A Study of the Impact of Digital Competence on Household Sports Consumption

A Study of the Impact of Digital Competence on Household Sports Consumption | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Study of the Impact of Digital Competence on Household Sports Consumption Ziyan Wang, Hemin Song, Yukun Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7472716/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In the era of the digital economy, digital competence is a crucial skill for navigating the digital landscape and an invaluable asset in the information society. The consumption of sports is influenced by many factors, including personal characteristics, family characteristics and the characteristics of the region in which the family is located. The enhancement of digital competence is of great significance in broadening income channels, improving income levels and upgrading the consumption structure. Based on data from the 2017 China Household Finance Survey (CHFS), the article empirically examines the impact of digital competence on household sports consumption based on constructing household digital capability scores. The results indicate that digital competence has a significant positive impact on household sports consumption. This conclusion remains robust even after considering potential endogeneity and conducting robustness tests. Further analyses show that digital competence is more effective in increasing household sports consumption for households characterized by higher levels of personal education, urban households, higher total household assets, and higher participation in household entrepreneurship. Digital competence promotes household sports consumption mainly by raising the level of household income. It is recommended to accelerate the development of the digital economy, establish a comprehensive mechanism for cultivating digital competencies, and fully leverage these capabilities to promote household sports consumption. Business and commerce/Economics Social science/Economics Earth and environmental sciences/Environmental social sciences Digital competence Household sports consumption Household income Mediating effect 1. Introduction Digital economy development in China has a robust global presence [ 1 , 2 ] . Recent statistics indicate that China's digital economy ranks among the world's most remarkable in size and growth rate. China's digital economy has been developing rapidly in recent years, and according to the Digital China Development Report (2023), the added value of China's core industries in the digital economy is estimated to exceed about $ 1637.208 billion in 2023, accounting for about 10 percent of GDP. The digital economy has become an important engine driving economic development. In China, the availability of digital technologies and services builds a realistic foundation for the rapid expansion of the digital economy, including emerging technologies (e.g., big data, cloud computing, the Internet of Things and 5G communications), digital services and applications. They have permeated all levels of social life, exerting a profound influence on the production and lifestyle of human beings. To adapt to a digital context, enhancing one's digital competence is required [ 3 ] . At the macro level, digital competence has gradually become an important indicator for measuring international competitiveness and soft power. At the micro level, digital competence has been shown to be not only a right but also a requirement of citizens, as it is necessary to be functional now [ 4 ] . Research has revealed a strong interest in the study of digital competence in (tertiary) educational settings [ 5 ] , teachers [ 6 ] , students [ 7 ] and enterprises [ 8 ] . According to scholars, the scope of digital competence is beyond digital literacy [ 9 , 10 ] and includes attitude and mindset besides skills [ 11 ] . Recent studies suggest digital competence should be replaced with digital literacy in educational contexts since digital competence pays more attention to the ethical, safety, and social dimension [ 12 , 13 ] and identifies more diverse knowledge, abilities, and desires of individuals [ 14 ] . Digital competence, the ability of an individual to understand and use digital technology in social activities such as work, life and recreation, is a reflection of the individual's ability to adapt and comprehensive quality in the era of the digital economy [ 15 ] . With the rapid development of the digital economy, the disposable income of residents has increased significantly, and emerging digital technologies are widely used in production and consumption, which profoundly affects residents' consumption choices. The rapid growth of the digital economy has caused the Chinese consumer market to speed and change, giving rise to new types of consumption, new scenes of consumption, and new consuming content [ 16 ] ; the residents' consumption structure has been upgraded [ 17 ] , leading to a significant increase in sports consumption. According to the viewpoint of sports economics, sports consumption belongs to enjoyment-centric consumption and development-centric consumption, which is affected by individual factors, family factors, and regional factors. Digital competence empowers people to utilize the Internet and digital technology effectively. It facilitates individuals' participation in the digital economy, thereby expanding income channels and increasing sources of revenue. This access enables them to gain digital dividends, improve their consumption structures, and ultimately boost sports consumption. From the macro level, the innovation of digital technology and the development of the digital economy promote national economic growth; from the micro level, the family is the cell of society. In the era of the digital economy, what impact will digital competence have on family sports consumption? This paper focuses on the effect of digital competence on family sports consumption and the mechanism of action. Based on the micro-data of more than 30,000 households in the 2017 China Household Finance Survey and Tobit regression model, this paper aims to provide empirical evidence for the promotion of China's residents' sports consumption upgrading. 2. Literature Review 2.1 Digital Competence Digital competence plays a key role in various industries. As a composite competence, it is considered an important survival skill in the digital era [ 18 ] and an important asset in the information society [ 19 ] . It refers to an individual's ability to correctly understand and creatively use digital technology in social activities such as work, life, and recreation. It reflects an individual's adaptability and comprehensive quality in the digital economy. Currently, the field suffers from a lack of clarity regarding the terminology (i.e., digital literacy, digital skills, digital competence, 21st-century skills,. .. ) [ 20 ] . As a dynamic concept, digital competence is relevant to policy and educational research, and it is linked to the development of digital technologies as well as the political objectives and expectations of people in knowledge societies [ 21 ] ; in 2011, the European Commission published Mapping Digital Competence: Towards a Conceptual Understanding and explicitly proposed that Digital competence is defined as the set of knowledge, skills and attitudes needed to use computer information technologies confidently, critically and creatively to achieve goals related to work, employment, learning, leisure and recreation, decision-making or social participation. In 2018, the meaning was enriched, and the EU defined digital competence as the confident, critical and responsible use of digital technologies in learning, work and participation in society. Despite the lack of agreement concerning the definition of digital competence, literature agrees that it is not only constituted of technological skills but encompasses multiple literacies. In other words, for measuring digital competence, several sub-dimensions are proposed. The European Union has provided two versions of reference: in 2014, the EU developed four primary indicators, namely "information," "communication," "content creation," and "problem-solving," along with 20 secondary indicators; in 2022, the EU updated the four primary indicators, namely "information and data literacy," "communication and cooperation," "digital content creation," "security," and "problem-solving," along with 30 secondary indicators. Although there is no one standard for digital competence indicators in China, some scholars have also proposed digital competence indicators suitable for China's national conditions. For example, Lu Jingming et al. (2023) [ 22 ] constructed a digital competence index system for herdsmen based on research data from three dimensions of digital technology access, digital platform use, and digital information acquisition; Wang Xiaohua (2023) [ 23 ] constructed a personal digital competence index from three dimensions which are digital access, digital use, and digital creation. 2.2 Relevant Studies on Sports Consumption In terms of the concept of sports consumption, there are a variety of interpretations in current academic circles. According to the perspective of whether or not it is directly related to physical activity, some scholars divide it into two categories, i.e. direct consumption and indirect consumption. The direct consumption is explicitly associated with sports practice (e.g. equipment, sporting events and public facilities), while the indirect consumption belongs to other economic fields, but could/should also be taken into account when they are consumed for sports purposes (e.g. transport, consumption of sports beverages and sports food, and the shadow price of time) [ 24 ] . For example, the 2024 Boston Marathon attracted 30,000 participants and participants' ticket expenses are direct sports consumption. Indirect sport consumption was significant. The reported data show that 68% of participants stayed in Boston-area hotels, and the median weekend spend by participants was $ 500. A survey conducted by the Boston Restaurant Group showed a 15% increase in sales at the city's restaurants [ 25 ] . From the perspective of the research object of sports consumption, early studies were mostly based on individuals, and in recent years, studies have tended to focus on the family level [ 26 ] . Since the family is the basic unit of social structure, the main part of family consumption is public family consumption, in addition to personal consumption, so the individual-based consumption research is fundamentally limited [ 27 ] . Sports consumption-related research uses more quantitative analyses, and the research data is divided into primary and secondary data. Papers using primary data mostly employ the questionnaire survey method. Kevin K. Byon (2010) used the Sports Consumption Motivation Scale to measure the motivation of wheelchair rugby spectators and predict their online consumption behaviour [ 28 ] . Sylvia Chan-Olmsted (2019) conducted an online survey (n = 646) to figure out factors influencing sports consumption on smartphones. It is found that the motivation to acquire knowledge of smartphone use for sports, social sports activity participation on smartphones and fandom interactions online increase the willingness to consume sports on smartphones [ 29 ] . Brian H. Yim(2020) designed an online survey questionnaire to study factors that may impact the millennial fans' decision-making process in connection with various sports consumption behaviors, which revealed five key characteristics of Millennial fan consumption: technology-driven, community-driven, peer pressure, emotional consumption, and fan engagement [ 30 ] . Secondary data are mostly from national databases, and such studies tend to focus on regional sports consumption and studies of sports consumption by specific groups. Adam Gemar (2020) explores the place of sport participation in the cultural lifestyles of Canadians based on cultural consumption theory and omnivore thesis, using data from a large-scale survey of the Canadian government. The results suggest that omnivorous cultural consumption includes people with high levels of cultural and economic capital and that the cultural domain of sport may be a more distinctive area of consumption for omnivores [ 31 ] . Grace Yan (2018) investigated the Twitter networks of the Champions League hashtag (#UCL) across the 2017 UEFA Champions League Final, including 19,869 pre-match posts, 3,276 halftime posts, and 5,691 post-match posts from Twitter. It is shown that large sports clubs have the ability to dominate the social network due to their stable and privileged position [ 32 ] . From the perspective of the influencing factors of sports consumption, at the macro level, the international experience is that a large-scale demand for sports consumption will be formed when the economic development enters the upper-middle-income stage, i.e., when it exceeds 6,500 US dollars, and the sports industry will become a pillar-type industry for economic development after entering the high-income stage [ 33 ] . That is, sports consumption is built on a certain economic foundation. In addition, conditions such as income level, education level, age, and time constraints all have an impact on sports consumption. Income promotes sports consumption [ 34 – 36 ] ; the higher the education level, the more people understand the value of sports, and the more sports consumption will be [ 34 ] ; with the increase of age, people will pay more attention to the loss of health capital, and to compensate for it they will invest more in sports consumption [ 37 ] . At the micro level, sports consumption behavior is affected by individual consumption motives and other factors. Some scholars have examined the differences in the motivation of Koreans living in the United States to consume sports in their home country (Korea in this study) and their host country (the United States in this study) in terms of eight aspects of motivation, namely, social, informational, recreational, escapism, fan expression, time-killing, fandom, and technical knowledge [ 38 ] . These studies combine economics and psychology to expand the influences on sports consumption. 2.3. The Impact of Digital Competence on Sports Consumption. At present, there is a lack of literature studying the direct impact of digital competence on sports consumption in China. Conversely, more studies have focused on the effects of digital competence, such as income-enhancing, household allocation of risky financial assets, entrepreneurial decision-making, and developmental resilience. Li Mengfan (2023) takes digital competence as an endogenous variable and finds that Internet use empowers personal income enhancement through technological effects and capital effects (physical, human, and social capital), and that the Internet itself exhibits dynamic heterogeneity in the process of its development [ 39 ] ; Wang Yako and Wang Yi-Wei (2024) demonstrate that digital competence and digital financial capability enhance household consumption levels via three primary channels: income enhancement, online purchasing, and easing liquidity constraints. Besides, the impact of digital competence on consumption is more pronounced among rural and low-income households [ 40 ] ; Wang Xiaohua et al. (2023) classified digital competence into three dimensions: digital access, digital use and digital creation. Constructing an LPM model, their study concluded that digital competence promotes financial literacy, which in turn facilitates the allocation of risky financial assets by household. Among these dimensions, digital creation gives residents more initiative and flexibility, allowing them to choose a variety of financial assets. Residents can choose credit cards to provide short-term credit, so that the household intertemporal smoothing, which in turn promotes consumption [ 23 ] ; Concerning the object of digital competence studied in China, most of them are farmers and herdsmen. Digital competence has an empowering role in fostering entrepreneurship in farming households and increasing farmers' financial engagement, both of which enhance the resilience and development of the farming household family [ 41 ] . In addition, to empirically analyze the relationship between household consumption and digital capability, Li Rui (2024) constructed a benchmark regression model based on the income effect. The study confirmed that digital competence plays a significant role in promoting various forms of consumption, including enjoyment-centric, development-centric, and subsistence-centric consumption, and it also found that increasing digital competence can significantly increase household consumption [ 15 ] . The digital economy is currently the primary focus of Chinese research on the factors influencing sports consumption, and studies have shown that it can greatly enhance the high-quality development of the sports industry, encourage the upgrading of industrial structure, and accelerate the upgrading of sports consumption structure. The popularity of the digital economy has brought about the application of digital technology, which not only promotes the digital and intelligent transformation of traditional industries but also gives rise to many new industries, new forms and new scenarios. Additionally, the degree of supply and demand matching has increased, which has given sports consumption new vitality [ 16 ] . Technology provides consumers with data, analytics, and insights and it allows fans to engage with athletes, teams, brands, etc. by means of the internet, social media, etc. Therefore, the content of sports consumption will continue to change significantly [ 42 ] . Sports consumption, as both enjoyment-centric and development-centric consumption, is not only affected by the digital economy at the macro level but also at the micro level by consumers’ cognitive ability, consumption intention, and consumption demand. As a critical survival and lifestyle skill in the digital era, digital competence exerts a significant impact on residents' sports consumption. Thus, this paper explores the impact and the mechanism of the role of digital competence on sports consumption. 