Given Names Change Consumption Decisions: Evidence from the Female Body Image Market in China

doi:10.21203/rs.3.rs-1944631/v1

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Given Names Change Consumption Decisions: Evidence from the Female Body Image Market in China

https://doi.org/10.21203/rs.3.rs-1944631/v1

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Names play important roles in Chinese culture; people often obtain their first impression of others by interpreting their names. As “feminine qualities” are favored in the naming of girls, many females receive names with implications of beauty in China. How do these names impact the life of Chinese females? In this paper, which uses survey data from 314 Chinese female as a sample, we tested the impact of beauty in a name on people’s consumption decisions in the body image market. According to the results of a ordinary linear regression, we found that a direct implication of beauty in names results in an increase of 9.51k CNY in appearance improvement consumption on average, which is approximately 43% of the average spending in appearance improvement in our sample.. Additionally, with mediation analysis, we found that this impact is partly caused by the Chinese culture in which people obtain impressions of others through their given names.

Psychology

naming

body image

given name

Chinese name

beautiful

Names are meaningful, and many of them have implications. Alice means noble and kind, Della means bright, Charlie means freedom, and David means beloved. Although many parents pick names with love, wishes, and expectations, people usually do not expect others to know the implications behind all the names. In most cases, people only see the meaning and implications of the names of their family, best friends, and themselves.

However, it is an entirely different story in many Asian cultures (such as in China, Korea, Japan, Vietnam, and Singapore) where two random people can usually tell the implications, expectations, and wishes behind the name of one another.

Taking China as an example, their names are much more straightforward, as people use characters as the basic units of the language (as words in English). As names are just words or combination of words we use in our everyday life, it is straightforward to derive the implication of the names (Su and Ren 2015). For example, my childhood friend is “Cong (smart) Chen”; everyone understands that his parents wish their child to be talented. In other words, most Chinese names are as straightforward as English names like “Snow-white”, so there is very little confusion.

Therefore, Asian names have a much more significant impact on people’s social lives than English names. You can hardly imagine people saying, “Your name is Alice, so you must be kind.” However, if “Cong” is naughty and doing poorly at his study, it is pervasive for people to say, “Think of your name, you should do better”; in some cases, people will even make fun of his name, such as “You failed the exam again, you shouldn’t be named Cong Chen, your name should be Chun (dumb, stupid) Chen.”

As a given name is believed to have the power of motivating and inspiring individuals, Asian parents usually bestow given names that are meaningful and that bear their wishes (Gao 2011). For boys, their parents often wish them to be successful in the future and give them names such as Cong (Chinese, bright), Xun (Chinese and Korean, honor), and Ki (Japanese, shining); “feminine qualities” are favored in the naming of girls, and many famous names contain elements of “beauty” (Gao 2011). They appear in the name either in the form of characters and words that mean beautiful, such as Mei or Li (Chinese, attractive), Yeon (Korean, beautiful), Mi (Korean and Japanese, beautiful), and Yuan (Chinese, beauty), or in the form of beautiful things such as flowers and jades, such as Hua (Chinese, flower) and Sakura (Japanese, cherry).

In addition, unlike some given names related to specific groups of people (i.e., usually only people in the less developed areas name their children “money”), “beautiful” names are prevalent across all social classes. You can find them everywhere in a local community, but people in higher social classes use them as well. For example, the “first lady” of China, Liyuan Peng, has both Li (beautiful) and Yuan (beauty) in her name; Princess Ka-ko in Japan has Ka in her name which means beautiful.

Just as the “Congs” are expected to be brilliant at school, it is reasonable that people also hope the Meis, Lis, and Yuans to have an attractive appearance. Do these names influence their owners’ attitude toward appearance? For example, do the owners of these names care more about their appearance and compare themselves more with others? Moreover, do these names increase their owners’ demand for goods and services that improve their appearance?

