Exploring the Impact of Persuasive Features
on Customer Satisfaction Levels of E-Commerce
Websites Based on the Kano Model
Muna M. Alhammad1 , Isaac Wiafe2(B) , and Stephen R. Gulliver3
1 King Saud University, Riyadh 11451, Saudi Arabia
malhammad@ksu.edu.sa
2 Department of Computer Science, University of Ghana, Accra, Ghana
iwiafe@ug.edu.gh
3 University of Reading, Reading RG6 6UD, UK
s.r.gulliver@henley.ac.uk
Abstract. This study investigated user needs and expectations in relation to the 28
persuasive features of the Persuasive SystemsDesignmodel. It adopted theKano’s
model of customer needs and expectations to examine perceive user satisfaction or
dissatisfaction levels of the various system features on e-commerce websites. The
findings provide guidelines for designing e-commerce platforms and websites that
seek to employ persuasive strategies to enhance user experience. It was observed
that persuasive system features do not consistently affect user satisfaction and
dissatisfaction levels. Features relating to dialogue support had the highest influ-
ence on user satisfaction levels; followed respectively by credibility support and
primary task support. Social support features do not have a high influence on user
satisfaction. No persuasive system feature emerged as a “must-be” feature.
Keywords: Persuasive system features · User satisfaction · Interactive
e-commerce design · Kano model
1 Introduction
Research has demonstrated that although usability plays a key role in the success of
e-commerce websites, it is not the main factor for success [1]. A successful e-commerce
website needs to attract users, emphasize credibility to stimulate trust, enhance customer
interactions, present products and services in an inspirational and attractive manner, and
persuade customers (via application of persuasive features) to purchase from the website
[2]. It must ultimately result in an experience that produces ‘customer satisfaction’. As
the argument on which factors are more relevant on e-commerce websites and its related
ethical considerations continue to progress [3, 4], this study focuses its investigation on
persuasive features that influences user satisfaction on e-commerce websites. Although
persuasive features play a major role in e-commerce platforms [5], they have not been
adequately explored [6]. In particular, the ability to select effective persuasive features
to enhance persuasive experience remains unsolved.
© Springer Nature Switzerland AG 2021
R. Ali et al. (Eds.): PERSUASIVE 2021, LNCS 12684, pp. 178–192, 2021.
https://doi.org/10.1007/978-3-030-79460-6_14
Exploring the Impact of Persuasive Features on Customer Satisfaction Levels 179
Persuasive features trigger activities that promote continuous interactions on web-
sites [7] and motivate consumers to use services or buy products from e-commerce
websites [8, 9]. It has been argued that e-commerce websites need to exhibit persua-
sive functionalities in addition to providing information on product and services [2].
Although influence strategies have always been part of commercial activities and thus
the need to incorporate them on e-commerce websites is not novel, the introduction of
persuasive technologies, and behavior change support systems, presents an opportunity
to e-commerce designers to enrich e-commerce websites. Persuasive systems design
is the use of technological artefacts and features to influence user cognition to a pre-
determined one [10, 11]. Existing research has demonstrated that persuasive systems
have been effective in areas such as health, education, environmental issues, and energy
conservation; yet studies that focus on the use of persuasive features in e-commerce is
lacking. In particular, the contextual value of specific types of persuasive features has not
been adequately explored. Limited studies have examined or evaluated the persuasive
power of website features and components in e-commerce [12]. However, this is vital,
considering that e-commerce websites must influence customers in addition to providing
brochures and catalogues of products and services. E-commerce website users may per-
ceive satisfaction based on their interactions and experiences of the websites they use.
Studies have shown that user satisfaction and persuasion are strongly correlated [13].
Accordingly, it is imperative to investigate persuasive features that promote customer
satisfaction on e-commerce websites [14].
This study, therefore, seeks to examine how different persuasive features of e-
commerce websites influence customers’ satisfaction levels. Specifically, the perception
of users concerning the 28 persuasive system features, proposed by Oinas-Kukkonen
and Harjumaa [15], was assessed using Kano’s model [16] to determine how customers
classify them in terms of relevance. The findings of this study provide relevant infor-
mation for further investigations on methods for selecting the most effective persuasive
features: an issue which is challenging to persuasive systems designers.
