reports. The words used in the annual report can reflect the strategic characteristics and prospects of the enterprise, and to a large extent reflect the business philosophy and development path of the enterprise. This study focuses on the use of four major digital technologies (big data, artificial intelligence, mobile internet and blockchain) in the sports industry with reference to existing research. Referring to the practice of related studies, the indicator is assigned a value of 1 if the company has used any of these technologies during the year, and 0 otherwise.
Role mechanism variables. In this paper, operational efficiency (EFF) and operational cost (Cost) are chosen as the variables influencing the mechanism of digital transformation’s effect on the economic efficiency of sports enterprises, in which operational efficiency is quantified by the total asset turnover ratio, and operational cost is measured by the total operating costs and expenses.
Control variables. With reference to previous studies, this study selects firm age (AGE), firm size (SIZE), equity concentration (Share), gearing ratio (LEV), unlimited shares outstanding (LnLT), and research and development investment (RD) as the control variables for the economic performance of sports enterprises. Where firm age is expressed as the logarithm of the year of establishment, firm size is measured by the logarithm of the firm’s total assets, and equity concentration is the ratio of the number of shares held by the first largest shareholder to the total share capital.
Multiple linear regression
Multiple linear regression analysis is a method to study the system under the influence of multiple factors and establish a predictive model of the overall change pattern of the system [29]. Generally, the multiple linear regression model is of the form:
This equation is a hyperplane in
When the number of independent variables increases from two to
The element
The component
Solving the system of regular equations,
Multiple linear regression analysis requires a test of the applicability of the model. The test steps are as follows:
First, the entire regression model is tested for significance. Because of the large number of independent variables in the multiple linear regression model, after the total significance analysis, it is also necessary to know which factors have a large effect on the dependent variable, which have a small effect, and which can be ignored. Then the interval estimates are determined.
Stepwise regression method
Stepwise regression is a traditional linear regression method that constructs regression models by continuously screening independent variables [30]. The basic idea is to introduce variables step by step, and the introduction condition is that the partial regression square of the variable has extreme significance. At the same time, after the introduction of new variables, the old variables that have been introduced before are tested one by one, and the variables that are not considered significant by the test are deleted to ensure that each variable in the resulting subset of independent variables is significant. Keep testing the deletion of variables until no new variables can be introduced, ensure that the relationship between the independent variables and the dependent variable in the regression model are all significant, screen out the significant influences as independent variables, and establish the regression equation. The regression equation is as follows:
The advantage of stepwise regression is that it screens the variables, removes insignificant independent variables, and ensures that there are no covariance issues between all variables in the regression model. It is worth noting that stepwise regression also has the potential to remove important variables related to the dependent variable due to significance issues between the variables, which can lead to setting bias.
Empirical model construction
Based on the previous theoretical analysis, in order to further test the extent of the impact of digital transformation on the economic efficiency of sports enterprises, the econometric model set in this study is as follows:
In the model,
In addition, for the role of digital transformation in improving the economic efficiency of the sports industry, this paper mainly examines the mechanism variables, and realizes the improvement of the economic efficiency of the sports industry by improving the mechanism variables.
This paper adopts the practice of existing research on mechanism effect test, and pays more attention to improving the credibility of identifying the causal relationship of mechanism variables on explanatory variables, and the influence of mechanism variables on explanatory variables should be direct and obvious. To this end, this paper constructs the mechanism effect model on the basis of the above model as follows:
In the model,
Currently, we are at the historical intersection of a new round of scientific and technological revolution and the breakthrough of industrial change. With the digital economy as the representative of innovation in multiple fields, group accelerated breakthroughs, the real economy using the digital economy, the breadth and depth of the expansion of new models, new business models continue to emerge, industrial organization and the shape of the real economy continue to reshape the digital economy leading kinetic advantage is obvious, is accelerating to lead the depth of integration of the industry. In the face of the arduous industrial change, how to accelerate the digital transformation of the sports industry, and constantly meet the new demand of people’s quality, high-end and fashionable sports consumption, and better enhance the economic benefits of the sports industry has become a problem that must be paid attention to at present.
