Towards Evaluating e-Commerce Sites Purchase Intention using

Affective Computing, a Preliminary Study

Hayri Yigit

, Javier De Andrés

, Martin González-Rodríguez

, Daniel Fernández-Lanvin

Kathia Marçal de Oliveira

and Emmanuelle Grislin-Le Strugeon

4,5

Department of Computer Science, Mehmet Akif Ersoy University, Antalya Burdur Yolu, 15030 Yakaköy/Burdu, Turkey

Depatment of Accounting, University of Oviedo, c/San Francisco 3, 33001, Oviedo, Spain

Depatment of Computer Science, University of Oviedo, c/San Francisco 3, 33001, Oviedo, Spain

Univ. Polytechnique Hauts-de-France, LAMIH, CNRS, UMR 8201, F-59313 Valenciennes, France

INSA Hauts-de-France, F-59313 Valenciennes, France

Keywords: Usability, Affective Computing, e-Commerce.

Abstract: The evaluation of an e-commerce website usability and effectiveness is traditionally measured through

surveys. These tools provide subjective information about the user perception of the website, and their use is

expensive and involve some drawbacks, since it requires the user to collaborate in the study and to control the

sample. In this work, we explore the relation between what the users perceives and shares when they are asked

about their experience, and the emotion they show while using the system under study. We use Affective

Computing algorithms to analyse user expressions, and we compare the results with the information provided

by means of a TAM based survey.

1 INTRODUCTION

The success of any e-commerce website is strongly

determined by the usability of its user interface, and

thus, by the satisfaction of the user that access the site

(Sahi, 2015). The most popular and traditional way to

measure the satisfaction of the user is applying

Technology Acceptance Models (TAMs)

(Marangunić & Granić, 2015), that is, using surveys

that the subject must fulfil after using the product.

Although this method is well tested and accepted, it

has many drawbacks that make it difficult and

expensive to use, especially in frequently updated

web sites, since it requires the subject collaboration

and a controlled sample. Moreover, the results of the

evaluation are based on the subjective perception

from the user, who can be influenced by many factors

(familiarity, learning effect, sympathy for the

website, reluctance to changes, etc.) that could bias

the results, and favour a website over others with

better design and usability.

https://orcid.org/0000-0001-6887-4087

https://orcid.org/0000-0002-9695-3919

https://orcid.org/0000-0002-5666-9809

In order to remove the subjective component, there

are other strategies like, for example, the use of eye-

tracking technologies, which measure the

performance of the user with objective parameters

(Cooke, 2004; Lasa et al., 2015; Rakoczi & Pohl,

2012). However, they usually require onsite

evaluation and technological infrastructure.

On the other hand, one of the emerging and most

active lines of research in Human Computer

Interaction is the one related to the perception of user

emotions though artificial vision algorithms. This is

part of what we know as Affective Computing (AC)

(Ma & Yarosh, 2021). AC algorithms can process the

picture of a subject and classify their emotions

analysing their facial expression. The relation

between emotions, usability and purchase intentions

has been already demonstrated by previous research

works (Eroglu et al., 2001).

In this paper, we try to explore whether the user

emotion captured by AC algorithms while using an e-

commerce site is consistent with the user subjective

perception of usability according to responses to a

Yigit, H., De Andrés, J., González-Rodríguez, M., Fernández-Lanvin, D., Marçal de Oliveira, K. and Strugeon, E.

Towards Evaluating e-Commerce Sites Purchase Intention using Affective Computing, a Preliminary Study.

DOI: 10.5220/0011537200003323

In Proceedings of the 6th International Conference on Computer-Human Interaction Research and Applications (CHIRA 2022), pages 163-170

ISBN: 978-989-758-609-5; ISSN: 2184-3244

163

TAM based survey. We designed a prototype that

simulates the purchase process in an e-commerce

website, gathering pictures of the subject and

evaluating them with three different AC algorithms,

and at the same time, we asked users to fulfil the

survey at the end of the process. The results we

obtained, despite the reduced size of the sample, point

out that there is a significant correlation between the

output of the AC algorithms and some questions of

the survey.

