# Sociological Analysis of Chinese Sports Viewers with Differences in Social Capital

Authors: Wei Liu; Xing Liu
Journal: Complexity
 (2021)

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## Abstract

Despite the swift growth of China’s sports programs, the sports audience in China vary markedly in terms of social class and urban types. Thus, understanding the audience characteristics of Chinese sports programs is of utmost socioeconomic significance for the development of sports programs. This study aims to investigate the population of different social capital and regions through the Internet and uses an ologist to perform regression analysis. People with different social capital exhibited significant differences in the love of sports programs, with significant social divisions. In addition, the differences in the groups’ love for sports programs in different regions were significant, suggesting field differences among the audiences of Chinese sports programs. Besides, we found that although sports programs are a type of public social resources, the Chinese audience’s preference for sports programs is affected by social capital and regional differences. In general, this study is helpful for the government and the media to formulate targeted sports planning policies, further promoting the development of sports and promoting the improvement of business model under industry 4.0.


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## Body

## 1. Introduction

In the era of industry 4.0, what is the business model of manufacturing industry in the future? The answer is to solve customer problems. For Chinese sports program production, understanding customer characteristics is helpful to solve customer problems. Sports programs are of considerable social significance to the public in terms of propagating sports health knowledge, founding rule consciousness, conveying correct values, and enriching people’s free time [1]. In addition, sports programs act as a vital link in elevating, expanding, and developing the sports industry. Currently, with the alteration of people’s lifestyle and the revolution of communication mode, the transmission mode of sports programs has transitioned from single TV transmission to the competition state of concurrent development with network communication [2]. A survey revealed a massive gap between the ratings of sports programs in China and the United States; indeed, the ratings of sports programs in China are low even compared with the domestic variety shows.In the Beijing Olympic Games, some studies reported that the audience group of sports programs in China comprised the group with more years of education, good work, and high income [3]. Some studies also reported that gender, age, region, and other objective factors constructed a different “field” [4] and “habitus” [5]. Of these, Yuanzhen had a huge gender difference in sports participation [6]. Over 85% of the sports media pages were occupied by more than 85% of men, whereas the images of women in the media were full of gender hints, such as sports babies and female fans, which publicized the inaccurate values that men are stronger than women. Wei argued that urban residents watch sports programs more frequently than their rural counterparts [7]. Zhang reported that the segregation of household registration in Chinese society is a significant reason for the inequality of sports participation [8]. Conversely, Fang deduced from the research of sports stakeholders that regional differences resulted in the differences in citizens’ sports interests in different regions [9]. Evidently, social demographic factors influence people’s preference for sports programs in varying degrees, and the social stratification system has been rebuilt in the lifestyle of indirect sports participation.To some extent, occupation depicts the daily social identity and social network of the research object. Typically, sociologists understand occupation as the reason for the inequality of social resources and life opportunities in different positions [10]. Michael Savage believed that class differentiation correlated with different lifestyles and consumption patterns. In his view, public service professionals with higher cultural capital but lower economic capital tended to pursue a healthy and positive lifestyle, including exercise, less drinking, active participation in cultural activities, and community activities. Conversely, managers and bureaucrats represented the “mediocre and boring” consumption pattern, including inactive or lack of exercise, limited participation in cultural activities, and preference for a traditional style of home and time [11]. Through investigation, Wang and other scholars are keen to buy tickets for sports competitions or performances [12]. Reportedly, technical personnel rank second after service personnel, whereas Liu reported that management personnel ranked first through CGSS data, followed by secondary occupation personnel [13]. Of note, it is easy to see the effect of occupation on access to resources and that watching sports programs in action has almost no resource barriers [14].Balzac once stated, “the soul of a person can be known by the gesture he grasps his walking stick.” In the same society, people with similar cultural capital typically exhibit the same “taste” and “sentiment” [15], which is primarily attributed to the combined effect of educational capital and other capital [16]. Yuanzhen demonstrated that the sports population rate of people with a university degree is the highest; this finding would stabilize people who like watching sports programs in a specific field, meanwhile, shape their habits by influencing their cultural structure dimensions, and, finally, create a “separation” under the combined efforts of “field” and “habits.”In the field of sports, people’s economic capital would affect the breadth and depth, content, and form of people’s participation in sports [17]. Regarding breadth, the better the economic capital base, the more selections people have to participate in sports [18]. Sports belong to the “leisure class” activities. If people cannot guarantee their fundamental life, such as food and clothing, participating in sports is unnecessary. Regarding depth, the better the economic capital base, the robust the sustainability of people’s participation in sports. The longer you move, the more energy you will consume in sports. If one aspires to take sports as a way of life, one must only spend significant time and energy on sports practice. Maron argued that income difference accounts for sports participation, and the results are consistent [19]. The ownership of media is in the economic field and driven by the field logic [20]. Alternatively, the media producing sports programs have an economic pursuit, and with the popularity of network media, the pursuit of the highest commercial interests has always been the driving force of the media. Then, the media produces sports programs aligning with their “field” and “habits” based on their preferences.Generally speaking, since the reform and opening up, profound changes have taken place in the Chinese society, the prominent feature of which is the change of social strata. The Chinese society is witnessing unparalleled social transformation, with rapidly developing urbanization process. As the majority of the rural population migrates into the city, the “habitus” might still exist in the rural population; however, the “field” would change markedly. Large cities, medium-sized cities, and small towns do have not only much isolation on ornamental consumption barriers but also significant differences in their field habit acquisition. Studying the sports program preference under the change of social stratum in China will play a positive role in excavating the sports program market and promoting the development of sports industry.


