Research ArticlePSYCHOLOGY

Moving chairs in Starbucks: Observational studies find rice-wheat cultural differences in daily life in China

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Science Advances  25 Apr 2018:
Vol. 4, no. 4, eaap8469
DOI: 10.1126/sciadv.aap8469
  • Fig. 1 About 90% of the people in Starbucks were from the local rice or wheat cultural region.

    The Human Development Index is a United Nations index of health, education, and wealth for 2015. GDP per capita data are from 2013, converted to U.S. dollars. The population density is as of 2013.

  • Fig. 2 Percentage of people sitting alone in cafes.

    People in the wheat area were more likely to be sitting alone on weekdays (left) and weekends (right). Bars represent 1 SEM.

  • Fig. 3 People were more likely to be alone earlier in the day, although rice-wheat differences persisted across the day.

    Yellow represents wheat region; green represents rice region. Bars represent 1 SEM.

  • Fig. 4 Demonstrations of the chair-moving test.

    A research assistant demonstrating how difficult it is to walk through the chair trap (left). To standardize chair width, researchers set the chairs to the width of their hips. Researchers only used light wooden chairs like these (right) to set the chair traps, never large stools or large plush chairs like those in the background of the picture.

  • Fig. 5 People in wheat areas were about three times more likely to move the chair than people in rice areas.

    Bars represent 1 SEM.

  • Fig. 6

    Employees were about five times more likely than customers to move the chair (left). Among customers, men were more likely than women to move the chair (center). Comparing China, Japan, and the United States, Americans were about twice as likely to move the chair (right). Bars represent 1 SEM.

  • Table 1 Rice-wheat differences in sitting alone.

    Rice-wheat differences in sitting alone.. Note that day of the week is coded numerically: Monday, 1 to Sunday, 7. Time of day is rounded to the nearest hour. Model is a hierarchical linear model (HLM) using the binomial GLMER function. Data are grouped at the city level in each model except the model with district GDP per capita. Table S10 presents models with districts nested within cities.

    B/γSEzP
    Percent rice−0.420.16−2.570.010
    Time of day−0.070.01−6.55<0.001
    Day of the week−0.070.01−6.46<0.001
    Percent rice−0.430.14−2.970.003
    Time of day−0.070.01−6.77<0.001
    Day of the week−0.070.01−6.38<0.001
    International chain0.170.063.050.002
    Percent rice−0.450.17−2.700.007
    Time of day−0.060.01−6.60<0.001
    Day of the week−0.070.01−6.62<0.001
    City GDP per capita0.0180.0053.380.001
    Percent rice−0.560.09−6.37<0.001
    Time of day−0.070.01−5.50<0.001
    Day of the week−0.070.01−5.82<0.001
    City population density0.020.030.590.559
    Percent rice−0.510.22−2.330.020
    Time of day−0.070.01−5.65<0.001
    Day of the week−0.070.01−5.87<0.001
    District GDP per capita0.0100.0061.740.082
    Percent rice−0.530.16−3.390.001
  • Table 2 Rice, GDP, and demographic predictors of chair moving.

    Note that models are HLMs using the binomial GLMER function. Data are grouped at the city level, except for the bottom two models, which are grouped at the district level. See table S11 for models with districts nested in cities.

    B/γSEzP
    Percent rice−1.860.44−4.19<0.001
    Employee1.930.306.52<0.001
    Below 40 years old0.030.380.090.928
    Female (civilians only)−1.060.44−2.410.016
    Employee2.030.316.57<0.001
    Female−0.460.30−1.510.131
    Percent rice−2.020.48−4.24<0.001
    Employee2.030.355.80<0.001
    Female−4.530.31−1.470.141
    City GDP per capita (10,000 RMB)−0.020.17−0.130.895
    Percent rice−1.990.51−3.91<0.001
    Employee2.070.326.53<0.001
    Female−0.460.32−1.460.145
    District GDP per capita (10,000 RMB)0.020.020.940.347
    Percent rice−2.300.62−3.69<0.001
    Employee2.100.326.65<0.001
    Female−0.410.32−1.270.205
    District population density−0.010.10−0.120.902
    Percent rice−1.960.74−2.640.008

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/4/4/eaap8469/DC1

    fig. S1. Sample chair trap in a Starbucks in Shanghai.

    table S1. Are people in international chains more likely to be sitting alone?

    table S2. Rice-wheat differences controlling for international chain.

    table S3. Sitting alone and GDP.

    table S4. Sitting alone and district-level data.

    table S5. Basic predictors of chair moving.

    table S6. City and district census predictors of chair moving.

    table S7. International comparison of chair moving.

    table S8. How well do other major theories of culture predict sitting alone?

    table S9. How well do other major theories of culture predict chair moving?

    table S10. Sitting alone models with stores nested in districts nested in cities.

    table S11. Chair moving models with stores nested in districts nested in cities.

    table S12. Chair moving models with stores nested in districts nested in cities.

    section S1. Rice statistics

    section S2. Chair moving

    section S3. Controlling for observer

    section S4. Hong Kong GDP per capita

    section S5. Age in districts

    section S6. Calculating effect sizes in GLMER

    section S7. Graphing mean percent sitting alone

    section S8. GDP per capita

    section S9. Alternative predictors

    section S10. Chair moving validity checks

    section S11. Ethics statement

    References (41)

  • Supplementary Materials

    This PDF file includes:

    • fig. S1. Sample chair trap in a Starbucks in Shanghai.
    • table S1. Are people in international chains more likely to be sitting alone?
    • table S2. Rice-wheat differences controlling for international chain.
    • table S3. Sitting alone and GDP.
    • table S4. Sitting alone and district-level data.
    • table S5. Basic predictors of chair moving.
    • table S6. City and district census predictors of chair moving.
    • table S7. International comparison of chair moving.
    • table S8. How well do other major theories of culture predict sitting alone?
    • table S9. How well do other major theories of culture predict chair moving?
    • table S10. Sitting alone models with stores nested in districts nested in cities.
    • table S11. Chair moving models with stores nested in districts nested in cities.
    • table S12. Chair moving models with stores nested in districts nested in cities.
    • section S1. Rice statistics
    • section S2. Chair moving
    • section S3. Controlling for observer
    • section S4. Hong Kong GDP per capita
    • section S5. Age in districts
    • section S6. Calculating effect sizes in GLMER
    • section S7. Graphing mean percent sitting alone
    • section S8. GDP per capita
    • section S9. Alternative predictors
    • section S10. Chair moving validity checks
    • section S11. Ethics statement
    • Reference (41)

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