2 RELATED WORK

Results from earlier studies demonstrate a strong and

consistent association between user emotions and

purchase behaviour. The most important factor

affecting user emotion on an e-commerce website is

undoubtedly the user interface and user experience

(Eroglu et al., 2001). These authors developed an

exemplary e-commerce site in their study and

conducted a survey with 328 people who tried the

site. They concluded that the environment of the

online shopping site will affect the emotion and

attitude of the user, which in turn will affect the

shopping results (Eroglu et al., 2001). Makkonen et

al. emphasized that, in cases of successful sales, users

are generally in a positive mood. Moreover, they

found that positive emotions are more effective than

negative ones in situations such as satisfaction,

repurchase and recommendation (Makkonen et al.,

2020). In addition, Kemppainen et al. found that the

user interface is a critical factor that can cause

negative emotions (Kemppainen et al., 2020).

Numerous studies have been conducted to evaluate

interface performance based on user feelings.

Although the selected methods are different from

each other, the main idea is to detect the emotions of

the user with the help of sensors while trying a

product, and then to direct a survey to the users and

analyse the data obtained to measure the performance

of the user interface.

2.1 Emotion Detection

Several different dimensions can be used to represent

emotions. For example, the Pleasure, Arousal and

Dominance (PAD) emotion model developed by

Mehrabian et al. uses 3 dimensions to detect all

emotions (Mehrabian & Russell, 1974). In contrast,

Fontaine and Plutchick examined emotions using

more than two dimensions (Fontaine et al., 2007;

Plutchick, 1980). Bhandari et al. used arousal and

valence dimensions to analyse user emotions in their

study examining the effects of design factors

(Bhandari et al., 2017). Johanssen et al., on the other

hand, developed a framework to define usage issues

in mobile applications based on user sentiment, and

tended to divide sentiment into 7 different dimensions

(Johanssen et al., 2019). In their study, Eroglu et al.

experienced that the PAD scheme could not capture

most of the effects of the site atmosphere and

suggested the use of a more inclusive scheme in

nature that includes reactions such as interest, anger,

surprise and disgust (Eroglu et al., 2003). In our

study, similar to such suggestion, we examined

emotions in 7 different dimensions.

Furthermore, many methods such as Galvanic

Skin Response (GSR), Electrodermal Activity

(EDA), Electroencephalogram (EEG),

Electromyography (EMG) and Facial Expression

Recognition (FER) have been used to detect emotions

in research. Gasah et al. used an EEG device to detect

emotions in their study, which was aimed to

determine whether a user interface would create

positive emotions in students (Gasah et al., 2019).

Bhandari et al. used more than one method: Facial-

EMG for valence and EDA for arousal in their study

(Bhandari et al., 2017). Lasa et al. developed a UX

evaluation tool using FER and an eye tracker (Lasa et

al., 2015). Psychophysiological sensors (GSR/EDA,

EEG, EMG etc.) can give the user's emotional state in

real time, but there are some disadvantages associated

with the use of such sensors. Firstly, measurements

made with them can be expensive. Moreover, the use

of these sensors is so intrusive that can disrupt human

nature by distracting the user and hence change the

results of the experiment (Maia & Furtado, 2016). In

our study, we used the facial expression detection

method to detect emotions. No physical contact was

made with the participants, the user carried out the

experiment in his own personal environment, and

only the user's own computer and camera were used

to collect data.

2.2 Measuring Satisfaction with

Surveys

The Technology Acceptance Model (TAM) pretends

to understand how users accept and use a technology

(Marangunić & Granić, 2015). TAM has been used in

many studies covering the subject of Human

Computer Interaction (Tang & Hsiao, 2016). It was

developed by Fred Davis in his study, “What causes

people to accept or reject information technology?”

(Davis, 1989). In response to the question, he

proposed the following bivariate approach.

CHIRA 2022 - 6th International Conference on Computer-Human Interaction Research and Applications

164

Perceived Usefulness: The degree to which a user

believes that a new technology will improve business

performance.

Perceived Ease of Use: The degree to which users

believe that they will not have difficulty using a new

technology.

We used a questionnaire survey method based on

the Technology Acceptance Model in our work.