## 2. Materials and Methods

### 2.1. Data Sources

In this study, the data were obtained from the database of “Netziens’ Social Awareness Survey” conducted by Prof. Ma Deyong, School of Politics, School of International Relations, Renmin University of China. The survey since 2012, researchers began to explore the way of collecting the questionnaire of netizens’ political attitude through online questionnaire survey. In 2012, more than 2200 questionnaires were collected. In 2013, based on the 2012 survey, the researchers further improved the questionnaire and survey methods and collected 2662 valid questionnaires on the Internet. From May to June 2014, the researchers summarized their previous experience and organized a survey team to conduct online questionnaire survey. At the same time, they published answer links in the questionnaire website, Sina Weibo, Kaidi community, Tianya forum, and other online communities, invited netizens to answer questions voluntarily, and collected 4231 valid questionnaires. Questionnaire network (http://www.wenjuan.com) is a company specialized in network research; the site has more than 4 million registered users; time of the database was primarily between April and May 2017. To ensure the reliability of the network questionnaire, we took the following control measures: (i) to prevent answer repetition, each IP address could only answer the questionnaire once; (ii) the questionnaire with answer time <7-8 min was eliminated. In the later stage, some questionnaires that were not answered thoughtfully were eliminated by browsing the questionnaire manually. A total of 2379 samples were collected, mostly from “questionnaire network” users. After removing the missing value and abnormal value, 2128 valid questionnaires were obtained.


### 2.2. Methods

Using the Internet, we investigated the population of different social capital and regions. We performed methodological evaluations and regression analysis using Stata 12.0 software.


### 2.3. Variables

#### 2.3.1. Result Variable: Liking Sports Programs

This study was conducted because of the love degree of sports programs. Thus, it is adequate and reasonable to use “love of sports programs” in the questionnaire as the result variable. The options for this question were as follows: like very much, like relatively, generally, do not like it very much, and do not like it at all; the values assigned to the options were 5, 4, 3, 2, and 1, respectively.


#### 2.3.2. Independent Variable: Demographic Characteristics and Cultural Structure Stratum

As this study aimed to assess the social class characteristics of sports program loving groups, we selected demographic characteristics and cultural structure class as independent variables.The demographic characteristics included gender, age, and region. Gender is a sociological concept, and the Chinese feudal society has prevailed with the idea that “men are superior to women,” resulting in the social psychology of women’s obedience and cowardice. Even today, traditional ethical values continue affecting the value standards of Chinese women. Based on life experience, it can be projected that men prefer to participate in sports activities more than women; thus, in gender, men = 1, women = 0. Different ages experience significant differences in social problems, leisure time, and social resources; thus, age is also an indispensable independent variable in demographic characteristics. Age in the original questionnaire data was interval grouping. To compare the difference of age in sports program preference, we set 18–29 years as 0, 30–44 as 1, 45–59 as 2, and >60 as 3. From the standpoint of China’s social development, the urban-rural dual system structure caused significant differences in social “fields” between urban and rural areas, followed by affecting the “habits” of urban and rural residents with varying preferences for sports programs. The original questionnaire had six living areas, namely, big cities, medium-sized cities, small cities, towns, villages, and overseas areas. To facilitate the differentiation, we set small cities, towns, and villages as 2, medium-sized cities as 1, and large cities as 0 based on the differences of public sports services and social economy. Of note, the data of overseas areas were excluded, and the samples of small towns were recorded. The sample size was 131, the sample size of medium-sized cities was 381, and the sample size of large cities was 1616.Per Bourdieu’s Social and Cultural Stratum perspective framework, from the sociological standpoint, observing the fields and habits of sports programs should primarily include the following three types of variables—social capital, economic capital, and cultural capital. In this study, based on the International Occupational Status Index (isei88) of 1988, it can be categorized into three categories—management = 2, technology = 1, and others = 0. Usually, economic capital includes family income and wealth. Based on the questionnaire, we used annual family income as the measurement variable. Cultural capital was typically measured by the education level. To instinctively comprehend the effect of different educational capital on watching sports programs, educational capital was transformed into categorical variables, including below junior high school = 0, senior high school (technical secondary school) = 1, undergraduate (major) = 2, and graduate student above = 3. Table1 presents the descriptive statistics of the variables mentioned above.Table 1
Descriptive statistics.
VariableObs.MeanStd. dev.MinMaxLove of sports programs21283.211.08615Gender21280.5310.49901Age21280.7530.75703Area21281.2850.82802Occupation21280.9240.82402Income21282.2050.877−2.3036.215Education21282.0990.57103


