International Journal of Information Management 48 (2019) 96–107
Contents lists available at ScienceDirect
International Journal of Information Management
journal homepage: www.elsevier.com/locate/ijinfomgt
Exploring the psychological mechanisms from personalized advertisements
to urge to buy impulsively on social media
T
⁎
Virda Setyania, Yu-Qian Zhub, , Achmad Nizar Hidayantoa, Puspa Indahati Sandhyaduhitaa,
Bo Hsiaoc
a
Faculty of Computer Science, University of Indonesia, Indonesia
Department of Information Management, National Taiwan University of Science and Technology, Taiwan, ROC
c
Department of Information Management, Chang Jung Christian University, Taiwan, ROC
b
1. Introduction
Online impulse buying has become an epidemic as a result of advances in information technology and e-commerce. Considerable efforts
have been devoted to identifying the factors that lead to impulse
buying, and a multitude of antecedents were identified, such as user
characteristics (e.g., hedonic needs and impulsiveness), online store
characteristics (e.g., ease of use, interactivity), marketing stimuli (e.g.,
bonus and discounts), and product characteristics (e.g., type and price)
(Chan, Cheung, & Lee, 2017). A systematic review of literature has
found that most research has been focused on the general belief of the
online store and user traits, while there is a lack of understanding of
context-specific stimuli that trigger users’ online impulse buying behavior (Chan et al., 2017).
Personalization is one of the technologies that create context-specific stimuli for users and is reportedly being used by online retailers to
entice shopper's impulsive purchases (Dawson & Kim, 2010). Personalized advertisement incorporates information about the individual, such
as demographic information, browsing history, and brand preferences
(Bang & Wojdynski, 2016). With social media providing a continuous
stream of data from billions of users about their likes and preferences,
personalized advertisement has become the prevalent way for digital
advertisers to communicate effectively with users (Chung, Wedel, &
Rust, 2016), fueling the growth of social commerce. Personalized ads
can increase click-through rates by as much as 670% relative to nonpersonalized advertisements (Beales, 2010; Summers, Smith, & Reczek,
2016), and has been considered as a key factor that contributes to user
impulse buying (de Kervenoael, Aykac, & Palmer, 2009)
Despite the impressive effectiveness of personalized advertisement
in attracting people to click and buy on impulse, our knowledge about
how this effectiveness is achieved is still limited. Prior research has
reported that personalization increases advertising value and flow experience (Kim & Han, 2014), leads to favorable attitude (Xu, 2006), and
receives higher attention (Köster, Rüth, Hamborg, & Kasper, 2015).
Howard and Kerin (2004) discovered that users are more likely to have
higher purchase intention for the product recommended in their personalized ads. However, prior research has not provided a coherent and
integral account of how exactly personalized advertisement translates
into click-through rates, and ultimately, urge to buy impulsively. The
mechanism underlying the effectiveness of personalization in achieving
higher click-through rates and impulse buying intentions remains, to a
large degree, a black box to be opened.
The present research tries to explore the psychological mechanisms
and constructs underlying user's reactions to personalized advertisement on social media. Specifically, we examine how personalization
increases the value of advertisement measured by perceived informativeness, credibility, creativity, and entertainment; we then look at how
advertisement value translates into two click-through motivations:
utilitarian click-through motivation and hedonic click-through motivation, which in turn, enhance impulsive buying intentions on social
media.
We contribute to extant literature with a more nuanced map of how
personalization enhances click-through rates, thereby helping personalized advertiser to better understand, and strengthen their value to
users. We also contribute to the impulsive buying literature by linking
click-through motivations to impulsive buying intentions, which enhances our understanding how hedonic and utilitarian click-through
motivations are related to impulsive buying intentions. With social
commerce providing an environment even more conducive for impulsive buying (Xiang, Zheng, Lee, & Zhao, 2016), we enrich the social
commerce literature by exploring how personalization, one of its
technical environment elements, enhances impulsive buying intentions.
In the next section, we begin with a discussion of the conceptual
framework, and develop our hypotheses. We next describe our participants, data collection, and measures. The analysis and results are then
presented, followed by a discussion of the results and conclusions.
⁎
Corresponding author at: Department of Information Management, National Taiwan University of Science and Technology, No. 43, Keelung Road, Sec. 4, Da’an
Dist., Taipei City 10607, Taiwan, ROC.
E-mail addresses: setyani.virda@gmail.com (V. Setyani), yuqian@gmail.com (Y.-Q. Zhu), nizar@cs.ui.ac.id (A.N. Hidayanto),
p.indahati@cs.ui.ac.id (P.I. Sandhyaduhita).
https://doi.org/10.1016/j.ijinfomgt.2019.01.007
Received 4 September 2017; Received in revised form 26 November 2018; Accepted 7 January 2019
Available online 23 February 2019
0268-4012/ © 2019 Elsevier Ltd. All rights reserved.
International Journal of Information Management 48 (2019) 96–107
V. Setyani, et al.
2. Conceptual framework and hypotheses
sensation and novelty(hedonic) seeking. Voss, Spangenberg, and
Grohmann (2003) further empirically tested and validated that hedonic
motivations are related to affective attributes such as fun, exciting,
delightful, enjoyable; and utilitarian motivations are related to cognitive attributes such as effective, helpful, practical and necessary. The
theory was widely adopted in the IS and e-commerce literature and
researchers have attributed informativeness, usefulness, cost-saving,
credibility, convenience, selection to utilitarian motivation; and enjoyment, engagement, social, adventure and discovery to hedonic motivation to use IS services, buy online, or consume online content (Chiu,
Wang, Fang, & Huang, 2014; Li & Mao, 2015; To, Liao, & Lin, 2007;
Wang, Yeh, & Liao, 2013). Hedonic and utilitarian dimensions are
ubiquitous in any consumption behavior and can provide building
blocks for researchers attempting to develop models that explain a
greater proportion of the variance in consumer behavior. They enable
practitioners to test the effectiveness of advertising campaigns that
stress experiential or functional strategies and reveal differences that
may not be apparent when a single dimension measure is used (Voss
et al., 2003).
Integrating the advertising value and hedonic and utilitarian motivation theories, we build our research framework based on the
Stimulus-Organism-Response (S-O-R) Model (Mehrabian & Russell,
1974). The S-O-R model provides a systematic view of users’ behavior
as a response to external stimuli, such as advertising, display or music
(Bagozzi, 1986). The stimuli are external to the person and can consist
of both marketing mix variables and other environmental inputs. In the
S-O-R model, users respond to external stimulus with organism (O),
which is the internal processes and structures consisting of perceptual,
physiological, feeling, and thinking activities (Bagozzi, 1986). Organism intervenes between stimuli external to the person and the final
actions, decisions, reactions, or responses, which is represented with R
(response) (Bagozzi, 1986). In this study, the stimuli are personalized
advertisements, while perceived advertising value and click-through
motivation represent a two-stage internal process in organism. More
specifically, we propose that personalized ads create value for users in
the following dimensions: perceived informativeness, perceived credibility, perceived creativity, and perceived entertainment. We argue
that these values created by personalized ads fuel users’ motivation to
click through the advertisement and find out more about it on social
media. Consistent with prior literature, cognitive values such as perceived informativeness and perceived credibility are linked to utilitarian click-through motivation, while affective or sensory values such
as perceived creativity and perceived entertainment are linked to hedonic click-through motivation. Motivations to click through, in turn,
lead to urge to buy impulsively. Our research model is depicted in
Fig. 1.