2 Related Literature
2.1 Customer Satisfaction
It is almost impossible to initiate and establish long-term relationships with customers if
their needs and expectations are not fully understood or met [17]. A good understanding
of customers’ needs and expectation is a necessary step to fulfil their satisfaction [18].
In interactive e-commerce websites, customer satisfaction is relevant [19] and thus sev-
eral organizations use customer satisfaction ratings as a key indicator of performance
[20]. Customer satisfaction is the assessment of the perceived difference between prior
expectations and the actual performance of the product or service [21], and is an indicator
of positive fulfilment of customer needs. Some researchers argue that customer needs
and satisfaction are the ultimate objectives of every organization [22]. Accordingly, the
need to meet their expectations cannot be overemphasized, particularly considering that
these needs evolve over time [16]. In contemporary interactive e-commerce websites,
customer satisfaction is considered as a key ingredient of success [19]. Thus, several
180 M. M. Alhammad et al.
theories, models, and frameworks have been proposed to extend knowledge and pro-
vide guidelines on how customer satisfaction can be achieved. Perhaps one of the most
dominant theories applied in customer satisfaction research is Herzberg’s motivation-
hygiene theory (i.e. the two-factor theory) [23]. The theory distinguishes concepts of
satisfaction and dissatisfaction as two distinct constructs. This notion is contrary to argu-
ments made by Zhan and Dran [8] that considers satisfaction as distinct values of one
dimension. Herzberg [24] argued that motivation and hygiene factors play an essential
role in customer satisfaction. Even though these factors continue to remain relevant in
contemporary investigations of customer satisfaction research, treating satisfaction and
dissatisfaction as mutually exclusive constructs is problematic.
Due to the limitation of Herzberg’s motivation-hygiene theory, some studies adopt
SERVQUAL [25] for investigating satisfaction. SERVQUAL is a multi-item scale for
measuring fivemain dimensions (i.e., Tangibles, Reliability, Responsiveness, Assurance
and Empathy) of customer needs and perceptions on quality performance. SERVQUAL
seeks tomeasure service quality, nonetheless, it has been adopted for accessing customer
satisfaction in several studies: with the assumption that there is a relationship between
service quality and customer satisfaction [26]. However, its use for measuring customer
satisfaction is contentious, since arguments on the relationship between service quality
and customer satisfaction are diverging [27–29]. It has been argued that the relationship
between service quality and customer satisfaction is non-linear [16]. So, SERVQUAL
is not suitable for assessing customer satisfaction, although it is useful for measuring
service quality. The Kano’s model [16] on the other hand addresses the limitations of
SERVQUAL. The model categorizes service quality features based on their effects on
customer satisfaction that support a business’s strategic and tactical decisions [30]. This is
a key step as it measures customers’ perception of service quality features and categories
them based on their influences on customer satisfaction. In this study, the Kano’s model
is used to measure customer satisfaction since it provides the relevant variables needed
for the study.
2.2 Kano’s Model of Customer Satisfaction
The Kano model distinguishes between four forms of customer expectations for product
and service quality. This includes i) must-be features, ii) one-dimensional features, iii)
attractive features and iv) indifferent features. Themodel has been applied inmany fields,
including service quality assessment [19], user experience assessment [8], and compen-
sation systems [31]. It argues that for businesses to excel, the first three forms must
be met. However, although the presence of “must-be” features is not noticeable by cus-
tomers, their absence or non-performance leads to total dissatisfaction. One-dimensional
features (i.e. performance or linear quality) are specific needs that are noticeable by cus-
tomers when present. Hence, the presence of one-dimensional features is always noted,
and the absence of one-dimensional features leads to customers dissatisfaction. Attrac-
tive or excitement features are attributes that are not expected by customers, yet their
presence will delight them and inspire loyalty. Attractive features increase satisfaction
levels, but their absence does not affect dissatisfaction. This is because attractive features
seek to address hidden and unarticulated needs and it is mostly a challenge to identify
them. Indifferent features do not influence customer satisfaction or dissatisfaction level
Exploring the Impact of Persuasive Features on Customer Satisfaction Levels 181
and thus, they do not have an impact on customer satisfaction. The model provides a
logical extension of Herzberg’s motivation-hygiene theory [24] where Must-be features
are similar to hygiene features, Attractive features maps to motivational features and
One-dimensional features maps to bivalent features.