Questionnaires are useful tools for collecting

subjective data containing users' feelings and

thoughts, and can be used to support the results of the

experiment. To the best of our knowledge, there is not

any de facto standard survey model used in studies

that perform facial expression analysis for emotion

detection. For example, in the research driven by

Mcduff et al., two questionnaires were administered,

one of which was conducted prior to the experiment

(McDuff et al., 2015). The prior experiment questions

were mostly about whether the user liked the ad or

bought the product featured in the ad. The

questionnaire used by Staiano et al. consisted of 3

different sections: (i) UX evaluation; (ii) user's

media-player usage information and (iii)

demographic information. In the user experience

evaluation section, they examined 6 different

dimensions of web site design (Staiano et al., 2012).

Faisal et al. developed a 26-question questionnaire, in

which they assessed web design features (typography,

colour, content quality, interactivity, and navigation)

to determine trust, satisfaction, and loyalty (Faisal et

al., 2017). In this study we used a questionnaire based

on this last paper. The final set of items, which were

measured using a 7-point Likert scale, was the

following:

1. It is easy to read the text on this website with the

used font type and size.

2. The font color is appealing on this website.

3. The text alignment and spacing on this website

make the text easy to read.

4. The color scheme of this website is appealing.

5. The use of color or graphics enhances navigation.

6. The information content helps in buying decisions

by comparing the information about products or

services.

7. The information content provided by this website

meets my needs.

8. Contents and information support for reading and

learning about buying process.

9. This website provides adequate feedback to assess

my progression when I perform a task.

10. This website offers customization.

11. This website offers versatility of ordering process.

12. This website provides content tailored to the

individual.

13. In this website everything is consistent.

14. Navigation aids serve as a logical road map for

buying.

15. Obviousness of buying button and links in this

website.

16. It is easy to personalize or to narrow buying

process.

17. It is easy to learn to use the website.

18. This website supports reversibility of action.

19. Overall I am satisfied with the interface of this

website.

20. My current experience with this website is

satisfactory.

21. Overall. I am satisfied with the amount of time it

took to complete the tasks for buying products.

22. Overall. I am satisfied with accuracy for this

website related to the buying process.

23. I trust the information presented on this website.

24. This website is credible for me.

25.I would visit this website again.

26. I would recommend this website to my friend.

3 OUR APPROACH

Our purpose was to check whether it is possible to

measure the satisfaction of the user with a website

through the analysis of the expressions of their face,

processed by means of affective computing

algorithms. We developed a shopping site simulation

to collect data. To know their subjective perception of

the website, participants were asked to complete two

tasks, browsing the website and completing the

questionnaire about their experience using the

website. Figure 1 shows the method pipeline.

Towards Evaluating e-Commerce Sites Purchase Intention using Affective Computing, a Preliminary Study

165

Figure 1: Method pipeline.

3.1 Data Collection

To develop the website user interface, we used the

popular JavaScript library ReactJS

. We adapted the

design from a popular shopping website and created

a different look and feel by changing some features in

the design, such as the logo, colours and layout, so as

not to bias participants. Before starting the

simulation, we included a start page that explains how

the participant must use the website, asks them to

remove their masks (when this is relevant) while

using the website, and for the necessary permissions

from the participant so that we can use the camera and

take snapshots.

After obtaining the necessary permissions, the

participant is redirected to the web site and the

purchase simulation starts. To distinguish the

participants, an artificial unique id is assigned to each

of them. The participant is asked to do some simple

tasks: browse the products and add at least 1 product

to the shopping cart to complete the simulation. A

photo of the subject is taken every 2 seconds while

they are browsing the website.

Additionally, photos are taken when the subject

adds a product to the shopping cart and finish the

shopping. The captured photos are saved in the

database in Base64 format in real-time. Moreover, the

information of the page viewed by the user at the time

the photo is taken, and the time information are also

recorded in the database. Due to possible errors,

simulation sessions are limited to 10 minutes in order

https://es.reactjs.org/

to avoid taking too many photos of the participants

and therefore to fill the database unnecessarily.

At the end of the simulation, the questionnaire is

shown to the subjects. The questionnaire is presented

in 3 different languages, English, Spanish and

Turkish. This questionnaire is designed based on the

one used by Faisal et al. in a similar study (Faisal et

al., 2017), which was designed based on well tested

previous relevant TAM studies.