2.1. Value of advertisement
Based on the Uses and Gratifications Theory (UGT), Ducoffe (1996)
developed a model that focused on advertising value, e.g. the subjective
evaluation of the relative worth or utility of advertising to users, and
explored what factors determine advertising value from a user's perspective. Predictors of advertising value can be categorized as either
cognitive or affective (Ducoffe, 1996; Kim & Han, 2014). Cognitive
factors include the perception of informativeness and credibility of the
advertisement (Kim & Han, 2014). Informativeness refers to the ability
to inform users of product alternatives for their greatest possible satisfaction (Gao & Koufaris, 2006). In advertising, there are three types
of perceived credibility: the advertising message itself, the advertising
site, and finally, the sponsor of the ad (Flanagin & Metzger, 2007).
Ducoffe's model focuses on message credibility, which refers to the
perceived truthfulness and believability of the advertising message
(MacKenzie, Lutz, & Belch, 1986). Affective factors include perceptions
of entertainment (Ducoffe, 1996; Kim & Han, 2014), which is positively
related to advertisement value, and irritation, which is negatively related to advertisement value (Ducoffe, 1996). Entertainment denotes
the ability to fulfill users’ needs for diversion, esthetic enjoyment or
emotional release (McQuail, 2005), while irritation is the extent to
which the advertising message is annoying and irritating to users (Kim
& Han, 2014). Recently, creativity, which refers to the extent to which
an ad is original and unexpected, is added as a new affective factor of
advertising value (Lee & Hong, 2016; Reinartz & Saffert, 2013). These
perceived values serve as perceptual antecedents that affect user attitude toward advertising (Ducoffe, 1996; Lee & Hong, 2016).
Ducoffe's model has been extensively applied and modified in various research covering different contexts. Table 1 shows a brief summary of these studies. We integrate different studies and focused on
four most commonly used advertising values in prior literature; namely,
informativeness, credibility, entertainment and creativity. In the personalization context, one recent research reported the personalization is
not significantly related to irritation (Kim & Han, 2014). In addition,
irritation in the context of personalized ads was repeatedly found to be
not significantly related to ad attitudes in various settings such as
China, U.S. and Taiwan (Logan, Bright, & Gangadharbatla, 2012; Tsang,
Ho, & Liang, 2004; Xu, 2006). Therefore, it is unlikely that irritation
will be a significant mediator of personalization to urge to buy impulsively relationship in our context. Hence, we exclude irritation from
the model to explore other more important constructs in the personalized ad context.
2.2. Hedonic and utilitarian dimensions of consumption
2.3. Personalized ads and advertisement value
Hirschman (1980) argued that humans are endowed with two essential modes of consumption: thinking and sensing. He asserted that all
consumption can be viewed as either cognition seeking (utilitarian) or
How do personalized advertisements create value for users? We
argue that it could create value for users in four different ways. First, as
Table 1
Summary of Advertising values in prior literature.
Source
Context
Results
Xu (2006)
Personalized ad in mobile
advertising
Perceived credibility and entertainment are positively related to ad attitude; informativeness and irritation are not
significantly related to ad attitudes
Logan et al. (2012)
General social media advertising
Perceived informativeness and entertainment are positively related to ad value; irritation is not significantly
related to ad value
Kim and Han (2014)
Personalized ad in mobile
advertising
Perceived credibility, incentive and entertainment are positively related to ad value; irritation and informativeness
are not significantly related to ad value
Lee and Hong (2016)
General social media advertising
Perceived informativeness and creativity are positively related to attitude
Tsang et al. (2004)
General web advertising
Perceived credibility and entertainment are positively related to ad attitude; informativeness and irritation are not
significantly related to ad attitudes
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V. Setyani, et al.
what is considered original, unexpected or meaningful, perceived
creativity may well differ by factors such as user age, gender, education
and culture (Smith & Yang, 2004). Instead of a general ad for all demographic groups, personalization allows advertisers to design content
for each group, factor in the differenes between different groups (i.e.,
what would be considered “the usual” for a certain group) and thus
come up with relevant and original content that is different from the
pack and delivery it to the right audience. With high level of personalization, the advertisment content can be tailored to the target audience, thereby satisfying the need to be novel, different, and relevant for
each group; accordingly, the messages will be perceived as more
creative.
Fig. 1. Proposed research model.
H1c. Level of ad personalization is positively related to users’ perceived
creativity.
the internet content grows every day, users are typically overwhelmed
by information and find it difficult to locate the exact piece of information that is useful to them. It is typical for a user to go through
pages of search results before he/she finally finds the right piece of
information. Gao and Koufaris (2006) found that users appreciate being
given information that is important for making a purchasing decision.
One of the main purposes of advertising is to distribute information of
certain goods or services (Kim & Han, 2014), and personalized advertisements meet this purpose by tailoring the advertisement content
according to user's personal information such as age, gender, preferences etc. Thus, it could provide more targeted and relevant information that meets user needs (Chen & Hsieh, 2012) and reduces
information overload by channeling relevant information directly to
individual users (Liang, Lai, & Ku, 2006). Information is valuable only
when it is relevant and needed. Therefore, the higher the perceived
personalization level of the advertisement, the higher the users’ perceive informativeness will be.
Advertising entertainment lies in the ability to fulfill audience needs
for diversion, esthetic enjoyment, or emotional release (McQuail,
2005). Like creativity, esthetics and tastes are very personal and differ
greatly from person to person, and from group to group. With high level
of personalization, the advertisments delievered are more appropriate
for the audience, as the ads are tailored for their taste, preference and
esthetics need. For example, cartoon characters may be perceived as
quite entertaining and enjoyable by teenagers, but not so much by seniors. Accordingly, the higher the level of personalization, the more
likely the messages will be perceived as entertaining.
H1d. Level of ad personalization is positively related to users’ perceived
entertainment.
2.4. Impact of advertising value on user's click-through motivation
H1a. Level of ad personalization is positively related to users’ perceived
informativeness.
How does advertising value translate to users’ clicks? We try to link
the values personalized advertisements create to two different types of
click-through motivations: utilitarian and hedonic. In ecommerce, utilitarian motivation is linked with cognitive and functional factors such
as convenience, information availability, product selection, efficiency,
etc., while hedonic motivation seeks affective and sensory outcomes
such as aesthetics, emotions, adventure, happiness, fantasy, awakening,
sensuality, and enjoyment (To et al., 2007). Based on Mikalef,
Giannakos, and Pateli (2013), we define utilitarian click-through motivation as the degree to which users perceive clicking-through personalized advertisements to browse further content to be a useful and
effective means to find products or services, whereas hedonic clickthrough motivation is the degree to which users perceive clickingthrough personalized advertisements to browse further content to be a
fun and emotionally stimulating experience. We argue that advertising
values are positively related to both utilitarian and hedonic clickthrough motivations. Specifically, perceived informativeness and perceived credibility are cognition-based constructs and are related to
utilitarian click-through motivation; while perceived entertainment and
creativity are affection and experience-based constructs and are related
to hedonic click-through motivation.
First, personalized advertising provides tailored content based on
users’ preferences, therefore increasing the availability of relevant information to users while at the same time reducing information overflow (Liang et al., 2006). The availability of product information is
important for users, and is a key predictor of purchase intentions
(Childers, Carr, Peck, & Carson, 2001). Prior research has found informativeness is positively related to utilitarian shopping motivation in
ecommerce (Burke, 1997; To et al., 2007). When users perceive high
informativeness from personalized advertisement (the ads are relevant,
timely, and accurate), they are more likely to find clicking through the
ads to be helpful and effective in satisfying their shopping needs.
