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Examining the Acceptance of WhatsApp Stickers Through Machine Learning Algorithms

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WhatsApp stickers are gaining popularity among university students due to their pervasiveness, specifically in educational WhatsApp groups. However, the acceptance of stickers by university students is still in short supply. Thus, this research aims to empirically examine the determinants affecting the acceptance of WhatsApp stickers through a proposed theoretical model by integrating the technology acceptance model (TAM) with the uses and gratifications theory (U&G). A questionnaire survey was circulated to collect data from 372 university students who have been engaged in a “Group Talk” in WhatsApp. A novel approach was employed to analyze the hypothesized relationships among the constructs in the research model through the use of machine learning algorithms. The results pointed out that IBk and RandomForest classifiers have performed better than the other classifiers in predicting the actual use of stickers with an accuracy of 78.57%. The research findings are believed to provide future directions for stickers developers to better promote stickers in educational activities.
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Examining the Acceptance of WhatsApp
Stickers Through Machine Learning
Algorithms
Rana A. Al-Maroof, Ibrahim Arpaci, Mostafa Al-Emran, Said A. Salloum,
and Khaled Shaalan
Abstract WhatsApp stickers are gaining popularity among university students due
to their pervasiveness, specifically in educational WhatsApp groups. However, the
acceptance of stickers by university students is still in short supply. Thus, this research
aims to empirically examine the determinants affecting the acceptance of WhatsApp
stickers through a proposed theoretical model by integrating the technology accep-
tance model (TAM) with the uses and gratifications theory (U&G). A questionnaire
survey was circulated to collect data from 372 university students who have been
engaged in a “Group Talk” in WhatsApp. A novel approach was employed to analyze
the hypothesized relationships among the constructs in the research model through
the use of machine learning algorithms. The results pointed out that IBk and Ran-
domForest classifiers have performed better than the other classifiers in predicting the
actual use of stickers with an accuracy of 78.57%. The research findings are believed
to provide future directions for stickers developers to better promote stickers in
educational activities.
R. A. Al-Maroof (B)
Department of English Language, Al Buraimi University College, Al Buraimi, Oman
e-mail: rana@buc.edu.om
I. Arpaci
Department of Computer Education and Instructional Technology, Tokat Gaziosmanpasa
University, Tokat, Turkey
e-mail: ibrahim.arpaci@gop.edu.tr
M. Al-Emran
Department of Information Technology, Al Buraimi University College, Al Buraimi, Oman
e-mail: mustafa.n.alemran@gmail.com
S. A. Salloum
Research Institute of Sciences and Engineering, University of Sharjah, Sharjah, UAE
e-mail: ssalloum@sharjah.ac.ae
K. Shaalan
Faculty of Engineering and IT, The British University in Dubai, Dubai, UAE
e-mail: khaled.shaalan@buid.ac.ae
© Springer Nature Switzerland AG 2021
M. Al-Emran et al. (eds.), Recent Advances in Intelligent Systems
and Smart Applications, Studies in Systems, Decision and Control 295,
https://doi.org/10.1007/978-3-030-47411-9_12
209
210 R. A. Al-Maroof et al.
Keywords WhatsApp stickers ·Technology acceptance model ·Uses and
gratifications theory ·Machine learning algorithms
1 Introduction
Social media technologies have become part of our daily lives [15]. Online commu-
nication through these technologies enables individuals to interact away from time
and space limitations. Online communication is a replacement for writing messages
and post them on the chat rooms [6]. Stickers represent one of the up-to-date tools
in online communications. Recently, stickers become a way of communication that
encourages people all over the world to discuss various social, education, and polit-
ical issues. One of the benefits of stickers that sets it apart from other mechanisms
in online communication is that it encourages users to create their sticker forms that
meet their personal needs [7]. The mechanism of creating stickers adds a new expe-
rience to the users that connect them emotionally with stickers. It raises the idea of
belonging and feeling of possessing [8,9]. Stickers have their own unique features,
such as size, uniqueness, and animation. In this respect, it is argued that stickers are
bigger, static, and animated [10]. They can be added or deleted and could be sent
separately without insertion in text messages. This paves the way to another impor-
tant benefit of stickers, which is motivation. Users feel that they are being motivated
whenever the question of stickers is raised. Motivation affects the frequency of using
stickers and makes them more pervasive. Users do not only send stickers to represent
emotions but also use them for strategic or functional purposes [11].