Based on the relevant economic data of Shanghai and Shenzhen A-share listed sports companies from 2012 to 2023, descriptive statistics and multiple covariance analysis (VIF) are performed for each variable in the model. The purpose of the multicollinearity analysis is to explore whether there is covariance between the variables, which has a greater impact on the results of the benchmark regression. Table 1 shows the results of descriptive statistics and multicollinearity test for each variable.
Descriptive statistics and multiple conlinear tests
| Variable | Means | SD | Min | Max | VIF | 1/VIF |
|---|---|---|---|---|---|---|
| ROA | 0.041 | 0.084 | -0.306 | 0.273 | - | - |
| DT | 0.056 | 0.135 | 0.003 | 0.776 | 1.915 | 0.522 |
| EFF | 0.834 | 0.331 | 0.198 | 2.183 | 1.343 | 0.745 |
| Cost | 0.953 | 0.118 | 0.435 | 1.679 | 1.285 | 0.778 |
| AGE | 15.247 | 5.239 | 2.347 | 27.113 | 1.804 | 0.554 |
| SIZE | 20.179 | 1.427 | 18.174 | 22.458 | 2.937 | 0.340 |
| Share | 33.287 | 22.216 | 0.253 | 88.935 | 2.268 | 0.441 |
| LEV | 40.539 | 17.548 | 11.475 | 83.174 | 1.464 | 0.683 |
| LnLT | 16.315 | 5.132 | 0.015 | 20.135 | 1.958 | 0.511 |
| RD | 3.607 | 1.953 | 0.064 | 8.606 | 1.619 | 0.618 |
As can be seen from the table, the mean value of the economic efficiency (return on net assets) of sports enterprises is 0.041, and the mean value of the degree of digitization is 0.056, which both have a large room for improvement compared with enterprises in other fields. From the standard deviation, it can be seen that the return on net assets and the degree of digitization have a large difference and a high degree of dispersion. According to Accenture statistics, between 2019 and 2023, the digitization gap between digital transformation leaders and other enterprises is widening, and their economic benefits are doubly divergent, a phenomenon that has become more pronounced in the wake of the epidemic, with the snowball effect coming to the fore. Therefore, we should focus on synergistic development among enterprises, create a favorable environment for industrial development, and promote the balanced development of performance and digital transformation among enterprises. The experience of international manufacturing industry shows that when the R&D investment ratio of enterprises is between 4.5% and 9.5%, it helps to improve the competitiveness of enterprises. In the table, the percentage of R&D investment is 3.607%, which indicates that the current level of R&D investment in sports enterprises is low, and it is necessary to further strengthen the investment in R&D and innovation. In the covariance test, the variance inflation factors of all variables are less than 10, indicating that there is no obvious covariance, and the influence of multiple covariance on the regression results can be excluded.
For the impact of digital transformation on the economic efficiency of the sports industry, this paper designed a fixed-effects model based on multiple linear regression, and used stepwise regression to study the specific impact of digital transformation on the economic efficiency of sports enterprises. Table 2 shows the baseline regression results, in which model (1)~(4) is the regression results of stepwise adding control variables, ***,**,* indicates significant at 1%, 5% and 10% significance level, respectively, and the value in parentheses is the value of the z-test, which is the same as in the following text.