Therefore, we propose:
Komiak and Benbasat (2006) argued that high level of personalization means that the advertisements users see effectively articulate the
user's personal needs, and are consistent with the users’ personal
shopping strategy. Highly personalized advertisment delivers better
representation of user needs, generate more relevant and better-customized recommendations, and is more likely to be perceived as having
high information quality and accuracy. Flanagin and Metzger (2007)
argued that message credibility is determined by the accuracy and information quality of the message itself in the online environment.
Hence, we infer that highly personalized ad can make users feel that the
message in the personalized ad is more credible. Furthermore, personalization can serve as a cue for users, trigger cognitive heuristics about
the nature of the underlying content, and shape user judgements of the
content crediblity (Sundar, Kim, & Gambino, 2017). In particular, with
high level of personalization, the similarity heuristic (if there is a similarity between my interest and what this ad offers, it is credible) and
the helper heuristic (if this ad helps me find things I like, it is credible)
are likely to be triggered, which enhance the perceived crediblity of the
ad (Sundar et al., 2017). Therefore, we propose:
H1b. Level of ad personalization is positively related to users’ perceived
credibility.
Advertising creativity is the extent to which an ad is original and
unexpected (Haberland & Dacin, 1992). There has been a strong focus
on creativity in advertising from both academia and industry as creativity has long been recognized as an important determinant of ad effectivenss (Smith & Yang, 2004). Divergence and relevance are important factors leading to creativity perceptions (Lee & Hong, 2016).
Divergence is associated with being novel and different from the usual,
while relevance is concerned with being meaningful, appropriate,
useful, and valuable (Smith, MacKenzie, Yang, Buchholz, & Darley,
2007). As different demographics groups have different ideas about
H2. Perceived informativeness is positively related to utilitarian click98
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V. Setyani, et al.
central component in the unplanned buying process (Verhagen & van
Dolen, 2011). Exposure to situational stimuli has been found to be a
main driver of impulsive buying (Chan et al., 2017). Verhagen and van
Dolen (2011) reported browsing activities lead to higher impulsive
buying urges. When people click through the ads, regardless of utilitarian or hedonic motivation, they are likely to be exposed to more
information about the product or service, more opportunities for not
only exposure to extrinsic stimuli, but also positive affects, which all
increase the urge to buy impulsively (Huang, 2015; Verhagen & van
Dolen, 2011). Therefore, we propose:
through motivation.
Although there is ample information on social media, people tend to
be more cautious and do not believe every piece of information blindly
(Kang, Höllerer, & O’Donovan, 2015). Cognitive response theory suggests that when persuasive communications are perceived as more
credible, both the cognitive responses and attitude toward the ad are
more favorable (Petty, Cacioppo, & Schumann, 1983). Perceived credibility of the source can determine the next action to be undertaken by
users, including the willingness in receiving further information (Li &
Suh, 2015). Credibility can effectively reduce the situational complexity
as users can focus on evaluating the product without worrying whether
the product information is biased or untruthful. In this way, credibility
increases the efficiency of exploring product/services via clickingthrough personalized ads, which is a key aspect of utilitarian motivation. When you are confident that you can believe the information you
see, time and efforts are saved from checking and verifying the truthfulness of the information. Thus, when users think of the personalized
advertisement as credible, they are more likely to click-through the
advertisements.
H6. Utilitarian click-through motivation is positively related to the urge
to buy impulsively.
H7. Hedonic click-through motivation is positively related to the urge
to buy impulsively.
2.6. The mediation hypotheses
Based on the Stimulus-Organism-Response (S-O-R) Model, we propose a research model that systematically examines users’ response to
personalized advertising. The S-O-R model is the most widely adopted
framework in online impulsive buying research as it emphasizes the
role of environmental cues in online impulse buying behavior and can
be largely reconciled with research drawn on the environmental psychology paradigm (Chan et al., 2017). In our model, personalized advertising serves as the stimuli, perceived advertising value and clickthrough motivations serves as the organism between stimuli and the
final responses of impulsive buying intentions. Ducoffe (1996) maintained that perceived values serve as perceptual antecedents that affect
user attitude toward advertising. Thus, perceived advertising value is
likely to influence people's attitude about the personalized ad on
whether they would think it is helpful or fun to click through the link
and learn more about it. Finally, the urge to buy impulsively represents
the response component in our study. In essence, our model depicts the
four advertising values and click-through motivations as the internal
mechanisms that mediate the relationship between personalized ads
and urge to buy impulsively. Therefore, we develop the following
mediation hypotheses:
H3. Perceived credibility is positively related to utilitarian clickthrough motivation.
Hedonic motivation is associated with need for novelty, variety, and
surprise (Hausman, 2000; Hirschman, 1980; Holbrook & Hirschman,
1982). Mehrabian and Russell (1974) suggest that novelty and unfamiliarity in the environment lead to arousal. Poels and Dewitte
(2008) explored the question of how ad creativity can be characterized
from an emotional point of view and found that creativity leads to
pleasure and arousal reactions in consumers. Yang and Smith (2009)
reasoned that processing creative ads should be deemed as intrinsically
interesting and enjoyable because consumers have the internal dispositions to appreciate stimuli that is different and surprising. They
found that creative ads lead to positive affects such as interested, excited, and inspired. Im, Bhat, and Lee (2015) found that novelty in ads
is linked to perceived hedonic value of the ads. As creative content is
novel, out-of-ordinary, intriguing and surprising (Lee & Hong, 2016), it
is likely to arouse experiential or sensory reactions and satisfy the need
for novelty, variety and surprises that correspond with hedonic-seeking
consumers (Voss et al., 2003). Therefore, the higher the perceived
creativity, the higher the users’ hedonic motivation to click through the
ad in search of novelty, variety and surprise would be.
H8a. Perceived informativeness and utilitarian click-through
motivations mediate the relationship between level of ad
personalization and urge to buy impulsively.
H4. Perceived creativity is positively related to hedonic click-through
motivation.
H8b. Perceived credibility and utilitarian click-through motivations
mediate the relationship between level of ad personalization and urge
to buy impulsively.
Another factor that attracts user attention is entertainment. Tsang
et al. (2004) found that perceived entertainment is the biggest predictor
of user's attitude toward the advertising message on their mobile
phones. Enjoyment, fun, and entertainment reflect the hedonic aspects
of user's motivation to use technologies (Weijters, Rangarajan, Falk, &
Schillewaert, 2007). Ads with high entertainment value are exciting,
enjoyable and fun (Ducoffe, 1996). As hedonic motivation seeks happiness, fantasy, awakening, sensuality, and enjoyment (To et al., 2007),
high entertainment value is likely to increases hedonic motivation to
click-through the ads to experience more and see more content.
H8c. Perceived creativity and hedonic click-through motivations
mediate the relationship between level of ad personalization and urge
to buy impulsively.
H8d. Perceived entertainment and hedonic click-through motivations
mediate the relationship between level of ad personalization and urge
to buy impulsively.
As perceived advertising value and click-through motivations are
the organism between stimuli and the final responses of impulsive
buying intentions, we expect that they fully mediate the relationship
between level of ad personalization and urge to buy impulsively.
H5. Perceived entertainment is positively related to hedonic clickthrough motivation.
H8e. Perceived advertising value and click-through motivations fully
mediate the relationship between level of ad personalization and urge
to buy impulsively.