The KANO model: facilitates the identification of quality expectations and time
transition of these quality factors [8]; is versatile, and has been adapted for a range of
different purposes [32]; puts customer satisfaction first in design features of products
and services; and has been successfully applied to assess attributes of website quality
[8]. As such the KANO model is appropriate for assessing persuasive system features,
i.e. to categorize features based on the influence of features on customer satisfaction, in
order to provide a better understanding of customer requirement priority, and address
the nature of changing quality features.
2.3 Persuasive Systems Features and E-Commerce Websites
Persuasion is a communication process involving an individual (persuader) sending a
message to a recipient (persuadee), intending to influence the recipient’s attitude and/or
behavior; whilst leaving the recipient with the power of decision. Although this activity
is not new, advancement in computer technology has enhanced its ease of application.
Increasingly, systems and technologies are designed with the sole intention of altering
cognition. Accordingly, new theories and frameworks [10, 14, 26, 27] have been pro-
posed to facilitate the use of technological artefacts for influencing target user behav-
ior. The Persuasive Systems Design model [15] is arguably the most used framework
for designing and evaluating persuasive systems and has been used to assess persua-
sive experiences in several domains including enterprise resource management systems
[36], alcohol and smoking management systems [37], and knowledge sharing among
academics [38]. The Persuasive Systems Design model highlights seven postulates for
analyzing and designing persuasive systems. It further argues that there are twenty-
eight (28) persuasive features that can be categorized into four main areas: primary
tasks (Reduction, Tunneling, Tailoring, Personalization, Self-Monitoring, Simulations,
Rehearsal); dialogue support (Praise, Rewards, Reminders, Suggestion, Similarity, Lik-
ing, Social Role); Credibility support (Trustworthiness, Expertise, Surface credibility,
Real-world feel, Authority, Third-party endorsements, Verifiability) and Social support
(Social Learning, Social Comparison, Normative Influence, Social facilitation, Cooper-
ation, Competition, Recognition (see [15] for a more detailed definition of each feature).
These features have been used in a range of different domains to encourage certain target
behavor. Some studies have argued that they influence users’ purchase intentions [39] by
providing good navigational usability, eliminate trust and security doubts, and promote
smooth transactions.
3 Research Approach
An exploratory studywas designed to investigate user preferences of persuasive features,
i.e., how each persuasive feature influences user satisfaction levels with regard to e-
commerce. A questionnaire was used to identify and classify customer perception into
182 M. M. Alhammad et al.
four categories as suggested by Kano et al. [16]. The functional or dysfunctional form
of each of the 28 persuasive features was measured. As suggested by Xu et al. [40], the
functional formwas specified as ‘howwould you feel if this particular feature is presented
in a product/service’. The dysfunctional form was specified as ‘how would you feel if
this particular feature is not presented in the product/service’. It is expected that Kano’s
model will classify persuasive features based on their impact on user satisfaction. Based
on responses to the functional and dysfunctional forms of each question, customers’
requirements for each feature were classified into different categories using the Kano
Evaluation (see Table 2). The frequency distribution for each feature determined Kano’s
category classification [19]: with the highest response frequency being the dominant
class. However, in situations where the dominant class is absent or sensitive to change
in frequency, two additional measures are used to determine it (i.e. category strength
and total strength) [41]. The quality response is measured by calculating the Satisfaction
Coefficient (CoS) and the Dissatisfaction Coefficient (CoD) - as shown respectively
in Eqs. 1 and 2; where A is Attractive, O is One-dimensional, M is Must-be and I is
Indifferent (Table 1).
= (A+ O)CoS (1)
A+ O+M+ I
and ( + )= (M O)CoD + + + × (−1) (2)A O M I
Table 1. Kano’s evaluation framework
Customer requirements Dysfunctional (negative) question
1 2 3 4 5
Functional (positive) question 1 Q A A A O
2 R I I I M
3 R I I I M
4 R I I I M
5 R R R R Q
A: Attractive, O: One-dimensional, M:Must-be, I: Indifferent, R: Reversal, Q: Questionable
1:I like it, 2:I expect it, 3:I’m neutral, 4:I can tolerate it, 5:I dislike it
Two independent sampleswere used for evaluating the same set of persuasive features
in relation to physical and digital products. The separation of the product sought to reduce
the length of the questionnaire and multiple evaluations. A pre-test was conducted using
6 participants and their recommendations were considered in the final version of the
questionnaire. The CVSCALE was used and the pre-test results showed high internal
reliability, with Cronbach’s α values above the 0.7.
