Work-in-Progress

CHI 2015, Crossings, Seoul, Korea

Effects of Agent Appearance on
Customer Buying Motivations on
Online Shopping Sites
Kazunori Terada
Gifu University
1-1 Yanagido, Gifu
501-1193, JAPAN
terada@gifu-u.ac.jp
Liang Jing
Tokyo Institute of Technology
2-12-1 Ookayama, Meguro-ku
Tokyo 152-8550 JAPAN
jing@ntt.dis.titech.ac.jp
Seiji Yamada
National Institute ofInformatics / SOKENDAI
2-1-2 Hitotsubashi, Chiyoda,
Tokyo, 101-8430 JAPANseiji@nii.ac.jp

Abstract
Although product recommendation virtual agents(PRVAs) are used in a large number of online shopping
websites, the optimal types of agents in this context
remain unclear. In the present study, we tested whether
agent appearance affects people’s buying motivations andanalyzed the key factors in persuading people to buy
products. The experimental results confirmed thatrecommendation effects vary according to agent
appearance. Furthermore, we obtained a partial order
ranking of the agent types, representing the effectivenessof their recommendations. The factor analysis results
indicated that the perceptions of familiarity andintelligence in relation to appearance are the key factors inpersuading people to buy products.

Author Keywords
virtual agent; recommendation; on-line shopping

ACM Classification Keywords
Permission to make digital or hard copies of part or all of this work for personalor classroom use is granted without fee provided that copies are not made or
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Copyright is held by the author/owner(s).
CHI’ 15 Extended Abstracts, Apr 18-23, 2015, Seoul, Republic of Korea.
ACM 978-1-4503-3146-3/15/04.
http://dx.doi.org/10.1145/2702613.2732798

H.5.2 [User Interfaces].

Introduction
Many consumers routinely shop online to purchase various
types of products, including electrical appliances, foods,
books, clothes, and other commodities. In online shopping
environments, product recommendations, which were929

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CHI 2015, Crossings, Seoul, Korea

previously provided by salespersons, were once expected tobe taken over by product recommendation virtual agents(PRVAs) [10]. However, recent research has suggested
that a substantial number of virtual agents on commercial
websites have disappeared [8]. Minoun et al. noted thatappearance inadequacy and lack of intelligence are the key
factors in the disappearance of PRVAs. However, theoptimal types of agents for product recommendations
remain unclear. Hence, we investigated which types of
PRVAs, including those composed of text only or with avideo of a real person, are effective through systematic
experiments with participants in an online shopping
environment.
Although studies on the evaluation of virtual agents inbasic tasks [1] and various experimental studies on theproperties (e.g., behaviors, appearance, or adaptation) of
virtual agents [7, 9] have been conducted, few
experimental studies have examined the recommendation
effect of PRVAs in online shopping environments. The
recommendation effect refers to the positive influence of
PRVA recommendations on a user’s motivation to buy.
Qiu et al. investigated the influence of productrecommendation agents on users in different modalities
and embodiments in an online shopping environment [10].
This study provided strong evidence for the influence ofhumanoid embodiments and output modalities inenhancing social interactions. Their main area of interest
was the investigation of agent trustworthiness and social
presence through systematic experiments using
questionnaires.

(f) Buddha-statue agent
Figure 1: The six PRVAs used in
the experiment

Various studies have also investigated the properties ofvirtual agents from a multi-modal perspective [2]. Suitablegestures for virtual agents have also been studied [3].
Unfortunately, few systematic investigations have been

conducted on the properties of PRVAs in online shopping.
We conducted experiments to investigate which types of
PRVAs are effective in making recommendations in an
online shopping environment. For this purpose, we
prepared a simulated online shopping website withrepresentative PRVAs and products. Participants used thewebsite with all combinations of PRVAs and products,
evaluated the recommendation effects for each conditionand scored their impressions of the products.

Experiment
Design and Materials
We used a six (PRVA) by six (product) within-participants
factorial design.
We selected six characteristic PRVAs with regard to theagency and experience aspects that were investigated byGray et al., who conducted an experiment with many
participants and found that these two significant
dimensions characterize various agents [4]. Because wealso considered these two dimensions to be significant inclassifying PRVAs, we prepared the six PRVAs (250 × 250
pixels) shown in Figure 1, including videos of a real person
and text.
We selected six products (Figure 3) for the experiments.
We conducted a preliminary experiment to gaugeuser-buying motivations with 41 participants using aquestionnaire that was scored on a seven-point Likert scale
and identified the six products whose scores were closest
to the intermediate value of four points. The average ofthe selected products’ prices was approximately $12.
We prepared the PRVA recommendations for the products
using customer reviews. These recommendations wereapproximately 200 Japanese words in length and took930

Work-in-Progress(a) Akapakun

CHI 2015, Crossings, Seoul, Korea

approximately 20 seconds for participants to read. A
balloon that included the recommendation appeared
directly above each PRVA in the experiment.