## 2.1. Data Sources

In this study, the data were obtained from the database of “Netziens’ Social Awareness Survey” conducted by Prof. Ma Deyong, School of Politics, School of International Relations, Renmin University of China. The survey since 2012, researchers began to explore the way of collecting the questionnaire of netizens’ political attitude through online questionnaire survey. In 2012, more than 2200 questionnaires were collected. In 2013, based on the 2012 survey, the researchers further improved the questionnaire and survey methods and collected 2662 valid questionnaires on the Internet. From May to June 2014, the researchers summarized their previous experience and organized a survey team to conduct online questionnaire survey. At the same time, they published answer links in the questionnaire website, Sina Weibo, Kaidi community, Tianya forum, and other online communities, invited netizens to answer questions voluntarily, and collected 4231 valid questionnaires. Questionnaire network (http://www.wenjuan.com) is a company specialized in network research; the site has more than 4 million registered users; time of the database was primarily between April and May 2017. To ensure the reliability of the network questionnaire, we took the following control measures: (i) to prevent answer repetition, each IP address could only answer the questionnaire once; (ii) the questionnaire with answer time <7-8 min was eliminated. In the later stage, some questionnaires that were not answered thoughtfully were eliminated by browsing the questionnaire manually. A total of 2379 samples were collected, mostly from “questionnaire network” users. After removing the missing value and abnormal value, 2128 valid questionnaires were obtained.


## 2.2. Methods

Using the Internet, we investigated the population of different social capital and regions. We performed methodological evaluations and regression analysis using Stata 12.0 software.


## 2.3. Variables

### 2.3.1. Result Variable: Liking Sports Programs

This study was conducted because of the love degree of sports programs. Thus, it is adequate and reasonable to use “love of sports programs” in the questionnaire as the result variable. The options for this question were as follows: like very much, like relatively, generally, do not like it very much, and do not like it at all; the values assigned to the options were 5, 4, 3, 2, and 1, respectively.


### 2.3.2. Independent Variable: Demographic Characteristics and Cultural Structure Stratum

As this study aimed to assess the social class characteristics of sports program loving groups, we selected demographic characteristics and cultural structure class as independent variables.The demographic characteristics included gender, age, and region. Gender is a sociological concept, and the Chinese feudal society has prevailed with the idea that “men are superior to women,” resulting in the social psychology of women’s obedience and cowardice. Even today, traditional ethical values continue affecting the value standards of Chinese women. Based on life experience, it can be projected that men prefer to participate in sports activities more than women; thus, in gender, men = 1, women = 0. Different ages experience significant differences in social problems, leisure time, and social resources; thus, age is also an indispensable independent variable in demographic characteristics. Age in the original questionnaire data was interval grouping. To compare the difference of age in sports program preference, we set 18–29 years as 0, 30–44 as 1, 45–59 as 2, and >60 as 3. From the standpoint of China’s social development, the urban-rural dual system structure caused significant differences in social “fields” between urban and rural areas, followed by affecting the “habits” of urban and rural residents with varying preferences for sports programs. The original questionnaire had six living areas, namely, big cities, medium-sized cities, small cities, towns, villages, and overseas areas. To facilitate the differentiation, we set small cities, towns, and villages as 2, medium-sized cities as 1, and large cities as 0 based on the differences of public sports services and social economy. Of note, the data of overseas areas were excluded, and the samples of small towns were recorded. The sample size was 131, the sample size of medium-sized cities was 381, and the sample size of large cities was 1616.Per Bourdieu’s Social and Cultural Stratum perspective framework, from the sociological standpoint, observing the fields and habits of sports programs should primarily include the following three types of variables—social capital, economic capital, and cultural capital. In this study, based on the International Occupational Status Index (isei88) of 1988, it can be categorized into three categories—management = 2, technology = 1, and others = 0. Usually, economic capital includes family income and wealth. Based on the questionnaire, we used annual family income as the measurement variable. Cultural capital was typically measured by the education level. To instinctively comprehend the effect of different educational capital on watching sports programs, educational capital was transformed into categorical variables, including below junior high school = 0, senior high school (technical secondary school) = 1, undergraduate (major) = 2, and graduate student above = 3. Table1 presents the descriptive statistics of the variables mentioned above.Table 1
Descriptive statistics.
VariableObs.MeanStd. dev.MinMaxLove of sports programs21283.211.08615Gender21280.5310.49901Age21280.7530.75703Area21281.2850.82802Occupation21280.9240.82402Income21282.2050.877−2.3036.215Education21282.0990.57103


