# SEER: Sustainable E-commerce with Environmental-impact Rating

Md Saiful Islam<EMAIL_ADDRESS>Department of Computer Science,
University of Rochester, United States  Adiba Mahbub Proma
<EMAIL_ADDRESS>Caleb Wohn  Karen Berger  Department of Earth and
Environmental Sciences, University of Rochester, United States  Serena Uong
Varun Kumar  Katrina Smith Korfmacher  Department of Environmental Medicine,
University of Rochester Medical Center, United States  Ehsan Hoque
<EMAIL_ADDRESS>
###### Abstract

With online shopping gaining massive popularity over the past few years,
e-commerce platforms can play a significant role in tackling climate change
and other environmental problems. In this study, we report that the “attitude-
behavior” gap identified by prior sustainable consumption literature also
exists in an online setting. We propose SEER, a concept design for online
shopping websites to help consumers make more sustainable choices. We
introduce explainable environmental impact ratings to increase knowledge,
trust, and convenience for consumers willing to purchase eco-friendly
products. In our quasi-randomized case-control experiment with 98 subjects
across the United States, we found that the case group using SEER demonstrates
significantly more eco-friendly consumption behavior than the control group
using a traditional e-commerce setting. While there are challenges in
generating reliable explanations and environmental ratings for products, if
implemented, in the United States alone, SEER has the potential to reduce
approximately 2.88 million tonnes of carbon emission every year.

###### keywords:

sustainable consumption , human behavior , eco-friendliness rating ,
environmental impact , climate change , e-commerce

††journal: Journal of Cleaner Production

##  1 Introduction

In recent years, the effects of climate change and environmental pollution
have become devastating, affecting communities worldwide. While awareness is
increasing among the general population, many people lack the knowledge or
motivation to make sustainable choices. This is specifically evident in
consumer buying behavior – despite individuals being concerned about the
environment and willing to opt for greener consumption, the intentions are
often not translated into appropriate actions (Young et al., 2010). This
phenomenon, termed as the “attitude-behavior gap”, is commonly identified in
consumption behavior literature (Tanner and Wölfing Kast, 2003; Vermeir and
Verbeke, 2006; Wheale and Hinton, 2007). High prices, difficulty in
identifying green products, not having enough time for research, lack of
environmental information in the product description, and lack of trust in the
“eco-friendly” labels provided by the manufacturers have been identified as
the contributing factors to the attitude behavior gap(Gleim et al., 2013;
Joshi and Rahman, 2015). Individual and social factors like habit, behavioral
control, social norms, and so on also contribute to the phenomenon, but their
impacts are not well established (Eze and Ndubisi, 2013; Tsakiridou et al.,
2008; Wang et al., 2014). While the factors responsible for the attitude-
behavior gap are well studied, existing literature falls short when providing
viable solutions or strategies to mitigate this gap.

Prior research suggests that green consumers prefer being presented with
environmental impact information in a simple, compact, and user-friendly
manner (Mondelaers et al., 2009). At the same time, they often find it hard to
trust “eco-friendly” labels provided by companies (Chen and Chang, 2012). The
increasing popularity of online shopping brings a unique opportunity - the
architecture of the website makes it easy to present information through the
incorporation of high-resolution images, videos, and hypertexts that can point
users to further resources. E-commerce websites can facilitate additional
information about a product and present it in a way that does not overwhelm
consumers but is still trustworthy and well-explained. In addition, whether an
additional piece of information should be presented to a consumer can be
customized based on consumer preference and behavior analysis. We find this as
an excellent opportunity to convey the required information about a product’s
environmental impact, and aid users in making more eco-friendly decisions. For
example, when users search for a specific product, they can sort the results
based on the eco-friendliness rating. However, the question remains – would
consumers trust these ratings? To build trust, an explanation for the rating
must be provided. There can be a summary of the product’s environmental
impacts that explains the rating of the product. In addition, from the
description of the product, words or phrases (keywords) that are related to
environmental impact can be highlighted and explained. This would also
increase consumers’ environmental knowledge, as they learn about the
environmental impacts of various product ingredients, thereby raising public
awareness. In this paper, we analyze the “attitude-behavior gap” for an online
setting and propose SEER (Sustainable E-commerce with Environmental-impact
Rating), a concept design (Figure 1) that would facilitate making eco-friendly
choices by incorporating the features discussed above.

Figure 1: (Left) shows our proposed design for displaying a product used by
the case group. The _environmental rating_ (1) indicates how eco-friendly a
product is and the _environmental concerns_ (2) summarizes the possible
environmental impacts of the product. Keywords related to environmental impact
analysis are also highlighted with green (eco-friendly) or red (harmful to the
environment) color (3). If a user hovers the mouse/pointer over the
highlighted keywords, additional environmental information about the keywords
is presented. (Right) shows the traditional product display design presented
to the control group. In both cases, brand names and logos are removed from
all products to avoid bias towards popular brands.