Although the Chinese and other Asian names have long been studied in the onomastics setting (Goldin 2000, Kawashima 2010, Zhongti and Millward 1989), the power of them on economic decisions is barely tested. Social scientists and linguistics such as Stanley Lieberson conducted early research of given names and focused on the parental choice of names in the United States over time (Lieberson and Bell 1992, Lieberson 1995). Later, economists found first names to be predictors of many lifetime economic outcomes, such as education, happiness, and early fertility (Aura and Hess 2010).

However, there has been almost no follow-up on the impact of first names in economics until recently. In population economics, Jurajda and Kovac used first names to predict people’s behavior in a war based on data concerning approximately one-half million soldiers and showed that having been given a first name that is synonymous with the leader(s) of the Croatian state during World War II predicted volunteering for service in the 1991–1995 Croatian war of independence and dying during the conflict (Jurajda and Kovac 2021).

Moreover, to the best of our knowledge, the study most relevant to our study is an economic study about Chinese given names, where CEOs with a given name that has a moral meaning were found to be more corporative in investment decisions on environmental protection (Jia et al. 2021). We believe that the impact of given names in economics is an important field that remains undeveloped, as names play an essential role in our personal and social life. Notably, the relationship between names and one’s consumption preference should be of great interest to economists and industries in the big data era.

This study seeks to address the research gap with a special dataset: female given names in China and the appearance improvement consumption of the names’ owners. We choose this dataset as “beauty” elements are popular among female given names, and appearance improvement (such as cosmetic and apparel) is a huge market of great economic importance.

We aim to address the following research question: do names with beauty implications increase people’s spending on appearance improvement? To study the above research question, we conducted an online survey with observations of given names and appearance-related consumption in the past year. The remaining part of the paper proceeds as follows. The next section explains our study design, Section 3 presents the results, and Section 4 concludes.

A total of 314 Chinese females were invited to participate in the study, which involved an online survey (9 data were removed due to both a lack of quality and significant outliers). Given the importance, representativeness, and scientific nature of the survey, this study signed an agreement with Survey Star, a professional data collection platform in China, and commissioned it to survey in China for payment. To improve the validity of the questionnaire, we informed them that the research results would only be used for scientific research purposes and emphasized the importance of careful and truthful completion. Informed consent was obtained from all participants.

Measurements and Sample Distributions

In the study, we used the following measurement/variables.

Dependent variables

Spending on appearance improvement (SA): The participants were asked about their SA in the following 6 categories: clothing and jewelry; beauty salons; cosmetics and skincare; food and supplements for appearance; fitness; and others. The amounts are measured in CNY (1 USD = 6.37 CNY). The latest disposable income per capita in China is 32,189 CNY (and 43,834 CNY as urban residents) as of 2020. The detailed descriptive statistics of spending on appearance improvement (SA) are shown in Table 1. In the rest of the paper, we use SA1 to SA6 for the subcategories of SA.

Table 1

Descriptive Statistics of spending on appearance improvement (SA)
	Min	Mean	Med	Max	Sd	SE
All (SA)	198	22591.70	13000	181500	26517.17	1521.23
Clothing and jewelry (SA1)	99	8947.78	5000	90000	12503.70	715.96
Beauty salons (SA2)	0	2219.15	1000	100000	6130.55	351.03
Cosmetic and skincare (SA3)	0	2438.81	500	35000	4390.47	251.81
Food and supplement for appearance (SA4)	0	2592.50	1000	45000	4545.28	260.26
Fitness (SA5)	0	1746.40	800	20000	2707.98	155.05
Other (SA6)	0	4697.67	2000	60000	8454.33	484.09

Level of care in appearance (LCA): The participants were asked to rank on a 5-point Likert scale about how much they cared for others’ ideas about their appearance. The level is recorded from 1 (I don’t care at all) to 5 (I truly care about it). The average LCA is 2.89, with a standard deviation of 0.85.

