Investigation 3- Consumer Preferences
Introduction:
Recently there have been massive social movements shaming businesses for practices
that cause environmental and social harm, including contributing to carbon emissions and unfair
labor practices. However, for profitable businesses to abide by these standards, it is often
necessary to increase product prices because production costs increase. Because consumer
preferences so profoundly influence business choices, researchers at IBM surveyed
perspectives on environmental sustainability and whether consumers are willing to accept price
premiums for products that are labeled as “sustainably or socially responsible”.
Researchers surveyed 2,000 participants from 10 different countries (Brazil, Canada,
China, France, Germany, India, Mexico, Spain, United Kingdom, and the United States) to
examine the question from the perspective of worldwide developed countries. Researchers
collected data on changes in consumer opinions on responsible business choices, the extent
that individuals are personally addressing environmental sustainability and the barriers
preventing individuals from taking more action on sustainability, along with demographic data
such as age, income, employment, and shopping decisions. This included both qualitative and
quantitative data. The website nor the summary of the experiment explain how these survey
participants were chosen, but for the sake of this investigation, I am going to assume they were
randomly selected from some list of citizens of the ten countries. However, without knowing
exactly what pools the participants were drawn from, it is difficult to eliminate the possibility of
bias in the results.
Research Question:
What percentage of consumers are willing to pay a premium for products branded as
sustainable or socially responsible?
Statistical Estimate:
The statistical estimate is the answer to this question from the survey data, which gives a
good picture of the population parameter that we are trying to measure. In this case the
statistical estimate is 49%. The estimate was found by creating a dummy variable, where people
who are not willing to pay a premium are entered into a chart as a zero and people who are
willing are entered as a one. Then, by averaging all the values in the table, you find the
percentage who are willing to pay premiums. Finding statistical estimates is a reasonable thing
to do because the survey data is in theory representative of the overall population, therefore the
estimate found from the survey data should be representative of the population parameter.
Bootstrap Model:
Although the statistical estimate is a reasonable guess at the population parameter, in
order to have good evidence to prove our case, we need to calculate a measure of sampling
variability to see how much the data could vary in future samples. However, we are limited to
only the data from the original sample (we can’t go out and collect 500 more samples), so we
must run a bootstrap simulation.
A bootstrap simulation takes the data from the initial sample and puts it in a sampler.
Bootstrap simulations are run with replacement so that there is possibility for sampling variation
in a sample that is still representative of the overall population.
The standard deviation found through the bootstrap model is 0.01139, which means there is
very little variability in the data. The SD helps us measure sampling variability, and in this case it
is very small (1%), meaning there is very little sampling variability.
Uncertainty:
Using the SD, we can now account for the uncertainty in the data, which is found using
the margin of error and confidence interval.
The margin of error is found by multiplying the standard deviation by 2.
0.01139 X 2, which is 0.02278, our margin of error.
The compatibility Interval is the range within the margin of error around the mean,
therefore by subtracting and adding the margin of error from the mean, the compatibility interval
is from .46622 to .51178. The compatibility interval gives a range of plausible results for what
the overall population could be expected to respond with about whether they would be willing to
pay the premium for responsibly sourced products. Using the data from the simulation, we could
reasonably predict that between 46.6 and 51.1 percent of the population is willing to pay an
increased premium for responsibly sourced products. Using the margin of error, this could be
phrased as we could expect 48.9% of people to be willing to pay, plus or minus 2.28%.
Generalizability:
Now that we have data from the sample and have simulated sampling variability, we
must now look at whether our sample data is generalizable (applicable) to the larger populations
of people in those countries. There are two factors that play into generalizability,
representativeness and uncertainty.
To evaluate representativeness, we must examine whether our sample is representative
of the overall population. This means evaluating bias in the sampling method.
Bias is most commonly avoided through random sampling. However, our study does not
specify how each participant was chosen to complete the survey. For the sake of analyzing
generalizability, I will assume in the absence of information that the sample was taken randomly
from some list of citizens of each country in the study. While this would include random
sampling, there are also aspects of bias. One possibility is convenience bias because the
people on the lists were a convenient place to draw names from, but the lists may not have
been representative of the overall consumer population (for example: non-citizen consumers).
Additionally, surveys typically have an aspect of voluntary response bias because there is the
possibility that some of the randomly chosen participants chose not to respond. This could lead
to bias if there is some factor that sets people who chose not to respond as systematically
different than people who chose to respond. However, voluntary response bias is very difficult to
avoid in human studies (as involuntary studies are unethical), therefore we cannot let it affect
our perception of the data’s generalizability.
Given that the surveys were sent out to randomly selected participants from a list of
citizens in each of the ten countries in the study, this study has pretty good generalizability on
the issue of representativeness. We can reasonably expect that the 2,000 survey participants
generally represent the opinions of the entire population of those countries.
We must also evaluate generalizability in regards to uncertainty. This was calculated
above, and as there is a relatively small amount of uncertainty. This allows us to conclude that
the survey results can be expected to be representative of the overall populations of those
countries.
Conclusion- Roughly 48.9% of consumers in the given countries are willing to pay
premiums to support responsible business practices. This is a significant percentage of the
population, however, it may not be enough for businesses to find it worth reworking their entire
business models to reflect ethical and sustainable practices. And perhaps a better question isn’t
whether people are willing to pay more for responsibly sourced products, but how much more
people are willing to pay before they default to the harmful cheaper option. Finding this breaking
point would likely be more useful to businesses who are determining whether or not to go
sustainable because they would be able to calculate the specific cost-benefits of each option.