Group Case Paper 2-1

MAR 4503 Consumer Behavior
February 7th, 2014.

b.
Taking in consideration Figure 7–9, the first half of the 5 percent of the population would fall in the category of innovators, as they are the quickest to adapt to an innovation. Consequently, the remaining 2.5 percent would fall into the early adopters category, which accounts for next 13.5 percent to adopt.

#4

Important Note: Public government data on rural China are limited. To compensate, I have adapted and extrapolated public data on urban China that is available. As critical factors for automobile adoption I have used: per capita income, paved road infrastructure, demographics, lifestyle and values. The sources for the data are the International Monetary Fund (IMF) and University of Virginia.
Basis for Adoption Curves: (1) According to the IMF, as shown in the graph, probability of a household owning an automobile is 0% before reaching per capita income over $5,000 USD. (2) After $11,000 USD in per capita income, the ability of a household to acquire an automobile reaches 100%. (3) According to the National Bureau of Statistics, in 2012 China’s rural disposable income was only $1,306 USD (7,917 Yuan) and China’s urban income was $4,051 USD (24,565 Yuan). We can appreciate how rural disposable income is 40% less than urban disposable income. Using this relation, we can adjust the income levels and adoption curve in rural China. (4) According to the Chicago Journal, about 61% of intra-village roads in rural China are paved and automobile ready. This is a ratio of 2:3 or 61:100 (rural:urban) compared to the 100% level of intra-city, paved roads in urban China. As paved roads predetermine automobile adoption, we can deduce that adoption in rural China will be 1/3 slower than in urban China (visible when comparing both curves). (5) Lastly, since lifestyles and values in rural China are way more conservative than in urban China, and thus less primed to changing from older, traditional ways of transportation (i.e. bicycle), we will adjust the previous 2:3 ratio on argument “(4)” to 1:2 ratio (25% decrease) to reflect lifestyle effect on adoption. Hence, an urban income level “x” that results in a 20% adoption rate, will only result in a 10% adoption rate for the same income level in rural China. Thus reflecting the cumulative effect of lack of road infrastructure and more conservative lifestyle values. Reference page.
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