Independent variables of interest

The implication of beauty (IB): We categorized the names of participants into the following three categories: the direct implication of beauty, coded as “2” (contains characters or words that mean beautiful or beauty); the indirect implication of beauty, coded as “1” (contains characters or words that are symbols of beautiful or beauty); and no implications of beauty, coded as “0”; the numbers of the samples in the three categories are 235, 37, and 33, respectively.

Control variables

Living condition (LC): The participants were asked who they lived with. The options were living with parents (coded as 0), living by her/himself (including living in dorms as a student, coded as 1), and living with spouse and/or children (coded as 2). There was also an option, “other,” with one observation, which we removed from our dataset. The numbers of the samples in the living with parents, living by her/himself, and living with spouse and/or children categories are 73, 65, and 167, respectively.

Age: The age of the participants ranged from 18 to 53 years, with an average of 29.4 years (Sd = 6.20).

Job: We offered 14 categories of jobs to choose from, which are the default options in most Chinese questionnaires about jobs. Job-1: students are the reference category in our regressions.

Education level (EL): Six levels of education were given from “below high school” (1) to “Doctorate, including doctoral student” (6). The median and mode education levels are both 4: 4-year college degree and students. The participants were also asked about their parents’ education levels, which were coded as ELF for fathers and ELM for mothers.

Disposable income (Income): The participants were also asked about their monthly disposable income in 6 levels. The monthly disposable income includes all the money one can spend after essentials, and it includes gifted money from parents and others (e.g., pocket money for students). Most participants were in the range of level 3, which is 60,000 to 120,000 CNY annually.

Main results

Table 2 displays our main results (we switched the unit to 1k CNY). Column 1 presents the OLS results with Spending on appearance improvement (SA) as dependent variable, the implication of beauty (IB) as independent variable of interest; jobs, education levels of the responders and their parents, living conditions, and income as control variables. Detailed information of the variables is stated as footnotes under the table. Some levels of the variables are not shown in the table as we do not have any observations, for example, none of the mothers hold a doctorate degree.

The results suggest a direct implication of beauty in names results in an increase of 9.51k CNY in appearance improvement consumption on average, which is approximately 43% of the average spending in appearance improvement in our sample.

Other than the independent variable of interest, we also found that some particular jobs, income, and the education level of the responders’ mothers are predictive in the model. We will discuss these results in detail in the discussion section.

Column 2–5 present the OLS results with the same independent variables in each submarket; in other words, the dependent variables are spending on clothing and jewelry (SA1) spending on beauty salons (SA2), spending on cosmetic and skincare (SA3), spending on food and supplement for appearance (SA4), and spending on fitness (SA5). We didn’t conduct a regression for spending on other appearance improvement categories (SA6) because it’s less informative.

The results in Column 2–5 suggest that a direct implication of beauty in names is also significant regressor in three of the sub-categories: clothing and jewelry (SA1), beauty salons (SA2), and food and supplement for appearance (SA4). The cosmetic and skincare market (A3) seems unaffected, the information is important as it’s the second largest submarket in appearance improvement according to our data.