We conduct a quasi-randomized case-control study of 98 participants, where
participants are asked to select products for a local school with a limited
budget. Before the start of the study, the participants are shown a climate
awareness video and how their choices can make a difference. To elicit real-
life behavior, everyone gets financial rewards for saving budget while
selecting the products. The participants then complete a post-study survey
related to whether their attitude towards the environment has changed (both
groups) and the design interface of the website (case group only). From our
observational study, we identify that the “attitude-behavior gap” also exists
in an online setting – participants who are more concerned about the
environment do not choose a higher number of eco-friendly products. However,
providing more information about the environment through our interface makes a
difference. We find that the purchasing behavior of _case_ subjects using SEER
differs significantly from the _control_ subjects using a traditional
e-commerce website setting ($p<0.01$). The case group demonstrates more eco-
friendly behavior than the control group ($p<0.005$) and reports that the
components introduced in SEER made finding eco-friendly products more
convenient, simultaneously increasing their trust in the labels because of the
provided explanation. At the same time, SEER has been rated as nearly
excellent in terms of usability, as established by a score of $79.18$ in
System Usability Scale (Brooke et al., 1996).

In 2020, the worldwide e-retail sale was more than 4.2 trillion USD with over
two billion e-commerce customers (Coppola, 2021). According to the quarterly
sales report of the U.S. Census Bureau, e-commerce sales in the second quarter
of 2021 accounted for 12.5 percent of total sales (Bureau, 2021). This shows
that, despite the challenges of generating reliable environmental ratings and
explanations, our proposed idea can have a huge impact in tackling climate
change by potentially reducing harmful environmental effects from every online
purchase. Adaptation of SEER will raise general awareness about climate change
and individual responsibility, thus promoting sustainable products and
encouraging the industry to invest more in sustainable production. Therefore,
SEER can be the seer of a greener future.

##  2 Study Design

###  2.1 Interface design

In this study, we develop a prototype website named SEER (Sustainable
E-commerce with Environmental-impact Rating) that targets addressing three
major factors responsible for the “attitude-behavior” gap observed in
sustainable consumption literature: (i) inconvenience (ii) lack of knowledge,
and (iii) lack of trust. The key component of SEER is an _environmental
rating_ (1-5 scale) that rates a product based on its environmental impact,
with a higher rating indicating higher eco-friendliness of a product. However,
for facilitating green consumption behavior, both the presence of a singular
rating and its credibility are important (Riskos et al., 2021; Young et al.,
2010). Therefore, our second proposed component is an environmental concerns
statement which briefly conveys an explanation of the rating provided by
discussing the product’s potential impact on the environment. These
explanations can help to increase trust (Pu and Chen, 2006). At the same time,
we try to educate people by highlighting words or phrases (keywords) that are
related to the environment, simultaneously explaining what these keywords
mean. This is our proposed third component, named as environmental keyword
highlights.

###  2.2 Participant recruitment and pre-study survey

We use Amazon Mechanical Turk (Paolacci et al., 2010) to recruit 98
participants living in the United States. We run a case-control study by
splitting the participants into two groups: case group selecting products
using SEER ($N=49$), and control group using a prototype of a traditional
website ($N=49$). Figure 1 shows both of the websites – although the look and
feel of both prototypes are the same, SEER additionally incorporates the three
components proposed above.

At first, the participants are asked to fill out some demographic information
such as age range, ethnicity, gender, household income, the highest degree of
education, and zip code. Then they are asked a standard set of questions used
in prior literature to infer their environmental sentiment (i.e., how
concerned they are about the environment how actively they are trying to
prevent environmental harm) (Lin and Huang, 2012). Depending on their answers,
they are assigned an “environmental sentiment score” (0-40). The participants
scoring at least 20 are considered sensitive or concerned about the
environment, and a quasi-randomized process is used to assign a participant to
either case or control group in such a way that both groups have a similar
distribution in terms of the environmental sentiment score. In addition, we
ask 5 standard questions to infer the knowledge of the participants about
environmental issues and climate change. All the questions are on a 5-point
Likert scale (strongly disagree: 0, disagree: 1, neutral: 2, agree: 3,
strongly agree: 4). The quasi-randomized assignment is successful, as we
observe similar distribution in both case and control groups – not only for
environmental sentiment score but also for gender, age, race, education,
environmental knowledge, and income level. The detailed demographic
distribution of the participants is presented in Table 1.