2.5. User's click-through motivation and urge to buy impulsively
When users click-through the ad in social media, they are linked to
the product web site where they could browse and examine the product
or service with more detailed information. Browsing, defined as the
examination of a merchandize for recreational and informational purposes without an immediate intent to buy, has been argued to be a
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3. Methodology
strongly disagree (1) to strongly agree (5). Personalization items were
adopted from Kim and Han (2014). Items for perceived entertainment,
perceived informativeness, perceived creativity, and perceived credibility were adapted from Lee and Hong (2016), Kim and Han (2014),
and Buil, Chernatony, and Martinez (2013). Utilitarian and hedonic
click-through motivation items were from Mikalef et al. (2013). Finally,
urge to buy impulsively measures were derived from Huang (2015), and
Kacen and Lee (2002). Complete questionnaire items for each dimension can be found in Appendix. We controlled for age and shopping
budget for shopping to partial out any possible confounding effects as
suggested by prior research on impulsive buying (Jeffrey & Hodge,
2007; Stilley, Inman, & Wakefield, 2010) and independently tested
gender's effect on our dependent variable.
3.1. Participants and procedure
The target population of this study is social media users in
Indonesia. With a population of 261 million, Indonesia is the world's
fourth most populous country, largest Muslim population, and
Southeast Asia's largest economy. Indonesia is considered as quite representative of developing countries, especially in the Asia Pacific region. Samples from Indonesia could possibly be generalized to other
developing countries, especially those with similar cultural, political,
techno-logical, legal and socioeconomic conditions (Kurnia, Karnali, &
Rahim, 2015).
We enlisted respondents that were familiar with social media advertising to ensure that they would able to answer the questions in the
questionnaire. The initial screening questions excluded respondents
that (1) did not have a social media account and (2) were not
Indonesian to ensure that our sample was consistent with our population. In the beginning of the questionnaire, we gave them an example of
social media advertising taken from a personalized Facebook advertising page and asked the respondents to login into their social media
account and view social media advertising that has been personalized.
The respondents then were asked to reflect upon their experience when
viewing the ad and fill out the questionnaire.
We distributed questionnaire online through various social media
platforms and forums in Indonesia. The survey was administrated for
approximately two months starting from March 24, 2016. Data from
963 respondents were collected, of which 101 were invalid or incomplete. Finally, 862 data points were used in our analysis.
The distribution of respondent demographics is summarized in
Table 2. There were more female respondents (72%) than male respondents (28%). Respondents aged between 21 and 25 year represented the majority of the samples (51%). Most respondents were
seeing/reading advertising social media several times in a day
4. Results and analysis
4.1. Data normality, linearity and homoscedasticity
Before conducting SEM analysis, we checked our data for normality,
linearity and homoscedasticity (Hair, Ringle, & Sarstedt, 2011). Hair
et al. (2011) argued that to meet normality requirements, the skewness
should be between −2 to +2 and kurtosis should be between −7 to
+7. The data normality test showed that the skewness and kurtosis
stats for all our measures are within the recommended range. Therefore, data normality requirement was satisfied.
Linearity is checked by examining the correlation matrix. As we can
see from Table 3, no correlation between any two constructs is greater
than 0.8, satisfying the requirement for linearity (Katz, 2006). Finally,
homoscedasticity requires that all dependent variables exhibit equal
level of variance across the range of predictor variables. We ran the
Levene's test of equality of error variances and all constructs had a
significance higher than 0.05, indicating that the variability in our
dependent variables are not significantly different from each other.
With these results, we proceeded to data analysis.
4.2. Measurement model testing
3.2. Measurements
Confirmatory factor analysis (CFA) was conducted using structural
equation model (SEM) with AMOS 2. First, we performed the convergent validity testing for each indicator, and found that all indicators
except one (P4) met the 0.6 factor loadings threshold (Kline, 1994). We
deleted P4 due to low factor loading. Next, we evaluated the AVE value
of each variable, and all variables met the requirement of AVE > 0.5
(Hair et al., 2011) as exhibited in Table 5. We also conducted discriminant validity test per Hair et al. (2011). Table 3 shows that the
square roots of AVE are greater than the correlation between variables.
Furthermore, the cross-loading values of this research model, as shown
by Table 4, also met the requirement such that each indicator has the
largest correlation to its construct variable (Barclay, Higgins, &
Thompson, 1995). Reliability in the forms of Composite Reliability (CR)
and Cronbach's Alpha (CA) for each variable exceeded 0.7 (Fornell &
Larcker, 1981; Hair et al., 2011). Lastly, we addressed the concern for
common method variance with two tests. First, we performed Harman's
one-factor test (Podsakoff, MacKenzie, & Podsakoff, 2012). All items
were included in an unrotated principal components factor analysis.
The analysis yielded ten factors with eigenvalue > 1.0, with the first
factor explaining 34.89% of the total variance. Second, we conducted
the Common Latent Factor test in AMOS (Podsakoff, MacKenzie, Lee, &
Podsakoff, 2003). This technique was similar to the Harman's one-factor
test; however, the research model's latent variables and their relationships were kept in this analysis. The common bias was estimated by
using the square of the common factor of each path before standardization. The resulting value of the common latent factor is 0.45. The
results of both tests were below the recommended threshold of 50%
(Eichhorn, 2014), suggesting that common method variance is unlikely
to confound the interpretations of results (Eichhorn, 2014; Podsakoff
The questionnaire was developed using five-point Likert scale from
Table 2
Respondent demographics.
Demographic variables
Frequency
Percentage
Gender
Female
Male
623
239
72
28
Age
15–20 years
21–25 years
26–30 years
31–40 years
41–50 years
> 50 years
313
441
54
34
18
2
36
51
6
4
2
0.2
Occupation
Student
Government
employees
Private employees
Entrepreneur
The others
649
28
75
3
115
23
47
13
3
6
Social media usage
Several times a day
Once a day
1–2 times a week
3–5 times a week
Seldom
703
91
18
36
14
81
11
2
4
2
Impulsive buying
experience
Never
These several days
A few weeks ago
A few months ago
More than a year
220
128
206
261
47
26
15
24
30
5
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V. Setyani, et al.
Table 3
Correlation matrix.
P
PE
PI
PC
PD
UM
HM
UB
Age
Budget
Mean
STD
P
PE
PI
PC
PD
UM
HM
UB
Age
Budget
3.055
2.942
3.104
3.014
2.682
3.049
2.937
3.170
1.849
2.441
.859
.887
.698
.784
.763
.782
.895
.956
.887
.816
(.832)
.355**
.448**
.310**
.342**
.258**
.292**
.237**
.067*
.171**
(.846)
.650**
.667**
.451**
.541**
.626**
.469**
(.009)
.140**
(.727)
.621**
.593**
.569**
.560**
.435**
.014
.134**
(.784)
.500**
.583**
.632**
.484**
(.036)
.094**
(.829)
.469**
.494**
.369**
(.006)
.152**
(.800)
.729**
.472**
−.092**
.159**
(.871)
.559**
(.064)
.133**
(.847)
−.142**
.179**
–
.234**
–
Square root of AVE in bold on diagonals are Pearson correlation of constructs.
* Correlation is significant at the 0.05 level (2-tailed).
** Correlation is significant at the 0.01 level (2-tailed).
Table 4
Loading and cross loading.
UB5
UB4
UB3
UB2
UB1
HM1
HM2
HM3
HM4
HM5
UM5
UM4
UM3
UM2
UM1
PE1
PE2
PE3
PC1
PC2
PC3
PC4
PD3
PD2
PD1
PI5
PI4
PI3
PI2
PI1
P1
P2
P3
Table 5
Reliability and convergent validity measures.