Exploring the Impact of Persuasive Features on Customer Satisfaction Levels 183
The questionnaire (see https://www.dropbox.com/s/dthnyjnviagpn0u/Questionaire.
pdf?dl=0) was administered online using ‘SmartSurvey.com’. The survey link was sent
through different media channels, including Facebook, Twitter, LinkedIn, Emails and
Amazon Mechanical Turk. A total of 500 links were sent. Amazon Mechanical Turk
was used to target a diverse range of responses. All in all, 250 responses were obtained
indicating a 50% response rate. A non-response bias analysis was conducted using
the first 50 responses, and the findings indicated that non-responses did not affect the
findings. Out of the 250 responses, 45 were excluded because 32 had no online shopping
experience, and although the remaining 13 had shopped online before they have never
browsed online shopping websites. The latter demonstrated a conflict in their responses
and thus were excluded from the study.
4 Results
4.1 Respondents’ Characteristics
A larger proportion of respondents were those who buy physical products. This was
almost three-quarters (76.5%) of the respondents (see Table 2). Out of the total of 157
respondents who buy physical products often, 56% were females and 44% were males.
Forty-nine per cent (49%) of respondents who buy physical product were between
25–34 years. Most respondents were either students (38.8%) or in full-time employ-
ment (36.3%). With respect to age, majority of respondents (49.8%) were between
25–34 years, and the remainder were 18–24 (23.9%), 35–54 (20.5%) and 5.8% for those
over 55 years old. In terms of the highest qualification, 44.4% of the respondents hold
a bachelor’s degree, 35.1% have master’s degree and 13.2% have a doctoral degree or
equivalent. The least educational qualification was high school degrees (5.4%). The dis-
tribution of those who tend to buy digital products is similar to those who buy physical
products except for gender, where male tend to buy digital products while female tend
to buy more physical products. All respondents had experienced online shopping and
bought an item online within the past six months. Respondents were from a range of
different nationality including theUK,USA, Saudi Arabia, India and Pakistan. See Table
2 for details of respondents’ characteristics.
4.2 Kano’s Categorization of Persuasive Features
TheKano’s categories for the 28 persuasive features were generated and the extent of sat-
isfaction and dissatisfaction were also calculated (see Table 3). Variations in satisfaction
and dissatisfaction levels were observed among the various persuasive features. Features
relating to primary task support (PT) (i.e., reduction, tunneling, tailoring, personaliza-
tion, self-monitoring, simulation and rehearsal) recorded coefficient of satisfaction (CoS)
values ranging from 0.30–0.45. Thus, they impact customer satisfaction between 30–
45%, whereas their absence may result in customers dissatisfaction (CoD) between 8
and 64% - see Table 3. The presence of personalization features in e-commerce websites
was observed to increase customer satisfaction by 31%, however, its absence does not
significantly affect customer dissatisfaction (8%). Tunneling and rehearsal increase cus-
tomer’s satisfaction (i.e., by 39% and 36% respectively) and their absence also impacts
184 M. M. Alhammad et al.
dissatisfaction levels (i.e., 47% and 46% respectively). Out of the seven primary task sup-
port features, one persuasive feature (i.e., self-monitoring) was found to be of significant
importance since it had total strength value above 60%. Thus, it is classified as a “one-
dimensional” feature (see Table 3). This indicates that the presence of self-monitoring
increases customer satisfaction by 45% and affect dissatisfaction by 64%.
Dialogue support (DS) features (i.e., praise, rewards, reminders, suggestion, similar-
ity, liking and social role) was observed to impact customer satisfaction levels between
23% and 63%; whilst their absence impacts dissatisfaction between 13% and 46% -
see Table 3. Reward significantly impacts satisfaction because it recorded 65.9% for
total strength value and was classified as being “attractive”, i.e., presence of reward
significantly improves customers satisfaction by 63%, yet its absence will not result in
customers dissatisfaction (i.e. 23%). Liking had a total strength value of 58.5%, while
customers extents of satisfaction and dissatisfaction were 45% and 46% respectively.