Even though emojis have been investigated by researchers and attracted the atten-
tion of academic and non-academic situations [1214], a few studies have focused
on the role of stickers in online communication. More specifically, Ghobadi and Taki
[15] examined the role of stickers in Telegram. The study aimed at examining the
impact of using Telegram stickers on EFL learners’ vocabulary learning. The results
have proven that teaching vocabulary through Telegram stickers could assist the
learning process and provide learners with a variety of advantages. In addition, Van
De Bogart and Wichadee [16] focused on the usage of stickers in LINE networking
sites. The results showed that perceived usefulness and attitude toward using stickers
by college students had a positive relationship with their intention to use.
Although previous studies have highlighted the importance of emojis in social
media [1722], there is a scarce of knowledge regarding the understanding of the
factors affecting the use of stickers in everyday communication in general, and educa-
tional activities in particular. To verify the usage of stickers in online communications
by university students, this research adopts the technology acceptance model (TAM)
and the uses and gratifications theory (U&G) by integrating them together to measure
the students’ acceptance of stickers. The reason behind the adoption and integration
of these two theories stems from several causes. First, previous studies have shown
that these two theories have been successfully adopted to pinpoint the acceptance or
adoption of several technologies. Second, each theory handles certain features that
Examining the Acceptance of WhatsApp Stickers … 211
are critical to understand the acceptance of stickers and their features among uni-
versity students. More specifically, the TAM takes into account the characteristics
of the technology itself, whereas the U&G focuses on the experiences generated by
that technology. Third, based on the existing literature, the integration of TAM and
U&G has not been experienced to examine the usage of stickers through WhatsApp
text messages among students at the university level. In line with the aforementioned
reasons, this research aims at integrating the TAM and U&G to understand the factors
affecting the students’ acceptance of WhatsApp stickers at the university level.
2 Theoretical Framework and Research Model
2.1 Technology Acceptance Model (TAM)
TAM is considered as a model to predict the acceptability of technology as it is
governed by two main variables, namely “perceived usefulness (PU)” and “perceived
ease of use (PEOU)” [23]. PU refers to “the degree to which a person believes
that using a particular system would enhance his or her job performance”, whereas
PEOU refers to “the degree to which a person believes that using a particular system
would be free from effort” [24]. Many researchers have emphasized the importance
of these two variables in predicting the acceptance or adoption of technology. In
that, these two variables were shown to have a significant positive impact on the
adoption of Google classroom [25], e-learning [2628], m-learning [29], e-payment
systems [30], social networks [31], metamodelling [32], and search-based software
engineering techniques [33], among many others. Based on that, the TAM is adopted
in this research as it focuses on the “ease of use” and “usefulness” variables, which
are closely related to the powerful features of stickers, including animation, size, and
social or emotional connection with other users.
2.2 Uses and Gratifications Theory (U&G)
The uses and gratification theory (U&G) has been used by many scholars in the
domain of media and communication [34]. The theory has a functional approach that
is grounded within the concept of “functionalism” [35]. The U&G theory is generated
to measure the usage of media by users to gratify their needs. It is believed that users
of media have certain needs, motivations, and expectations that can be fulfilled and
gratified through the use of media. The U&G focuses on individuals’ motivation,
which is one of the influential factors in using the media [36]. Gratification can
cover various aspects, including hedonic, content, utilitarian, and social gratification.
212 R. A. Al-Maroof et al.
Within each aspect of gratification, multiple dimensions may be found, such as
self-documentation, self-expression, social interaction, and information sharing. The
main aim behind using these features is entertainment and spending time [37].
Further, the main essential factors that are embedded inside the U&G theory are
the inclusion of both “novelty” and “being there” [17]. Novelty is considered a funny
way to create innovation, whereas “being there” refers to the social media users who
may click social buttons to feel immersed in a mediated reality. The two factors are
crucial to the usage of stickers in communication Apps. Given that stickers are one
of the featured additions in WhatsApp, users may use it to fulfill the sense of novelty.
Besides, the U&G theory pays attention to the feature of “being there” as it is one
of the distinctive features behind using stickers. Stickers can be used in different
religious and social situations to reflect the idea of the uniqueness of being there.
Accordingly, the U&G dimensions can serve the basic aim of this study. It has a
close relation with motivation and entertainment, which in turn, are considered as
influential features that urge users to use stickers. Furthermore, social interaction
and information sharing are critical aspects that can measure the importance of using
stickers by students.