P
PE
PC
PD
PI
HM
UM
UB
Indicator
Loading
Variable
AVE
CR
CA
0.115
0.130
0.133
0.121
0.124
0.245
0.261
0.259
0.262
0.261
0.216
0.249
0.264
0.286
0.269
0.308
0.337
0.321
0.262
0.243
0.299
0.313
0.261
0.304
0.244
0.336
0.355
0.348
0.375
0.379
0.839
0.939
0.680
0.329
0.372
0.382
0.348
0.356
0.558
0.592
0.588
0.596
0.594
0.390
0.449
0.476
0.516
0.485
0.804
0.880
0.837
0.539
0.499
0.690
0.647
0.366
0.426
0.479
0.546
0.545
0.487
0.526
0.531
0.321
0.284
0.260
0.341
0.386
0.396
0.360
0.369
0.594
0.630
0.626
0.635
0.632
0.440
0.506
0.537
0.582
0.547
0.615
0.648
0.640
0.705
0.653
0.804
0.843
0.410
0.478
0.532
0.588
0.550
0.486
0.524
0.529
0.312
0.251
0.253
0.269
0.304
0.313
0.284
0.291
0.400
0.425
0.422
0.427
0.425
0.310
0.357
0.378
0.410
0.385
0.369
0.404
0.384
0.363
0.336
0.482
0.434
0.798
0.929
0.745
0.393
0.415
0.504
0.440
0.444
0.274
0.252
0.222
0.313
0.355
0.364
0.331
0.339
0.500
0.531
0.528
0.535
0.532
0.417
0.481
0.510
0.553
0.519
0.579
0.633
0.624
0.506
0.468
0.599
0.583
0.480
0.559
0.571
0.654
0.690
0.677
0.731
0.738
0.431
0.363
0.349
0.437
0.495
0.508
0.462
0.474
0.837
0.889
0.883
0.895
0.891
0.594
0.559
0.593
0.643
0.604
0.536
0.575
0.558
0.500
0.463
0.593
0.599
0.381
0.444
0.468
0.471
0.454
0.405
0.437
0.441
0.246
0.206
0.199
0.366
0.414
0.425
0.386
0.396
0.662
0.644
0.639
0.648
0.645
0.671
0.772
0.819
0.888
0.834
0.468
0.512
0.499
0.462
0.428
0.549
0.541
0.369
0.430
0.457
0.511
0.430
0.435
0.455
0.459
0.270
0.208
0.219
0.778
0.880
0.904
0.822
0.842
0.479
0.500
0.496
0.503
0.501
0.351
0.363
0.385
0.417
0.392
0.340
0.368
0.356
0.309
0.286
0.504
0.368
0.276
0.321
0.348
0.306
0.292
0.275
0.295
0.297
0.124
0.101
0.100
P1
P2
P3
0.849
0.936
0.693
Personalization (P)
0.692
0.869
0.835
PI1
PI2
PI3
PI4
PI5
0.76
0.696
0.728
0.747
0.704
Informativeness (PI)
0.529
0.849
0.848
PD1
PD2
PD3
0.736
0.934
0.804
Credibility (PD)
0.687
0.867
0.862
PC1
PC2
PC3
PC4
0.81
0.784
0.75
0.79
Creativity (PC)
0.614
0.864
0.864
PE1
PE2
PE3
0.803
0.888
0.844
Entertainment (PE)
0.715
0.883
0.881
UM1
UM2
UM3
UM4
UM5
0.827
0.886
0.826
0.767
0.68
Utilitarian motivation (UM)
0.64
0.898
0.902
HM1
HM2
HM3
HM4
HM5
0.848
0.881
0.878
0.884
0.863
Hedonic motivation (HM)
0.758
0.94
0.948
indices were good: CMIN/DF = 2.184 (CMIN = 1168.7; DF = 535;
suggested value < 5); GFI = 0.948 (suggested value > 0.9);
CFI = 0.971 (suggested value > 0.9); TLI = 0.966 (suggested
value > 0.9); RMSEA = 0.037 (suggested value < 0.10), pClose = 1.
The results of structural model testing are summarized in Table 6 and
Fig. 2.
Our results show that the level of personalization is positively related to all four advertising values, with perceived informativeness
having the largest effect size (beta = 0.57, P < 0.01). The four advertising values positively relates to utilitarian and hedonic clickthrough motivations. Utilitarian and hedonic click-through motivations, in turn, contributes to urge to buy impulsively. We controlled for
age and spending budget. Age is significantly negatively related to urge
to buy impulsively (beta = −0.13), while budget is significantly positively related to urge to buy impulsively (beta = 0.10). We independently tested the effect of gender and it was not significantly
related to our dependent variable.
P: personalization; PE: perceived entertainment, PC: perceived creativity; PD:
perceived credibility; PI: perceived informativeness; HM: hedonic click-through
motivation; UM: utilitarian click-through motivation; UB: urge to buy impulsively.
et al., 2012).
The last step of measurement model testing is the goodness of fit
(GOF) testing. The fitness indices we obtained indicate that our data fit
the measurement model well: CMIN/DF = 3.518 (CMIN = 1643;
DF = 467; suggested value < 5); GFI = 0.927 (suggested value >
0.9); CFI = 0.946 (suggested value > 0.9); TLI = 0.939 (suggested
value > 0.9);
RMSEA = 0.054
(suggested
value < 0.10),
pClose = 0.01.
4.3. Structural model testing
We tested the structural model with AMOS. The structural model fit
101
International Journal of Information Management 48 (2019) 96–107
V. Setyani, et al.
Table 6
Summary of results.
Hypotheses
H1a
H1b
H1c
H1d
H2
H3
H4
H5
H7
H6
Table 7
Mediation test results.
Estimate
S.E.
C.R.
P
Result
Support
Support
Support
Support
Support
Support
Support
Support
Support
Support
P → PI
P → PD
P → PC
P → PE
PI → UM
PD → UM
PC → HM
PE → HM
HM → UB
UM → UB
0.568
0.392
0.4
0.412
0.609
0.14
0.489
0.295
0.43
0.161
0.036
0.034
0.038
0.041
0.07
0.051
0.068
0.062
0.052
0.056
13.048
9.387
9.76
10.2
10.245
3.274
8.395
5.048
8.849
3.381
***
Age
Budget
−0.126
0.098
0.029
0.032
−4.435
3.477
***
***
***
***
***
***
***
***
***
***
Parameter
Estimate
SE
Bootstrapping
Bias-corrected 95% CI
Indirect effect
P → PI → UM
P → PD → UM
P → PE > HM
P → PC → HM
P → PI → UM → UB
P → PD → UM → UB
P → PE > HM → UB
P → PC → HM → UB
Direct effect
P → UM
P → HM
P → UM → UB
P → HM → UB
P → UB
Total effect
P → UM
P → HM
P → UM → UB
P → HM → UB
P → UB
***
*** Significant at the 0.01 level (2-tailed).
Lower
Upper
P
0.334
0.053
0.130
0.210
0.063
0.010
0.059
0.096
0.045
0.021
0.039
0.041
0.023
0.005
0.019
0.022
0.256
0.013
0.053
0.145
0.022
0.002
0.024
0.061
0.435
0.095
0.204
0.307
0.116
0.023
0.099
0.149
0.000
0.008
0.006
0.000
0.002
0.006
0.006
0.000
−0.125
0.003
−0.024
0.001
0.081
0.047
0.037
0.013
0.017
0.037
−0.216
−0.066
−0.056
−0.031
0.010
−0.03
0.076
−0.005
0.035
0.156
0.008
0.936
0.006
0.933
0.027
0.262
0.343
0.050
0.156
0.287
0.045
0.045
0.018
0.028
0.046
0.177
0.256
0.018
0.107
0.199
0.355
0.436
0.091
0.219
0.381
0.000
0.000
0.002
0.000
0.000
P: personalization; PE: perceived entertainment, PC: perceived creativity; PD:
perceived credibility; PI: perceived informativeness; HM: hedonic click-through
motivation; UM: utilitarian click-through motivation; UB: urge to buy impulsively.