Table 2. Samples and total sample overview
Physical Digital Total sample
Age 18–24 22.3% 29.2% 23.9%
25–34 49% 52% 49.8%
35–54 22.3% 14.6% 20.5%
55+ 6.4% 4.2% 5.8%
Gender Male 44% 58.3% 47.3%
Female 56% 41.7% 52.7%
Level of education High school 5.7% 4.2% 5.4%
Bachelor’s degree 42.7% 50% 44.4%
Master’s degree 36.3% 31.2% 35.1%
Doctoral degree 12.7% 14.6% 13.2%
Other 2.5% 0% 1.9%
Current occupation Employed 49.7 49.9 49.7
unemployed 4.5% 0% 3.4%
Homemaker 4.5% 6.2% 4.9%
Retired 1.9% 2% 1.9%
Student 38.8% 41.7% 39.5%
Other 0.6% 0% 0.5%
Mean shopping experience in 6 months? 7 times 8 times 8 times
Browsing frequency Rarely 7.6% 6.3% 7.3%
Sometimes 27.4% 20.8% 25.8%
Often 49.7% 47.9% 49.3%
Always 15.3% 25% 17.6%
Exploring the Impact of Persuasive Features on Customer Satisfaction Levels 185
Table 3. Results of Kano’s model analysis
Motivation Category based on Category strength Total strength Category based on CoS CoD
frequency strength
Primary task Reduction I 27.8 44.9 I 0.37 −0.32
support Tunnelling I 12.7 53.7 I 0.39 −0.47
Tailoring I 34.6 42.9 I 0.30 −0.28
Personalisation I 30.7 24.9 I 0.31 − 0.08
Self-Monitoring O 8.3 65.9 O 0.45 − 0.64
Simulations I 40.5 38.0 I 0.31 − 0.22
Rehearsal I 16.1 53.7 I 0.32 − 0.46
24.39 46.29 0.35 − 0.35
Dialogue Praise I 52.7 25.4 I 0.23 −0.13
support Rewards A 13.7 65.9 A 0.63 −0.23
Reminders I 40.0 36.1 I 0.26 −0.30
Suggestion I 36.6 39.5 I 0.33 −0.28
Similarity I 13.2 55.6 I 0.43 −0.43
Liking I 8.8 58.5 I 0.45 −0.46
Social Role I 38.5 38.0 I 0.33 −0.22
29.07 45.57 0.38 −0.29
Credibility Trustworthiness O 17.1 71.7 O 0.52 −0.66
support Expertise O 9.3 68.8 O 0.43 −0.66
Surface I 38.0 30.7 I 0.22 −0.28
credibility
Real-world feel O 12.2 69.3 O 0.49 −0.64
Authority I 31.2 45.4 I 0.30 −0.34
3rd party I 25.9 48.3 I 0.31 −0.37
endorsement
Verifiability I 20.0 49.8 I 0.28 −0.46
21.96 54.86 0.36 −0.49
Social support Social Learning I 50.2 26.8 I 0.24 −0.14
Social I 43.4 33.7 I 0.26 −0.22
Comparison
Normative I 48.8 29.8 I 0.24 −0.22
Influence
Social facilitation I 49.8 31.7 I 0.22 −0.24
Cooperation I 42.9 37.6 I 0.25 −0.29
Competition I 62.9 20.5 I 0.13 −0.14
Recognition I 34.1 40.0 I 0.35 −0.23
47.44 31.44 0.24 −0.21
Credibility Support (CS) features (i.e., trustworthiness, expertise, surface credibil-
ity, real-world feel, authority, third-party endorsement and verifiability) were observed
to impact customers satisfaction between 22% and 52%, and dissatisfaction between
28 and 66% - see Table 3. Trustworthiness, real-world feel, and expertise had total
strength values of 71.7%, 69.3% and 68.8% respectively. These features fell into the
category of “one-dimensional”. Hence, their presence in e-commerce websites may
increase customer’s satisfaction by 52%, 49%, and 43% respectively and their omission
will negatively impact it by 66%, 64%, and 66%.
186 M. M. Alhammad et al.
Social support (SS) features (i.e., social learning, social comparison, normative influ-
ence, social facilitation, cooperation, competition and recognition) affects satisfaction
levels between 13% and 35%, and dissatisfaction levels between 14% and 29% - see
Table 3. This indicates a relatively weak form of influence on customer’s satisfaction
and dissatisfaction. All social support feature was observed to be in the “indifferent”
category.