3. Theoretical basis and research hypothesis According to AISAS consumer behavior Theory, in the era of the Internet and wireless applications, digital platforms provide consumers with the convenience of actively obtaining information. Enterprises attract consumers' attention by disseminating advertisements online. Once consumers develop an interest in the content, they actively search for product information, which then leads to purchasing action. Finally, consumers will share their consumption experiences on the Internet. In the Internet era, the development of digital technology has revolutionized the sports consumption scene. Based on 5G, big data, artificial intelligence, etc., new scenes such as online live streaming and online consumption have been derived [16] . On the one hand, the emergence of online shopping platforms and the development of modern logistics systems enable individuals with digital competence to buy sports products on online shopping platforms such as Taobao, Jingdong, Tmall, Pinduoduo, etc. During the ‘Double Eleven’ shopping festival in 2023, Tmall announced a sports consumption list, in which Nike, FILA, Anta and other brands have harvested more than 100 million yuan in sales. On the other hand, the development of digital technology makes online and offline interoperability, providing people with more choices in sports consumption. However, the emergence of the digital divide is inevitable due to the inequality of ICT in terms of physical connectivity and access [43] . Also, there are regional differences in economic, cultural, and transport development, which lead to differences in digital competence between urban and rural areas as well as among individuals. These differences in using digital platforms result in differences in household sports consumption. Individuals who have sufficient knowledge of digital platforms and digital competence are more likely to acquire sports consumption information through the Internet, social media, and mobile applications. Based on this, the first research hypothesis of this paper is formulated: Hypothesis 1: Digital competence contributes to the level of household sports consumption. According to asymmetric information theory, the more information one has, the more advantageous it is in market economic activities. Digital competence represents an individual's ability to correctly understand and creatively use digital technology, and Internet use is a major component of digital competence [40] . By using the Internet to break down information barriers, people can understand market dynamics in a timely and comprehensive manner, which helps to optimize production decisions and increase incomes. On the one hand, digital competence can increase business income, and the rise of digital platforms attracts the public to actively participate in ‘Internet +’ entrepreneurship, everyone can open a shop on the Internet and conduct transactions, which inspires the public to try to innovate and start a new business, thus increasing their business income [44] ; on the other hand, digital competence can increase wage income. Digital recruitment platforms help broaden the channels for people to submit CVs and look for jobs, and digital service platforms, such as online live broadcasting and self-media creation, directly provide jobs to alleviate the pressure of unemployment, thus increasing their wage income [45] . Consumption is affected by economic growth, income distribution, population, employment and other factors, of which income distribution is a direct factor affecting the level of consumption. The rise of disposable income is the prerequisite for a higher level of consumption by residents. Residents' income is also an objective and fundamental condition for sports consumption [46] . Erik Thibaut (2014) used the theory of household production, which states that expenditure increases with income, capital and time, and that higher incomes provide households with the funds to consume so that higher levels of utility can be realized. Thibaut's study confirms that when household incomes increase, households will spend more on sports participation in terms of Money [34] . At the individual level, the majority of studies verify the positive correlation between income and sports consumption [47-49] . Accordingly, the paper proposes the following hypothesis: Hypothesis 2: Digital competence contributes to the rise in the level of household income, which in turn increases the level of household sports consumption. 4. Research design 4.1. Sample Selection and Data Source The data in this paper comes from the 2017 China Household Finance Survey (CHFS), a questionnaire that covers micro-level variables in household consumption, household expenditure, employment, payment habits, assets and liabilities, meeting the data quality requirements needed for the study. The China Household Finance Survey is conducted under the auspices of the China Household Finance Survey and Research Centre of the Southwestern University of Finance and Economics and is conducted every two years. Up to now, five years of data are available for 2011, 2013, 2015, 2017, and 2019. Given that sports and healthcare consumption are not listed separately in the data for 2011, 2013, 2015, and 2019, this paper only adopts the 2017 China Household Finance Survey database and processes the data as follows: ① Combine the individual-level data, household-level data, and master data according to the correspondence between households and household heads; ② Remove missing samples of key variables; ③ Retain only samples where the household head is 16 years old and above; ④ Take 1% upper and lower truncation of the total household assets and total household income in this data and then take the natural logarithm. The final number of observations is 39,763. 4.2. Model Setting 4.2.1. Benchmark regression model There are a large number of ‘0’ values in household sports consumption, which is restricted data. The data presents the characteristics of ‘subsumed data’, and if the OLS model is used, it will produce a large estimation bias, so this paper draws on the research of Zhang Wei et al. (2022) and adopts the Tobit model for regression: $$\:lnsportscon={\alpha\:}_{0}+{\beta\:}_{0}DigitalScore+{\rho\:}_{0}{X}_{i}+\mu\:+{\epsilon\:}_{i}$$ 1 In this paper, \(\:lnsportscon\) is the core dependent variable, representing the sports consumption of household i in the sample for the year 2017, which has been log-transformed. \(\:{X}_{i}\) denotes head-level and household-level control variables. \(\:\mu\:\) represents a province-fixed effect, and \(\:{\epsilon\:}_{i}\:\) is the random error term. DigitalScore's coefficient \(\:{\beta\:}_{0}\) measures the impact of the head of household's digital competence on household sports consumption. 4.2.2. Modelling of the mediating mechanism To deeply study the influence mechanism of digital competence on household sports consumption, the following mediating effect model is constructed for empirical testing: \(\:lnsportscon={\alpha\:}_{0}+{\beta\:}_{0}DigitalScore+{\rho\:}_{0}{X}_{i}+\mu\:+{\epsilon\:}_{i}\) (2) \(\:{TotalIncome}_{i}={\alpha\:}_{1}+{\beta\:}_{1}DigitalScore+{\rho\:}_{1}{X}_{i}+\mu\:+{\epsilon\:}_{i}\) (3) \(\:lnsportscon={\gamma\:}_{2}+{\gamma\:}_{2}DigitalScore+{\gamma\:}_{2}{TotalIncome}_{i}+{\rho\:}_{2}{X}_{i}+\mu\:+{\epsilon\:}_{i}\) (4) In the formula, \(\:{TotalIncome}_{i}\) denotes the total income of household i. 4.3. Variable Setting 4.3.1. Explained variables The explanatory variable of this paper is household sports consumption. Given the availability of data and the research results of Ma Tianping (2022) [ 50 ] , the question [G1020] in the CHFS questionnaire: ‘Last year, how much did your family spend on health care and fitness and exercise expenditures (unit: yuan)’ is used as a proxy variable for household sports consumption. The data plus 1 and take the natural logarithm for processing. 4.3.2. Explanatory variables Digital competence is the explanatory variable of this paper. In the era of the digital economy, there are differences in the digital competence of each individual due to uneven regional development and uneven distribution of digital technologies, which results in unequal access to digital technologies as well as equal acquisition of digital skills by each individual. As a result, a digital divide between individuals exists, which means the gap between individuals in acquiring information and communication knowledge and using information and communication technology. It is interdependent with digital competence and has an inherent correspondence in terms of concepts and dimensions. Given that the digital divide includes the access divide, the skills divide, and the transformation divide [ 51 – 53 ] , and based on Wang Xiaohua's approach, this paper divides digital competence into three dimensions, namely digital access, digital use, and digital creation. The first dimension is digital access. The uneven distribution of information and communication devices and services such as mobile phones, computers, and the Internet among different regions and groups has resulted in some households being at an ‘information disadvantage’. These households lack Internet connectivity and information updates and face a digital access divide [ 54 ] . This not only limits their possibilities of enjoying a digital life but may also have a negative impact on their overall well-being. The second dimension is digital use. Internet penetration does not imply natural access to digital technology use, and differences in digital technology use stem from differences in users' physical, human, and social capital [ 55 ] , leading to unequal access to digital resources for different households. The third dimension is digital creation. It connotes that groups in an information-advantageous position who master relevant knowledge and skills in using digital technology can acquire more opportunities for participation, and can transform digital competence into income through online entrepreneurship, and financial investment via online platforms. This paper combines the connotation of digital competence and the above dimensions while taking into account the scientific and comprehensive nature of the indicators, the availability of data and comparability, and takes digital competence as a first-level indicator, digital access, digital use, and digital creation as a second-level indicator. Based on the China Household Finance Survey questionnaire, 12 specific tertiary indicators are selected to construct a digital capability evaluation framework, as shown in Table 1 . The digital capability index is calculated using the entropy method, and the scores for the primary, secondary, and tertiary indicators are derived accordingly. Table 1 Construction of digital competence indicator system Dimensions The questions in CHFS Assignment criteria Weights from entropy method digital access Do you currently have a computer in your home? yes = 1, no = 0 0.052 Do you currently use a mobile phone? yes = 1, no = 0 0.002 Do you currently use a smart phone? yes = 1, no = 0 0.035 Is your home currently connected to fixed broadband? yes = 1, no = 0 0.048 digital use Do you use the Internet? yes = 1, no = 0 0.057 When shopping at your home, do you use the computer to pay, mobile terminal such as mobile phone or Pad to pay? yes = 1, no = 0 0.092 Do you use social chatting tools such as WeChat and QQ? yes = 1, no = 0 0.071 digital creation Do you have online shopping experience? yes = 1, no = 0 0.067 Do you use financial APP or Internet mobile phone to follow financial news? yes = 1, no = 0 0.143 Do you use the Internet to sell products and services? yes = 1, no = 0 0.249 Do you use the Internet to engage in stock speculation, scientific research and other businesses? yes = 1, no = 0 0.183 4.3.3. Control variables Based on Li Rui et al. (2023), this paper includes two types of control variables: individual and household level, which may affect household sports consumption. The first is the control variables at the head of household level, which include age, age squared, gender (male = 1, female = 0), marital status (married = 1, unmarried = 0), education level (no schooling = 1; primary or junior high school = 2; senior high school or Vocational High School = 3; High School or College = 4); Bachelor's Degree and above = 5). The second is household-level control variables, including whether or not an urban household (urban = 1, rural = 0), household size (total household size), total household assets (logarithmic treatment), household holding of financial products (yes = 1; no = 0), and household entrepreneurial participation (yes = 1, no = 0). Table 2 represents the definition of the main variables in this paper. Table 2 Definition of key variables Variable type Variable name Variable measurement Explained variables Household sports consumption Household annual sports consumption (yuan), logarithmic treatment Explanatory variables Digital Competence 11 household-level indicators were constructed and the entropy method was used to calculate the digital competence score. Household head control variables Gender male = 1,female = 0 Marital status Married = 1,unmarried = 0 Educational attainment Undergraduate or above = 5, Higher vocational or college = 4, High school or vocational high school = 3, Elementary school or junior high school = 2, Never attended school = 1 age age age squared Square of age divided by 100100 Household Control Variables Urban or rural urban = 1, rural = 0 Household size Total number of people in the household Total household assets Amount of total household assets (yuan), logarithmic treatment Household holdings of financial products yea = 1, no = 0 Household entrepreneurial involvement yes = 1, no = 0 4.4. Descriptive statistical analysis of variables This paper uses data from the 2017 CHFS survey. Sports consumption is logarithmically treated, control variables at the head of household level and household level are introduced, and total household income is used as the mechanism variable. During data processing, a total of 39,763 household samples were selected, and the age of the head of the household was limited to 16 years old and above. All household sports consumption was then multiplied by 1, and the household sports consumption was After applying logarithmic treatment, the lowest value is zero, and the highest value is 12.206. Descriptive statistical analyses of digital access, digital use, and digital creation reveal that digital access has the largest mean value, indicating that digital access plays a fundamental role. Table 3 Descriptive statistics of main variables Variables Variable name Observed value Mean value Standard deviation Minimum value Maximum value Explained variable Household sports consumption 39,763 0.663 2.163 0.000 12.206 Explanatory variable Digital access 1 39,763 0.499 0.500 0.000 1.000 Digital access 2 39,763 0.969 0.173 0.000 1.000 Digital access 3 39,763 0.630 0.483 0.000 1.000 Digital access 4 39,763 0.525 0.499 0.000 1.000 Digital use 1 39,763 0.464 0.499 0.000 1.000 Digital use 2 39,763 0.292 0.455 0.000 1.000 Digital use 3 39,763 0.385 0.487 0.000 1.000 Digital creation 1 39,763 0.407 0.491 0.000 1.000 Digital creation 2 39,763 0.146 0.353 0.000 1.000 Digital creation 3 39,763 0.035 0.185 0.000 1.000 Digital creation 4 39,763 0.085 0.280 0.000 1.000 Household head control variables Gender 39,763 0.793 0.405 0.000 1.000 age 39,753 55.198 14.242 3.000 117.000 Age squared 39,753 32.497 15.881 0.090 136.890 Marital status 39,763 0.850 0.357 0.000 1.000 Educational attainment 39,712 3.428 1.682 1.000 9.000 Household Control Variables Urban or rural 39,763 0.681 0.466 0.000 1.000 Household size 39,763 3.173 1.551 1.000 15.000 Total household assets 39,741 10.733 1.440 0.000 17.973 Household holdings of financial products 39,589 0.041 0.198 0.000 1.000 Household entrepreneurial involvement 39,762 0.142 0.349 0.000 1.000 5. Analysis of empirical results 5.1Benchmark regression 5.1.1The impact of digital competence on household sports consumption This paper uses the Tobit model and the regression results are shown in Table 4 below. Column (1) adds household head level control variables and family level control variables, column (2) adds only household head level control variables and column (3) adds only the explanatory variable digital competence. The coefficients of digital competence are respectively 13.660, 19.085, and 22.572, which shows that the effects of digital competence on household sports consumption are all significant at the 1 percent level Table 4 The impact of digital competence on household sport consumption variables (1) (2) (3) Household sports consumption Household sports consumption Household sports consumption Digital competence 13.660*** 19.085*** 22.572*** (-23.697) (-37.46) (-60.988) gender -0.960*** -1.508*** (-3.533) (-5.501) age -0.185*** -0.116* (-4.128) (-2.536) Age squared 0.271*** 0.253*** (-6.747) (-6.178) Marital status -0.39 -0.316 (-1.114) (-0.935) Educational attainment 1.269*** 1.849*** (-17.093) (-27.075) Urban or rural 3.051*** (-8.567) Household size -0.656*** (-7.267) Total household assets 1.205*** (-14.442) Household holdings of financial products 2.440*** (-6.283) Household entrepreneurial involvement 0.946** (-3.16) N 39509 39703 39763 Pseudo R2 0.088 0.085 0.093 Note: ***, ** and * indicate that the coefficients are significant at the 1%, 5% and 10% levels, respectively, with t-values in parentheses. Same as below. In terms of control variables, the vast majority of control variables passed the significance test. Age has a significant negative effect on the level of household sports consumption. This may be attributed to the fact that young people are more aware of sports and fitness, have higher demands on their physical condition and spend more on sports. The effect of marital status on household sports consumption does not pass the significance test. The level of education passes the significance test with a positive coefficient. This is probably because households with a higher level of education are more culturally literate, have a wider range of career opportunities, and consequently are likely to have higher incomes. It can be seen that urban households have a significant positive effect on household sports consumption because urban households are located in areas with higher levels of economic development and pursue sports consumption. Household size has a significant negative effect on household sports consumption. Total household assets have a positive effect on the level of household sports consumption. Total household assets include financial assets and physical assets. The theory of wealth effect shows that when the total household assets increase, the wealth effect felt by the household makes its expenditure on consumption increase. Whether or not to hold financial products has a significant positive effect on the level of household sports consumption. This effect may be because when households own more financial products (e.g., stocks, bonds, funds, etc.), their level of wealth increases, which in turn may enhance their consumption ability and intention to consume. Household entrepreneurial participation has a significant positive effect on household sports consumption levels. It may be because households obtain some business income during the entrepreneurial process. Family size has a significant negative effect on the level of household sports consumption. From Table 4 , it can be found that in the regression results of the columns with the gradual addition of control variables, there is a significant positive relationship between the logarithm of household sports consumption and the digital competence of the head of household. When the household head's digital competence rises by 1 unit, household sports consumption increases by 13.66 times, illustrating the key role of digital competence in influencing consumption trends and providing impetus to stimulate consumer market dynamics. This finding provides support for hypothesis 1 . 