Fig. 2. Results.
we stratified our sample according to the general gender and age distribution of social media users’ profile in Indonesia (slightly more male
than female users and 90% of users under the age of 34) (eMarketer,
2016) and randomly discarded respondents from the over-represented
strata to achieve better sample representativeness. In the end, we discarded 539 respondents and ended up with a sample of 323 respondents
that were balanced according to age and gender. Chi-square tests
showed that our sample did not differ from the expected age and gender
distribution. Table 8 below shows our stratified sample distribution. We
re-estimated model fit, reliability and validity and every requirement
was met. We then reran the analysis and the results are reported in
Table 9 and Table 10.
We used multiple group structural model to test whether there are
differences between the full sample model and the stratified sample
4.4. Mediation testing
We tested whether advertising value and click-through motivations
mediate the relationship between personalization and urge to buy impulsively with an asymmetric bootstrap test of mediation using biascorrected confidence intervals for indirect effects (Hamby, Daniloski, &
Brinberg, 2015; Zhao, Lynch, & Chen, 2010). To establish mediation,
the only requirement is a significant indirect effect (Zhao et al., 2010).
The results show that all four advertising values serve as mediators
from personalization to utilitarian and hedonic click-through motivations. Perceived informativeness has the biggest indirect effect
(beta = 0.33, SE = 0.045, p < 0.01), followed by perceived creativity
(beta = 0.21, SE = 0.021, p < 0.01), perceived entertainment
(beta = 0.13, SE = 0.039, p < 0.01), and perceived credibility
(beta = 0.05, SE = 0.041, p < 0.01). The four advertising values together with utilitarian and hedonic click-through motivations serve as
serial mediators from personalization to urge to buy impulsively. Perceived creativity and hedonic motivation have the biggest indirect effect size (beta = 0.096, SE = 0.022, p < 0.01), followed by perceived
informativeness and utilitarian motivation (beta = 0.063, SE = 0.023,
p < 0.01), perceived entertainment and hedonic motivation
(beta = 0.059, SE = 0.019, p < 0.01), and perceived credibility and
utilitarian motivation (beta = 0.01, SE = 0.005, p < 0.01). The results
provide support for the proposed serial mediation model. Perceived
advertising value and click-through motivations, however, did not fully
mediate the relationship between level of ad personalization and urge
to buy impulsively, as the direct effects from personalization to utilitarian motivation and urge to buy impulsively are still significant.
Therefore, Hypothesis 8a, 8b, 8c, 8d received support, while Hypothesis
8e did not (Table 7).
Table 8
Stratified sample demographics.
Demographics
4.5. Stratified sample test
Because our sample was biased toward female and young respondents, our results could possibly be biased. To address this concern,
Frequency
Percent
Gender
Male
Female
167
156
51.7
48.3
Generationa
≤35 years
> 35
280
43
86.7
13.3
Age
15–20 years
21–25 years
26–30 years
31–40 years
41–50 years
> 50 years
91
151
27
34
18
2
28.2
46.7
8.4
10.5
5.6
.6
Occupation
Student
Government employees
Private employees
Entrepreneur
other
190
27
57
12
37
58.8
8.4
17.6
3.7
11.5
a
We estimated the number of people aged 31–35 to be 50% of the 31–40 age
bracket.
102
International Journal of Information Management 48 (2019) 96–107
V. Setyani, et al.
Hsieh, 2012) and reduce information overload (Liang et al., 2006), but
also expand the value of personalization to include a broader spectrum
of values such as perceived creativity, perceived entertainment and
perceived credibility.
Prior research that broadly discusses the impact of utilitarian and
hedonic motivation on impulsive buying has mostly found hedonic
motivation to be the main driver of impulsive buy (Amos, Holmes, &
Keneson, 2014; Chan et al., 2017), while utilitarian motivation is found
to reduce impulsive buying (Park, Kim, Funches, & Foxx, 2011). Like
prior research, this research found that in the social platform personalized advertisement context, hedonic motivation is a stronger driver
of impulsive buy behavior. In our results, however, utilitarian clickthrough motivation is also significantly associated with urge to buy
impulsively. We speculate that with the advancements in user data
analysis, personalized advertisements are able to reflect and address a
user's deeper needs, the ones that even the user him/herself may not be
aware of. For example, a busy mom may not have thought about
whether she need to buy a set of award-winning math tutorials for her
kids, but when she sees advertisement for it on Facebook, which obviously knows her identity as a mother of young children, she finds it
could be useful and helpful for her kids and immediately buys it.
Therefore, personalized advertisements based on user's interest, roles,
and preferences may profoundly change the landscape of impulsive
buying in that it uncovers user's hidden needs and desires even before
the user becomes aware of it. The classic driver of impulsive buying,
hedonic motivation, is still a significant driver of urge to buy impulsively in our study, however, its role may be complemented by
utilitarian click-through motivation in the personalized advertisement
context.
For our control variables, age and budget effects on urge to buy
impulsively were confirmed. The results are in-line with prior research
(Amos et al., 2014; Wells, Parboteeah, & Valacich, 2011). People are
more capable of regulating themselves from impulsive buying as they
advance in age. People with more budget, however, are more prone to
impulsive buying behaviors.
Advertising values are key mediators of the personalization to clickthrough motivation relationship. Specifically, perceived entertainment
and creativity fully mediate the personalization to hedonic clickthrough motivation relationship, while perceived credibility and informativeness partially mediate the personalization to utilitarian clickthrough motivation relationship. Interestingly, the direct effect of personalization to utilitarian click-through motivation turned out to be
negative, signaling a suppression effect, i.e., when the direct and
mediated effects of an independent variable on a dependent variable
have opposite signs (Cheung & Lau, 2008). Similarly, the indirect effect
from personalization to urge to buy impulsively mediated through
utilitarian click-through motivation is also negative. We suspect that
this may be due to the so-called “Personalization Privacy Paradox” that
people may feel toward personalization: on the one hand, personalized
services add value to customers and increase customer loyalty; on the
other hand, people are concerned about their privacy as personalization
requires collection of customer personal data (Awad & Krishnan, 2006).
When consumers perceive privacy invasion, they form less favorable
attitudes toward the ad, and are less likely to buy (Cases, Fournier,
Dubois, & Tanner, 2010). It is possible that these love/hate relationship
consumers have with personalization is manifested in the opposite signs
of the direct and indirect relationships personalization has on utilitarian
click-through motivation.
Advertising values along with utilitarian and hedonic click-through
motivations serve as serial mediators from personalization to urge to
buy impulsively, accounting for the majority of the personalization to
urge to buy impulsively relationship. However, they failed to fully
mediate the relationship, as the direct effect from personalization to
urge to buy impulsively remained significant. Just as Zhao et al. (2010)
have observed, most mediation studies report “partial mediation” with
a significant direct path that is rarely predicted or explained. Zhao et al.
Table 9
Stratified sample hypothesis testing.
Hypotheses
H1a
H1b
H1c
H1d
H2
H3
H4
H5
H7
H6
P
P
P
P
PI
PD
PC
PE
HM
UM
Age
Budget
Estimate
PI
PD
PC
PE
UM
UM
HM
HM
UB
UB
UB
UB
0.492
0.357
0.368
0.377
0.6
0.196
0.446
0.396
0.443
0.173
−0.178
0.088
S.E.
0.062
0.058
0.065
0.071
0.086
0.072
0.108
0.096
0.081
0.093
0.035
0.048
C.R.
P
Result
7.251
5.25
5.502
5.851
7.388
3.124
4.786
4.246
5.668
2.249
−4.072
2.019
***
Supported
Supported
Supported
Supported
Supported
Supported
Supported
Supported
Supported
Supported
***
***
***
***
***
***
***
***
**
***
**
** Correlation is significant at the 0.05 level (2-tailed).
*** Correlation is significant at the 0.01 level (2-tailed).
Table 10
Stratified sample mediation test results.