2.3 Research Model
Due to the narrow applicability of TAM concerning the “usefulness” and “ease of use”
variables, the U&G comes as an assistant theory that provides more insights into these
variables. The U&G provides an in-depth understanding of learners’ perspectives
with respect to the extension of TAM [38]. The U&G consists of three core utilities,
which were believed to have a significant relationship with the TAM constructs, such
as “social utility”, “hedonic utility”, and “functional utility”. Social utility is used
to reflect the interpersonal use, which has a close correlation with PU. WhatsApp
users can design stickers that indicate their social attitude towards different social
events in their life. Similarly, the hedonic utility is related to the PU from the novelty
and enjoyment perspectives. Users invented various forms of stickers to arouse the
feeling of fun and amusement through WhatsApp communications. When the users
of technology use a specific form of stickers to accomplish a specific task, it is then
related to the functional utility.
Based on the above discussion, the research model of this study is constructed
as depicted in Fig. 1. In that, this research suggests that social utility (including
socialization and self-presentation), hedonic utility (including enjoyment and nov-
elty), and functional utility (including unique function) are significantly influenced
by both PEOU and subjective norm. It was also postulated that the students’ intention
to use stickers are affected by PEOU, subjective norm, social utility, hedonic utility,
and functional utility. Further, the actual use of stickers is suggested to be influenced
by the students’ intentions. It is important to report that social, hedonic, and func-
tional utilities are embedded in PU. In line with this, the following hypotheses were
formulated:
Examining the Acceptance of WhatsApp Stickers … 213
Fig. 1 Research model
H1: PEOU has a significant effect on (a) socialization, (b) self-presentation, (c)
enjoyment, (d) novelty, and (e) unique function.
H2: Subjective norm has a significant effect on (a) socialization, (b) self-
presentation, (c) enjoyment, (d) novelty, and (e) unique function.
H3: socialization (a), self-presentation (b), enjoyment (c), novelty (d), and unique
function (e) have significant effects on students’ intention to use stickers.
H4: Subjective norm has a significant effect on students’ intention to use stickers.
H5: PEOU has a significant effect on students’ intention to use stickers.
H6: Students’ intention to use stickers has a significant effect on stickers actual
use.
214 R. A. Al-Maroof et al.
3 Research Methodology
3.1 Procedure and Sample
To understand the students’ intention to use stickers, a self-administered survey was
circulated to several groups of university students who have been engaged in a “Group
Talk” in WhatsApp. The use of questionnaire surveys in such type of research studies
is considered the best tool as it can effectively analyze the relationships among the
constructs in the proposed research model [39]. The sample was collected from the
University of Fujairah located in the United Arab Emirates (UAE). The purpose of
selecting this sample is attributed to the reason that most of the selected students have
already experienced the use of stickers in their daily university lives. It is imperative to
mention that the majority of the discussed issues in the WhatsApp groups are related
to educational matters such as courses, assignments, exams, and social activities. The
data were collected between February and March 2019. A total of 400 questionnaires
surveys were distributed. Among those, 28 responses were discarded due to the large
number of missing values, and thus, a total of 372 responses were retained for further
analysis.
3.2 Research Instrument
The research instrument consists of two main parts. The first part is dedicated to col-
lect students’ demographics, whereas the second part involves the items that measure
the constructs in the proposed research model. The second part was measured using a
five-point Likert scale ranging from “1 =strongly disagree” to “5 =strongly agree”.
The items used to measure the actual use, intention, and subjective norm were taken
from Shao and Kwon [17]. The items used to measure the PEOU were adopted from
Davis [24]. The items used to measure the enjoyment and self-presentation were
adopted from Gan [40], whereas the items used to measure novelty were taken from
Sundar and Limperos [41]. The items used to measure socialization were adopted
from the previous relevant studies [42,43].
4 Results
4.1 Descriptive Analysis
The participated students were selected from different faculties, enrolled in different
majors, and different levels. 53% of the participants were females, and 47% were
Examining the Acceptance of WhatsApp Stickers … 215
males. The majority of the respondents were aged between 18 and 29 years old with
66%, whereas the rest were above 29 years old.
4.2 Hypotheses Testing Using Machine Learning Algorithms
The current study employs supervised machine learning classification algorithms
through a wide range of methodologies such as decision trees, if-then-else rules, meta
classifiers, Bayesian networks, functions, and neural networks in order to examine
the relationships among the constructs in the proposed research model. Weka (version
3.8.3) was used to analyze the data by employing the percentage split (66%) test mode
based on the following classifiers: a bayesian classifier (NaiveBayes), a logistic-
regression classifier (SMO), a lazy-classifier (IBk), a meta-classifier (Bagging), a
rule-learner (PART), and a decision-tree (RandomForest) [4446].
The results shown in Table 1indicate that RandomForest had a better performance
in predicting socialization by the attributes of PEOU and subjective norm. The clas-
sifier predicted the socialization with an accuracy of 84.92%. The classifier also had
a better performance in TP rate (0.849), ROC area (0.949), and precision (0.846).