5 Discussion and Implications
Although some existing studies have investigated the relationship and impact of per-
suasion or persuasive systems features on e-commerce websites, none have investigated
the phenomenon using the Kano model. Yet, the Kano model provides considerable
insight on how customers/users of e-commerce websites evaluate their satisfaction lev-
els. The findings and observations made from this study present several implications on
persuasive system design features and e-commerce website designs in particular.
5.1 Primary Task Support Features
Persuasive system features such as primary task support are expected to facilitate user
performance in accomplishing their objectives. Almost all primary task support fea-
tures were indifferent to users of e-commerce platforms (except self-monitoring). This
demonstrates that current e-commerce users do not pay much attention to activities that
enhance their primary task. It is worth noting that the omission of primary task support
as a persuasive feature is not the same as the omission of the primary task itself. That is,
without primary task support, users are still be capable of perform all relevant activities
on the websites, yet persuasive features, e.g. “one-click” purchase recommendations,
the provision of virtual fitting rooms, and “wish-list”, are expected to enhance the pri-
mary shopping experience. The classification of most primary task support features
as indifferent is further supported by the argument that features, such as personaliza-
tion, recommendation, and tailoring, do not play much of a role in how users perceive
e-commerce websites [12]. Mostly, e-commerce website users prefer to enter their per-
sonal data at checkout rather than making use of personalization services provided by
the sites.
This finding, however, contradicts much existing knowledge that argues that the
provision of features that seek to support users to locate their desired products with ease
is paramount in e-commerce websites. According to Chu et al. [12] users who have no
specific product in mind find primary task support features more useful since, as such
features make it easier to locate items. Also, first-time users expect websites to provide
themwith guides as to how they register or checkout during product purchase. It has also
been argued that personalization eases information processing and thus creates positive
emotional states in users [42]. Others [43] have argued that personalization is the epitome
of persuasion on e-commerce websites. Additionally, even though tailored content is
provided to support product selection, users consider such content as ordinary product
categorization and do not believe that the tailored content meets their needs; a claim has
also been validated by [12]. Other studies have demonstrated that the provision of search
Exploring the Impact of Persuasive Features on Customer Satisfaction Levels 187
tools and clear layout of information are not paramount on e-commerce websites when
compared to education, medicine and financial websites [8].
Self-monitoring emerged as a “one-dimensional” feature. Self-monitoring features
provide e-commerce users with the tendency to monitor and adjust their activities or
purchase behavior to ensure that it is appropriate based on how it is perceived by others.
Findings from this study conform to studies that argue that the ability of users to monitor
one’s self is imperative in mobile shopping [44]. Self-monitoring plays a moderating
role in website use [45] and thus it impacts e-commerce customer satisfaction levels.
As a one-dimensional feature, it measures performance on a linear scale and thus it is
noticeable by customers when present. Accordingly, it is recommended that designers of
e-commerce website must ensure that they provide system support that facilitates users
to monitor their actions.
5.2 Dialogue Support
The provision of advance Dialogue support features has become rampant in recent
e-commerce platforms. For instance, features including ExpertClerk [46] have been
designed to support customers on e-commerce platforms to imitate sales clerks. Others
have advocated for the provision of search and choice support [47], yet findings from
this study suggest that dialogue support features do not play a key role in e-commerce
platform customer satisfaction. Six out of the seven persuasive dialogue support features
(Praise, Reminders, Suggestion, Similarity, Liking and Social Role) were observed to
be “Indifferent”. The exception was Rewards, which was classified as an “Attractive”.
It is intriguing to note that most dialogue support features were perceived as indifferent
in other studies [14], which also demonstrated that dialogue support features were the
most dominant e-commerce platform system features. Considering that majority of the
respondents were regular users of e-commerce websites, the findings corroborate with
studies that claim that dialogue support is significant to new customers when compared
to existing customer [48]. Dialogue support features facilitate buyer persuasion [49].
Accordingly, it can be inferred from this study that although the provision of dialogue
support features on e-commerce platforms facilitates persuasion, users of these platforms
do not perceive it as a contributor to their satisfaction levels. For instance, persuasive
features such as reminders become helpful when used to notify shoppers of important
issues but not when used as tools of product promotion.