Benchmark regression
| Variable | Model (1) | Model (2) | Model (3) | Model (4) |
|---|---|---|---|---|
| DT | 1.237***(0.548) | 1.206***(0.526) | 1.185***(0.493) | 1.158***(0.427) |
| AGE | - | -0.007(0.472) | -0.005(0.469) | -0.002(0.418) |
| SIZE | - | 0.318**(2.641) | 0.307**(2.457) | 0.285**(2.279) |
| Share | - | - | -0.005(-0.315) | -0.004(-0.306) |
| LEV | - | - | -0.014(-0.526) | -0.012(-0.438) |
| LnLT | - | - | - | -0.018***(-3.174) |
| RD | - | - | - | -0.006(-0.356) |
| (Con_) | 1.076***(0.069) | 0.974***(0.057) | 0.894***(0.051) | 0.825***(0.048) |
| Year | YES | YES | YES | YES |
| Ind | YES | YES | YES | YES |
| R2 | 0.176 | 0.184 | 0.193 | 0.215 |
Based on the results of the benchmark regression, it can be seen that in model (1), which only introduces the digital transformation level variable, the regression coefficient of the digital transformation level of sports enterprises is 1.237 and remains significantly positive at the 1% level. This indicates that there is a positive promotion effect of digital transformation on the economic efficiency of sports enterprises, and H1 is verified. After the gradual addition of control variables in model (2)~model (4), the impact of the level of enterprise digital transformation on the economic efficiency of sports enterprises is gradually weakening, from 1.237 to 1.158 without the addition of control variables, but always has a significant positive effect at the 1% level. In addition, from the regression coefficients of the control variables, the age of the firm (AGE), the concentration of equity (Share), the gearing ratio (LEV), and the research and development investment (RD) have a negative effect on the economic performance of sports firms, but there is no significance. And the coefficient of the effect of unlimited shares outstanding (LnLT) on the economic performance of sports firms is -0.018 and has a significant negative effect at the 1% level. Overall, the R2 of the model is 0.176 before adding control variables, and its value increases to 0.215 after adding control variables, which indicates that the explanatory strength of the model variables on the economic efficiency of sports enterprises has gradually increased, and the choice of variables has credibility.
The asset operation efficiency of sports enterprises is a direct driving force for their better operation and development, which can better reflect the health of their operation activities. Generally speaking, sports enterprises have the characteristics of short cycle, strong industrial association, etc. The improvement of capital operation efficiency can provide sufficient and stable funds for the operation of sports enterprises to a certain extent, and the digital transformation of sports enterprises can provide efficient resource turnover allocation. Based on this, this paper will use the stepwise test regression coefficient method to analyze the impact of sports enterprise operating efficiency on the relationship between enterprise digital transformation and enterprise economic efficiency, and the empirical results are shown in Table 3.
Test of the Mechanism of Corporate Operational Efficiency
| Variable | Model (1)-ROA | Model (2)-EFF | Model (3)-ROA |
|---|---|---|---|
| DT | 1.158***(0.427) | 0.059*(1.943) | 1.107***(2.759) |
| EFF | - | - | 0.398***(4.274) |
| AGE | -0.002(0.418) | 0.005(0.932) | 0.005**(1.756) |
| SIZE | 0.285**(2.279) | 0.006(0.871) | 0.003(1.064) |
| Share | -0.004(-0.306) | 0.004*(1.814) | 0.002**(2.137) |
| LEV | -0.012(-0.438) | 0.016(2.138) | 0.002(1.135) |
| LnLT | -0.018***(-3.174) | 0.003(1.507) | 0.002(1.248) |
| RD | -0.006(-0.356) | -0.138***(-2.915) | 0.035***(2.764) |
| (Con_) | 0.825***(0.048) | -0.186*(-1.674) | -0.025(-0.807) |
| Year | YES | YES | YES |
| Ind | YES | YES | YES |
| R2 | 0.215 | 0.327 | 0.694 |
| F | 11.64 | 29.15 | 29.98 |
Through observation, it is found that the coefficient of the level of digital transformation on the economic efficiency of sports enterprises in model (1) is 1.158, and the regression coefficient of the level of digital transformation on the operational efficiency of sports enterprises in model (2) is 0.059, which indicates that the digital transformation can improve the operational efficiency of sports enterprises, and the H2 is verified. The coefficient of the level of digital transformation on the economic efficiency of sports enterprises in model (3) is 1.107, and the coefficient of the operational efficiency of enterprises is 0.398, both of which are statistically significantly positive at the 1% level and pass the bootstrap test. It shows that there is a partial mediation effect in the model, i.e., digital transformation improves the operational efficiency of sports enterprises, which in turn improves the level of economic efficiency of sports enterprises. This is mainly because the sports industry market is close to a perfectly competitive market, the price of the unit product is similar to the cost, the profit gap between the leading sports enterprises is small, and the digital transformation of sports enterprises will be able to effectively compress the cost of information acquisition and resource acquisition.