Variables | Options | N (number) | Case (%) | Control (%)
---|---|---|---|---
Gender | Female | 28 | 46.4% | 53.6%
Male | 70 | 51.4% | 48.6%
Age | 18-24 | 4 | 100.0% | 0.0%
25-34 | 47 | 46.8% | 53.2%
35-44 | 25 | 40.0% | 60.0%
45-54 | 12 | 58.3% | 41.7%
55-64 | 7 | 71.4% | 28.6%
65 and above | 4 | 25.0% | 75.0%
Race | Asian | 7 | 57.1% | 42.9%
| Black or
---
African American
12 | 41.7% | 58.3%
White | 78 | 50.0% | 50.0%
Other | 1 | 100.0% | 0.0%
Education | Below High School | 1 | 0.0% | 100.0%
Associate | 7 | 57.1% | 42.9%
Bachelor | 54 | 44.4% | 55.6%
Graduate | 15 | 66.7% | 33.3%
Yearly Family Income (USD) | Below 20,000 | 11 | 45.5% | 54.5%
20,000-35,000 | 13 | 53.8% | 46.2%
35,000-50,000 | 16 | 56.3% | 43.7%
50,000-75,000 | 34 | 52.9% | 47.1%
75,000-100,000 | 30 | 36.7% | 63.3%
Above 100,000 | 5 | 40.0% | 60.0%
Total | - | 98 | 50.0% | 50.0%
Table 1: Demographic details of the study participants

###  2.3 Experiment setup

Figure 2: A simple overview of our study design.

Figure 2 summarizes a participant’s activity in our study. Both case and
control groups are given the prompt that a local school is just opening, and
the school needs help to purchase some products due to a shortage of school
staff (note that although the school is imaginary, participants are not aware
of this until the end of the experiment). The prompt includes that the school
has a limited budget, and if participants can save some money from the budget,
they would receive 10% of the saved money as a token of thanks. All
participants are also shown a motivational video (3-minute length) explaining
the adverse effects of environmental pollution and climate change, and how
their individual actions can make a difference. The objective of showing this
video is to prime the participants from both case and control groups into
being more concerned about the environment and indirectly elicit eco-friendly
behavior. In addition, showing the video helped us avoid adding any possible
confounding factor between case and control groups. Since the case group would
be provided with information on environmental impact, they are more likely to
infer that the study is about making eco-friendly choices. Alternatively, the
control group would have no such information, thereby going into the study
with a completely different thought process. Priming both case and control
groups with a motivational climate video removes this bias. Therefore,
participants would have two possibly conflicting objectives in mind: (i) save
money and get monetary benefits instantly, and/or (ii) invest in eco-friendly
products which has no short-term benefits but will be helpful for the
environment in the long run.

All participants are then shown a pair of products side-by-side from 12 pre-
selected product types. Between each pair, one is more eco-friendly than the
other. Figure 1 shows how each product was presented to the participants. The
products are arranged in the same order, and in the same arrangement for all
participants. Whether the eco-friendly product from a certain pair will be
displayed on the left side or right side was pre-determined randomly (but kept
the same for all participants). This avoids possible confounding factors
(e.g., users being biased towards the product on the left side, being more
attentive at the beginning, etc.). When selecting a product from a pair, the
participants from the case group are shown three additional components (see
Figure 1) proposed in SEER – (i) environmental rating, (ii) environmental
concerns, and (iii) environmental keyword highlights, along with traditionally
available features: product name, an image of the product, price, user rating,
number of users who rated this product, and product description. The control
group has no access to SEER components but saw the traditional features.

After the participants finish product selection, they complete a post-study
survey. Both case and control groups answer the same questions related to
climate sentiment as before. This time, however, the questions are asked in
the future tense to determine the effectiveness of our intervention strategy
in raising awareness of climate change and increasing concern for the
environment among the participants. The participants from the case group
answer additional questions regarding the effectiveness of the SEER
components, system usability (Brooke et al., 1996), and their experience
(i.e., likes, dislikes, and suggestions) of the study.

###  2.4 Products and their environmental impact assessment

Based on the theme, we select 12 types of products displayed in Table 2 to be
included in the study. We choose two products for each type, one being more
eco-friendly than the other. For selecting the products and generating their
relevant environmental information, we consult a domain expert (an Associate
Professor of Environmental Science who is actively engaged in several
sustainability programs).