Table 2

Spending on appearance improvement (SA) and its subcategories (SA1-5) – OLS results
Response variable	SA	SA1	SA2	SA3	SA4	SA5
Intercept	21.41* (10.49)	6.85 (5.12)	1.90 (2.78)	3.91^* (2.00)	1.29 (1.09)	0.94 (1.87)
IB-1	1.2 (4.18)	2.19 (2.04)	0.07 (1.10)	0.41 (0.79)	-0.71 (0.43)	0.36 (0.74)
IB-2	9.51* (4.61)	4.83* (2.25)	3.51** (1.22)	1.32 (0.88)	0.86^* (0.48)	1.3 (0.82)
Job-2	4.08 (7.19)	1.43 (3.51)	0.65 (1.90)	0.97 (1.37)	-0.03 (0.75)	0.63 (1.28)
Job-3	10.43 (7.81)	3.15 (3.81)	-0.32 (2.07)	0.92 (1.49)	0.7 (0.81)	1.54 (1.39)
Job-4	14.73 (9.5)	2.23 (4.63)	0.88 (2.51)	2.23 (1.81)	0.76 (0.99)	0.98 (1.69)
Job-5	-1.04 (9.06)	-2.3 (4.42)	-1.34 (2.40)	-0.03 (1.73)	0.24 (0.94)	0.93 (1.61)
Job-6	13.14 (12.09)	7.18 (5.89)	2.65 (3.20)	1.60 (2.30)	0.39 (1.26)	1.91 (2.15)
Job-7	45.79** (16.06)	23.89** (7.83)	3.98 (4.25)	-3.47 (3.06)	3.91* (1.67)	13.18*** (2.86)
Job-8	19.36* (9.19)	10.2* (4.48)	-0.38 (2.43)	1.52 (1.75)	1.06 (0.96)	1.79 (1.64)
Job-11	23.75 (18.19)	5.74 (8.87)	2.95 (4.82)	2.58 (3.47)	0.5 (1.89)	2.75 (3.24)
Job-14	19.98 (24.29)	7.58 (11.85)	0.01 (6.43)	-0.25 (4.63)	4.01 (2.53)	9.84* (4.33)
Age	-0.21 (0.26)	-0.03 (0.12)	0.02 (0.06)	-0.08 (0.05)	0.01 (0.02)	0.00 (0.04)
EL-2	14.71 (15.99)	7.55 (7.8)	2.44 (4.23)	-0.5 (3.05)	1.46 (1.66)	3.48 (2.85)
EL-3	-9.45 (14.21)	-5.01 (6.93)	-1.45 (3.76)	1.66 (2.71)	-0.9 (1.48)	-1.23 (2.53)
EL-4	4.75 (11.13)	3.47 (5.43)	-0.41 (2.95)	-1.59 (2.12)	0.16 (1.16)	0.84 (1.98)
EL-5	-1.66 (7.89)	0.04 (3.85)	0.28 (2.09)	0.55 (1.50)	0.49 (0.82)	0.03 (1.4)
EL-6	4.91 (4.71)	2.62 (2.29)	0.31 (1.24)	0.22 (0.89)	-0.02 (0.49)	0.78 (0.84)
ELF-2	-17.00 (20.9)	-6.39 (10.19)	0.34 (5.53)	0.76 (3.98)	0.75 (2.18)	-1.42 (3.73)
ELF-3	-17.99 (16.51)	-3.9 (8.05)	-2.44 (4.37)	-0.29 (3.15)	-0.12 (1.72)	-2.15 (2.94)
ELF-4	-2.05 (11.12)	-0.79 (5.42)	0.56 (2.94)	1.34 (2.12)	0.00 (1.16)	-1.21 (1.98)