2. Impact of the digital competence sub-dimension on household sports consumption Since this paper divides digital competence into three dimensions, namely digital access, digital use, and digital creation, it further investigates the impact that each of these three dimensions will have on family sports consumption. The results are shown in Table 5 below, where the impact of digital access, digital use, and digital creation on family sports consumption all pass the significance test and are all significant at the 1 percent level. The largest of these is the coefficient of digital access (34.653), followed by the coefficient of digital use (27.318) and the coefficient of digital creation (16.538). It indicates that digital access has the greatest impact and provides families with access to sports consumption information channels, empowering families to learn about the sports consumption market, explore suitable sports products, and undertake sports consumption. Table 5 The impact of digital competence on household sports consumption: sub-dimensions variables Household sports consumption Digital acess 34.653*** (-12.089) Digital use 27.318*** (-17.262) Digital creation 16.538*** (-20.132) gender -0.994*** -0.945*** -1.084*** (-3.631) (-3.446) (-3.992) age -0.283*** -0.168*** -0.195*** (-6.230) (-3.711) (-4.303) Age squared 0.318*** 0.249*** 0.241*** (-7.703) (-6.14) (-5.915) Marital status -0.636 -0.474 -0.493 (-1.792) (-1.345) (-1.412) Educational attainment 1.557*** 1.494*** 1.433*** (-21.375) (-20.649) (-19.602) Urban or rural 3.386*** 3.323*** 3.725*** (-9.319) (-9.234) (-10.589) Household size -0.645*** -0.615*** -0.552*** (-7.122) (-6.761) (-6.262) Total household assets 1.429*** 1.434*** 1.399*** (-16.682) (-17.145) (-17.014_ Household holdings of financial products 3.617*** 3.182*** 2.714*** (-9.325) (-8.25) (-6.892) Household entrepreneurial involvement 1.449*** 1.289*** 1.120*** (-4.856) (-4.303) (-3.724) observations 39509 39509 39509 5.2. Endogeneity test In this paper, the instrumental variable method is chosen to further address the endogeneity issue. Referring to Yin, Zhichao et al. (2020) [ 56 ] , the instrumental variable ‘the mean value of digital competence of other households in the same community’ is chosen. Firstly, families within the same community share the same economic and geographical environment and exhibit similar digital competence. For one of them, the digital competence of other families will not directly affect the digital competence of that family; conversely, the digital competence of that family will not directly affect the digital competence of other families. Therefore, this instrumental variable is suitable for the endogeneity test. In this paper, the endogeneity test is conducted using the 2SLS method, and Table 6 shows the regression results. The joint F-value of 1116.03 in the first stage proves that there is no weak instrumental variable problem. In column (1), the first-stage regression results show that the regression coefficients of the instrumental variables are positive and significant, indicating that there is a positive relationship between digital competence and instrumental variables. In column (2), the results of the two-stage regression show that the regression results remain consistent with the baseline regression after the introduction of the instrumental variable, demonstrating a robust and significant positive effect of digital competence on household sports consumption. Table 6 Results of the instrumental variables approach to endogeneity testing Variables (1) (2) Digital competence Household sports consumption instrumental variable 0.508*** (-50.89) Digital competence 3.860*** (-14.22) Control variables yes yes _Cons 0.001 -1.405*** (-0.1) (-7.58) observations 33,233 33,233 R-squared 0.567 0.093 F 1116.03 Province fixed no no 5.3. Robustness test To verify the robustness of the benchmark regression results, this paper changes the core explanatory variables, the explained variables, and excludes outliers, etc. The relevant results are shown in Table 7 . Table 7 Results of the robustness test Variables (1) (2) (3) (4) replace the explained variable: sports consumption per capita replace the explanatory variable: average digital competence Truncated: Household sports consumption Exclude samples where the head of the household is over 70 years old: Household sports consumption average digital competence 17.406*** (-16.222) digital competence 5.965*** 13.679*** 13.949*** (-22.515) (-22.237) (-16.765) Control variables yes yes yes yes observations 39509 39509 32,698 26,776 Pseudo R2 0.1066 0.0867 0.0731 0.0947 5.3.1. Replacement of the explained variable: sports consumption per capita Per capita sports consumption refers to the sports consumption of a family member, namely, dividing family sports consumption by family size, and replacing the explanatory variables with average digital competence to verify the effect of digital competence on family per capita sports consumption. As shown in column (1) of Table 7 , digital competence has a significant contribution to per capita household sports consumption, with a regression coefficient of 5.965, significant at the 1% level. It differs from the coefficient of digital competence of the head of the household in the benchmark regression results only in size, verifying the robustness of the basic conclusions of this paper. This indicates that the above empirical test is reliable and further verifies hypothesis 1 . 5.3.2. Replace the explanatory variable: average digital competence. Average digital competence refers to the digital competence of a family member, namely, dividing the digital competence index by the family size, and replacing the core explanatory variable with average digital competence to verify the impact of digital competence on family sports consumption. As shown in column (2) of Table 7 , it can be seen that the coefficient of average household digital competence is significant at the 1% level, 17.406, larger than the regression coefficient of digital competence of the head of the household in the results of the benchmark regression, which verifies the robustness of the basic conclusions of this paper. 5.3.3. Eliminate outliers. First, the truncated treatment is used in this paper to exclude samples with extreme values of 5% on both sides of the household sports consumption variable. The second is to exclude samples where the head of the household is over 70 years old, given the age limitations of sports. The results are shown in columns (3) and (4) of Table 7 . From the regression results, the digital competence regression coefficients differ only in magnitude after the outliers are excluded, and the regression coefficients are positive, which still has a significant promotion effect on household sports consumption. Consistent with the previous results, this further validates the robustness of the paper's findings. 5.4. Analyses of mechanisms Table 8 Impact of digital competence on total household income variables (1) (2) (3) household sports consumption Total household income household sports consumption Digital competence 1.656*** 0.951*** 1.568*** (-25.307) (-25.691) (-23.491) Total household income 0.101*** (-11.087) gender -0.117*** -0.018 -0.112*** (-4.255) (-1.149) (-4.018) age -0.021*** 0.007* -0.021*** (-4.247) -2.518 (-4.273) age squared 0.030*** 0 0.030*** (-7.069) (-0.205) (-6.946) marital status -0.04 0.225*** -0.066* (-1.193) -11.949 (-1.968) Educational attainment 0.173*** 0.144*** 0.159*** (-21.263) (-31.389) (-19.076) Urban or rural 0.047 0.326*** 0.013 (-1.806) (-22.357) (-0.501) Household size -0.056*** 0.188*** -0.075*** (-7.258) -43.175 (-9.389) Total household assets 0.096*** 0.211*** 0.075*** (-13.873) (-53.866) (-10.354) Household holdings of financial products 0.740*** 0.207*** 0.710*** (-13.631) (-6.775) (-12.993) Household entrepreneurial involvement 0.066* 0.042* 0.071* (-2.108) (-2.361) (-2.224) Constant -1.275*** 6.154*** -1.918*** (-7.875) -67.111 (-11.079) Observations 39509 38853 38853 Adj R-squared 0.113 0.3679 0.1152 Theoretically, digital competence can promote income level. It has been shown that there is a positive correlation between the coverage of new media and income. The use of the Internet can directly or indirectly promote employment, diversify employment channels, and play a positive role in raising income levels [ 57 ] . Since income level is one of the primary determinants of sports consumption, this paper uses total household income as a mediating variable to confirm the mechanism of digital competence on household sports consumption. Column (1) of Table 8 represents the regression results of model (1) and examines the total effect of digital competence on household sports consumption without the mediating variable. The coefficient of digital competence is 1.656 and passes the 1% significance test. Column (2) of Table 8 presents the regression results of model (3), examining the effect of digital competence on total household income. Digital competence is positive at the 1% significance level, indicating that digital competence significantly enhances total household income. Column (3) of Table 8 reports the regression results of Model (4), which investigates the direct effect of digital competence on household sports consumption, incorporating the mediator variable. The coefficient for digital competence is 1.568, significant at the 1% level, while the coefficient for total household income is 0.101, also significant at the 1% level. Combining the regression results in columns (1) (2) (3) of Table 8 , it can be seen that total household income plays a partially mediating role in the process of digital competence affecting household sports consumption. In particular, while other factors remain constant, every 1 unit increase in digital competence will directly raise household sports consumption by 1.568 units, and will also cause total household income to rise by 0.951 units, and every 1 unit increase in digital competence will raise household sports consumption by 0.101 units. Therefore, each unit increase in digital competence indirectly raises household sports consumption by 0.0961 units (0.951*0.101 ≈ 0.0961) through total household income, for a total effect of 1.656 units. This indicates that the increase in household sports consumption due to digital competence is realized through an increase in total household income, so hypothesis 2 is valid. 6. Conclusions and recommendations 6.1Conclusion This paper is based on the 2017 China Household Finance Survey (CHFS). It constructs a household-level digital capability index using the entropy method, calculates the scores for various dimensions of digital competence, and analyzes the impact and mechanism of digital competence on household sports consumption using the Tobit regression model, mediation effect model, endogeneity test, and robustness test, arriving at the following conclusions: First, digital competence can significantly promote the level of family sports consumption, and the three sub-dimensions under digital competence: digital access, digital use, and digital creation all have a promotional effect on the level of family sports consumption. Notably, digital access exerts the most substantial effect, followed by digital use, with digital creation having the least impact. This conclusion still holds after considering endogeneity issues and conducting robustness tests. Second, the mechanism analysis shows that digital competence affects household sports consumption by influencing household income, and household income serves as a significant mediating factor. In other words, digital competence contributes to an increase in household income, thereby promoting higher levels of household sports consumption. 6.2Recommendations The improvement of digital competence has a significant contribution to the increase in the income and consumption levels of households, which means that it is important to stimulate consumption and expand domestic demand through the development of digital competence as human capital. Therefore, the following recommendations are made: 6.2.1. Governments The priority should be given to establishing digital network communication infrastructure and advancing the upgrading of digital infrastructure. This would enable broader digital access, enhance the digital competence of the population, and unlock new potential in sports consumption. First, to ensure systematic development of infrastructure, it is essential to optimize the layout of digital infrastructure networks. In constructing digital sports infrastructure, advanced information technologies such as big data, cloud computing, the Internet of Things (IoT), 5G, and artificial intelligence (AI) should be actively employed. For example, Guangzhou's Ersha Island Sports Park, the nation's first intelligent sports park, incorporates multiple smart fitness facilities. Some fitness equipment is equipped with solar power generation systems, enabling the park to generate electricity through physical activity, thereby integrating digital fitness with green fitness. Additionally, the park features intelligent jogging tracks and smart fitness paths, offering innovative opportunities for public sports consumption. Secondly, efforts should be made to bridge the digital divide by increasing investments in digital infrastructure in central and western regions, as well as rural areas, thereby establishing a solid foundation for internet access. The uneven and insufficient development of digital infrastructure across regions is an objective reality. Governments should promote the integrated development of urban and rural digital infrastructure, enhance internet penetration and coverage in rural and central-western regions, and advance the equalization and accessibility of digital sports services. This will enable a broader population to benefit from the dividends of the digital era; Thirdly, extensive digital skills training programs should be implemented for the general population to enhance human capital. Efforts should focus on the high-quality development and open sharing of digital education resources, digital skills training, digital products, and information services. 6.2.2. Sports Enterprises Efforts should focus on increasing investment in technological components, allocating greater resources to research and development, and enhancing digital innovation capabilities to meet the growing sports consumption demands of the public. Firstly, to improve consumers‘ sports information literacy, sports enterprises can use official websites and social media platforms such as WeChat, Weibo and TikTok to publicize graphics and videos displaying digital sports products and services to attract consumers’ attention. Also, it is beneficial to understand and increase users' participation through online interactions, such as polls and user feedback, which not only help consumers gain a deeper understanding of the dynamics of the digital sports market but also enhance their intention to purchase sports products and services, thus promoting the development of the sports consumption market. Secondly, sports enterprises should leverage internet platforms to actively cultivate emerging sports services industries, such as digital fitness services and online sports training. By utilizing technologies such as virtual reality (VR), artificial intelligence (AI) large models, and virtual anchors, they can expand e-commerce live-streaming scenarios and create innovative consumer experiences. Besides, promoting the digital transformation of sports venues, integrating digital technology into the operation of sports venues, and realizing a comprehensive digital informatization upgrade of the entry process, online booking, and the sports process, to make the venues a platform linking sports and the masses. Thirdly, new scenarios for sports consumption should be created. For example, the construction of large-scale urban complexes of ‘sports center + commercial center’ and sports towns of ‘tourism + sports and leisure’. Information technology can be employed to empower and enhance these developments, facilitating more engaging and interactive consumer experiences. Fourthly, it is essential to strengthen information supervision and encourage rational consumption among sports consumers. This can be achieved through technological means, manual reviews, and reporting mechanisms to enhance the regulation of harmful content and false information. Creating a healthy online sports consumption environment will help protect consumers from being misled or deceived, guide them in adopting healthy consumption habits, and promote rational purchasing decisions. These efforts will contribute to the sustainable development of digital sports consumption. 