We remove the name and logo of the brand of the products since consumers can
be biased towards certain brands (Chovanová et al., 2015; Hillenbrand et al.,
2013; Mellens et al., 1996). We also consider the price of the products as an
additional context, and selected products to evenly represent each of the four
different conditions (3 product pairs from each condition):

  * (i)

Eco-friendly product is cheaper

  * (ii)

Both products are equally priced

  * (iii)

Eco-friendly product is slightly more expensive (additional cost is below 5
USD)

  * (iv)

Eco-friendly product is significantly more expensive (additional cost is at
least 5 USD)

These conditions would allow us to probe into the question of how strong the
influence of price is for consumers when buying eco-friendly products.

| Product Type
---
Code
Product Type
P1 | Decaf coffee
P2 | All-purpose cleaner
P3 | Toilet paper
P4 | Clubhouse playset
P5 | 4-in-a-row board game
P6 | Copy paper
P7 | Kitchen trash bags
P8 | Ballpoint pen
P9 | Chair
P10 | Table
P11 | Glue sticks
P12 | File folder
Table 2: 12 types of products selected for the study

The domain expert rated the eco-friendliness of the products on a scale of 1
to 5 (higher value means more eco-friendly), and provided justifications for
the rating. This justification is used as the “environmental concerns” in
SEER. Furthermore, three undergraduate students highlighted keywords in the
product description in either red or green, where red indicated a keyword that
is not eco-friendly (e.g., pollution), green being the opposite (e.g.,
recycled). Additional justification as to why these keywords are marked in red
or green and are also available to the case group upon hovering over the
highlighted keywords.

##  3 Results

Participants from both groups demonstrate similar distribution in terms of
other factors like race, gender, education, income, and environmental
knowledge (Table 1). We study statistical correlations between independent
variables (gender, age range, level of education, yearly income range,
environmental sentiment score, and knowledge) and the target variable (eco-
friendliness). Gender, age range, level of education, yearly income range are
categorical values, while environmental sentiment score and knowledge are
numerical. For variables with categorical values, we use the ANOVA test and
report the $p$-values wherever appropriate. For variables with numerical
values, we use the Pearson correlation co-efficient and report $r$. Also, to
test the major hypotheses, we use the $t$-test and report $p$-values. System
Usability Scale (SUS) score is measured exactly as in literature (Brooke et
al., 1996).

###  3.1 People have good intention

We ask 10 standard questions (Table 3) to infer the environmental sentiment of
all the participants. For each question, we assign a score from 0 to 4 based
on the response (strongly disagree: 0, disagree: 1, neutral: 2, agree: 3,
strongly agree: 4). So, each participant can score between 0 and 40 – 20 if
they are neutral on average, above 20 if they demonstrate an eco-friendly
attitude, below 20 otherwise. In general, we find that people have good
intention (Figure 3(a)). 73 subjects (74.5%) demonstrate an eco-friendly
attitude, reporting they are concerned about the environment and intend to
take actions to prevent harm while 2 subjects remain neutral, and 23 do not
demonstrate such an intention.

Environmental sentiment question | SD | D | N | A | SA
---|---|---|---|---|---
| I make a special effort to buy paper and plastic products that
---
are made from recycled materials (Q1)
7 | 17 | 14 | 41 | 19
I have switched products for ecological reasons (Q2) | 8 | 9 | 21 | 41 | 19
| When I have a choice between two equal products, I purchase
---
the one less harmful to other people and the environment (Q3)
2 | 0 | 12 | 51 | 33
| I have voted for a candidate in an election at least in part because
---
he or she was in favor of strong environmental protection (Q4)
6 | 11 | 21 | 29 | 31
| I have avoided buying a product because it had potentially
---
harmful environmental effects (Q5)
4 | 7 | 17 | 35 | 35
| I have read newsletters, magazines or other publications
---
written by environmental groups (Q6)
9 | 5 | 14 | 43 | 27
I have signed a petition in support of protecting the environment (Q7) | 17 | 13 | 17 | 20 | 31
I have given money to an environmental group (Q8) | 21 | 14 | 13 | 27 | 23
| I have written a letter or called the member of Congress or another
---
government official to support strong environmental protection (Q9)
34 | 9 | 13 | 24 | 18
| I have boycotted or avoided buying the products of a company
---
because I felt that company was harming the environment (Q10)
18 | 11 | 12 | 37 | 20

Table 3: Summarized responses of all the participants on the environmental
sentiment questions. Here SD, D, N, A, and SA denote “Strongly Disagree”,
“Disagree”, “Neutral”, “Agree”, and “Strongly Agree” respectively.

(a)

(b)

Figure 3: Although people have good intentions, the intentions do not
translate well to actual action. (a) shows the environmental sentiment of the
subjects (higher value indicates greener sentiment), (b) shows the correlation
between environmental sentiment and eco-friendliness as defined by their
selection of eco-friendly products ($r=0.088$; the green line indicates the
trend).