In addition to these, Google Analytics web

analytics service, was used to analyse website traffic

and audience. The unique ids we assigned to the

participants enabled us to track each subject

separately.

The Google Cloud Firestore service is used to

store collected data. The website is hosted on Vercel,

a free platform for hosting static websites.

We kept the website open for 3 months. At the end

of the experiment, we wrote a simple JavaScript script

to retrieve data from the database. Survey data was

stored in CSV format. Subjects’ photos were

converted from Base64 format to PNG image format

using a Python script and stored temporarily for use

in the analysis.

3.2 Participants

At the end of the data gathering process, a total of 119

participants visited the site. Although we offer the

survey in 3 different languages (English, Spanish and

Turkish), we made the site accessible to all countries.

Most of the participants were from Pakistan and

CHIRA 2022 - 6th International Conference on Computer-Human Interaction Research and Applications

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Spain. We did not try to balance factors such as the

gender of the participant or the platform they used so

that the shopping process would proceed naturally.

Figure 2 shows the distribution of participants by

gender, age, and the platform they use.

Figure 2: Distribution of participants by gender, age, and

the platform they use.

Not all respondents who visited the website

completed the survey or were willing to allow their

photos and responses to be recorded. In these cases,

participants' data were not used in the analysis. 61%

of the participants who visited the web site allowed

their photos to be saved and completed the survey.

In addition, not all the participants had a properly

working webcam or they did not read the warnings

carefully and take off their masks before starting the

simulation. After examining the collected

photographs and cleaning the data, 49.3% of the

participants who completed the survey (30.25% of all

participants) provided valid data to be used in the

analysis.

In summary, data from 36 participants, with an

average of 57 photos, were used for the analysis.

3.3 Detecting Facial Expressions

To predict facial expressions, we first detected the

faces of the participants from the captured photos and

then saved these facial photos in the subjects’ folders.

For the sentiment prediction, we used 3 pre-trained

deep learning models that were previously trained on

the facial expression dataset. We mentioned this

process in the Emotion Detection section. Figure 3

shows the facial expression recognition pipeline.

Figure 3: The facial expression recognition pipeline.

3.3.1 Face Detection

MediaPipe is a framework that provides cross-

platform machine learning solutions. We used the

MediaPipe framework's face detection tool to detect

faces. We set the MODEL_SELECTION parameter

to 0. 0 value, which works better at finding faces up

to 2 meters, and parameter 1, better at finding faces

up to 5 meters. For the MIN_DETECTION_CON-

FIDENCE parameter we set 0.5, which means that

face detection is considered successful if the value is

greater than 0.5.

In some photos, more than one face was detected,

in which case we saved the detected faces in a

separate folder and then manually filtered the faces

that did not belong to the participant. We manually

reviewed the obtained data again and removed any

invisible, blurred or duplicate faces.

3.3.2 Facial Expression Detection

We used 3 pre-trained models, trained with ready-

made datasets, to detect facial expressions.

RMN: It was developed using Pytorch, trained using

the FER2013 and VEMO datasets, and achieved

76.82% and 65.94% accuracy on these datasets,

respectively (Pham et al., 2020).

FER-Tensorflow: It was developed using

TensorFlow, trained using the FER2013 dataset and

achieved 65% accuracy (GitHub - Xionghc/Facial-

Expression-Recognition: Facial-Expression-

Recognition in TensorFlow. Detecting Faces in Video

and Recognize the Expression(Emotion)., n.d.).

FER-Pytorch: It was developed using Pytorch,

trained using the FER2013 and CK+ datasets and

achieved 73.12% and 94.64% accuracy on these

datasets, respectively (GitHub - WuJie1010/Facial-

Expression-Recognition.Pytorch: A CNN Based

Pytorch Implementation on Facial Expression

Recognition (FER2013 and CK+), Achieving

73.112% (State-of-the-Art) in FER2013 and 94.64%

in CK+ Dataset, n.d.).

Each model predicts 7 emotions, namely happy, sad,

neutral, angry, fearful, disgusted, surprised. To get

the final result from 3 different predictions, we

averaged each emotion and assumed the emotion with

the highest probability to be the overall emotion of

the participants while shopping. We combined the

results with the survey results and saved them in CSV

format.