Computer Engineering department at the Tokyo Instituteof Technology who did not have any previous knowledge
on PRVAs, especially with regard to their appearance.

Akapakun Bath Cleaner

Each participant was asked to attempt all 36
combinations of the six PRVAs and six products in an
online shopping environment in counter-balanced orders.
We set the orders of the combinations carefully so that no
PRVA and product appeared two times in a row.

Just put this into a bath to clean it!!
Price: 1,200 JPY FREE Shipping
In Stock.

I’d like to introduce thisuseful product forcleaning your bath.

(b) Round cushion(c) Folding board(d) PC glasses(e) Mask(f) Barometer

Figure 3: The six products usedin the experiment

Figure 2: The main page used in the experiment

We prepared the simulated online shopping website shownin Figure 2. The brief information on each productincluded a photo 1 , the price, the shipping cost, a short
explanation of the product as well as a PRVA 2 and its
recommendation 3 for the product, all of which appeared
as in a real online shopping environment. The participants
viewed the information and read the PRVA’s
recommendation. They then rated their buying
motivations on a scale that ranged from 0-100 points (0:very weak, 50: neutral, 100: very strong) through a
pop-up window that appeared 30 seconds after the web
page opened. On the web page, all PRVAs except the one
composed of text behaved in the same manner, i.e.,slightly moving their heads and making pointing gestureswith their right hands, without sound. We set thesebehavioral restrictions to ensure that PRVAs differed onlyin terms of appearance.
Participants, procedures and measurements
We recruited 41 participants (28 males and 13 females
between the ages of 22 and 26 years). The groupconsisted of graduate students and staff from theWe quantitatively recorded subjective buying motivation.
The participants were requested to answer, on a scaleranging from 0 to 100, “How much do you want topurchase this product?”
Additionally, we included 20 questions on a questionnaire
to investigate the participants’ impressions of the PRVAs,
as shown in Table 1. The participants were asked toanswer the questions directly after completing theirexperiments. The responses were recorded on aseven-point Likert scale (1 = “strongly disagree”; 7 =“strongly agree”).
Table 1: Experiment questions (adjectives) (SD on aseven-point Likert scale)
Q1
Q4
Q7
Q10
Q13
Q16
Q19

Trustworthy
Assertive
Confident
Thoughtful
Optimistic
SturdySensible

Q2
Q5
Q8
Q11
Q14
Q17
Q20

Friendly
Bold
Responsible
Intelligent
Warm
Dominant
Outgoing

Q3
Q6
Q9
Q12
Q15
Q18

HonestInteresting
Communicative
Attractive
StrongKnowledgeable

Results
A two-way analysis of variance (ANOVA) on buying
motivation confirmed that there were no significant
interactions between agents and products (F25,14 = .92,
p = .57), whereas the main effects of both agents

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CHI 2015, Crossings, Seoul, Korea(F5,14 = 13.6, p < .01) and products(F5,14 = 23.2,p < .01) were statistically significant. A
post-hoc Tukey’s test on agents revealed that significant
differences were evident between the following eight pairs:
human-like agent and real person (p = .0019), robot-likeagent (p = .0003), Buddha statue agent (p = .0000), text
(p = .0000), Buddha statue agent and real person(p = .0052), robot-like agent (p = .0225), dog-like agent(p = .0000), and dog-like agent and text (p = .0072).
Figure 4 shows the average buying motivation for
products and participants plotted against agent type,sorted in ascending order to clarify these eight differences.

four questions with the top factor loadings, are shown in
Table 2. We interpreted the first four factors as“Familiarity,” “Intelligence,” “Power,” and “Rightness”
through a careful summarization of the adjectives listed in
the answers to the questionnaire (Table 2).
Table 2: Factors [eigenvalue, contribution ratio] and questions(factor loadings)
Factors
Familiarity [5.4,27.2%]
Intelligence [3.2,15.8%]
Power [2.4,12.2%]
Rightness [2.3,11.5%]

Questions with high factor loadings
Q14(.84),Q12(.79),Q2(.78),Q13(.74)
Q10(.87),Q11(.81),Q18(.73),Q9(.68)
Q16(.89),Q15(.83),Q17(.64),Q5(.57)
Q3(.61),Q7(.58),Q4(.46),Q8(.39)

Buying motivation

Figure 5 shows the 2D-coordinate of the Familiarity andIntelligence factors. In this figure, the participants’
average impressions of the six PRVAs were plotted andadjusted on the x-y axes.
0.4
0.3
0.2