Thereby, H1a and H2a were supported.
TheresultsshowninTable2indicate that both RamdomForest and IBk performed
better than the other classifiers in predicting self-presentation by the attributes of
PEOU and subjective norm. The classifiers predicted the self-presentation with an
accuracy of 84.92%. Further, they had a better performance in TP rate (0.833), ROC
area (0.952), and precision (0.856). Thereby, H1b and H2b were supported.
As per the readings in Table 3, the results suggested that RandomForest performed
better than the other classifiers in predicting the enjoyment by the attributes of PEOU
and subjective norm. The classifier predicted the enjoyment with an accuracy of
76.98%. Further, it had a better performance in TP rate (0.770), ROC area (0.918),
and precision (0.784). Thereby, H1c and H2c were supported.
Tabl e 1 Predicting the socialization by PEOU and subjective norm
Classifier CCIa
(%)
TPb
rate
FPc
rate
Precision Recall F-Measure ROCd
area
NaiveBayes 69.05 0.690 0.194 0.690 0.690 0.665 0.804
SMO 62.70 0.627 0.243 0.649 0.627 0.659 0.712
IBk 83.33 0.833 0.087 0.828 0.833 0.829 0.956
Bagging 72.22 0.722 0.156 0.708 0.722 0.710 0.885
PART 74.60 0.746 0.141 0.738 0.746 0.734 0.900
RandomForest 84.92 0.849 0.082 0.846 0.849 0.844 0.949
aCCI Correctly Classified Instances, bTP True Positive, cFP False Positive, dROC Receiver
Operating Characteristic
216 R. A. Al-Maroof et al.
Tabl e 2 Predicting the self-presentation by PEOU and subjective norm
Classifier CCI
(%)
TP rate FP rate Precision Recall F-Measure ROC
area
NaiveBayes 64.29 0.643 0.236 0.666 0.643 0.636 0.823
SMO 63.49 0.635 0.251 0.684 0.635 0.620 0.774
IBk 84.92 0.849 0.104 0.856 0.849 0.848 0.952
Bagging 80.95 0.810 0.124 0.814 0.810 0.810 0.909
PART 83.33 0.833 0.117 0.843 0.833 0.833 0.907
RandomForest 84.92 0.849 0.104 0.856 0.849 0.848 0.952
Tabl e 3 Predicting the enjoyment by PEOU and subjective norm
Classifier CCI
(%)
TP rate FP
Rate
Precision Recall F-Measure ROC
area
NaiveBayes 51.59 0.516 0.305 0.543 0.516 0.517 0.716
SMO 56.35 0.563 0.289 0.634 0.563 0.559 0.648
IBk 78.57 0.786 0.113 0.829 0.786 0.788 0.923
Bagging 72.22 0.722 0.164 0.794 0.722 0.718 0.873
PART 75.40 0.754 0.137 0.789 0.754 0.757 0.889
RandomForest 76.98 0.770 0.136 0.784 0.770 0.772 0.918
The results shown in Table 4indicate that both SMO and PART performed better
than the other classifiers in predicting novelty by the attributes of PEOU and subjec-
tive norm. The classifiers predicted the novelty with an accuracy of 52.38%. Thereby,
H1d and H2d were supported.
The results shown in Table 5reveal that SMO and PART performed better than
the other classifiers in predicting unique function by the attributes of PEOU and
subjective norm. The classifiers predicted the novelty with an accuracy of 42.07%.
Thereby, H1e and H2e were supported.
Tabl e 4 Predicting the novelty by PEOU and subjective norm
Classifier CCI
(%)
TP rate FP
Rate
Precision Recall F-Measure ROC
area
NaiveBayes 50.00 0.500 0.479 0.393 0.500 0.416 0.510
SMO 52.38 0.524 0.524 0.524 0.524 0.688 0.500
IBk 38.89 0.389 0.362 0.388 0.389 0.388 0.552
Bagging 50.00 0.500 0.472 0.456 0.500 0.416 0.534
PART 52.38 0.524 0.524 0.524 0.524 0.688 0.500
RandomForest 38.10 0.381 0.362 0.391 0.381 0.385 0.548
Examining the Acceptance of WhatsApp Stickers … 217
Tabl e 5 Predicting the unique function by PEOU and subjective norm
Classifier CCI
(%)
TP
rate
FP
rate
Precision Recall F-Measure ROC
area
NaiveBayes 38.89 0.389 0.420 0.273 0.389 0.282 0.491
SMO 420.07 0.421 0.421 0.421 0.421 0.592 0.500
IBk 37.30 0.373 0.382 0.356 0.373 0.353 0.547
Bagging 40.48 0.405 0.407 0.427 0.405 0.372 0.532
PART 42.07 0.421 0.421 0.421 0.421 0.592 0.500
RandomForest 38.89 0.389 0.398 0.3691 0.389 0.356 0.528
The results shown in Table 6suggest that RandomForest performed better than
the other classifiers in predicting the intention. The RandomForest predicted the
intention by the attributes of all dependent variables with an accuracy of 88.89%.