6.2.3. Individuals It is important to enhance personal digital competence and establish a new concept of sports consumption. Firstly, a shift in mindset is necessary to align with the policy requirements of enhancing digital capabilities in the digital age, thereby being qualified digital citizens in the new era. Continuously study and update their digital literacy and skills, and examine online information from a critical perspective. When faced with confusing and inducing consumer information, we should analyze it in depth, and improve the ability to identify the false information. Secondly, individuals should establish new concepts of sports consumption. At the stage of demand identification and information collection, it is necessary to clarify one's budget level and examine one's purchasing motives. When browsing relevant information on social network platforms, avoid blindly following the shopping recommendations of Key Opinion Leaders (KOLs) and Key Opinion Consumers (KOCs), just reasonably formulate consumption lists. At the program evaluation and purchase decision stage, a comparative analysis of selected sports products should be conducted. The product that best meets both needs and budget should be chosen, leading to a purchase decision. In the post-purchase behavior stage, a comprehensive evaluation of the purchased sports products or services is necessary. The goal is to highlight the value of the product, extend its usage time and lifespan, and reduce the frequency of replacement. Additionally, it is important to recognize that overemphasizing fashion trends is not advisable. Declarations Acknowledgments The authors extend their sincere gratitude to the anonymous reviewers for their invaluable advice. Author Contributions Z.W.: The author contributed to the conceptualization and writing of the original draft, as well as to formal analysis and data analysis. H.S. and Y.T.: They contributed to writing， reviewing, and editing, as well as to supervision. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7472716","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":518388897,"identity":"be7d7d2c-3280-45f0-b170-b55143898057","order_by":0,"name":"Ziyan Wang","email":"","orcid":"","institution":"Beijing Sport University","correspondingAuthor":false,"prefix":"","firstName":"Ziyan","middleName":"","lastName":"Wang","suffix":""},{"id":518388898,"identity":"94a0d82d-635a-4f44-aa24-5058067bd4ac","order_by":1,"name":"Hemin Song","email":"","orcid":"","institution":"Beijing Sport University","correspondingAuthor":false,"prefix":"","firstName":"Hemin","middleName":"","lastName":"Song","suffix":""},{"id":518388899,"identity":"fad530aa-48c7-43fc-b674-4c5c01b5bb40","order_by":2,"name":"Yukun Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYJACZiCWk2fvAXMYGwgp54FqMTbsOUOilsSGGzlEarFnP3v4dcGfw4yNM98e/szDYCO74QDzswd4beHJS7Oe2XaYmV06L02ahyHNeMMBNnMD/A7LMTPmbTjMxjg7x4yZh+Fw4oYDPGwSeLXwvzEz5vlzmIfh5hljoMP+E6FFIsf4MQ/bYQmGGzwGQIcdIELLjTdmzDPb0g0Me/LSJOcYJBvPPMxmhlcLe3+O8eeCP9b184FB9+FNhZ1s3/HmZ3i1AAGyM0BBxUxAPUjJB8JqRsEoGAWjYEQDAHRaROx5mRbyAAAAAElFTkSuQmCC","orcid":"","institution":"Beijing Sport University","correspondingAuthor":true,"prefix":"","firstName":"Yukun","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2025-08-27 14:38:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7472716/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7472716/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92163810,"identity":"ff61be75-6b63-4ec1-8bc2-57b8fbf9ed09","added_by":"auto","created_at":"2025-09-25 10:36:07","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":111051,"visible":true,"origin":"","legend":"","description":"","filename":"AStudyoftheImpactofDigitalCompetenceonHouseholdSportsConsumption826.docx","url":"https://assets-eu.researchsquare.com/files/rs-7472716/v1/56a38d11f1b874c4ca8106db.docx"},{"id":92163811,"identity":"7e3a2191-53e3-49ad-8e1d-c5bb800ff3c9","added_by":"auto","created_at":"2025-09-25 10:36:07","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5139,"visible":true,"origin":"","legend":"","description":"","filename":"02a42d84dbf14a9ca69f82cb81703e50.json","url":"https://assets-eu.researchsquare.com/files/rs-7472716/v1/307ea01d962e6a82b018a15d.json"},{"id":92163812,"identity":"f31a413d-b767-4503-a3fb-c064cdb32b6a","added_by":"auto","created_at":"2025-09-25 10:36:07","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":191005,"visible":true,"origin":"","legend":"","description":"","filename":"02a42d84dbf14a9ca69f82cb81703e501enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7472716/v1/e33cd04f3b4fae759dd45f8c.xml"},{"id":92163813,"identity":"e9c63ba3-d3de-4163-aea5-a68eb32d72fe","added_by":"auto","created_at":"2025-09-25 10:36:07","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":190339,"visible":true,"origin":"","legend":"","description":"","filename":"02a42d84dbf14a9ca69f82cb81703e501structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7472716/v1/cd32a86cdfc71eb9eca1805b.xml"},{"id":92163814,"identity":"2e83f38b-cb55-44fb-b04d-72d83084b2a0","added_by":"auto","created_at":"2025-09-25 10:36:07","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":198689,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7472716/v1/eec12e6bd20edfdc8a2dcac6.html"},{"id":98219652,"identity":"0324d63e-7d96-4c64-a705-930109d0887d","added_by":"auto","created_at":"2025-12-15 11:10:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1970708,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7472716/v1/c5a654fc-b0d6-4830-bc1a-05fd24d827f9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Study of the Impact of Digital Competence on Household Sports Consumption","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDigital economy development in China has a robust global presence\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Recent statistics indicate that China's digital economy ranks among the world's most remarkable in size and growth rate. China's digital economy has been developing rapidly in recent years, and according to the Digital China Development Report (2023), the added value of China's core industries in the digital economy is estimated to exceed about \u003cspan\u003e$\u003c/span\u003e1637.208\u0026nbsp;billion in 2023, accounting for about 10 percent of GDP. The digital economy has become an important engine driving economic development.\u003c/p\u003e\u003cp\u003eIn China, the availability of digital technologies and services builds a realistic foundation for the rapid expansion of the digital economy, including emerging technologies (e.g., big data, cloud computing, the Internet of Things and 5G communications), digital services and applications. They have permeated all levels of social life, exerting a profound influence on the production and lifestyle of human beings. To adapt to a digital context, enhancing one's digital competence is required\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. At the macro level, digital competence has gradually become an important indicator for measuring international competitiveness and soft power. At the micro level, digital competence has been shown to be not only a right but also a requirement of citizens, as it is necessary to be functional now\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eResearch has revealed a strong interest in the study of digital competence in (tertiary) educational settings\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, teachers\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, students\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e and enterprises \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. According to scholars, the scope of digital competence is beyond digital literacy\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e and includes attitude and mindset besides skills\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Recent studies suggest digital competence should be replaced with digital literacy in educational contexts since digital competence pays more attention to the ethical, safety, and social dimension\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e and identifies more diverse knowledge, abilities, and desires of individuals\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDigital competence, the ability of an individual to understand and use digital technology in social activities such as work, life and recreation, is a reflection of the individual's ability to adapt and comprehensive quality in the era of the digital economy\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWith the rapid development of the digital economy, the disposable income of residents has increased significantly, and emerging digital technologies are widely used in production and consumption, which profoundly affects residents' consumption choices. The rapid growth of the digital economy has caused the Chinese consumer market to speed and change, giving rise to new types of consumption, new scenes of consumption, and new consuming content\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e; the residents' consumption structure has been upgraded\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, leading to a significant increase in sports consumption. According to the viewpoint of sports economics, sports consumption belongs to enjoyment-centric consumption and development-centric consumption, which is affected by individual factors, family factors, and regional factors. Digital competence empowers people to utilize the Internet and digital technology effectively. It facilitates individuals' participation in the digital economy, thereby expanding income channels and increasing sources of revenue. This access enables them to gain digital dividends, improve their consumption structures, and ultimately boost sports consumption.\u003c/p\u003e\u003cp\u003eFrom the macro level, the innovation of digital technology and the development of the digital economy promote national economic growth; from the micro level, the family is the cell of society. In the era of the digital economy, what impact will digital competence have on family sports consumption? This paper focuses on the effect of digital competence on family sports consumption and the mechanism of action. Based on the micro-data of more than 30,000 households in the 2017 China Household Finance Survey and Tobit regression model, this paper aims to provide empirical evidence for the promotion of China's residents' sports consumption upgrading.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Digital Competence\u003c/h2\u003e\u003cp\u003eDigital competence plays a key role in various industries. As a composite competence, it is considered an important survival skill in the digital era\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e and an important asset in the information society\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. It refers to an individual's ability to correctly understand and creatively use digital technology in social activities such as work, life, and recreation. It reflects an individual's adaptability and comprehensive quality in the digital economy.\u003c/p\u003e\u003cp\u003eCurrently, the field suffers from a lack of clarity regarding the terminology (i.e., digital literacy, digital skills, digital competence, 21st-century skills,. .. )\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. As a dynamic concept, digital competence is relevant to policy and educational research, and it is linked to the development of digital technologies as well as the political objectives and expectations of people in knowledge societies\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e; in 2011, the European Commission published Mapping Digital Competence: Towards a Conceptual Understanding and explicitly proposed that Digital competence is defined as the set of knowledge, skills and attitudes needed to use computer information technologies confidently, critically and creatively to achieve goals related to work, employment, learning, leisure and recreation, decision-making or social participation. In 2018, the meaning was enriched, and the EU defined digital competence as the confident, critical and responsible use of digital technologies in learning, work and participation in society.\u003c/p\u003e\u003cp\u003eDespite the lack of agreement concerning the definition of digital competence, literature agrees that it is not only constituted of technological skills but encompasses multiple literacies. In other words, for measuring digital competence, several sub-dimensions are proposed. The European Union has provided two versions of reference: in 2014, the EU developed four primary indicators, namely \"information,\" \"communication,\" \"content creation,\" and \"problem-solving,\" along with 20 secondary indicators; in 2022, the EU updated the four primary indicators, namely \"information and data literacy,\" \"communication and cooperation,\" \"digital content creation,\" \"security,\" and \"problem-solving,\" along with 30 secondary indicators. Although there is no one standard for digital competence indicators in China, some scholars have also proposed digital competence indicators suitable for China's national conditions. For example, Lu Jingming et al. (2023) \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003econstructed a digital competence index system for herdsmen based on research data from three dimensions of digital technology access, digital platform use, and digital information acquisition; Wang Xiaohua (2023) \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003econstructed a personal digital competence index from three dimensions which are digital access, digital use, and digital creation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Relevant Studies on Sports Consumption\u003c/h2\u003e\u003cp\u003eIn terms of the concept of sports consumption, there are a variety of interpretations in current academic circles. According to the perspective of whether or not it is directly related to physical activity, some scholars divide it into two categories, i.e. direct consumption and indirect consumption. The direct consumption is explicitly associated with sports practice (e.g. equipment, sporting events and public facilities), while the indirect consumption belongs to other economic fields, but could/should also be taken into account when they are consumed for sports purposes (e.g. transport, consumption of sports beverages and sports food, and the shadow price of time)\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. For example, the 2024 Boston Marathon attracted 30,000 participants and participants' ticket expenses are direct sports consumption. Indirect sport consumption was significant. The reported data show that 68% of participants stayed in Boston-area hotels, and the median weekend spend by participants was \u003cspan\u003e$\u003c/span\u003e500. A survey conducted by the Boston Restaurant Group showed a 15% increase in sales at the city's restaurants\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFrom the perspective of the research object of sports consumption, early studies were mostly based on individuals, and in recent years, studies have tended to focus on the family level\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Since the family is the basic unit of social structure, the main part of family consumption is public family consumption, in addition to personal consumption, so the individual-based consumption research is fundamentally limited\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSports consumption-related research uses more quantitative analyses, and the research data is divided into primary and secondary data.\u003c/p\u003e\u003cp\u003ePapers using primary data mostly employ the questionnaire survey method.