## 2.3.1. Result Variable: Liking Sports Programs

This study was conducted because of the love degree of sports programs. Thus, it is adequate and reasonable to use “love of sports programs” in the questionnaire as the result variable. The options for this question were as follows: like very much, like relatively, generally, do not like it very much, and do not like it at all; the values assigned to the options were 5, 4, 3, 2, and 1, respectively.


## 2.3.2. Independent Variable: Demographic Characteristics and Cultural Structure Stratum

As this study aimed to assess the social class characteristics of sports program loving groups, we selected demographic characteristics and cultural structure class as independent variables.The demographic characteristics included gender, age, and region. Gender is a sociological concept, and the Chinese feudal society has prevailed with the idea that “men are superior to women,” resulting in the social psychology of women’s obedience and cowardice. Even today, traditional ethical values continue affecting the value standards of Chinese women. Based on life experience, it can be projected that men prefer to participate in sports activities more than women; thus, in gender, men = 1, women = 0. Different ages experience significant differences in social problems, leisure time, and social resources; thus, age is also an indispensable independent variable in demographic characteristics. Age in the original questionnaire data was interval grouping. To compare the difference of age in sports program preference, we set 18–29 years as 0, 30–44 as 1, 45–59 as 2, and >60 as 3. From the standpoint of China’s social development, the urban-rural dual system structure caused significant differences in social “fields” between urban and rural areas, followed by affecting the “habits” of urban and rural residents with varying preferences for sports programs. The original questionnaire had six living areas, namely, big cities, medium-sized cities, small cities, towns, villages, and overseas areas. To facilitate the differentiation, we set small cities, towns, and villages as 2, medium-sized cities as 1, and large cities as 0 based on the differences of public sports services and social economy. Of note, the data of overseas areas were excluded, and the samples of small towns were recorded. The sample size was 131, the sample size of medium-sized cities was 381, and the sample size of large cities was 1616.Per Bourdieu’s Social and Cultural Stratum perspective framework, from the sociological standpoint, observing the fields and habits of sports programs should primarily include the following three types of variables—social capital, economic capital, and cultural capital. In this study, based on the International Occupational Status Index (isei88) of 1988, it can be categorized into three categories—management = 2, technology = 1, and others = 0. Usually, economic capital includes family income and wealth. Based on the questionnaire, we used annual family income as the measurement variable. Cultural capital was typically measured by the education level. To instinctively comprehend the effect of different educational capital on watching sports programs, educational capital was transformed into categorical variables, including below junior high school = 0, senior high school (technical secondary school) = 1, undergraduate (major) = 2, and graduate student above = 3. Table1 presents the descriptive statistics of the variables mentioned above.Table 1
Descriptive statistics.
VariableObs.MeanStd. dev.MinMaxLove of sports programs21283.211.08615Gender21280.5310.49901Age21280.7530.75703Area21281.2850.82802Occupation21280.9240.82402Income21282.2050.877−2.3036.215Education21282.0990.57103