Rewards emerged as the only attractive system feature. These findings agree with
existing studies [49–51], which confirms that buyers consider rewards as a key factor to
customer satisfaction level. Considering that rewards on e-commerce platforms provide
direct and observable benefits to users, it is not surprising that users perceive it as
an attractive feature. It is therefore suggested that designers of e-commerce websites
must ensure that they involve features that reward users. As explained earlier, attractive
features increase satisfaction levels. Considering that persuasive system designs intend
to make a user perform a predetermined activity, it is vital that they include attractive
features. It is however emphasized that rewards feature on e-commerce websites must
target all category of users, this will facilitate regular visits to these websites.
188 M. M. Alhammad et al.
5.3 Credibility Support
Credibility support features had the highest total strength (54.86%). Besides, it was
observed to be the only category of features that recorded three features to be one-
dimensional (i.e. Trustworthiness, Expertise and Real-world feel). This demonstrates
that credibility plays a crucial role in customer satisfaction levels. System credibility has
been demonstrated to be vital, as it contributes to users intention to use a system [38].
Perhaps, it can be considered to be the most important feature in persuasive systems
design. With respect to e-commerce platforms, website credibility is essential to both
users and customers.
The emergence of Trustworthiness, Expertise and Real-world feel as one-
dimensional features show that customers and users of e-commerce websites always
look out for these features as a measure of satisfaction and are dissatisfied when absent.
It has been confirmed that the use of enhanced methods to improve credibility yields
favorable customer responses on e-commerce platforms [52]. Some studies [14] have
explained that Trustworthiness and Expertise are among the most used system features
on e-commerce websites.
Surface credibility, Authority, Third Party Endorsement and Verifiability were all
observed to be indifferent. Surface credibility relates to the appearance of thewebsite, and
this result suggests that e-commerce users do not consider the aesthetics features of the
website as a key ingredient of their satisfaction levels. These findings support arguments
by [53], that stipulates that surface credibility has less influence on regular application
users. More importantly, further investigations need to be conducted to ascertain why
credibility features including Authority, Third Party Endorsement and Verifiability were
observed to be indifferent features.
5.4 Social Support
Although Social support features have been identified to promote e-commerce plat-
forms [54], users perceive these features as indifferent. All social support features were
observed to be indifferent and they also recorded the lowest total strength (i.e. 31.44%).
Thus, they have no impact on user satisfaction levels. Particularly, Competition was
observed to be perceived as the feature with the least impact on user satisfaction. It
recorded the lowest total strength value of 20.5. Yet, others [38] have explained that
perceive social support promotes user’s intention to continuous use.
Studies have argued that social supports in virtual environments mostly seek to
address user problems through the direct and indirect provision of information, expe-
rience, and advice [55]. Therefore, although such a feature may enhance trust among
customers and users of e-commerce platforms [56], it has no impact on how these users
perceive satisfaction. It is important to note that Social support features are the least
utilized system features on e-commerce websites [14].
However, considering that shopping is a social event [57], it is surprising to observe
that social support is not perceived to influence e-commerce website user satisfaction.
Thus, there is a need for further studies to investigate this phenomenon to understand
the drivers of such findings.
Exploring the Impact of Persuasive Features on Customer Satisfaction Levels 189
6 Conclusions and Future Work
This study explored user perceptions of persuasive features on e-commerce websites that
influence their satisfaction or dissatisfaction levels using the Kano model. According
to the Kano model, user satisfaction evolves, and although there are possibilities that
perceptions of system features that influence user satisfaction levels on e-commerce
websites may change, current findings indicate that no persuasive feature is perceived to
be a “must-be” feature. This observation confirms that persuasive features are perceived
as add-ons and is not perceived by customers as critical to the ecommerce experience.
They enhance overall system qualities, create engagement and support decision-making
processes. In this sense, they complement e-commerce design and support user appeal
in a competitive environment. Hence, this study serves as a guide to understanding user
preferences and contributes to the development of practical guidelines for designing
persuasive e-commerce websites.
Three credibility support features (trustworthiness, Expertise and Real-world feel)
were observed to be perceived by customers to impact satisfaction. That is, their presence
is noticed by users and their absence results in dissatisfaction. Rewards emerged as the
only system feature that users perceive to be attractive. All social support features were
observed to be perceived by users as indifferent. Future work needs to investigate the
causal effects of these findings. In addition, further investigations may be conducted on
the impact of within-group cultural differences which might reveal differences on users’
preferences of persuasive feature that impacts their satisfaction and dissatisfaction levels.
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