\u003c/p\u003e\u003cp\u003eKevin K. Byon (2010) used the Sports Consumption Motivation Scale to measure the motivation of wheelchair rugby spectators and predict their online consumption behaviour\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Sylvia Chan-Olmsted (2019) conducted an online survey (n\u0026thinsp;=\u0026thinsp;646) to figure out factors influencing sports consumption on smartphones. It is found that the motivation to acquire knowledge of smartphone use for sports, social sports activity participation on smartphones and fandom interactions online increase the willingness to consume sports on smartphones\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Brian H. Yim(2020) designed an online survey questionnaire to study factors that may impact the millennial fans' decision-making process in connection with various sports consumption behaviors, which revealed five key characteristics of Millennial fan consumption: technology-driven, community-driven, peer pressure, emotional consumption, and fan engagement\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSecondary data are mostly from national databases, and such studies tend to focus on regional sports consumption and studies of sports consumption by specific groups. Adam Gemar (2020) explores the place of sport participation in the cultural lifestyles of Canadians based on cultural consumption theory and omnivore thesis, using data from a large-scale survey of the Canadian government. The results suggest that omnivorous cultural consumption includes people with high levels of cultural and economic capital and that the cultural domain of sport may be a more distinctive area of consumption for omnivores\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Grace Yan (2018) investigated the Twitter networks of the Champions League hashtag (#UCL) across the 2017 UEFA Champions League Final, including 19,869 pre-match posts, 3,276 halftime posts, and 5,691 post-match posts from Twitter. It is shown that large sports clubs have the ability to dominate the social network due to their stable and privileged position\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFrom the perspective of the influencing factors of sports consumption, at the macro level, the international experience is that a large-scale demand for sports consumption will be formed when the economic development enters the upper-middle-income stage, i.e., when it exceeds 6,500 US dollars, and the sports industry will become a pillar-type industry for economic development after entering the high-income stage\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. That is, sports consumption is built on a certain economic foundation. In addition, conditions such as income level, education level, age, and time constraints all have an impact on sports consumption. Income promotes sports consumption\u003csup\u003e[\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e; the higher the education level, the more people understand the value of sports, and the more sports consumption will be \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e; with the increase of age, people will pay more attention to the loss of health capital, and to compensate for it they will invest more in sports consumption\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. At the micro level, sports consumption behavior is affected by individual consumption motives and other factors. Some scholars have examined the differences in the motivation of Koreans living in the United States to consume sports in their home country (Korea in this study) and their host country (the United States in this study) in terms of eight aspects of motivation, namely, social, informational, recreational, escapism, fan expression, time-killing, fandom, and technical knowledge\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. These studies combine economics and psychology to expand the influences on sports consumption.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. The Impact of Digital Competence on Sports Consumption.\u003c/h2\u003e\u003cp\u003eAt present, there is a lack of literature studying the direct impact of digital competence on sports consumption in China. Conversely, more studies have focused on the effects of digital competence, such as income-enhancing, household allocation of risky financial assets, entrepreneurial decision-making, and developmental resilience. Li Mengfan (2023) takes digital competence as an endogenous variable and finds that Internet use empowers personal income enhancement through technological effects and capital effects (physical, human, and social capital), and that the Internet itself exhibits dynamic heterogeneity in the process of its development\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e; Wang Yako and Wang Yi-Wei (2024) demonstrate that digital competence and digital financial capability enhance household consumption levels via three primary channels: income enhancement, online purchasing, and easing liquidity constraints. Besides, the impact of digital competence on consumption is more pronounced among rural and low-income households\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e; Wang Xiaohua et al. (2023) classified digital competence into three dimensions: digital access, digital use and digital creation. Constructing an LPM model, their study concluded that digital competence promotes financial literacy, which in turn facilitates the allocation of risky financial assets by household. Among these dimensions, digital creation gives residents more initiative and flexibility, allowing them to choose a variety of financial assets. Residents can choose credit cards to provide short-term credit, so that the household intertemporal smoothing, which in turn promotes consumption\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e; Concerning the object of digital competence studied in China, most of them are farmers and herdsmen. Digital competence has an empowering role in fostering entrepreneurship in farming households and increasing farmers' financial engagement, both of which enhance the resilience and development of the farming household family\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. In addition, to empirically analyze the relationship between household consumption and digital capability, Li Rui (2024) constructed a benchmark regression model based on the income effect. The study confirmed that digital competence plays a significant role in promoting various forms of consumption, including enjoyment-centric, development-centric, and subsistence-centric consumption, and it also found that increasing digital competence can significantly increase household consumption\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe digital economy is currently the primary focus of Chinese research on the factors influencing sports consumption, and studies have shown that it can greatly enhance the high-quality development of the sports industry, encourage the upgrading of industrial structure, and accelerate the upgrading of sports consumption structure. The popularity of the digital economy has brought about the application of digital technology, which not only promotes the digital and intelligent transformation of traditional industries but also gives rise to many new industries, new forms and new scenarios. Additionally, the degree of supply and demand matching has increased, which has given sports consumption new vitality\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Technology provides consumers with data, analytics, and insights and it allows fans to engage with athletes, teams, brands, etc. by means of the internet, social media, etc. Therefore, the content of sports consumption will continue to change significantly\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSports consumption, as both enjoyment-centric and development-centric consumption, is not only affected by the digital economy at the macro level but also at the micro level by consumers\u0026rsquo; cognitive ability, consumption intention, and consumption demand. As a critical survival and lifestyle skill in the digital era, digital competence exerts a significant impact on residents' sports consumption. Thus, this paper explores the impact and the mechanism of the role of digital competence on sports consumption.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Theoretical basis and research hypothesis","content":"\u003cp\u003eAccording to AISAS consumer behavior Theory, in the era of the Internet and wireless applications, digital platforms provide consumers with the convenience of actively obtaining information. Enterprises attract consumers\u0026apos; attention by disseminating advertisements online. Once consumers develop an interest in the content, they actively search for product information, which then leads to purchasing action. Finally, consumers will share their consumption experiences on the Internet.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the Internet era, the development of digital technology has revolutionized the sports consumption scene. Based on 5G, big data, artificial intelligence, etc., new scenes such as online live streaming and online consumption have been derived\u003csup\u003e[16]\u003c/sup\u003e. On the one hand, the emergence of online shopping platforms and the development of modern logistics systems enable individuals with digital competence to buy sports products on online shopping platforms such as Taobao, Jingdong, Tmall, Pinduoduo, etc. During the \u0026lsquo;Double Eleven\u0026rsquo; shopping festival in 2023, Tmall announced a sports consumption list, in which Nike, FILA, Anta and other brands have harvested more than 100 million yuan in sales. On the other hand, the development of digital technology makes online and offline interoperability, providing people with more choices in sports consumption. However, the emergence of the digital divide is inevitable due to the inequality of ICT in terms of physical connectivity and access\u003csup\u003e[43]\u003c/sup\u003e. Also, there are regional differences in economic, cultural, and transport development, which lead to differences in digital competence between urban and rural areas as well as among individuals. These differences in using digital platforms result in differences in household sports consumption. Individuals who have sufficient knowledge of digital platforms and digital competence are more likely to acquire sports consumption information through the Internet, social media, and mobile applications.\u003c/p\u003e\n\u003cp\u003eBased on this, the first research hypothesis of this paper is formulated:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 1:\u003c/strong\u003e Digital competence contributes to the level of household sports consumption.\u003c/p\u003e\n\u003cp\u003eAccording to asymmetric information theory, the more information one has, the more advantageous it is in market economic activities. Digital competence represents an individual\u0026apos;s ability to correctly understand and creatively use digital technology, and Internet use is a major component of digital competence\u003csup\u003e[40]\u003c/sup\u003e. By using the Internet to break down information barriers, people can understand market dynamics in a timely and comprehensive manner, which helps to optimize production decisions and increase incomes. On the one hand, digital competence can increase business income, and the rise of digital platforms attracts the public to actively participate in \u0026lsquo;Internet +\u0026rsquo; entrepreneurship, everyone can open a shop on the Internet and conduct transactions, which inspires the public to try to innovate and start a new business, thus increasing their business income\u003csup\u003e[44]\u003c/sup\u003e; on the other hand, digital competence can increase wage income. Digital recruitment platforms help broaden the channels for people to submit CVs and look for jobs, and digital service platforms, such as online live broadcasting and self-media creation, directly provide jobs to alleviate the pressure of unemployment, thus increasing their wage income\u003csup\u003e[45]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eConsumption is affected by economic growth, income distribution, population, employment and other factors, of which income distribution is a direct factor affecting the level of consumption. The rise of disposable income is the prerequisite for a higher level of consumption by residents. Residents\u0026apos; income is also an objective and fundamental condition for sports consumption\u003csup\u003e[46]\u003c/sup\u003e. Erik Thibaut (2014) used the theory of household production, which states that expenditure increases with income, capital and time, and that higher incomes provide households with the funds to consume so that higher levels of utility can be realized. Thibaut\u0026apos;s study confirms that when household incomes increase, households will spend more on sports participation in terms of Money\u003csup\u003e[34]\u003c/sup\u003e. At the individual level, the majority of studies verify the positive correlation between income and sports consumption\u003csup\u003e[47-49]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccordingly, the paper proposes the following hypothesis:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 2:\u003c/strong\u003e Digital competence contributes to the rise in the level of household income, which in turn increases the level of household sports consumption.\u003c/p\u003e"},{"header":"4. Research design","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Sample Selection and Data Source\u003c/h2\u003e\u003cp\u003eThe data in this paper comes from the 2017 China Household Finance Survey (CHFS), a questionnaire that covers micro-level variables in household consumption, household expenditure, employment, payment habits, assets and liabilities, meeting the data quality requirements needed for the study. The China Household Finance Survey is conducted under the auspices of the China Household Finance Survey and Research Centre of the Southwestern University of Finance and Economics and is conducted every two years. Up to now, five years of data are available for 2011, 2013, 2015, 2017, and 2019. Given that sports and healthcare consumption are not listed separately in the data for 2011, 2013, 2015, and 2019, this paper only adopts the 2017 China Household Finance Survey database and processes the data as follows: ① Combine the individual-level data, household-level data, and master data according to the correspondence between households and household heads; ② Remove missing samples of key variables; ③ Retain only samples where the household head is 16 years old and above; ④ Take 1% upper and lower truncation of the total household assets and total household income in this data and then take the natural logarithm. The final number of observations is 39,763.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Model Setting\u003c/h2\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e4.2.1. Benchmark regression model\u003c/h2\u003e\u003cp\u003eThere are a large number of \u0026lsquo;0\u0026rsquo; values in household sports consumption, which is restricted data. The data presents the characteristics of \u0026lsquo;subsumed data\u0026rsquo;, and if the OLS model is used, it will produce a large estimation bias, so this paper draws on the research of Zhang Wei et al. (2022) and adopts the Tobit model for regression:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:lnsportscon={\\alpha\\:}_{0}+{\\beta\\:}_{0}DigitalScore+{\\rho\\:}_{0}{X}_{i}+\\mu\\:+{\\epsilon\\:}_{i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn this paper, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:lnsportscon\\)\u003c/span\u003e\u003c/span\u003e is the core dependent variable, representing the sports consumption of household i in the sample for the year 2017, which has been log-transformed. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}\\)\u003c/span\u003e\u003c/span\u003e denotes head-level and household-level control variables. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:\\)\u003c/span\u003e\u003c/span\u003e represents a province-fixed effect, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003eis the random error term. DigitalScore's coefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e measures the impact of the head of household's digital competence on household sports consumption.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e4.2.2. Modelling of the mediating mechanism\u003c/h2\u003e\u003cp\u003eTo deeply study the influence mechanism of digital competence on household sports consumption, the following mediating effect model is constructed for empirical testing:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:lnsportscon={\\alpha\\:}_{0}+{\\beta\\:}_{0}DigitalScore+{\\rho\\:}_{0}{X}_{i}+\\mu\\:+{\\epsilon\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{TotalIncome}_{i}={\\alpha\\:}_{1}+{\\beta\\:}_{1}DigitalScore+{\\rho\\:}_{1}{X}_{i}+\\mu\\:+{\\epsilon\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:lnsportscon={\\gamma\\:}_{2}+{\\gamma\\:}_{2}DigitalScore+{\\gamma\\:}_{2}{TotalIncome}_{i}+{\\rho\\:}_{2}{X}_{i}+\\mu\\:+{\\epsilon\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the formula, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{TotalIncome}_{i}\\)\u003c/span\u003e\u003c/span\u003e denotes the total income of household i.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Variable Setting\u003c/h2\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e4.3.1. Explained variables\u003c/h2\u003e\u003cp\u003eThe explanatory variable of this paper is household sports consumption. Given the availability of data and the research results of Ma Tianping (2022)\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e, the question [G1020] in the CHFS questionnaire: \u0026lsquo;Last year, how much did your family spend on health care and fitness and exercise expenditures (unit: yuan)\u0026rsquo; is used as a proxy variable for household sports consumption. The data plus 1 and take the natural logarithm for processing.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e4.3.2. Explanatory variables\u003c/h2\u003e\u003cp\u003eDigital competence is the explanatory variable of this paper. In the era of the digital economy, there are differences in the digital competence of each individual due to uneven regional development and uneven distribution of digital technologies, which results in unequal access to digital technologies as well as equal acquisition of digital skills by each individual. As a result, a digital divide between individuals exists, which means the gap between individuals in acquiring information and communication knowledge and using information and communication technology. It is interdependent with digital competence and has an inherent correspondence in terms of concepts and dimensions. Given that the digital divide includes the access divide, the skills divide, and the transformation divide\u003csup\u003e[\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e, and based on Wang Xiaohua's approach, this paper divides digital competence into three dimensions, namely digital access, digital use, and digital creation.