###  3.2 Intention does not always imply action

We find that consumers’ eco-friendly intention rarely translates into action.
The eco-friendly behavior of the subjects is measured by the number of eco-
friendly products they choose (0-12). We find no statistically significant
correlation of gender, age, level of education, or yearly income with
subjects’ eco-friendliness behavior. This suggests these are either unrelated
or may have weak correlation, which would require a larger population to find
out. The _attitude-behavior gap_ demonstrated in previous literature is also
found in our study. As we see in Figure 3(b), the correlation between one’s
environmental attitude (sentiment) and actual eco-friendly behavior is very
weak ($r=0.088$). In addition, we split the participants such that those
scoring less than 20 are in the “less green attitude” group, and those scoring
at least 20 are in “greener attitude” group. The behavior is marginally
different between these two groups. Using the $t$-test, we fail to reject the
null hypothesis that “there is no relationship between one’s greener
environmental attitude and actual action” ($p=0.75$). Consequently, we fail to
establish that greater concern about the environment actually translates to
more eco-friendly action ($p=0.38$).

###  3.3 SEER can reduce the attitude-action gap

In the pre-survey questionnaire, we ask all the participants 5 questions to
infer their knowledge about climate change and environmental issues (Table 4).
Participants scoring below the median total score are regarded as part of the
“less knowledgeable” group and those scoring at least the median are in the
“more knowledgeable” group. Based on the one-tailed $t$-test, we find that
subjects with higher environmental knowledge show more eco-friendly behavior
than subjects who are less knowledgeable ($p<0.0005$). This shows that
environmental knowledge can be a powerful tool in increasing purchase of eco-
friendly products, which SEER tries to convey to its users.

Environmental knowledge question | SD | D | N | A | SA
---|---|---|---|---|---
| I am aware of the environmental and human
---
effects of climate change (Q1)
0 | 4 | 13 | 31 | 50
| I know that my consumption choices can
---
make a difference (Q2)
3 | 5 | 14 | 45 | 31
| When I read the description of a product, I can understand
---
whether it is harmful for the environment (Q3)
2 | 4 | 27 | 37 | 28
It is easy for me to recognize an eco-friendly product (Q4) | 1 | 9 | 20 | 43 | 25
| I like to read about sustainability, climate change,
---
and the environment often (Q5)
7 | 10 | 21 | 39 | 21

Table 4: Summarized responses of all the participants on the questions asked
to infer their knowledge about climate change and environmental issues. Here
SD, D, N, A, and SA denote “Strongly Disagree”, “Disagree”, “Neutral”,
“Agree”, and “Strongly Agree” respectively. Figure 4: The distribution of eco-
friendliness in the case and control group. Eco-friendliness is measured by
the number of eco-friendly products selected by a participant (out of 12; a
higher score indicates more eco-friendly behavior).

The one-tailed $t$-test reveals that the participants from the case group are
significantly more eco-friendly than the control group ($p<0.005$). Figure 4
shows the eco-friendliness scores of case and control group participants. On
average, a subject from the case group selected $8.57$ eco-friendly products
(mode = $11$, median = $9$, SD111SD: Standard Deviation = $2.13$) while the
average for the control group is $7.47$ (mode = $6$, median = $7$, SD =
$1.92$). Our experiment suggests that SEER is capable of helping consumers
choose more eco-friendly products while shopping online. In the post-study
survey, participants from the case group are also asked about their level of
agreement on the impact of the SEER prototype and its components on their
purchasing decisions. 39 participants (79.6%) at least agree (agree/strongly
agree) that comparing eco-friendliness of products is easy using SEER (29
strongly agreed), 9 participants are neutral, while only one disagrees (no
strong disagreement). The proposed prototype not only increases convenience
but also increases trust. 30 participants (61.2%) express that they trust the
environmental labels presented in the SEER prototype more than the traditional
websites they use (e.g., Amazon). The above findings indicate that SEER can
reduce the “attitude-behavior” gap for online shopping by addressing two
fundamental barriers: inconvenience and lack of trust in the “eco-friendly”
labels.

We also analyze the individual impacts of the proposed three components. 41
participants (83.7%) express their agreement (28 strongly agree) that the
environmental rating makes it easy to identify eco-friendly products,
establishing this component as the strongest factor for convenience. The
environmental impact summary is primarily responsible for building consumer
trust in the provided labels, as 41 participants (83.7%) at least agree (24
strongly agree) that this component helped them trust the labels. The effect
of the environmental keyword highlights is primarily to increase consumer
knowledge as it describes what the words/phrases mean and how it is related to
the environment. 36 participants (73.5%) from the case group self-report that
they are more aware of the environment after participating in this study, 22
(44.9%) expressing that they will read more articles related to the
environment.