## 3. Discussion and Result

### 3.1. Interpretation and Analysis of the Results of the Ologist Regression Model of Sports Program Preference

In this study, the dependent variable was a continuous variable of the degree of preference for sports programs; thus, we used ordered multiple linear regression (ologist) to analyze the impact of various social classes on the preference for sports programs. Table2 shows the estimated results of five ologist equations as follows: model 1 is the fundamental model, including all control variables; models 2–5 are based on model 1 with occupation, education, and income added. In addition, we categorized variables to validate the social level’s preference for favorite programs. Table 3 shows the estimation results of four ologist equations as follows: models 1–3 are market domain models for small towns, medium-sized cities, and large-scale cities; and model 4 is a national benchmark model. In this study, gender and age were unavoidable issues of demographic characteristics. Model 1 exhibited that men’s preference for sports programs was 2.46 times higher than that of women (e0.9 = 2.46). Currently, no analysis is available to examine gender differences in gender preference sports programs at home and abroad. We found that from model 1 to model 5, the gender coefficient changed marginally. Despite adding education, occupation, and income, the gender influence coefficient maintained a balanced state. Of note, even if women were at the same level as men, the chances of liking sports programs did not increase with social capital, cultural capital, and educational capital. For a long time, the traditional Confucian ideas of women in the Chinese society, such as “comprehensive husband and child” and “keeping women’s morals” have nurtured the introverted and reserved Chinese women, implying that restricting Chinese women’s preference for watching sports programs could be historical and cultural reasons. Regarding variable age analysis, we divided the age composition into four groups. Based on the fitting results of model 1, we found that with the youth group as the reference group, the regression coefficient of the retirement group was not significant, whereas the regression coefficient of the young and middle-aged group was the most significant 0.326 (P<0.01), followed by the middle-aged group 0.240 (P<0.05), which is precisely the opposite of the “U”-shaped characteristic of the age distribution of direct participation in sports, suggesting that watching sports programs among the masses and actually participating in sports activities exert a complementary effect. Alternatively, after experiencing the double squeeze of “family life” and “professional life,” the young and middle-aged groups in China have no time to exercise, resulting in their switch from physical exercise to watching sports programs. Regarding regional differences, the regression coefficients of medium-sized cities and large cities were significantly and positively correlated with small towns as the reference group. From a horizontal standpoint, after adding social capital, educational capital, and economic capital, the regression coefficients of different cities exhibited the similar changing trend; that is, medium-sized cities preferred watching sports programs, followed by large cities, and finally small cities.Table 2
The results of the ologist regression model of sports program preference.
Model 1Model 2Model 3Model 4Model 5Gender (female = 0)0.905∗∗∗0.892∗∗∗0.907∗∗∗0.906∗∗∗0.897∗∗∗(0.082)(0.082)(0.082)(0.082)(0.082)Age (18–30 = 0)31–45 = 10.326∗∗∗0.267∗∗∗0.335∗∗∗0.259∗∗∗0.233∗∗(0.087)(0.089)(0.088)(0.092)(0.094)46–60 = 20.240∗∗0.1860.258∗∗0.1930.172(0.121)(0.122)(0.122)(0.124)(0.126)>61 = 3−0.259−0.298−0.176−0.263−0.235(0.305)(0.305)(0.309)(0.306)(0.309)Area (large cities = 0)Medium-sized cities = 1−0.448∗∗∗−0.382∗∗∗−0.437∗∗∗−0.423∗∗∗−0.366∗∗∗(0.103)(0.105)(0.104)(0.104)(0.107)Towns and villages = 2−0.560∗∗∗−0.444∗∗∗−0.525∗∗∗−0.495∗∗∗−0.384∗∗(0.164)(0.167)(0.166)(0.165)(0.170)Occupation (others = 0)Technology = 10.289∗∗∗0.192∗(0.099)(0.101)Management = 20.1500.092(0.102)(0.106)Edu (below junior high school = 0)Senior high school = 10.2520.264(0.354)(0.356)Undergraduate = 20.582∗0.515(0.330)(0.333)Graduate student = 30.2900.225(0.340)(0.344)Income0.158∗∗∗0.145∗∗∗(0.048)(0.049)Obs.21282128212821282128PseudoR20.0300.0320.0320.0310.034Standard errors are in parenthesis.∗∗∗P<0.01; ∗∗P<0.05; and ∗P<0.1.Table 3
The ologist regression model of different city types’ preference for sports programs.
Large citiesMedium-sized citiesTowns and villagesNationwideGender0.834∗∗∗1.154∗∗∗1.639∗∗∗0.902∗∗∗(0.094)(0.201)(0.381)(0.082)Age (18–30 = 0)31–45 = 10.264∗∗0.077−0.1900.242∗∗(0.106)(0.233)(0.519)(0.094)46–60 = 20.0390.660∗∗−0.0760.178(0.144)(0.290)(0.741)(0.126)>61 = 3−0.1860.535−1.722−0.211(0.352)(1.055)(1.091)(0.309)Occupation (others = 0)Technology = 10.225∗∗0.117−0.3580.205∗∗(0.114)(0.257)(0.571)(0.101)Management = 20.1540.059−0.3440.057(0.123)(0.242)(0.484)(0.106)Edu (below junior high school = 0)Senior high school = 10.583−0.072−0.6450.328(0.572)(0.665)(0.787)(0.354)Undergraduate = 20.839−0.003−0.0530.618∗(0.546)(0.623)(0.765)(0.330)Graduate student = 30.4340.2340.2940.363(0.554)(0.664)(0.910)(0.340)Income0.131−0.1570.459∗0.105(0.100)(0.195)(0.269)(0.083)Obs.16163811312128PseudoR20.0290.0450.0810.033Standard errors are in parenthesis.∗∗∗P<0.01; ∗∗P<0.05; and ∗P<0.1.Model 2 added the social capital (occupation) variable. The regression equation revealed that compared with the control group, technical personnel preferred watching sports programs. In this study, the technical personnel ranked the first. Perhaps, the nature of work and the objective reasons for the time of technical personnel resulted in their more on-site consumption and preference of watch sports programs at home. In model 5, after adding cultural capital and economic capital, although the explanatory coefficient was marginally lower but still significant, it showed that social capital alone exerted a significant effect on the viewing of sports programs.Model 3 added the cultural capital (education) variable. Compared with the control group, the coefficient was significant in the undergraduate (diploma). After adding economic capital and social capital to model 5, cultural capital lost its significance, suggesting that cultural capital affected sports program preferences through social capital and economic capital. Hence, we believe that sports programs are not professional programs and are related to cultural capital.Model 4 added economic capital (income) variables. Economic capital was significant in both model 4 and model 5. In model 5, compared with lower-income earners, the middle- and high-income groups were still significant, and the trends were consistent, suggesting that economic capital played a positive role in promoting whether people liked to watch sports programs. Meanwhile, we found that the number of middle-income groups was marginally higher than that of high-income groups.