ELF-5	5.99 (7.44)	2.2 (3.63)	1.54 (1.97)	1.44 (1.42)	0.04 (0.77)	0.54 (1.32)
ELF-6	-4.55 (4.66)	-0.57 (2.27)	-1.46 (1.23)	-0.64 (0.88)	-0.65 (0.48)	-0.42 (0.83)
ELM-2	16.32 (14.14)	8.45 (6.89)	-0.42 (3.74)	-0.91 (2.69)	-0.47 (1.47)	1.48 (2.52)
ELM-3	18.93^* (11.19)	5.28 (5.46)	3.15 (2.96)	-0.76 (2.13)	0.15 (1.16)	2.08 (1.99)
ELM-4	11.29^* (6.75)	4.6 (3.29)	0.33 (1.79)	-0.67 (1.28)	0.01 (0.7)	1.81 (1.2)
ELM-5	-9.71* (4.22)	-3.13 (2.06)	-1.99^* (1.11)	-2.24** (0.80)	-0.95* (0.44)	-1.09 (0.75)
LC-2	-3.18 (4.28)	-0.79 (2.09)	-2.23^* (1.13)	-0.78 (0.81)	-0.25 (0.44)	-0.55 (0.76)
LC-3	-0.85 (4.07)	0.44 (1.98)	-2.36* (1.07)	0.02 (0.77)	-0.12 (0.42)	-0.07 (0.72)
Income-2	39.12*** (6.94)	17.21*** (3.39)	5.26** (1.84)	3.25* (1.32)	4.13*** (0.72)	3.98** (1.24)
Income-3	8.74 (5.32)	6.41* (2.59)	-0.44 (1.40)	-0.10 (1.01)	1.51** (0.55)	0.7 (0.94)
Income-4	-0.32 (4.52)	1.22 (2.2)	-0.45 (1.2)	-0.77 (0.86)	0.67 (0.47)	-0.43 (0.8)
Income-5	-0.04 (3.91)	-0.02 (1.91)	0.33 (1.03)	-0.88 (0.74)	0.13 (0.4)	-0.05 (0.69)
Income-6	4.71 (3.06)	1.57 (1.49)	1.50 (0.81)	-0.12 (0.58)	0.46 (0.31)	1.43** (0.54)
Significant levels: 1 “” 0.1 “^” 0.05 “” 0.01 “” 0.001 “*” 0 The columns 2–6 are: Clothing and jewelry (SA1) Beauty salons (SA2) Cosmetic and skincare (SA3) Food and supplement for appearance (SA4) Fitness (SA5). The educations levels of the responders and their parents, EL, ELF(ather),ELM(other) are leveled as: 1 (under high), 2(high school), 3(associate), 4 (bachelor), 5 (master), 6(doctorate). The job titles are: 1 (student), 2 (enterprise office staff), 3 (enterprise manager), 4 (government), 5 (professional), 6 (service), 7 (household business), 8 (freedom worker), 9(factory worker), 10 (farmer/producer), 11 (housewife), 12 (retired), 13 (unemployed), 14 (other). The income levels are: 1 (under 3000), 2 (3000–5000), 3(5001–10000), 4(10001–15000), 5 (15001–20000), 6 (above 20000), the unit is CNY/month.