Sports enterprises in the process of digital transformation, operating activities with data as the key element, will reduce information asymmetry by optimizing the flow of information, effectively reducing transaction costs. In addition, digital transformation can improve the efficiency of enterprise operation and management, and realize the combination of online and offline management and operation based on digital data, which can effectively reduce the operation and management costs, and then promote the enhancement of enterprise economic efficiency. Based on this, we will continue to use stepwise test regression coefficient method to analyze the impact of sports enterprise operating costs on the relationship between enterprise digital transformation and enterprise economic efficiency, and the specific empirical results are shown in Table 4.
Test of the Mechanism of Corporate Operational Costs
| Variable | Model (1)-ROA | Model (2)-EFF | Model (3)-ROA |
|---|---|---|---|
| DT | 1.158***(0.427) | -0.203*(2.198) | 0.978***(3.942) |
| EFF | - | - | -0.026***(-3.271) |
| AGE | -0.002(0.418) | -0.005(-0.813) | 0.003**(2.306) |
| SIZE | 0.285**(2.279) | -0.002(-0.051) | 0.005(1.075) |
| Share | -0.004(-0.306) | -0.003(-0.739) | 0.002***(2.214) |
| LEV | -0.012(-0.438) | -0.085(-1.382) | 0.007***(2.735) |
| LnLT | -0.018***(-3.174) | 0.005(0.816) | 0.002(1.515) |
| RD | -0.006(-0.356) | -0.051(0.789) | 0.073(0.679) |
| (Con_) | 0.825***(0.048) | 1.943(1.415) | -0.089**(-2.137) |
| Year | YES | YES | YES |
| Ind | YES | YES | YES |
| R2 | 0.215 | 0.421 | 0.218 |
| F | 9.64 | 12.73 | 11.26 |
The coefficient of digital transformation on the economic efficiency of sports enterprises in model (1) is 1.158, and the coefficient of digital transformation on the operating costs of sports enterprises in model (2) is -0.203, which is significant and negative at the 1% level of detection, indicating that the higher the degree of digitalization of sports enterprises, the lower the operating costs of the enterprises, i.e., the digital transformation will inhibit the increase of the enterprise operating costs. The coefficient of digital transformation on the economic efficiency of sports enterprises in model (3) is 0.978, and the coefficient of sports enterprises’ operating costs on the economic efficiency of enterprises is -0.026, both of which are statistically significant at the 1% level of detection. In addition, the bootstrap test shows that there is a partial mediation effect in the model, i.e., the digital transformation will improve the economic efficiency of the enterprise by reducing the operating costs of the enterprise. Although digital transformation increases the cost of sports enterprises in the early stage of transformation, with the deepening of the transformation, the organizational structure becomes more and more reasonable, and ultimately will achieve the reduction of enterprise costs, which in turn will bring the effect of promoting the enhancement of enterprise performance.
In order to ensure the accuracy and reliability of the results of this regression, and taking into account that there is a lag in the implementation effect of digital transformation, this paper adopts the lagged term of digital transformation (DT) as an instrumental variable to construct the Hausmann test diagnostic model and replaces the explanatory variables to carry out endogeneity diagnosis and robustness test. In this paper, the return on net assets is used as a measure when analyzing the economic efficiency of sports enterprises, and it is replaced with gross profit margin (GPM) when conducting the robustness test, and the specific results are shown in Table 5.