### 3.2. Interpretation and Analysis of the Ologist Regression Model of Different City Types’ Preference for Sports Programs

The relevant analysis results in Table2 revealed significant urban-type differences in sports program preference groups, suggesting considerable differences in sports program preference groups in urban-type fields. Thus, whether the characteristics of this difference are consistent or have their own characteristics remains unclear. Hence, this research established models of different types of cities in response to this question.From the comparative analysis results of city types shown in Table3, from the model coefficients and statistical test results, some differences were found in the preference for sports programs in different city types. Specifically, in small towns, only the 46–60 age group was significant, which could be related to the relatively small economic pressure and stable working conditions of such groups in small towns. In medium-sized cities, social capital and economic capital were significant and consistent with the national model; this could be because the mean value of the national model was consistent with that of medium-sized cities. Meanwhile, compared with the inadequate public services and market development of smaller cities, the development of service facilities and the sports industry market has played a role in the field change mechanism for the increase of sports program preference groups, especially the upsurge in social attributes would increase the separation of social capital and economic capital. In large cities, in age groups, the upper 31–45 age range was significant. In large cities, both social capital and economic capital were much higher than small- and medium-sized cities; however, only differences were found in age groups, suggesting the preference for sports programs in large cities. The field was the social result created by various social reasons squeezing each other. In various types of cities, the explanatory power of educational capital was insignificant, suggesting that educational capital exerted no impact on the creation of the stratification mechanism for sports program preferences in various cities. Overall, neither population model, social capital, educational capital, or economic capital exerted an independent influence; this demonstrates that people who liked sports programs had different stratification mechanisms in different types of cities.