Mediation analysis results

As we discussed in the introduction section, people in Chinese culture are significantly impacted by their names in their social life; one of the most important sources of these impacts is filling others’ expectations. As our names contain the expectations and wishes of our parents, we want to show people that we are worthy of the characters (Su and Ren 2015).

Therefore, we asked the following question: does the impact of the implication of beauty (IB) on consumption come from caring about the opinions of others (measured by level of care in appearance, LCA)?

Column 1 in Table 3 presents the results of the OLS regression with level of care in appearance (LCA) as the dependent variable. The independent variables are the same as we had in the main results. The result suggests a direct implication (IB-2) increases LCA significantly by 0.38, on average.

Column 2 comes from the main regression results in Table 2 for comparison purpose; Column 3 represents the results after adding LCA as an extra control variable to the regression for Column 2. The results suggest that the direct implication is still predictive for the spending on appearance improvement, but less significantly and decreased in amount. Additionally, we found that every extra level of LCA increases spending on appearance improvement by 4.18k CNY.

Table 3

Impact of the implication of beauty (IB) on LCA and the impact of Level of care in appearance (LCA) on SA – OLS results
Response variable	LCA	SA (Without LCA)	SA (With LCA)
Intercept	1.62*** (0.39)	21.41* (10.49)	28.16** (10.69)
IB-1	0.12 (0.15)	1.2 (4.18)	0.71 (4.14)
IB-2	0.38** (0.17)	9.51* (4.61)	7.94^* (4.6)
Job-2	-0.37 (0.27)	4.08 (7.19)	5.64 (7.14)
Job-3	-0.21 (0.29)	10.43 (7.81)	11.32 (7.74)
Job-4	0.05 (0.35)	14.73 (9.5)	14.54 (9.4)
Job-5	0.05 (0.34)	-1.04 (9.06)	-1.22 (8.97)
Job-6	0.39 (0.45)	13.14 (12.09)	11.54 (11.97)
Job-7	-0.45 (0.6)	45.79** (16.06)	47.71** (15.9)
Job-8	0.07 (0.34)	19.36* (9.19)	19.08* (9.1)
Job-11	-1.02 (0.68)	23.75 (18.19)	28.05 (18.07)
Job-14	-0.03 (0.91)	19.98 (24.29)	20.14 (24.03)
Age	0.00 (0.00)	-0.21 (0.26)	-0.18 (0.26)
EL-2	0.23 (0.6)	14.71 (15.99)	13.76 (15.82)
EL-3	0.07 (0.53)	-9.45 (14.21)	-9.73 (14.06)
EL-4	0.37 (0.41)	4.75 (11.13)	3.24 (11.03)
EL-5	0.54^* (0.29)	-1.66 (7.89)	-3.88 (7.85)
EL-6	0.03 (0.17)	4.91 (4.71)	4.8 (4.66)
ELF-2	-0.52 (0.78)	-17.00 (20.9)	-14.83 (20.7)
ELF-3	-0.13 (0.62)	-17.99 (16.51)	-17.44 (16.34)
ELF-4	-0.08 (0.41)	-2.05 (11.12)	-1.71 (11)
ELF-5	-0.02 (0.28)	5.99 (7.44)	6.12 (7.36)
ELF-6	0.06 (0.17)	-4.55 (4.66)	-4.78 (4.61)
ELM-2	1.03^* (0.53)	16.32 (14.14)	12.03 (14.08)
ELM-3	0.52 (0.42)	18.93^* (11.19)	16.78 (11.11)
ELM-4	0.38 (0.25)	11.29^* (6.75)	9.73 (6.7)
ELM-5	0.14 (0.15)	-9.71* (4.22)	-10.25* (4.18)
LC-2	0.00 (0.16)	-3.18 (4.28)	-3.17 (4.23)
LC-3	0.09 (0.15)	-0.85 (4.07)	-1.2 (4.02)
Income-2	0.59* (0.26)	39.12*** (6.94)	36.68*** (6.93)
Income-3	-0.09 (0.2)	8.74 (5.32)	9.14^* (5.26)
Income-4	0.05 (0.17)	-0.32 (4.52)	-0.51 (4.48)
Income-5	0.12 (0.14)	-0.04 (3.91)	-0.51 (3.88)
Income-6	0.00 (0.11)	4.71 (3.06)	4.71 (3.03)
LCA	-	-	4.18** (1.59)
The columns 2–6 are: Clothing and jewelry (SA1) Beauty salons (SA2) Cosmetic and skincare (SA3) Food and supplement for appearance (SA4) Fitness (SA5). The educations levels of the responders and their parents, EL, ELF(ather),ELM(other) are leveled as: 1 (under high), 2(high school), 3(associate), 4 (bachelor), 5 (master), 6(doctorate). The job titles are: 1 (student), 2 (enterprise office staff), 3 (enterprise manager), 4 (government), 5 (professional), 6 (service), 7 (family business), 8 (freedom worker), 9(factory worker), 10 (farmer/producer), 11 (housewife), 12 (retired), 13 (unemployed), 14 (other). The income levels are: 1 (under 3000), 2 (3000–5000), 3(5001–10000), 4(10001–15000), 5 (15001–20000), 6 (above 20000), the unit is CNY/month.

Moreover, we performed a mediation analysis with standardized coefficients to better understand the relationship, the detailed results are reported in Table 4. The results shows that LCA can partially explain the impact of IB on SA.