(a)

(b)

Figure 5: Relation between eco-friendly behavior of the case group and the
extra price of the eco-friendly product. The extra price is shown as an exact
amount (USD) in (a) and as a percentage in (b). Negative additional cost
indicates the eco-friendly product is cheaper, where most of the participants
purchase it. The more the additional cost for the eco-friendly product, the
less the number of participants purchasing it ($p=0.007$). The green line
indicates the trend.

###  3.4 SEER is user friendly

To evaluate the usability of the SEER interface, participants from the case
group are asked 10 standard System Usability Scale (Brooke et al., 1996)
questions (Table 5). The average SUS score for SEER is $79.18$ (median =
$85$), $68.2$ being the average score for all websites (Bangor et al., 2009).
The mode of the SUS score was surprising – a perfect score (100), provided by
12 participants (24.5%). Typically, a SUS score between 70-80 is considered
good, more than 80 indicates excellent and less than 50 is not acceptable
(Bangor et al., 2008). Therefore, we can conclude that SEER has near excellent
usability and is acceptable as a user interface.

System usability scale questions | SD | D | N | A | SA
---|---|---|---|---|---
I think that I would like to use this system frequently (Q1) | 2 | 0 | 13 | 39 | 44
I found the system unnecessarily complex (Q2) | 51 | 14 | 9 | 11 | 13
I thought the system was easy to use (Q3) | 0 | 3 | 9 | 23 | 63
| I think that I would need the support of a technical person
---
to be able to use this system (Q4)
51 | 9 | 14 | 14 | 10
| I found the various functions in this system were
---
well integrated (Q5)
0 | 1 | 7 | 46 | 44
| I thought there was too much inconsistency in this
---
system (Q6)
49 | 12 | 15 | 19 | 3
| I would imagine that most people would learn to
---
use this system very quickly (Q7)
0 | 0 | 9 | 30 | 59
I found the system very cumbersome to use (Q8) | 47 | 10 | 9 | 22 | 10
I felt very confident using the system (Q9) | 0 | 2 | 12 | 27 | 57
| I needed to learn a lot of things before I could
---
get going with this system (Q10)
48 | 8 | 15 | 18 | 9

Table 5: Response of all the participants on the system usability scale
questions. Here SD, D, N, A, and SA denote Strongly Disagree, Disagree,
Neutral, Agree, and Strongly Agree respectively. Odd numbered questions (Q1,
Q3, $\cdots$, Q9) are in positive sentiment (SA is the most desired), while
the even numbered questions are in negative sentiment (SD is the most
desired).

###  3.5 Eventually, money matters

However, the price of a product significantly affects consumption behavior
(Ehrenberg et al., 1994; Lichtenstein et al., 1993; Ramya and Ali, 2016). As
in Figure 5, we see a significant negative correlation between the extra price
a consumer has to pay for an eco-friendly product and the number of consumers
who are still willing to pick the eco-friendly product (Pearson’s correlation
co-efficient $r=-0.73123$ and $p=0.007$) even while they are using the SEER
prototype. On average, more than 80% of the participants purchase the eco-
friendly product when it is cheaper than the non-eco-friendly product or when
the prices are similar. This drops to below 60% when the eco-friendly product
is slightly or substantially more expensive.

##  4 Discussion

###  4.1 Effectiveness of SEER

Our study establishes that the attitude-behavior gap also holds in an online
setting, and that it is indeed possible to nudge people towards eco-friendly
purchasing behavior by providing more information regarding the impact of the
products. The proposed system SEER aims to assist people who want to make eco-
friendly choices but do not end up acting on that sentiment. In our design, we
aim to mitigate three specific barriers - lack of trust, lack of knowledge,
and inconvenience. From our results, it can be said that addressing these
factors indeed increases purchasing of eco-friendly products. In addition,
according to the SUS score and thematic analysis of the open-ended feedback
from the participants, SEER seems to be well received by the participants. A
quote from a participant summarizes our contribution:

> “I genuinely liked this study a great deal as I thought it was very well-
> designed, streamlined, easy to interact with, and quite intuitive. Moreover,
> I liked the conceptual framework of the website showcased in this study as
> it provided a realistic prototype of a highly usable and user-friendly way
> of comparing specific parameters of products, which is currently quite
> difficult, cumbersome, and time-consuming.”

Our thematic analysis of open-ended feedback from participants suggests that
people tend to emphasize the price of the product. “I like the intention to
buy at the lowest price, and to save money” - is what one of our participants
said about her purchasing behavior, and it was a sentiment shared by many
other participants. Even our quantitative analysis shows similar results -
people are less likely to buy the more expensive product when price
differences are high, even if it is eco-friendly. However, aligning with prior
research, a majority of the participants were willing to pay a little bit
extra to purchase the eco-friendly products (Gregory-Smith et al., 2017;
Mostafa, 2016; Wei et al., 2018).