\u003c/p\u003e\u003cp\u003eThe first dimension is digital access. The uneven distribution of information and communication devices and services such as mobile phones, computers, and the Internet among different regions and groups has resulted in some households being at an \u0026lsquo;information disadvantage\u0026rsquo;. These households lack Internet connectivity and information updates and face a digital access divide\u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e. This not only limits their possibilities of enjoying a digital life but may also have a negative impact on their overall well-being. The second dimension is digital use. Internet penetration does not imply natural access to digital technology use, and differences in digital technology use stem from differences in users' physical, human, and social capital\u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e, leading to unequal access to digital resources for different households. The third dimension is digital creation. It connotes that groups in an information-advantageous position who master relevant knowledge and skills in using digital technology can acquire more opportunities for participation, and can transform digital competence into income through online entrepreneurship, and financial investment via online platforms.\u003c/p\u003e\u003cp\u003eThis paper combines the connotation of digital competence and the above dimensions while taking into account the scientific and comprehensive nature of the indicators, the availability of data and comparability, and takes digital competence as a first-level indicator, digital access, digital use, and digital creation as a second-level indicator. Based on the China Household Finance Survey questionnaire, 12 specific tertiary indicators are selected to construct a digital capability evaluation framework, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The digital capability index is calculated using the entropy method, and the scores for the primary, secondary, and tertiary indicators are derived accordingly.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eConstruction of digital competence indicator system\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDimensions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe questions in CHFS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAssignment criteria\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWeights from entropy method\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003edigital\u003c/p\u003e\u003cp\u003eaccess\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDo you currently have a computer in your home?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eyes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDo you currently use a mobile phone?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eyes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDo you currently use a smart phone?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eyes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIs your home currently connected to fixed broadband?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eyes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003edigital\u003c/p\u003e\u003cp\u003euse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDo you use the Internet?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eyes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhen shopping at your home, do you use the computer to pay, mobile terminal such as mobile phone or Pad to pay?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eyes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.092\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDo you use social chatting tools such as WeChat and QQ?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eyes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003edigital\u003c/p\u003e\u003cp\u003ecreation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDo you have online shopping experience?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eyes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDo you use financial APP or Internet mobile phone to follow financial news?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eyes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDo you use the Internet to sell products and services?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eyes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.249\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDo you use the Internet to engage in stock speculation, scientific research and other businesses?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eyes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e4.3.3. Control variables\u003c/h2\u003e\u003cp\u003eBased on Li Rui et al. (2023), this paper includes two types of control variables: individual and household level, which may affect household sports consumption. The first is the control variables at the head of household level, which include age, age squared, gender (male\u0026thinsp;=\u0026thinsp;1, female\u0026thinsp;=\u0026thinsp;0), marital status (married\u0026thinsp;=\u0026thinsp;1, unmarried\u0026thinsp;=\u0026thinsp;0), education level (no schooling\u0026thinsp;=\u0026thinsp;1; primary or junior high school\u0026thinsp;=\u0026thinsp;2; senior high school or Vocational High School\u0026thinsp;=\u0026thinsp;3; High School or College\u0026thinsp;=\u0026thinsp;4); Bachelor's Degree and above =\u0026thinsp;5). The second is household-level control variables, including whether or not an urban household (urban\u0026thinsp;=\u0026thinsp;1, rural\u0026thinsp;=\u0026thinsp;0), household size (total household size), total household assets (logarithmic treatment), household holding of financial products (yes\u0026thinsp;=\u0026thinsp;1; no\u0026thinsp;=\u0026thinsp;0), and household entrepreneurial participation (yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0).\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e represents the definition of the main variables in this paper.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDefinition of key variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVariable measurement\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExplained\u003c/p\u003e\u003cp\u003evariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHousehold sports\u003c/p\u003e\u003cp\u003econsumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHousehold annual sports consumption (yuan), logarithmic treatment\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExplanatory\u003c/p\u003e\u003cp\u003evariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDigital Competence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 household-level indicators were constructed and the entropy method was used to calculate the digital competence score.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eHousehold head control variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emale\u0026thinsp;=\u0026thinsp;1,female\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMarried\u0026thinsp;=\u0026thinsp;1,unmarried\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEducational\u003c/p\u003e\u003cp\u003eattainment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUndergraduate or above =\u0026thinsp;5, Higher vocational or college\u0026thinsp;=\u0026thinsp;4, High school or vocational high school\u0026thinsp;=\u0026thinsp;3, Elementary school or junior high school\u0026thinsp;=\u0026thinsp;2, Never attended school\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eage\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eage squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSquare of age divided by 100100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eHousehold\u003c/p\u003e\u003cp\u003eControl Variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban or rural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eurban\u0026thinsp;=\u0026thinsp;1, rural\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHousehold size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal number of people in the household\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal household assets\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAmount of total household assets (yuan), logarithmic treatment\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHousehold holdings of financial products\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eyea\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHousehold entrepreneurial involvement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eyes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Descriptive statistical analysis of variables\u003c/h2\u003e\u003cp\u003eThis paper uses data from the 2017 CHFS survey. Sports consumption is logarithmically treated, control variables at the head of household level and household level are introduced, and total household income is used as the mechanism variable. During data processing, a total of 39,763 household samples were selected, and the age of the head of the household was limited to 16 years old and above. All household sports consumption was then multiplied by 1, and the household sports consumption was After applying logarithmic treatment, the lowest value is zero, and the highest value is 12.206. Descriptive statistical analyses of digital access, digital use, and digital creation reveal that digital access has the largest mean value, indicating that digital access plays a fundamental role.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics of main variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eObserved value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStandard deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMinimum value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMaximum value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExplained variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHousehold sports\u003c/p\u003e\u003cp\u003econsumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39,763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e12.206\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"10\" rowspan=\"11\"\u003e\u003cp\u003eExplanatory\u003c/p\u003e\u003cp\u003evariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDigital access 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39,763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDigital access 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39,763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.969\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDigital access 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39,763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.483\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDigital access 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39,763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDigital use 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39,763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDigital use 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39,763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDigital use 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39,763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDigital creation 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39,763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.407\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDigital creation 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39,763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDigital creation 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39,763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDigital creation 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39,763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eHousehold head control variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39,763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39,753\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14.242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e117.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39,753\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e136.890\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39,763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEducational\u003c/p\u003e\u003cp\u003eattainment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39,712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e9.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eHousehold\u003c/p\u003e\u003cp\u003eControl Variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban or rural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39,763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHousehold size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39,763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e15.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal household assets\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39,741\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e17.973\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHousehold holdings of financial products\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39,589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHousehold entrepreneurial involvement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39,762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Analysis of empirical results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e5.1Benchmark regression\u003c/h2\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e5.1.1The impact of digital competence on household sports consumption\u003c/h2\u003e\u003cp\u003eThis paper uses the Tobit model and the regression results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e below. Column (1) adds household head level control variables and family level control variables, column (2) adds only household head level control variables and column (3) adds only the explanatory variable digital competence. The coefficients of digital competence are respectively 13.660, 19.085, and 22.572, which shows that the effects of digital competence on household sports consumption are all significant at the 1 percent level\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe impact of digital competence on household sport consumption\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003evariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHousehold sports\u003c/p\u003e\u003cp\u003econsumption\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHousehold sports\u003c/p\u003e\u003cp\u003econsumption\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHousehold sports\u003c/p\u003e\u003cp\u003econsumption\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigital competence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.660***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.085***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.572***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-23.697)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-37.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-60.988)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003egender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.960***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.508***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-3.533)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-5.501)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.185***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.116*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-4.128)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-2.536)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.271***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.253***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-6.747)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-6.178)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-1.114)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.935)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducational attainment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.269***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.849***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-17.093)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-27.075)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban or rural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.051***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-8.567)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.656***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-7.267)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal household assets\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.205***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-14.442)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold holdings of financial products\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.440***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-6.283)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold entrepreneurial involvement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.946**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-3.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39763\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePseudo R2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: ***, ** and * indicate that the coefficients are significant at the 1%, 5% and 10% levels, respectively, with t-values in parentheses. Same as below.