This archive allows you to explore different kinds of green storytelling
made by fast food companies nowadays and the common patterns behind them.
By choosing each recipe, you can consult actual examples of sentences extracted from
their websites. Recipes are a metaphor to represent the most recurring strategies
of green communication through ingredients that are combined together.
Keywords: setting up the dataset
This archive is based on research studying the websites of the 9 most popular fast food
chains in the US -as a matter of fact, these companies originated in the United States,
the world's second-largest carbon emitter, with its 5 billion tonnes of CO2 (2021)1.
They were selected by using the following criteria: annual revenue, total number of
stores open world wide, and has at list one sustainability-related section on their websites.
The companies that fulfilled the criteria are: McDonald’s, KFC, Burger King, Wendy’s, Domino’s, Subway,
Krispy Kreme, Starbucks, Pizza Hut
To analyse the language, it was necessary to create a custom-made green glossary.
To do so, firstly it was needed to get a deeper comprehension of the concept of sustainability.
The definition of sustainability from Britannica [Source] provided a
solid theoretical basis.
Moreover, the prototype of the glossary comes from the article “Clever’s Green Glossary:
46 Sustainable Key Words to Help You Shop Smarter” of the website Clever. In order to collect
keywords more effectively, we only selected the ones related to the food industry. Ultimately,
it features 41 keywords [Source] related to environmental issues, strongly connected to the fast
food
market and used in today's green narratives.
The research was further deepened by selecting only 23 keywords out of initial 41. The ones that
were
chosen
are directly related to environmental issues and/or are the most recurring ones.
In order to understand why and how these keywords are used in their context, the dataset was enriched by
collecting
all the sentences in which they appear. Furthermore, those keywords were searched on each “.com” of
the
company's
website to avoid results on a geographical basis and to reach a neutral view of the topic (i.e. www.mcdonalds.com instead www.mcdonalds.it).
Tags: understanding the context
Sentences were collected, read and evaluated manually. In order to analyze the context in which the
sentence
is placed,
labels were assigned to each of them to describe them fully and in depth.
The label Topic was made according to discussed matters in the sentences to create a frame
of reference for the user. The 7 topics were chosen to cover all possible topics dealing with
sustainability
in fast food companies websites:
Animal
Refers to every context in which there’s a trace of animal caring, treatment or responsible framing.
food
Refers to every context in which there’s a focus on raw materials and how they were treated before
becoming a final product.
resources
Refers to contexts in which is talked about responsible and ethical sourcing, as well as innovative
ways
of energy resources.
ACTIONS
Refers to every context in which the company takes a stand on environmental sustainability and
describes
how they deal with the problem on a daily basis.
WASTE
Refers to every context in which there is a reference to the waste disposal, sustainable packaging,
the
reduction of emissions and all types of waste.
Distribution
Refers to every context that concerns suppliers, logistics, technology and equipment they use.
MANAGEMENT
Refers to every context in which the company promises to find more sustainable solutions in the future
or
explains nowadays administrative strategy related to efficient resources management.
The label Aim targets those sentences that show whether the company talks about particular
commitment,
why they are talking about it and in which way. This label takes two approaches:
take action
Sentences labeled as such, talk about goals achieved in the past, daily actions against climate
change, promises for the future regarding environmental sustainability, etc.
dissemination
sentences labeled as such, talk about general information about the environment, without specific
goals nor commitments.
The label Evidence targets those sentences that show if the data is present or absent in the
company's
statements. This label takes two approaches:
data
Sentences labeled as such show only those sentences which contain statistical data about the company,
chronological records of goals achieved, references to partnerships with other companies or suppliers,
etc.
no data
Sentences labeled as such show those sentences that don't provide any statistical evidence that could
support the company’s statements.
The Typology of text was analysed and used to distinguish different forms of text that occur.
Typology
refers to the placement and function of each sentence: TITLE, statement or paragraph. In this way it was
possible to preserve the impact and appearance that these phrases had on one's website.
recipes: sifting the dataset
The 9 recipes are a synthesis of the dataset and they depict frequent communication
strategies:
from each of them users are able to read real examples extracted from the analyzed websites.
The recipes were made once every sentence has been collected and analyzed by labels. Therefore, the
dataset
was cross-referenced in order to recognize which are the most recurring patterns in green storytelling by
fast
food companies.
The cross-reference was done following this order:
1.
Firstly,the most frequent pairs of topics were extracted. They were extracted
by
calculating how many times each topic was used together with the other one (this was possible by
calculating ). To define the most occurring pairings, it was chosen to select the ones that had the
highest and/or second highest number of occurrence (based on spreadsheet rows).
This table was made possible by applying a formula to the previous 'Dataset'
spreadsheet
that automatically identifies the TRUE results (selected boxes) for each topic pair. For example, in
the
case of Food-Animals, the formula only takes into account results that are simultaneously TRUE within
the
columns of the topics FOOD and ANIMALS.
2.
Within each topic pair, the most repeated keywords were counted and extracted. Thus,
after
counting them, the keyword that was the most repeated in that specific topic pair was selected as an
ingredient.
3.
In this step, now it was possible to summarise each finding by counting and filtering
the
sentences according to the label Aim and the label Evidence. In other words, it was possible to see
how
many sentences contained Take action or Dissemination, and how many of them were labeled as Statistic
or
No Data.
The results in the table were obtained through a formula: for each row of the table
(therefore each recipe) we asked on the "Dataset" spreadsheet to count each URL, with at least one of
the
two recipe topics assigned, that the "Aim" column marked as "take action". An analogue process was
subsequently carried out to count how many URLs marked as Aim "Dissemination". Same logic to then
count
the results with "data/no data" evidence.From these results was then possible to get the
percentages that are shown in the recipes’ introduction.
As mentioned, the results of these computations led to the 9 recipes available for
consultation
in the archive.