###  4.2 Impact of particular products

Figure 6: Eco-friendliness in the case and control group for all the product
types chosen for this study.

It is possible that people may be more interested in buying eco-friendly for
certain types of products than others as shown in Figure 6. Some eco-friendly
products are easier to identify than others, and it is seen that in such
cases, both case and control groups almost equally choose the eco-friendly
product. For example, in the case of the second selection (P2), one product is
a plant-based cleaner while the alternative contains harsh chemicals. Being
plant-based is an obvious clue, and most of the subjects (>80%) from both case
and control could identify and purchase the eco-friendly product. Not all the
easily identifiable eco-friendly products are popular though – for P1 (ground
decaf, selected by <60% subjects), one choice is certified organic (thus
easily identifiable) but 42% more expensive than the alternative. In this
case, other factors such as price makes the eco-friendly option less
desirable. In general, when identifying the eco-friendly product is difficult,
more participants from the case group made the correct choice compared to the
control. For example, in the case of P10, one product is a wooden table and
another a plastic table. 81.6% of the subjects from the case group chose the
wooden table (more eco-friendly), compared to 51% in the control group. As
reported in the post-study survey, many participants from the control group
found it difficult to decide whether trees cut to make the wooden table had
more negative environmental impact than the plastic alternative. It is
noteworthy that, for both P2 and P10, the price of the eco-friendly and non-
eco-friendly products is almost the same, eliminating price as a defining
factor for driving consumer decisions.

While we acknowledge that there may be other factors involved, the study is
still valuable as a proof-of-concept. Future studies could include more than
12 product types and a deeper analysis of the relationship between product
attributes and the receptivity of eco-friendly products.

###  4.3 Policy implications

In addition to directly informing about specific purchase decisions, such a
system could be used to provide additional incentives or rewards for green
purchasing. For example, people could be given an “eco-friendliness score” or
earn “green points” depending on their purchasing history, and get rewarded
based on it. Providing eco-friendly ratings may also encourage corporations to
be more transparent about the materials they use in their products. If sales
of eco-friendly products indeed increase with increased awareness, a positive
feedback loop may be created, driving companies to follow more eco-friendly
practices.

However, it must be noted that the effectiveness, efficiency, and equity of
such a system depends on implementation details. For example, if companies
self-rate their products, the system could exacerbate “greenwashing" (Laufer,
2003). Companies may provide false information, omit harmful ingredients or
practices, or use more eco-friendly keywords to inflate their products’ eco-
friendliness scores. It is also important to recognize that because certain
eco-friendly products tend to be more expensive, any rewards associated with
green purchasing patterns could exacerbate economic disparities, as lower-
income consumers might not be able to take advantage of these rewards.
However, companies could tie this system to justice-promoting strategies, such
as making donations to climate justice or community environmental
organizations with a percentage of profits from eco-friendly purchases. It
must be noted that SEER does not directly address or mitigate underlying
social/environmental/economic issues related to climate change; it is simply a
tool to provide consumers with more information to aid in making more eco-
friendly choices. Therefore, the actual impact of our system depends on
actions at the policy level such as eco-friendly rating schemes, incentive
structures, and tie-in policies by participating companies.

###  4.4 Integrating SEER components as optional information

Some participants believe that individuals cannot prevent climate change, and
using a system like SEER would place the responsibility of climate change on
individuals. Moreover, it can cause emotional stress for individuals who
cannot afford to be eco-friendly as they can see the negative impact of their
choices on the environment. To address these issues, the components proposed
in SEER can be made optional so it does not burden the consumers with
information they do not want.

###  4.5 Immediate and long term impact

The study had an immediate impact on the subjects by increasing environmental
awareness among participants. Many reported learning more about the
environment, and expressed more willingness towards eco-friendly consumption
in our post-study survey:

> “Thanks for the reminder, sometimes I feel like individuals cannot do much
> to help. I think it is easier to blame big companies for not doing enough,
> but it is true that consumer pressure can make a difference.” “It is a
> humble reminder to think about the environment whenever we want to make a
> purchase” “I learned more about eco-friendly products and the effects of
> climate change. I will try to focus on buying more green products from now
> on.”