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn terms of control variables, the vast majority of control variables passed the significance test. Age has a significant negative effect on the level of household sports consumption. This may be attributed to the fact that young people are more aware of sports and fitness, have higher demands on their physical condition and spend more on sports. The effect of marital status on household sports consumption does not pass the significance test. The level of education passes the significance test with a positive coefficient. This is probably because households with a higher level of education are more culturally literate, have a wider range of career opportunities, and consequently are likely to have higher incomes. It can be seen that urban households have a significant positive effect on household sports consumption because urban households are located in areas with higher levels of economic development and pursue sports consumption. Household size has a significant negative effect on household sports consumption. Total household assets have a positive effect on the level of household sports consumption. Total household assets include financial assets and physical assets. The theory of wealth effect shows that when the total household assets increase, the wealth effect felt by the household makes its expenditure on consumption increase. Whether or not to hold financial products has a significant positive effect on the level of household sports consumption. This effect may be because when households own more financial products (e.g., stocks, bonds, funds, etc.), their level of wealth increases, which in turn may enhance their consumption ability and intention to consume. Household entrepreneurial participation has a significant positive effect on household sports consumption levels. It may be because households obtain some business income during the entrepreneurial process. Family size has a significant negative effect on the level of household sports consumption.\u003c/p\u003e\u003cp\u003eFrom Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, it can be found that in the regression results of the columns with the gradual addition of control variables, there is a significant positive relationship between the logarithm of household sports consumption and the digital competence of the head of household. When the household head's digital competence rises by 1 unit, household sports consumption increases by 13.66 times, illustrating the key role of digital competence in influencing consumption trends and providing impetus to stimulate consumer market dynamics.\u003c/p\u003e\u003cp\u003eThis finding provides support for hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003e2. Impact of the digital competence sub-dimension on household sports consumption\u003c/h3\u003e\n\u003cp\u003eSince this paper divides digital competence into three dimensions, namely digital access, digital use, and digital creation, it further investigates the impact that each of these three dimensions will have on family sports consumption. The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e below, where the impact of digital access, digital use, and digital creation on family sports consumption all pass the significance test and are all significant at the 1 percent level. The largest of these is the coefficient of digital access (34.653), followed by the coefficient of digital use (27.318) and the coefficient of digital creation (16.538). It indicates that digital access has the greatest impact and provides families with access to sports consumption information channels, empowering families to learn about the sports consumption market, explore suitable sports products, and undertake sports consumption.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe impact of digital competence on household sports consumption: sub-dimensions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003evariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eHousehold sports consumption\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigital acess\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.653***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-12.089)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigital use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.318***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-17.262)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigital creation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.538***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-20.132)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003egender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.994***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.945***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.084***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-3.631)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-3.446)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-3.992)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.283***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.168***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.195***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-6.230)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-3.711)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-4.303)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.318***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.249***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.241***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-7.703)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-6.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-5.915)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.636\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.493\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-1.792)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-1.345)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-1.412)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducational attainment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.557***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.494***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.433***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-21.375)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-20.649)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-19.602)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban or rural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.386***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.323***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.725***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-9.319)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-9.234)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-10.589)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.645***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.615***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.552***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-7.122)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-6.761)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-6.262)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal household assets\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.429***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.434***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.399***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-16.682)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-17.145)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-17.014_\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold holdings of financial products\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.617***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.182***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.714***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-9.325)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-8.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-6.892)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold entrepreneurial involvement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.449***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.289***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.120***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-4.856)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-4.303)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-3.724)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eobservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39509\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e5.2. Endogeneity test\u003c/h2\u003e\u003cp\u003eIn this paper, the instrumental variable method is chosen to further address the endogeneity issue. Referring to Yin, Zhichao et al. (2020)\u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/sup\u003e, the instrumental variable \u0026lsquo;the mean value of digital competence of other households in the same community\u0026rsquo; is chosen. Firstly, families within the same community share the same economic and geographical environment and exhibit similar digital competence. For one of them, the digital competence of other families will not directly affect the digital competence of that family; conversely, the digital competence of that family will not directly affect the digital competence of other families. Therefore, this instrumental variable is suitable for the endogeneity test.\u003c/p\u003e\u003cp\u003eIn this paper, the endogeneity test is conducted using the 2SLS method, and Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the regression results. The joint F-value of 1116.03 in the first stage proves that there is no weak instrumental variable problem. In column (1), the first-stage regression results show that the regression coefficients of the instrumental variables are positive and significant, indicating that there is a positive relationship between digital competence and instrumental variables. In column (2), the results of the two-stage regression show that the regression results remain consistent with the baseline regression after the introduction of the instrumental variable, demonstrating a robust and significant positive effect of digital competence on household sports consumption.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of the instrumental variables approach to endogeneity testing\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDigital competence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHousehold sports consumption\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003einstrumental variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.508***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-50.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDigital competence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.860***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-14.22)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eyes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eyes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e_Cons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.405***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-7.58)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eobservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33,233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33,233\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1116.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProvince fixed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eno\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eno\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e5.3. Robustness test\u003c/h2\u003e\u003cp\u003eTo verify the robustness of the benchmark regression results, this paper changes the core explanatory variables, the explained variables, and excludes outliers, etc.\u003c/p\u003e\u003cp\u003eThe relevant results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of the robustness test\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ereplace the explained\u003c/p\u003e\u003cp\u003evariable:\u003c/p\u003e\u003cp\u003esports consumption per capita\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ereplace the explanatory variable:\u003c/p\u003e\u003cp\u003eaverage digital competence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTruncated:\u003c/p\u003e\u003cp\u003eHousehold sports consumption\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExclude samples where the head of the household is over 70 years old:\u003c/p\u003e\u003cp\u003eHousehold sports consumption\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eaverage digital competence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.406***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-16.222)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edigital competence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.965***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.679***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.949***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-22.515)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-22.237)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-16.765)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eyes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eyes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eyes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eyes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eobservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32,698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26,776\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePseudo R2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0731\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0947\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003e5.3.1. Replacement of the explained variable: sports consumption per capita\u003c/h2\u003e\u003cp\u003ePer capita sports consumption refers to the sports consumption of a family member, namely, dividing family sports consumption by family size, and replacing the explanatory variables with average digital competence to verify the effect of digital competence on family per capita sports consumption. As shown in column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, digital competence has a significant contribution to per capita household sports consumption, with a regression coefficient of 5.965, significant at the 1% level. It differs from the coefficient of digital competence of the head of the household in the benchmark regression results only in size, verifying the robustness of the basic conclusions of this paper. This indicates that the above empirical test is reliable and further verifies hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section3\"\u003e\u003ch2\u003e5.3.2. Replace the explanatory variable: average digital competence.\u003c/h2\u003e\u003cp\u003eAverage digital competence refers to the digital competence of a family member, namely, dividing the digital competence index by the family size, and replacing the core explanatory variable with average digital competence to verify the impact of digital competence on family sports consumption. As shown in column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, it can be seen that the coefficient of average household digital competence is significant at the 1% level, 17.406, larger than the regression coefficient of digital competence of the head of the household in the results of the benchmark regression, which verifies the robustness of the basic conclusions of this paper.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003e5.3.3. Eliminate outliers.\u003c/h2\u003e\u003cp\u003eFirst, the truncated treatment is used in this paper to exclude samples with extreme values of 5% on both sides of the household sports consumption variable. The second is to exclude samples where the head of the household is over 70 years old, given the age limitations of sports. The results are shown in columns (3) and (4) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. From the regression results, the digital competence regression coefficients differ only in magnitude after the outliers are excluded, and the regression coefficients are positive, which still has a significant promotion effect on household sports consumption. Consistent with the previous results, this further validates the robustness of the paper's findings.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e5.4. Analyses of mechanisms\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eImpact of digital competence on total household income\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003evariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehousehold sports consumption\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal household income\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehousehold sports consumption\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigital competence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.656***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.951***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.568***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-25.307)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-25.691)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-23.491)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal household income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.101***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-11.087)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003egender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.117***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.112***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-4.255)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-1.149)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-4.018)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.021***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.007*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.021***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-4.247)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-4.273)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eage squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.030***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.030***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-7.069)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.205)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-6.946)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.225***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.066*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-1.193)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-11.949\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-1.968)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducational attainment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.173***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.144***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.159***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-21.263)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-31.389)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-19.076)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban or rural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.326***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-1.806)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-22.357)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-0.501)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.056***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.188***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.075***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-7.258)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-43.