Robustness and endogenous testing
| Variable | Model (1) | Model (2) | Model (3) | Model (4) |
|---|---|---|---|---|
| DT | 1.241***(0.682) | - | - | - |
| Ln.DT | - | 1.307***(0.743) | - | - |
| Ln2.DT | - | - | 1.318***(0.759) | - |
| Ln3.DT | - | - | - | 1.302***(0.724) |
| AGE | -0.005(0.423) | -0.005(0.423) | -0.006(0.435) | -0.006(0.435) |
| SIZE | 0.267**(2.158) | 0.287**(2.246) | 0.282**(2.231) | 0.284**(2.242) |
| Share | -0.008(-0.347) | -0.006(-0.315) | -0.007(-0.327) | -0.005(-0.295) |
| LEV | -0.009(-0.518) | -0.015(-0.469) | -0.013(-0.438) | -0.011(-0.435) |
| LnLT | -0.014**(-3.152) | -0.015**(-3.158) | -0.012**(-3.006) | -0.013**(-3.104) |
| RD | -0.008(-0.378) | -0.008(-0.378) | -0.005(-0.342) | -0.005(-0.342) |
| (Con_) | 3.292***(0.738) | 2.978***(0.633) | 2.741***(0.595) | 7.425***(0.573) |
| Year | YES | YES | YES | YES |
| Ind | YES | YES | YES | YES |
| R2 | 0.257 | 0.237 | 0.236 | 0.231 |
In model (1), the gross profit margin (GPM) is used to replace the original explanatory variable return on assets (ROA) to conduct regression validation on the sample of sports enterprises again, and the regression results show that the level of digital transformation (DT) is positively correlated with the economic efficiency variable of sports enterprises at the 1% level, with a regression coefficient of 1.241, which is significant compared with the previous regression results, and the sign of each control variable is increased, magnitude of the coefficients as well as the significance do not change to a large extent before and after the replacement. Therefore, it shows that the original conclusion is robust, and the robustness results are basically consistent with the empirical research results and their significance, which proves once again that the results of the study have a certain degree of accuracy and reliability.
In order to avoid the problem of estimation bias arising from endogeneity affecting the accuracy of the regression results and to fully ensure the robustness of the regression conclusions, the lagged period regression is further adopted, and the indicators of the degree of digital transformation lagged by one, two, and three periods are used as instrumental variables for the endogeneity test. The results of the model run (model (2)(3)(4)) indicate that the regression coefficients of Ln.DT, Ln2.DT and Ln3.DT with the economic performance of sports enterprises are all significantly positive, and the p-values of the statistic are all greater than 0.05, then it is considered that there is no endogeneity problem. The p-values in the over-identification sarganBasman test are 0.642 and 0.738 respectively are much greater than 0.05, then all instrumental variables are considered to satisfy the condition of exogeneity and are valid, i.e. there is no over-identification problem.
The above analysis shows that although the two regression methods differ in regression coefficients, the significance levels and conclusions are the same, and are consistent with the previous conclusion that replacing the metrics of the economic efficiency of sports enterprises still does not affect the core research conclusions, which fully proves that the regression results are of robustness significance.
In the previous test, the impact of digital transformation on the economic efficiency of sports enterprises was examined based on the full sample and passed the endogeneity and robustness tests. In order to explore the differentiation of enterprises with different attributes in realizing digital strategy orientation formulation, etc., this paper, with reference to the existing studies, introduces the interaction terms of digital transformation with size variables and industry variables as core explanatory variables on the basis of the variables of the fixed-measurement model (7) designed in the previous paper, respectively, and further group regression to examine the level of digital transformation of different sizes and industry attributes on the economic efficiency of sports enterprises’ heterogeneous influence effects. Firstly, this paper divides the sample sports enterprises into two groups of small and medium-sized enterprises (with less than 340 employees, take 0) and large-scale enterprises (with more than 350 employees, take 1) in accordance with the classification method of Scale in the Provisions of Small and Medium-sized Enterprises Delineation Standard. Secondly, the sample enterprises are classified into two groups of sports manufacturing industry (take 2) and sports service industry (take 3) according to the method of National Economy Industry Classification (NEC). Finally, the interaction term was introduced as the core explanatory variable (DID-DT), and the group regression was conducted to test for heterogeneity. The regression results of the heterogeneity test are shown in Table 6.
Regression results of heterogeneity testing
| Variable | Scale | Industry | ||
|---|---|---|---|---|
| Model (0) | Model (1) | Model (2) | Model (3) | |
| DID-DT | 0.085***(2.935) | -0.064(-1.372) | 0.121**(2.403) | 0.035(1.282) |
| DT | 0.047(1.316) | 0.016(0.268) | 0.085*(1.917) | 0.051(1.474) |
| Controls | YES | YES | YES | YES |
| DID-DT-(Con_) | 6.489***(3.057) | 9.062***(3.178) | 9.698***(3.274) | 9.135***(3.164) |
| DT-(Con_) | 8.476**(2.173) | 8.428***(3.106) | 4.015(0.937) | 7.626***(3.278) |
| Year | YES | YES | YES | YES |
| Ind | YES | YES | YES | YES |
The results of the regression of the size attribute grouping found that the effect of digital transformation on the economic efficiency produced by the digital transformation varies among different sizes of sports enterprises. The regression coefficient of model (0) DT is larger than that of model (1), and the sign of the coefficient of the interaction term added by model (0) is significantly positive and the coefficient is significantly higher. It indicates that there is enterprise size heterogeneity in digital transformation, and digital transformation has a more significant effect on the economic efficiency improvement of large-scale sports enterprises. Compared with small-scale enterprises, large-scale enterprises are more likely to produce agglomeration effects and competitive advantages in economic resources, so digital transformation has a positive impact on the growth of economic efficiency of large-scale sports enterprises. H4 can be verified.