While it is challenging to generate reliable environmental ratings and
explanations, our proposed idea can potentially have a huge impact in tackling
climate change by reducing carbon emission from potentially every online
purchase. According to the U.S. Department of Commerce, e-commerce retail
sales for the country accounted for more than 13% of total retail sales in
2021 (First - Third quarter) (Bureau, 2021). Earlier research shows that with
appropriate intervention like reminding consumers of eco-friendly behaviors,
consumers’ carbon footprint from food and drink purchases alone can be reduced
by 3 kg a week on average (Panzone et al., 2018). In the United States, almost
25% people (about 80 million) shop online at least once in a month
(OptinMonster, 2021). If one online purchase has a carbon footprint similar to
a single grocery trip (a modest assumption), it is possible to reduce 2.88
megatons of carbon emission annually if every U.S. online shopper uses SEER
($80\text{ million monthly shoppers}\times 12\text{ months}\times 3\text{ Kg
carbon reduction per monthly shopping}$). This is equivalent to more than half
a million people getting rid of their car, considering an average passenger
vehicle emits 4.6 tons of $CO_{2}$ per year (EPA, 2021).

##  5 Limitations and Future Work

A major limitation of our study is that the environmental impacts of the
products are simply hand-annotated by an expert. However, in the real world,
there are thousands of products, and it would be very difficult to manually
annotate them all. Moreover, as the product life-cycle is constantly changing,
product ratings must be updated accordingly. This naturally brings into
question – how do we generate environmental-impact information in the real
world? One method could be delegating this task to the manufacturers and an
independent, neutral group could monitor it. This approach brings consumer
trust into question, as consumers may not trust environmental labels provided
by the manufacturers (Delmas and Burbano, 2011). Another method can be an
autonomous body manually generating these ratings for the most popular types
of products first, and then increase coverage with time. But, this might be
difficult to scale and continuously update in the light of new knowledge. One
promising method seems to be crowd-sourcing, inspired by the success of
Wikipedia – the encyclopedia that maintains nearly 4 million articles using
crowd-sourcing, and remains as accurate as Encyclopedia Britannica (Giles,
2005). Finally, with the advances in machine learning (e.g., natural language
processing), it might be possible to generate the environmental ratings and
impact statements automatically. For example, a knowledge graph that captures
environmental entities and their relations (Hogan et al., 2021; Mishra and
Mittal, 2021) might help automatically identify the related keywords and
generate explanations to how those keywords interact with the environment.

In our study, the motivational climate video acted as an emotional incentive
for green consumption, similar to emotional green advertising used in prior
research (Matthes et al., 2014). Priming both case and control groups in this
way allows us to probe into the question - if someone wants to make eco-
friendly purchases, how effective would our proposed design be compared to the
traditional ones? In this context, priming the participants is appropriate.
Future studies could be done without priming the participants to simulate a
more real-life scenario, where not all people are always actively thinking
about the environment.

Another potential limitation of the study is the sample size and the
demography of the population. Scaling up the experiment would be challenging
as it would significantly increase the cost incurred in the experiment. Future
studies could be done in an entertaining way (e.g., gamification) so users
will be willing to participate for free. Alternatively, we could partner up
with an e-commerce business and present our interface to a larger number of
users. Moreover, all the subjects are from the United States, where literacy
rate and per capita income are higher compared to many other countries. Future
studies are needed to assess how receptive people will be to SEER in other
countries where literacy rate is lower and/or there are financial constraints.

Our study is a proof-of-concept that shows SEER is an effective, easy-to-use,
and well-received prototype for people willing to be eco-friendly. We propose
a new interdisciplinary research direction that connects user interface design
with sustainable consumption research. However, we also acknowledge that this
is a complex issue with other major contributing factors such as price,
availability, personal and social norms, habit, and so on, which are not
addressed by the study. Despite the limitations, we hope our study will
inspire future research for e-commerce re-design, and encourage
interdisciplinary research for promoting sustainability.

## Ethics

All the participants who completed the study received a payment of 10 USD, and
an additional bonus by saving their budget. The participants, at any point,
were permitted to quit the study without facing any consequence. After the end
of the study, all the participants were clarified that the products they
purchased are not for any real school, and their choices did not cause any
harm to the environment, considering some participants might feel guilty of
purchasing non-eco-friendly products. The entire study was approved and
supervised by the Institutional Review Board (IRB) of the University of
Rochester. All researchers who conducted the study and analyzed data are
certified by IRB to conduct human behavior studies.

## Acknowledgement

We thank Goergen Institute for Data Science for the Seed Funding of this
project. We also acknowledge Liudmila Paymukhina for her valuable feedback on
formulating the experiment.

## Code and Data Availability

Upon acceptance, both code and data will be made publicly available following
the IRB protocol of this project.

## Author contributions statement

M.S.I, A.M.P., C.W., K.B., and E.H. designed the experiment and the prototype;
C.W., V.K., and S.U. implemented the prototype; M.S.I., A.M.P., and C.W.
conducted the experiments; M.S.I. analyzed the results; M.S.I., A.M.P., and
E.H. wrote the manuscript; K.B. and K.K. helped improve the manuscript; E.H.
supervised the project. All authors reviewed the manuscript.

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