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-9.389)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal household assets\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.096***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.211***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.075***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-13.873)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-53.866)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-10.354)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold holdings of financial products\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.740***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.207***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.710***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-13.631)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-6.775)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-12.993)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold entrepreneurial involvement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.066*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.042*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.071*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-2.108)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-2.361)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-2.224)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.275***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.154***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.918***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-7.875)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-67.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-11.079)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38853\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdj R-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3679\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1152\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTheoretically, digital competence can promote income level. It has been shown that there is a positive correlation between the coverage of new media and income. The use of the Internet can directly or indirectly promote employment, diversify employment channels, and play a positive role in raising income levels\u003csup\u003e[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e. Since income level is one of the primary determinants of sports consumption, this paper uses total household income as a mediating variable to confirm the mechanism of digital competence on household sports consumption.\u003c/p\u003e\u003cp\u003eColumn (1) of Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e represents the regression results of model (1) and examines the total effect of digital competence on household sports consumption without the mediating variable. The coefficient of digital competence is 1.656 and passes the 1% significance test. Column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents the regression results of model (3), examining the effect of digital competence on total household income. Digital competence is positive at the 1% significance level, indicating that digital competence significantly enhances total household income. Column (3) of Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e reports the regression results of Model (4), which investigates the direct effect of digital competence on household sports consumption, incorporating the mediator variable. The coefficient for digital competence is 1.568, significant at the 1% level, while the coefficient for total household income is 0.101, also significant at the 1% level. Combining the regression results in columns (1) (2) (3) of Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, it can be seen that total household income plays a partially mediating role in the process of digital competence affecting household sports consumption. In particular, while other factors remain constant, every 1 unit increase in digital competence will directly raise household sports consumption by 1.568 units, and will also cause total household income to rise by 0.951 units, and every 1 unit increase in digital competence will raise household sports consumption by 0.101 units. Therefore, each unit increase in digital competence indirectly raises household sports consumption by 0.0961 units (0.951*0.101\u0026thinsp;\u0026asymp;\u0026thinsp;0.0961) through total household income, for a total effect of 1.656 units. This indicates that the increase in household sports consumption due to digital competence is realized through an increase in total household income, so hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e is valid.\u003c/p\u003e\u003c/div\u003e"},{"header":"6. Conclusions and recommendations","content":"\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e6.1Conclusion\u003c/h2\u003e\u003cp\u003eThis paper is based on the 2017 China Household Finance Survey (CHFS). It constructs a household-level digital capability index using the entropy method, calculates the scores for various dimensions of digital competence, and analyzes the impact and mechanism of digital competence on household sports consumption using the Tobit regression model, mediation effect model, endogeneity test, and robustness test, arriving at the following conclusions:\u003c/p\u003e\u003cp\u003eFirst, digital competence can significantly promote the level of family sports consumption, and the three sub-dimensions under digital competence: digital access, digital use, and digital creation all have a promotional effect on the level of family sports consumption. Notably, digital access exerts the most substantial effect, followed by digital use, with digital creation having the least impact. This conclusion still holds after considering endogeneity issues and conducting robustness tests.\u003c/p\u003e\u003cp\u003eSecond, the mechanism analysis shows that digital competence affects household sports consumption by influencing household income, and household income serves as a significant mediating factor. In other words, digital competence contributes to an increase in household income, thereby promoting higher levels of household sports consumption.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003e6.2Recommendations\u003c/h2\u003e\u003cp\u003eThe improvement of digital competence has a significant contribution to the increase in the income and consumption levels of households, which means that it is important to stimulate consumption and expand domestic demand through the development of digital competence as human capital. Therefore, the following recommendations are made:\u003c/p\u003e\u003cdiv id=\"Sec30\" class=\"Section3\"\u003e\u003ch2\u003e6.2.1. Governments\u003c/h2\u003e\u003cp\u003eThe priority should be given to establishing digital network communication infrastructure and advancing the upgrading of digital infrastructure. This would enable broader digital access, enhance the digital competence of the population, and unlock new potential in sports consumption. First, to ensure systematic development of infrastructure, it is essential to optimize the layout of digital infrastructure networks. In constructing digital sports infrastructure, advanced information technologies such as big data, cloud computing, the Internet of Things (IoT), 5G, and artificial intelligence (AI) should be actively employed. For example, Guangzhou's Ersha Island Sports Park, the nation's first intelligent sports park, incorporates multiple smart fitness facilities. Some fitness equipment is equipped with solar power generation systems, enabling the park to generate electricity through physical activity, thereby integrating digital fitness with green fitness. Additionally, the park features intelligent jogging tracks and smart fitness paths, offering innovative opportunities for public sports consumption. Secondly, efforts should be made to bridge the digital divide by increasing investments in digital infrastructure in central and western regions, as well as rural areas, thereby establishing a solid foundation for internet access. The uneven and insufficient development of digital infrastructure across regions is an objective reality. Governments should promote the integrated development of urban and rural digital infrastructure, enhance internet penetration and coverage in rural and central-western regions, and advance the equalization and accessibility of digital sports services. This will enable a broader population to benefit from the dividends of the digital era; Thirdly, extensive digital skills training programs should be implemented for the general population to enhance human capital. Efforts should focus on the high-quality development and open sharing of digital education resources, digital skills training, digital products, and information services.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec31\" class=\"Section3\"\u003e\u003ch2\u003e6.2.2. Sports Enterprises\u003c/h2\u003e\u003cp\u003eEfforts should focus on increasing investment in technological components, allocating greater resources to research and development, and enhancing digital innovation capabilities to meet the growing sports consumption demands of the public. Firstly, to improve consumers\u0026lsquo; sports information literacy, sports enterprises can use official websites and social media platforms such as WeChat, Weibo and TikTok to publicize graphics and videos displaying digital sports products and services to attract consumers\u0026rsquo; attention. Also, it is beneficial to understand and increase users' participation through online interactions, such as polls and user feedback, which not only help consumers gain a deeper understanding of the dynamics of the digital sports market but also enhance their intention to purchase sports products and services, thus promoting the development of the sports consumption market. Secondly, sports enterprises should leverage internet platforms to actively cultivate emerging sports services industries, such as digital fitness services and online sports training. By utilizing technologies such as virtual reality (VR), artificial intelligence (AI) large models, and virtual anchors, they can expand e-commerce live-streaming scenarios and create innovative consumer experiences. Besides, promoting the digital transformation of sports venues, integrating digital technology into the operation of sports venues, and realizing a comprehensive digital informatization upgrade of the entry process, online booking, and the sports process, to make the venues a platform linking sports and the masses. Thirdly, new scenarios for sports consumption should be created. For example, the construction of large-scale urban complexes of \u0026lsquo;sports center\u0026thinsp;+\u0026thinsp;commercial center\u0026rsquo; and sports towns of \u0026lsquo;tourism\u0026thinsp;+\u0026thinsp;sports and leisure\u0026rsquo;. Information technology can be employed to empower and enhance these developments, facilitating more engaging and interactive consumer experiences. Fourthly, it is essential to strengthen information supervision and encourage rational consumption among sports consumers. This can be achieved through technological means, manual reviews, and reporting mechanisms to enhance the regulation of harmful content and false information. Creating a healthy online sports consumption environment will help protect consumers from being misled or deceived, guide them in adopting healthy consumption habits, and promote rational purchasing decisions. These efforts will contribute to the sustainable development of digital sports consumption.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec32\" class=\"Section3\"\u003e\u003ch2\u003e6.2.3. Individuals\u003c/h2\u003e\u003cp\u003eIt is important to enhance personal digital competence and establish a new concept of sports consumption. Firstly, a shift in mindset is necessary to align with the policy requirements of enhancing digital capabilities in the digital age, thereby being qualified digital citizens in the new era. Continuously study and update their digital literacy and skills, and examine online information from a critical perspective. When faced with confusing and inducing consumer information, we should analyze it in depth, and improve the ability to identify the false information. Secondly, individuals should establish new concepts of sports consumption. At the stage of demand identification and information collection, it is necessary to clarify one's budget level and examine one's purchasing motives. When browsing relevant information on social network platforms, avoid blindly following the shopping recommendations of Key Opinion Leaders (KOLs) and Key Opinion Consumers (KOCs), just reasonably formulate consumption lists. At the program evaluation and purchase decision stage, a comparative analysis of selected sports products should be conducted. The product that best meets both needs and budget should be chosen, leading to a purchase decision. In the post-purchase behavior stage, a comprehensive evaluation of the purchased sports products or services is necessary. The goal is to highlight the value of the product, extend its usage time and lifespan, and reduce the frequency of replacement. Additionally, it is important to recognize that overemphasizing fashion trends is not advisable.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend their sincere gratitude to the anonymous reviewers for their invaluable advice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZ.W.: The author contributed to the conceptualization and writing of the original draft, as well as to formal analysis and data analysis. H.S. and Y.T.: They contributed to writing， reviewing, and editing, as well as to supervision. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and informed consent statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo animal studies and no human studies are presented in this manuscript. No potentially identifiable human images or data is presented in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLi, K., Kim, D. J., Lang, K. R., Kauffman, R. J. \u0026amp; Naldi, M. How should we understand the digital economy in Asia? Critical assessment and research agenda. \u003cem\u003eElectronic Commerce Research and Applications\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e, 101004, (2020).\u003c/li\u003e\n\u003cli\u003eMurthy, K. V. B., Kalsie, A. \u0026amp; Shankar, R. 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The consumption of sports is influenced by many factors, including personal characteristics, family characteristics and the characteristics of the region in which the family is located. The enhancement of digital competence is of great significance in broadening income channels, improving income levels and upgrading the consumption structure. Based on data from the 2017 China Household Finance Survey (CHFS), the article empirically examines the impact of digital competence on household sports consumption based on constructing household digital capability scores. The results indicate that digital competence has a significant positive impact on household sports consumption. This conclusion remains robust even after considering potential endogeneity and conducting robustness tests. Further analyses show that digital competence is more effective in increasing household sports consumption for households characterized by higher levels of personal education, urban households, higher total household assets, and higher participation in household entrepreneurship. Digital competence promotes household sports consumption mainly by raising the level of household income. It is recommended to accelerate the development of the digital economy, establish a comprehensive mechanism for cultivating digital competencies, and fully leverage these capabilities to promote household sports consumption.\u003c/p\u003e","manuscriptTitle":"A Study of the Impact of Digital Competence on Household Sports Consumption","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-25 10:36:02","doi":"10.21203/rs.3.rs-7472716/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fc5cda66-978f-4afb-966e-6c467a71537a","owner":[],"postedDate":"September 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55082117,"name":"Business and commerce/Economics"},{"id":55082118,"name":"Social science/Economics"},{"id":55082119,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2025-12-15T11:08:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-25 10:36:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7472716","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7472716","identity":"rs-7472716","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}