Buddha-agent

Text

Robot-agent Real person Dog-agent Virtual human

Figure 4: Agent type effect on buying motivation, averaged
over products and participants and listed in ascending order.
Error bars indicate standard errors. ** p < .01, * p < .05,
+ p < .1

Intelligence

0.1
0
-0.1

Robot-agent

-0.2
-0.3
-0.4

Text
Dog-agent-0.5

Furthermore, to investigate the relationship between the
PRVA factors and the recommendation effects, we
performed a factor analysis (varimax rotation) on theparticipants’ impressions of the PRVAs using the scores on
the 20 questions. The results, including the four factors
with the top eigenvalues and contribution ratios and the

Virtual humanReal person

Buddha-agent

-0.6

-0.8

-0.6

-0.4

-0.2

0

Familiarity

0.2

0.4

0.6

0.8

Figure 5: Averaged factor scores for six PRVAs plotted on the
Familiarity-Intelligence coordination

Table 3 shows the correlation coefficients between the932Work-in-Progress

CHI 2015, Crossings, Seoul, Korea

average buying motivation for the six products and thefactor scores for the six PRVAs for all participants.
Statistically significant relationships were observed
between buying motivation and the scores of three factors,
Familiarity, Intelligence, and Rightness.
Table 3: Correlation coefficient between buying motivationand factor scores. ** p < .01, * p < .05.
Buying motivation

Familiarity.45 **

Intelligence
.23 *

Power
-.07

Rightness.23 *Discussion
Interactions between agents and products were notstatistically observed, indicating that agent and producttypes independently affect buying motivation. It is quite
natural for product type to affect buying motivation
because people have their own demands. However, thecurrent results provide clear evidence that agent typeaffects buying motivation. The recommendation method
was controlled across all agents. All agents used the same
dialogue window and exhibited the same behaviors. The
only difference that the participants observed among theagents was in their appearance. Therefore, the hypothesisthat agent appearance affects customer-buying
motivations was confirmed.
We obtained a partial order ranking of the types of agentsthat have a stronger effect on product recommendations.
Surprisingly, the recommendation effect of a real person
was not particularly high. Rather, the recommendation ofa virtual human agent had the strongest effect onparticipant buying motivations. The reason for this result
is clearly shown in Figure 5. The average factor scores forboth Familiarity and Intelligence were high for the virtualhuman agent, indicating that both factors are necessary to
persuade people to buy products in an online shopping

environment. However, the factor score of Familiarity was
not high for a real person. This might explain why therecommendation effect of a real person was lower thanthat of a virtual human. The results partially replicatedthe findings of Mimoun et al., who noted that appearance
adequacy and intelligence are key factors for successful
PRVAs [8].
It is widely accepted that the human mind has two
distinct thought systems, System 1 and System 2 [5].
System 1 is fast, intuitive, and emotional. System 2 isslow, rational, and deliberative. Feelings of positive
recognition and familiarity toward others, which areprocessed in System 1, are known to increase trust [6].
On the other hand, an impression of an agent as having
rich, reliable knowledge about products might affect
System 2 thought. Therefore, Familiarity and Intelligencemight have contributed to the participants’ trust in therecommendations of the human agent through System 1
and System 2 thought, respectively.
The lowest factor score was for the dog agent’sIntelligence. Nevertheless, there were no significant
differences in the recommendation effects of the dog andvirtual human agents. This is because Familiarity is more
important than Intelligence in persuading people to buy
products. This interpretation is evident because thecorrelation coefficient between buying motivation and thefactor score for Familiarity was higher than that forIntelligence.
The Buddha statue agent performed the worst in terms ofrecommending products. Although participants perceived
it to have high Intelligence, based on associating it withomniscience and omnipotence, they did not emotionallyempathize with the statue’s recommendations.

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The results indicate that the use of text only is notparticularly effective in persuading people to buy products.
The reliability of information is typically assessed
according to the reliability of the person who is the sourceof the information. Participants might not be able toattribute personality to text recommendations. This isevident from the fact that the factor scores for bothIntelligence and Familiarity were low for text.

Conclusion
In the present study, we aimed to experimentallyinvestigate which types of PRVAs are effective inrecommending products for people to buy on an online
shopping site. An experiment with six PRVAs with varying
appearances confirmed that the recommendation effectvaries according to agent appearance. We obtained apartial order ranking of the types of agents that havestronger effects on product recommendations. The resultsof a factor analysis indicated that Familiarity andIntelligence are the key factors in determining which typesof agents are suitable for making recommendations.

Acknowledgements
This study was supported by MEXT KAKENHI“Cognitive Interaction Design” (26118005).

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