The results of model (2)~(3) show that compared with model (3), the regression coefficient of model (2) and the coefficient of interaction term are significantly positive, then it indicates that the digital transformation has a better effect on the improvement of economic efficiency in the sports manufacturing industry, i.e., there is a threshold effect of industry attributes in the impact of digital transformation on the economic efficiency of sports enterprises. Compared with the service industry, the supply chain system of the product manufacturing industry is more complex, involving raw material procurement, production planning, execution and product sales, etc. Digital transformation is more conducive to the integration of resources, real-time access to and monitoring of data changes, in order to provide customers with personalized products and services, and therefore it has a better impact on the enhancement of economic efficiency. On the other hand, in the service industry, there are usually multiple systems and platforms, and the problem of information silos is so serious that data sharing and integration cannot be realized properly, which leads to the poor performance of digital transformation on economic efficiency.
In today’s intelligent and data-driven era, a lack of insight into consumer needs is likely to lead to ineffective investments by enterprises. When expanding their business, sports enterprises should make full use of relevant information and data to grasp consumer needs and keep abreast of the latest consumption trends. When predicting market trends, sports enterprises can use statistical models, machine learning algorithms and other ways to analyze social media, consumer purchase records, web browsing records and other ways to understand consumers. They can also analyze consumer evaluations of related products and services to understand consumer preferences and needs, analyze the advantages of products that consumers attach more importance to, and select profit points with higher economic benefits based on relevant analysis results.
When sports enterprises initially explore new markets and consider the possibility of expanding their industrial layout, they should use big data technology to analyze the relevant factors of the market. By conducting market surveys and using digital technology to count and analyze the relevant findings, they can analyze the position and advantages of their products in the same industry, fully understand whether the enterprise is competitive in the market it wants to join, avoid the trial-and-error costs brought about by blind investment, and reduce the enterprise’s investment risks.
In addition to the initial confirmation of the industrial layout, sports enterprises should also make certain preparations. One is to use digital technology to analyze the needs and purchasing habits of target customers, and initially determine the business model of the new industry and related sales channels. And the enterprise can also choose to diversify the operation of sales channels, for example, it can choose the combination of online and offline sales, to avoid relying too much on a single sales channel, so as to reduce the sales risk. The second is to understand the competitors, through the network information, self-media platforms and other channels to understand the competitors’ advertising programs, marketing methods, etc., so as to analyze their own advantages and disadvantages, and to find more attractive marketing methods to attract customers. Utilize marketing methods and channels to warm up the new business of the enterprise, and also determine the direction of subsequent development by analyzing the attention as well as evaluation. Thirdly, the layout of the industry is regularly assessed, and the marketing channels are constantly optimized and adjusted in order to improve the performance of the enterprise and make the industrial layout develop smoothly.
The article analyzes the role and optimization path of digital transformation in the economic efficiency of sports industry by using multiple linear stepwise regression method. Based on the research results, it can be seen that digital transformation can significantly enhance the economic efficiency of sports enterprises, and its enhancement effect is more prominent in large-scale sports enterprises and sports manufacturing industries. In addition, in the test of the role mechanism of digital transformation in enhancing the economic benefits of sports enterprises, the level of digital transformation enhances the economic benefits by improving the operational efficiency and reducing the operational costs of sports enterprises. Therefore, the improvement of economic efficiency of sports industry needs to make full use of information technology to integrate data to improve marketing effect, and optimize the industrial layout with digital technology, so that the sports industry can develop smoothly with high quality.