## 3.1. Interpretation and Analysis of the Results of the Ologist Regression Model of Sports Program Preference

In this study, the dependent variable was a continuous variable of the degree of preference for sports programs; thus, we used ordered multiple linear regression (ologist) to analyze the impact of various social classes on the preference for sports programs. Table2 shows the estimated results of five ologist equations as follows: model 1 is the fundamental model, including all control variables; models 2–5 are based on model 1 with occupation, education, and income added. In addition, we categorized variables to validate the social level’s preference for favorite programs. Table 3 shows the estimation results of four ologist equations as follows: models 1–3 are market domain models for small towns, medium-sized cities, and large-scale cities; and model 4 is a national benchmark model. In this study, gender and age were unavoidable issues of demographic characteristics. Model 1 exhibited that men’s preference for sports programs was 2.46 times higher than that of women (e0.9 = 2.46). Currently, no analysis is available to examine gender differences in gender preference sports programs at home and abroad. We found that from model 1 to model 5, the gender coefficient changed marginally. Despite adding education, occupation, and income, the gender influence coefficient maintained a balanced state. Of note, even if women were at the same level as men, the chances of liking sports programs did not increase with social capital, cultural capital, and educational capital. For a long time, the traditional Confucian ideas of women in the Chinese society, such as “comprehensive husband and child” and “keeping women’s morals” have nurtured the introverted and reserved Chinese women, implying that restricting Chinese women’s preference for watching sports programs could be historical and cultural reasons. Regarding variable age analysis, we divided the age composition into four groups. Based on the fitting results of model 1, we found that with the youth group as the reference group, the regression coefficient of the retirement group was not significant, whereas the regression coefficient of the young and middle-aged group was the most significant 0.326 (P<0.01), followed by the middle-aged group 0.240 (P<0.05), which is precisely the opposite of the “U”-shaped characteristic of the age distribution of direct participation in sports, suggesting that watching sports programs among the masses and actually participating in sports activities exert a complementary effect. Alternatively, after experiencing the double squeeze of “family life” and “professional life,” the young and middle-aged groups in China have no time to exercise, resulting in their switch from physical exercise to watching sports programs. Regarding regional differences, the regression coefficients of medium-sized cities and large cities were significantly and positively correlated with small towns as the reference group. From a horizontal standpoint, after adding social capital, educational capital, and economic capital, the regression coefficients of different cities exhibited the similar changing trend; that is, medium-sized cities preferred watching sports programs, followed by large cities, and finally small cities.Table 2
The results of the ologist regression model of sports program preference.
Model 1Model 2Model 3Model 4Model 5Gender (female = 0)0.905∗∗∗0.892∗∗∗0.907∗∗∗0.906∗∗∗0.897∗∗∗(0.082)(0.082)(0.082)(0.082)(0.082)Age (18–30 = 0)31–45 = 10.326∗∗∗0.267∗∗∗0.335∗∗∗0.259∗∗∗0.233∗∗(0.087)(0.089)(0.088)(0.092)(0.094)46–60 = 20.240∗∗0.1860.258∗∗0.1930.172(0.121)(0.122)(0.122)(0.124)(0.126)>61 = 3−0.259−0.298−0.176−0.263−0.235(0.305)(0.305)(0.309)(0.306)(0.309)Area (large cities = 0)Medium-sized cities = 1−0.448∗∗∗−0.382∗∗∗−0.437∗∗∗−0.423∗∗∗−0.366∗∗∗(0.103)(0.105)(0.104)(0.104)(0.107)Towns and villages = 2−0.560∗∗∗−0.444∗∗∗−0.525∗∗∗−0.495∗∗∗−0.384∗∗(0.164)(0.167)(0.166)(0.165)(0.170)Occupation (others = 0)Technology = 10.289∗∗∗0.192∗(0.099)(0.101)Management = 20.1500.092(0.102)(0.106)Edu (below junior high school = 0)Senior high school = 10.2520.264(0.354)(0.356)Undergraduate = 20.582∗0.515(0.330)(0.333)Graduate student = 30.2900.225(0.340)(0.344)Income0.158∗∗∗0.145∗∗∗(0.048)(0.049)Obs.21282128212821282128PseudoR20.0300.0320.0320.0310.034Standard errors are in parenthesis.∗∗∗P<0.01; ∗∗P<0.05; and ∗P<0.1.Table 3
The ologist regression model of different city types’ preference for sports programs.
Large citiesMedium-sized citiesTowns and villagesNationwideGender0.834∗∗∗1.154∗∗∗1.639∗∗∗0.902∗∗∗(0.094)(0.201)(0.381)(0.082)Age (18–30 = 0)31–45 = 10.264∗∗0.077−0.1900.242∗∗(0.106)(0.233)(0.519)(0.094)46–60 = 20.0390.660∗∗−0.0760.178(0.144)(0.290)(0.741)(0.126)>61 = 3−0.1860.535−1.722−0.211(0.352)(1.055)(1.091)(0.309)Occupation (others = 0)Technology = 10.225∗∗0.117−0.3580.205∗∗(0.114)(0.257)(0.571)(0.101)Management = 20.1540.059−0.3440.057(0.123)(0.242)(0.484)(0.106)Edu (below junior high school = 0)Senior high school = 10.583−0.072−0.6450.328(0.572)(0.665)(0.787)(0.354)Undergraduate = 20.839−0.003−0.0530.618∗(0.546)(0.623)(0.765)(0.330)Graduate student = 30.4340.2340.2940.363(0.554)(0.664)(0.910)(0.340)Income0.131−0.1570.459∗0.105(0.100)(0.195)(0.269)(0.083)Obs.16163811312128PseudoR20.0290.0450.0810.033Standard errors are in parenthesis.∗∗∗P<0.01; ∗∗P<0.05; and ∗P<0.1.Model 2 added the social capital (occupation) variable. The regression equation revealed that compared with the control group, technical personnel preferred watching sports programs. In this study, the technical personnel ranked the first. Perhaps, the nature of work and the objective reasons for the time of technical personnel resulted in their more on-site consumption and preference of watch sports programs at home. In model 5, after adding cultural capital and economic capital, although the explanatory coefficient was marginally lower but still significant, it showed that social capital alone exerted a significant effect on the viewing of sports programs.Model 3 added the cultural capital (education) variable. Compared with the control group, the coefficient was significant in the undergraduate (diploma). After adding economic capital and social capital to model 5, cultural capital lost its significance, suggesting that cultural capital affected sports program preferences through social capital and economic capital. Hence, we believe that sports programs are not professional programs and are related to cultural capital.Model 4 added economic capital (income) variables. Economic capital was significant in both model 4 and model 5. In model 5, compared with lower-income earners, the middle- and high-income groups were still significant, and the trends were consistent, suggesting that economic capital played a positive role in promoting whether people liked to watch sports programs. Meanwhile, we found that the number of middle-income groups was marginally higher than that of high-income groups.