Table 4

Path coefficients for mediation models and test results (standardized)
	Path Coefficients		Causal Mediation Analysis
	To LCA	To SA	Estimate	95% CI Lower	95% CI Upper
Beauty	0.440* (0.203)	0.299^* (0.173)
LCA		0.134** (0.051)
ACME			0.060^*	-0.001	0.140
ADE			0.299^*	-0.029	0.063
Total Effect			0.359*	0.031	0.069

In this paper, we conducted a survey to test the impact of beauty in one’s name on people’s consumption decisions in the market for appearance improvement. We found that having a direct implication of beauty in names, such as “Mei,” “Li,” and “Hong,” significantly increases their spend in appearance improvement. The result is also significant in the 3 submarkets of clothing and jewelry, beauty salons, and fitness (but failed in the cosmetics and skincare market).

Additionally, we found that some jobs, income, living conditions (who people live with), and more importantly, the education levels of their mothers also have significant results. Interestingly, one’s own education level and age do not seem to play a role in one’s decisions.

For example, working in household business also significant influenced SA and some of its submarkets. The reason for this notable impact is that household business owners (or their wives) need to give signals that their businesses are in good condition to persuade people to conduct business with them. Many household business owners even purchase luxury dressing, houses, and cars when they are in debt (Joy et al. 2020, Correia, Kozak, and Kim 2018).

Another interesting result is that the age coefficients are very close to 0. Since our samples include participants with a broad range of ages (18–53 years), we believe that the market for appearance improvement is attracting them despite their age. There is also no evidence that the education level of the participants affects their SA.

The most unexpected but reasonable result is that the education levels of the participants’ mothers have a significant impact on their SA, especially in the cosmetic submarket (see ELM3-5). On average, the participants spend 9.71 k less for appearance when their mothers have graduate degrees. On the one hand, this implies that mothers have a much greater impact on daughters’ attitudes toward appearance improvement consumption than fathers (all ELF levels make no change of statistical significance); on the other hand, it reflects some general opinions in the past of China. Until recent decades, caring for appearance has been regarded as something wrong in schools. Even today, it has been written in some school regulations that using cosmetic/dye hair/tattoo is a serious violation of rules that may result in dismissal from school.

Although there are little academic discussions of this situation, thousands of news reports can be found on the internet. For example, at a school in Xiamen, students are not allowed to curl or dye their hair. Students with naturally curly or light hair need a document from the local hospital that proves it is natural (Koetse 2016). In another middle school in Guizhou, a teacher stand at the school gate with a dishcloth and a bucket of water to wipe cosmetics off female students' faces, and a survey on Weibo (China's equivalent to Twitter) shows 64 percent of participants agree with the school's policy (Daylimail.com 2019).

The situation was more severe in the old years when the mothers were in school. Teachers would even shout at female students who use cosmetics: “Do you come to school for study or prostitution?” As the old schools are so against consumption in appearance improvement, well-educated mothers are also often on the other side of the market. The only exception is that some of these mothers feel okay with buying nice-looking clothes for their daughters, but they definitely say “No” to cosmetics and salons, which they believe to be associated with street girls.

The last but exciting result is that income level 2 significantly impacts SA (and all of its submarkets), but it does not apply to levels 3, 4, 5, and 6. We can see that these consumers truly cannot wait to spend money on their appearance once they have spare money.

We also found that the impact can partly be explained by the fact that having IBs in one’s name increases the level of care about others’ judgment of one’s appearance due to the culture; however, this is not the only reason.

There are some limitations of our study. First, the consumption data we use are recollections of the responders, so there can be some biases, the results can be more convincing if we can obtain more accurate data (e.g., credit card history); second, although we explained the data will be used for research purpose only and their names will not be published, some responders worrying about information leakage might use fake names.

Although our findings are only about Chinese names and the body image market, they can easily be extended to other implications of other Asian names and the corresponding markets. As the name data of customers are currently much more accessible, we believe that the studies that follow ours will be an essential topic in marketing research that work as guidance to the industries where customers’ names can be obtained.

Conflict of Interest: The authors have no conflict of interest of any kind.

Funding: We did not receive any funding.

Ethical approval: The study is approved by IBS at Zhejiang Gongshang University. Informed consent was obtained from all participants.

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Given Names Change Consumption Decisions: Evidence from the Female Body Image Market in China

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