The classifier also had a better performance in TP rate (0.889), ROC area (0.981), and
precision (0.894). Therefore, H3a, H3b, H3c, H3d, H3e, H4, and H5 were supported.
The results shown in Table 7indicate that IBk and RandomForest performed bet-
ter than the other classifiers in predicting the actual use by intention. The classifiers
Tabl e 6 Predicting the intention
Classifier CCI
(%)
TP rate FP rate Precision Recall F-Measure ROC
area
NaiveBayes 56.35 0.563 0.240 0.573 0.563 0.560 0.728
SMO 55.56 0.556 0.309 0.745 0.556 0.501 0.660
IBk 69.84 0.698 0.170 0.708 0.698 0.698 0.768
Bagging 74.60 0.746 0.141 0.767 0.746 0.744 0.903
PART 81.75 0.817 0.109 0.822 0.817 0.817 0.920
RandomForest 88.89 0.889 0.065 0.894 0.889 0.890 0.981
Tabl e 7 Predicting the actual use by intention
Classifier CCI
(%)
TP rate FP rate Precision Recall F-Measure ROC
area
NaiveBayes 73.81 0.738 0.178 0.735 0.738 0.736 0.846
SMO 68.25 0.683 0.297 0.761 0.683 0.651 0.754
IBk 78.57 0.786 0.135 0.787 0.786 0.786 0.862
Bagging 75.40 0.754 0.130 0.773 0.754 0.756 0.854
PART 78.57 0.786 0.135 0.787 0.786 0.786 0.840
RandomForest 78.57 0.786 0.135 0.787 0.786 0.786 0.862
218 R. A. Al-Maroof et al.
predicted the actual use with an accuracy of 78.57%. The classifiers had a better per-
formance in TP rate (0.786), ROC area (0.862), and precision (0.787). Accordingly,
H6 was supported.
5 Conclusion
5.1 Research Implications
WhatsApp stickers have recently become widely accepted by students. This research
was designed with the aim of understanding the factors affecting the students’ accep-
tance of WhatsApp stickers. To serve this purpose, this research integrates the TAM
with the U&G theory to understand the determinants affecting the students’ decisions
to accept these tools.
Drawing on the research results, this study offers several implications. First, this
study is believed to be one of the few that attempts to understand the students’
acceptance of WhatsApp stickers in the UAE context. Second, this research integrates
the TAM with the U&G theory to understand the determinants affecting the students’
acceptance of stickers. Thus, this research validates the applicability of TAM and
U&G in predicting the acceptance of stickers by students. Third, this study employs
a novel approach in analyzing the hypothesized relationships among the constructs
in the proposed theoretical model through the use of machine learning algorithms.
This approach was rarely used in the previous literature, and thus, it is believed
that this approach would add a significant contribution to the IS literature. Fourth,
stickers developers can gain more insights from the research findings of this study
by considering the prominent factors and take them into account while developing
future stickers specified for educational purposes.
5.2 Limitations and Future Research Directions
Despite the results achieved, this research was limited in some ways. First, this
research examined the factors affecting the stickers acceptance by students and did
not evaluate the academics’ acceptance of these tools. Therefore, what is now needed
is another research which could consider the role of academics in using these stickers
and understand the determinants affecting their acceptance. Second, the data were
collected from only one institution in the UAE. Thus, the generalization of the results
to the other UAE institutions should be treated with caution. Third, the data were
collected through questionnaire surveys only. Therefore, it would be interesting to
use other qualitative methods such as focus groups or interviews to further enlighten
the quantitative results.
Examining the Acceptance of WhatsApp Stickers … 219
Acknowledgements This is an extended version of a conference paper published by the
International Conference on Advanced Intelligent Systems and Informatics 2019.
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... Various other research studies assessed the positive influence of social influence, perceived enjoyment, and computer self-efficacy on perceived ease of use, perceived usefulness, and E-learning acceptance. A continuing critical concern is the cross-cultural perspective related to the acceptance and use of education technologies and the integration in countries where there are issues related to bandwidth or other connectivity (Al-Maroof, 2020). ...
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