## 3.2. Interpretation and Analysis of the Ologist Regression Model of Different City Types’ Preference for Sports Programs

The relevant analysis results in Table2 revealed significant urban-type differences in sports program preference groups, suggesting considerable differences in sports program preference groups in urban-type fields. Thus, whether the characteristics of this difference are consistent or have their own characteristics remains unclear. Hence, this research established models of different types of cities in response to this question.From the comparative analysis results of city types shown in Table3, from the model coefficients and statistical test results, some differences were found in the preference for sports programs in different city types. Specifically, in small towns, only the 46–60 age group was significant, which could be related to the relatively small economic pressure and stable working conditions of such groups in small towns. In medium-sized cities, social capital and economic capital were significant and consistent with the national model; this could be because the mean value of the national model was consistent with that of medium-sized cities. Meanwhile, compared with the inadequate public services and market development of smaller cities, the development of service facilities and the sports industry market has played a role in the field change mechanism for the increase of sports program preference groups, especially the upsurge in social attributes would increase the separation of social capital and economic capital. In large cities, in age groups, the upper 31–45 age range was significant. In large cities, both social capital and economic capital were much higher than small- and medium-sized cities; however, only differences were found in age groups, suggesting the preference for sports programs in large cities. The field was the social result created by various social reasons squeezing each other. In various types of cities, the explanatory power of educational capital was insignificant, suggesting that educational capital exerted no impact on the creation of the stratification mechanism for sports program preferences in various cities. Overall, neither population model, social capital, educational capital, or economic capital exerted an independent influence; this demonstrates that people who liked sports programs had different stratification mechanisms in different types of cities.


## 4. Result

Based on Bourdieu’s “field-habituation” cultural structure theory, this study identified the audiences who like sports programs using the ologist regression analysis of the social awareness survey data of netizens. Precisely, from the viewpoint of the national model, the major findings and conclusions are as follows. First, the demographics of audiences who like sports programs are markedly different, suggesting that the audiences of sports programs have notable differences under the “field-habituation” theory. In addition, social division demonstrates that social history and culture play a vital role in the preferences of sports programs, and it also depicts the existence of gender inequalities and regional inequalities in Chinese society. Second, the impact of educational capital and social capital on the audience of sports programs is not completely positive, which also demonstrates that the “field” of sports programs has boundaries, and the impact of “habituation” is also in a certain “field.” Nevertheless, technical personnel and those with a university degree prefer watching sports programs, implying that sports programs warrant certain social understanding and emotional decoding in cultural transmission and program appreciation. Third, economic capital exerts an independent and continuous impact on the audience of sports programs, suggesting that economic factors remain as restrictive factors for the present social development in China. Besides, it demonstrates that economic divisions are higher than social divisions and educational divisions. At this stage, the development of the sports media industry remains a prerequisite for promoting national fitness or augmenting the level of socioeconomic development. Fourth, when looking at different types of cities, the impact mechanisms of different types of cities are different, showing that the field forms various social divisions in sports program preferences.


## 5. Conclusions

The empirical analysis results explained that people are in society, and any individual behavior is profoundly motivated by the “field” and class “habituation” of the small world, “field,” “habituation,” and “Behind the hidden lies the joint effect of social inequality, economic capital, cultural capital, and social capital, which is the reproduction of social and cultural structures.” Indeed, this study concludes that for the sports media industry, the keywords of the audience portrait of sports programs have been discovered, namely, male, city, 30–45 years old, university degree, technical personnel, and financial freedom. Through such a portrait of the media industry that forms the program, one can select female hosts, values with cultural tastes, content presentation methods that release social pressure, and audience interaction methods for new formats. For different types of city “fields,” sports program audiences could exhibit significant differences in characteristics. Moreover, this study reveals that favorite sports programs are the result of multiple social effects, and it is impossible to simply apply sports and sociological theories for research. Thus, it is essential to highlight the analysis of different factors such as “habituation” and “field.”This study has some limitations worth acknowledging. First, the sample was obtained from the “Netizens’ Social Awareness Survey” conducted by the Renmin University of China, which is not a questionnaire developed for this research question. Second, the research variables were not comprehensive, and the sample size was inadequate. Besides, the research conclusions could be improved further. However, theoretically and logically, it can be estimated that liking sports programs does not seem to have considerable isolation on consumption barriers. Indeed, through the impact of multiple “fields” and variables in social life, people’s liking for sports programs has created a social division. Finally, limited by the data itself, this study does not have differences in the analysis of the impact mechanism of specific cities and regions under the supply of public sports services; this is also a crucial topic for future research.



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