Towards Evaluating e-Commerce Sites Purchase Intention using Affective Computing, a Preliminary Study

167

3.4 Data Analysis

Once the data had been gathered, we assessed

whether certain facial expressions were statistically

associated with certain responses to the questions in

the questionnaire. To do this we discarded all the

facial expressions except neutral and sad, as they did

not have enough observations to conduct statistical

tests. For the case of neutral there are 16 cases in the

sample and for the sad expression 15.

As we have only two valid expressions and the

total sample is of small size, we used the non-

parametric Mann-Whitney test (Wilcoxon rank sum

test) instead of the usual t-tests to assess whether the

responses of the participants with sad expression were

significantly different from those of individuals with

neutral expression. Also, as the prior theoretical

developments do not suggest a direction in the

relationship, we used two-tailed tests.

4 RESULTS

Table 1 shows the results of the Mann-Whitney tests.

The results of each test (one for each question) are

displayed in a row in the table. We include the mean

rank for neutral and sad expressions, and the p-value

associated to the z statistic associated to the Mann-

Whitney’s U in the test. Tests evidencing significant

differences (p<5%) are displayed in bold typeface

while tests suggesting slight differences

(5%<p<10%) are displayed in italics.

Table 1: Results of the Mann-Whitney tests.

Question Mean rank

for neutral

(

n=16

)

Mean rank

for sad

(

n=15

)

p-value

1 10.44 21.93 <0.001

2 13.0

19.2

0.054

3 12.19 20.07 0.014

4 13.56 18.60 0.115

5 11.06 21.27 0.002

6 13.56 18.60 0.117

7 14.34 17.77 0.284

8 14.50 17.60 0.337

9 17.22 14.70 0.438

10 17.47 14.43 0.348

11 15.56 16.47 0.779

12 16.69 15.27 0.660

13 16.28 15.70 0.856

14 14.50 17.60 0.330

15 13.25 18.93 0.078

16 16.94 15.00 0.549

17 14.66 17.43 0.386

18 15.97 16.03 0.984

19 16.31 15.67 0.842

20 14.69 17.40 0.400

21 14.47 17.63 0.324

22 14.53 17.57 0.347

23 15.34 16.70 0.673

24 14.88 17.20 0.467

25 13.75 18.40 0.148

26 15.91 16.10 0.952

It is evidenced that for questions 1, 3 and 5 the mean

rank for the individuals with sad expression are

significantly higher than the rank for those with

neutral expression. This means that the values

obtained in the questionnaire were significantly

higher for individuals with a sad expression. Then,

this kind of users finds easier to read the text with a)

the used font type and size and b) the text alignment

and spacing. Users with sad expression have also a

stronger conviction that colour / graphics in the

website enhance navigation. For questions 2 and 15

the test evidences that differences, if not so strong, are

still present. So, the font colour is slightly more

appealing for sad users and they find a bit more

obvious the buying button and links in the website.

For the rest of the questions, tests do not show

significant differences.

5 CONCLUSION

These results of the study are initially confusing. The

three questions that evidence a strong relation with

subjects showing a sad expression are emphasizing

positive aspects of the design (regarding font, text

layout and readability and colours selection). That is,

results suggest that users who appreciate these aspects

of the design as positive are sad. After analysing the

results, we reviewed the pictures classified as sad by

the algorithms, and what we found out is that most of

them seem to be concentrated instead of sad. Thus, it

seems more reasonable to think that users that found

out the application more useable where more focused

on the task and so, were classified as sad.

Obviously, this is just a conjecture, and should be

checked with a new experiment, so we cannot assume

that as a valid result of the experiment. Nevertheless,

results suggest that there is a relation between the

subjective perception of some of the aspects of the

design and the information we gather using AC

algorithms. This encourages us to repeat the

experiment with a bigger and controlled sample of

users.

CHIRA 2022 - 6th International Conference on Computer-Human Interaction Research and Applications

168

ACKNOWLEDGEMENTS

This work was funded by the Scientific and

University Department – SCAC of the Embassy of

France in Spain (French Institute of Spain REF: Grant

02/2021) and the Department of Science, Innovation

and Universities (Spain) under the National Program

for Research, Development and Innovation (Project

RTI2018-099235-B-I00).

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