Parameter
Estimate
SE
Bootstrapping
Bias-corrected 95% CI
Indirect effect
P → PI → UM
P → PD → UM
P → PE → HM
P → PC → HM
P → PI → UM → UB
P → PD → UM → UB
P → PE → HM → UB
P → PC → HM → UB
Direct effect
P → UM
P → HM
P → UM → UB
P → HM → UB
P → UB
Total effect
P → UM
P → HM
P → UM → UB
P → HM → UB
P → UB
Lower
Upper
P
0.288
0.068
0.168
0.185
0.060
0.014
0.078
0.085
0.068
0.032
0.094
0.103
0.035
0.010
0.045
0.052
0.182
0.014
0.029
0.069
0.002
0.001
0.021
0.031
0.451
0.142
0.307
0.406
0.137
0.048
0.154
0.215
0.000
0.019
0.036
0.000
0.047
0.036
0.025
0.000
−0.152
−0.049
−0.032
−0.023
0.172
0.068
0.052
0.024
0.025
0.058
−0.294
−0.155
−0.095
−0.079
0.065
−0.025
0.050
0.000
0.020
0.294
0.020
0.307
0.051
0.259
0.001
0.204
0.304
0.043
0.140
0.355
0.078
0.077
0.027
0.044
0.076
0.051
0.158
0.004
0.070
0.217
0.361
0.465
0.114
0.247
0.512
0.007
0.000
0.030
0.000
0.000
model (Deng & Yuan, 2015). Specifically, measurement weights,
structural weights and structural covariance were used to estimate the
differences between the two models. The results show that all three
parameters: measurement weights, structural weights and structural
covariance are invariant between the full sample and the stratified
sample, suggesting that there are no statistically significant differences
between the results of the two models. Thus, the results seem to be
robust with the complete sample test.
5. Discussion and conclusion
5.1. Discussion
With the increasing popularity of personalized advertising in social
media, it is important to understand how personalized advertising
works and how it is linked to purchase intentions. This research explores the psychological mechanism and constructs underlying user's
reactions to personalized advertisement. Our results show that personalized advertisement has significant influence on all four aspects of
advertising value. The results not only resonance with prior literature
that views the key value of personalization to be providing more targeted and relevant information that could meet user needs (Chen &
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International Journal of Information Management 48 (2019) 96–107
V. Setyani, et al.
(2010) contented that this may be an indication that there is an omitted
mediator. Reflecting upon our context, we postulate that it is possible
that there are other mediators in the process that were not included in
our model. Some possible omissions are the social value and social
click-through motivations. Facebook and other social platforms have
the “like” button and often display ads with a line explaining, so and so
from your friend list have liked the product or service. Thus, personalized ads could help you to understand your friends better, by showing
you what they like, and could possible trigger you to click-through the
ads because you are curious to find out what they liked or why the liked
it.
5.3. Practical implications
The results of this study are meaningful for companies deploying
personalized advertisements in social media in several ways. As people
spend more and more time on social media, managers respond by devoting considerable more resources to promote their products and
services on social media. However, what really works for personalized
advertisement on social platforms? What motivates users to click, and
what makes them want to buy immediately? We offer the following
recommendations for managers wondering these questions.
First, provide relevant and useful information. Of the four advertising value, informativeness is the strongest predictor of click motivation, followed by creativity and entertainment. The results provide
some guidelines for managers hoping to boost their click-through rates
on social media personalized ads. To be informative, ads need to provide accurate and relevant information. With social media data, it is
easier than ever to identify what would be relevant for a user based on
their social media activities. For example, for young people troubled
with acne and posting on their Facebook complaining about acne, the
ad could provide information like “8 out of 10 find salicylic acid to be
effective for their acne” and promote their product with salicylic acid
ingredient. For young mothers perplexed by their babies’ fussiness, ads
for anti-colic bottle could say “air in the bottle makes babies fussy. Try
our new product”, which not only showcases the product, but also explains why this product is effective to keep the users informed.
Second, be creative. Creativity is the biggest driver for hedonic click
through motivation, and bears the biggest indirect effect from personalization to impulsive buying on social media personalized ads.
Therefore, it is important to incorporate some unexpected and new
elements in ads to attract people to buy. Creative ads are surprising and
out of ordinary. Depending on what is the norm in ads of similar products, creative ad content could work from the usual and achieve the
surprising element by going different directions. For example, instead
of using beautiful girls in cosmetics ads, some ads creatively showed a
beautiful “girl” removing all “her” makeup and in the end, turned out to
be a boy to highlight the beauty product's transformative power.
Third, keep them entertained. Entertainment bears the second largest indirect effect from personalization to impulsive buying. Therefore,
it is helpful for the ads to be interesting and enjoyable. Depending on
the user's interest and preferences, customized content could be applied. For example, Sci-Fi lovers would probably find an ad with Sci-Fi
elements in it to be interesting, while sports fans would enjoy an ad
with their favorite stars in it.
Finally, motivate them. Hedonic and utilitarian click-through motivations both lead to higher impulsive buying urges, with hedonic
motivation having a stronger effect size. Are there other ways to appeal
to the hedonic side of motivation besides creativity and entertainment?
How about aesthetics, adventure, happiness, fantasy or awakening (To
et al., 2007)? Are there other ways to increase the utilitarian motivation
of users besides credibility and informativeness? How about convenience, product selection and efficiency (To et al., 2007)? Personalization technology can be used to achieve these values. For example, if
a person likes content about African safari adventures, an African safari
ad would address the adventure side of hedonic motivation and is more
likely to be clicked and viewed. The value of personalization lies in that
it allows a multi-faceted understanding of the customer, and this understanding can be used to create customer value in different dimensions. Therefore, advertising value is not limited to the four that we
examined in this research, but could be expanded to include more dimensions that correspond to either hedonic or utilitarian motivation of
customer consumption.
5.2. Theoretical implications
The effectiveness of personalized advertising has been widely recognized, what is less understood is how personalized advertising
achieves these outcomes, necessitating the need to study the underlying
mechanism. The promise of mediation analysis is that it can identify
fundamental processes underlying human behavior that are relevant
across behaviors and contexts, and enable us to develop efficient and
powerful interventions to focus on variables in the mediating process
(MacKinnon & Fairchild, 2009). Our research contributes to extant
literature in three ways. First, our results uncovered the mechanism
from personalized advertising to click-through motivation, explaining
how personalized advertising lead to impressively higher click-through
rates. This research proposed, and tested advertising values as mediators to two kinds of click motivation. Our results show that there could
be multiple paths for personalized advertisement to exert its effect on
click-through rates. It could be through providing valued information,
enhancing advertiser's credibility, and providing tailored creative and
entertaining content. By integrating click-through motivations and the
advertising value framework, we unveiled the mediators that tie personalization to click-through motivation. The results not only contribute to extant literature with a better understanding of the process
through which personalization works, but also aid managers in tailoring their efforts to further enhance personalizing effectiveness with
four avenues of possible intervention, thereby helping personalized
advertiser to better understand, and strengthen their value to users.
Second, while prior research has examined impulsive shopping
motivations from various lenses (Amos et al., 2014; Chan et al., 2017),
few have investigated impulsive buying from a personalization perspective. Exposure to generic online advertisement in various formats
(text, video and image) has been linked to impulsive buying (Adelaar,
Chang, Lancendorfer, Lee, & Morimoto, 2003), yet little is known about
the role of personalized advertisements in impulsive buying online.
While 48% users reportedly spend more with personalized e-commerce
(Fletcher, 2012), a theoretical account for the relationship between
personalized advertising and impulsive buying is still missing. Based on
the S-O-R model, this research contributes to the impulsive buying literature by theoretically linking personalized advertisement to impulsive buying intentions and explaining the process with advertising
value and click-through motivations as the mediators. The findings
enriched the S-O-R model with the two-stage internal process in organism and provided evidence for the importance of click-through rates
in impulsive buying intentions.
Third, our study adds new insights to the role of hedonic and utilitarian clicking in the personalized advertisement context. Contrary to
prior research that identified utilitarian motivation as an inhibitor of
impulsive buying (Park et al., 2011), this research found that in the
social platform personalized advertisement context, utilitarian motivation also appears to be a driver of impulsive buying behavior. This
may be partly due to the ability of data analytics and personalization to
uncover users’ hidden needs and desires. This finding helps us to better
understand how personalized advertisement transforms user's reaction
in the social media platform and provide novel insights into buying
intentions subsequent to user's social media clicking activities.
5.4. Limitations and future research
Several limitations of this research should be noted. Although these
findings are interesting, we relied mainly on cross-sectional self104
International Journal of Information Management 48 (2019) 96–107
V. Setyani, et al.
social media platforms from different cultural background for personalized advertisement to achieve wider generalizability. Finally, we did
not explore the relationship between utilitarian motivation and hedonic
motivation, which could be a possible avenue for future research.
reported conventional sample and only measured the intentions to buy
impulsively. Future research could consider using actual sales numbers
with a longitudinal design to validate the causal relationship between
personalized advertisements and impulsive buying behavior. Second,
similar to other online impulsive buying literature (Lo, Lin, & Hsu,
2016; Verhagen & van Dolen, 2011), our sample is biased toward
women and young people. We accounted for this bias by stratifying our
sample according to our population parameters and compare their
differences. Third, we drew our sample from a single country. Thus, the
generalizability of our results may be limited in countries with different
cultural backgrounds. Future research could design tests with multiple
Acknowledgements
Yu-Qian Zhu is grateful for the support from the Ministry of Science
and Technology, Taiwan, ROC (Republic of China) (Funding #: 1052410-H-011-MY2).
Appendix A. Measurement items
Respondents were asked to respond to each statement by clicking the number that best indicated how strongly they agreed or disagreed with each
statement. Five-point Likert scale was used, where 1 denoted Strongly Disagree, 2 denoted Disagree, 3 denoted Neutral, 4 denoted Agree, and 5
denoted Strongly Agree.
Variable
Code
Items
Personalization
P1
P2
P3
P4
I feel that
I feel that
I feel that
I feel that
loading)
Perceived Entertainment
PE1
PE2
PE3
I feel that the ad on my social media is interesting
I feel that the ad on my social media is enjoyable
I feel that the ad on my social media is pleasant
Kim and Han (2014)
Perceived Informativeness
PI1
PI2
PI3
PI4
PI5
The
The
The
The
The
ad
ad
ad
ad
ad
on
on
on
on
on
my
my
my
my
my
social
social
social
social
social
media
media
media
media
media
supplies relevant information on products or services
provides timely information on products or services
provides accurate product information
is a good source of information
is a good source of up to date products or services
Hsu, Wang, Chih, and Lin (2015) and Kim
and Han (2014)
Perceived Creativity
PC1
PC2
PC3
PC4
The
The
The
The
ad
ad
ad
ad
on
on
on
on
my
my
my
my
social
social
social
social
media
media
media
media
is
is
is
is
Lee & Hong (2016)
Perceived Credibility
PD1
PD2
PD3
I feel that the ad on my social media is convincing
I feel that the ad on my social media is believable
I feel that the ad on my social media is credible
Utilitarian click-through motivation
UM1
Clicking through
services)
Clicking through
services)
Clicking through
services)
Clicking through
services)
Clicking through
services)
UM2
UM3
UM4
UM5
Hedonic click-through motivation
Urge to Buy Impulsively
HM1
HM2
HM3
HM4
HM5
UB1
UB2
UB3
UB4
UB5
Source
the ad on my social media is tailored to me
contents of the ad on my social media are personalized
the ad on my social media is personalized for my use
the ad on my social media is delivered in a timely way (deleted due to low factor
unique
really out of ordinary
intriguing
surprising
Kim and Han (2014)
Kim & Han (2014)
the ad to browse products on my social media is effective (to find product/
Mikalef et al. (2013)
the ad to browse products on my social media is helpful (to find product/
the ad to browse products on my social media is functional(to find product/
the ad to browse products on my social media is practical (to find product/
the ad to browse products on my social media is necessary (to find product/
Clicking through the ad to browse products on my social media is fun
Mikalef et al. (2013)
Clicking through the ad to browse products on my social media is exciting
Clicking through the ad to browse products on my social media is delightful
Clicking through the ad to browse products on my social media is enjoyable
Clicking through the ad to browse products on my social media is thrilling
I experienced a number a number of sudden urges to buy things after viewing the ad on my
Huang (2015)
social media
I saw a number of things on the ad on my social media I wanted to buy even though they were
not on my shopping list
I felt a sudden urge to buy something after viewing the ad on my social media
I want to buy things in the ad on my social media even though I had not planned to purchase
I want to buy things in the ad on my social media even though I do not really need it
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Puspa Indahati Sandhyaduhita is currently a lecturer at
the Faculty of Computer Science, Universitas Indonesia. She
received her master degree in Computer Science,
Information Architecture Track from TU Delft, the
Netherlands. Her research interests include information
systems, IS requirements, business process modeling, enterprise architecture, enterprise resource planning, IS
adoption, e-commerce, e-government and knowledge
management systems. Her researches have been published
in International Journal of E-health and Medical
Communications, Information Resources Management Journal,
International Journal of Management and Enterprise
Development, Electronic Government and other international
refereed journals and conference proceedings
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Virda Setyani is currently a developer in one of the biggest
Indonesian e-marketplace and part-time teaching assistant
at Faculty of Computer Science, Universitas Indonesia. She
received her bachelor's degree from Department of
Information Systems, Universitas Indonesia. Her research
interests are mainly in e-commerce, business intelligence,
and social media.
Bo Hsiao is an Associate Professor in the Department of
Information Management at Chang Jung Christian
University, Taiwan. He received his PhD degree in
Information Management from National Taiwan University,
Taiwan. His research interests include manufacturing information systems, data envelopment analysis, project
management, knowledge economy, and pattern recognition.
Yu-Qian Zhu is Associate Professor in the Department of
Information Management, National Taiwan University of
Science and Technology. She holds a PhD in Management of
Technology from National Taiwan University. Prior to her
academic career, she served as R&D engineer and R&D
manager in Fortune 100 and InfoTech 100 firms. Her research interests include knowledge management, R&D team
management, and social media. Her works can be found in
journals such as Journal of Management, R&D Management,
International Journal of Information Management, Government
Information Quarterly, Computers in Human Behavior etc. YuQian can be contacted at yzhu@mail.ntust.edu.tw
Achmad Nizar Hidayanto is an Associate Professor and
the Head of Information Systems/Information Technology
Stream, the Faculty of Computer Science, Universitas
Indonesia. He received his PhD in Computer Science from
Universitas Indonesia. His research interests are information systems/information technology adoption, e-commerce, e-government, social media analysis, knowledge
management, and strategic information technology management. Her researches have been published in
International Journal of Medical Informatics, Informatics for
Health and Social Care, Journal of Theoretical and Applied
Electronic Commerce Research, International Journal of
Innovation and Learning, and other international refereed
journals and conference proceedings
107