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What affects users to click social media ads and purchase intention? The roles
of advertising value, emotional appeal and credibility
Article  in  Asia Pacific Journal of Marketing and Logistics · November 2022
DOI: 10.1108/APJML-01-2022-0084
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What affects users to click social
media ads and purchase intention?
The roles of advertising value,
emotional appeal and credibility
Wen-Kuo Chen
Department of Money and Banking,
National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
Chia-Ju Ling
Ph.D. Program in Management, Dayeh University, Changhua, Taiwan, and
Chien-Wen Chen
Department of Business Administration, Feng Chia University College of Business,
Taichung, Taiwan
Abstract
Purpose – This study is based on a web-based advertising value model, adding emotional appeal, advertising
credibility, advertising clicks, product evaluation and incentive to examine the impact on consumers’ product
evaluation and purchase intention.
Design/methodology/approach – In total, 313 valid online questionnaires were collected from those who
had browsed the social media advertisement.
Findings – The results show that advertising credibility and emotional appeal have a higher impact on
advertising attitude than informativeness and irritation in the original web-based advertising value model.
Research limitations/implications – The findings strengthen the literature on the antecedents of
advertising attitude in social media context, confirming advertising clicks as a predictor of product evaluation,
and determining that advertising attitude, product evaluation and incentive play key roles as predictors of
consumer purchase intention.
Practical implications – The findings are particularly relevant to advertising marketing managers. In the
context of social media advertising, the advertising credibility of consumers is the key to increasing consumers’
willingness to purchase. Advertisers must establish a good advertising reputation to consumers in the social
media context.
Originality/value – This study combines the web-based advertising value model, advertising clicks, product
evaluation and incentive to investigate the factors that influence customers’ purchase decisions in the context
of social media.
Keywords Advertising value model, Ad clicks, Product evaluation, Incentive, Purchase intention
Paper type Research paper
1. Introduction
The rapid development of Internet technology has led to an increase in the popularity of
e-commerce. The growing use of e-commerce worldwide indicates that e-commerce and retail
sales are predicted to reach $7,385 trillion by 2025 (eMarketer, 2021). About 53% of the global
population is on social media; 70% of people have bought goods as a result of online
advertising (We Are Social and Hootsuite, 2021). This indicates that consumers have a higher
level of trustworthy and acceptance of mobile advertising, which drives businesses to spend
more on mobile advertising each year (Business Next, 2017). According to Research and
Markets (2021), the mobile advertising market was valued at US$180.610 billion in 2019 and
is projected to grow to US$724.460 billion in 2026. Moreover, taking US mobile advertising as
an example, it was US$117.35 billion in 2021 and is expected to reach US$174.14 billion in
Click social
media ads and
purchase
intention
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1355-5855.htm
Received 29 January 2022
Revised 12 October 2022
7 November 2022
9 November 2022
Accepted 9 November 2022
Asia Pacific Journal of Marketing
and Logistics
© Emerald Publishing Limited
1355-5855
DOI 10.1108/APJML-01-2022-0084
2024 (eMarketer, 2020). About half of adult US Internet users said that when brands use their
data in advertising, it helps them discover (50%) and find (49%) products and services that
interest them, but 44% said this often feels invasive (eMarketer, 2021). In Taiwan, 73.4% of
online consumers reported clicking on social media advertisements via their smartphones
(Insightxplorer, 2021); however, according to an eMarketer survey (2017), as many as 47% of
smartphone users worldwide notice and interact with mobile advertising. However,
advertising click behavior and product evaluation are ignored in previous studies. Most
enterprises have delivered targeted advertising in social media based on consumer preference
of mobile devices to attract consumers’ attention to their products. However, when are
consumers willing to click on advertisements on social media? These interesting research
issues attract much attention from both enterprises and researchers (Yu et al., 2020).
Ducoffe (1996) developed an approach to investigate the effectiveness of website advertising
attitudes, focusing on advertising value; that is, the advertising value model. This model has
been extensively used, which can usefully explain the formation of attitude towards
advertising. The advertising value model indicated that informativeness, entertainment and
irritation are the three antecedents of the perception of consumers’ advertising attitude.
Informativeness and entertainment play important roles in forming consumers’ advertising
attitudes (Lee and Cho, 2019), and consumers will want to avoid being disturbed by interfering
advertisements. Hence, if the number of advertisements is too frequent to make consumers
think that they are disturbed, it may irritate users and hurt advertising attitudes (Hausman and
Siekpe, 2009). Ahmadi et al. (2013) pointed out in the mobile advertisement that credibility
emphasizes the trust of advertisers (Okazakiet al., 2007). Consumers will judge the credibility of
the mobile information before focusing on the information content and subsequent
consumption decision-making behavior (Liu et al., 2012). In addition, Aslam et al. (2016)
believe that emotional appeal is an effective factor in advertising effectiveness, and good
advertising appeal will deepen the consumer’s impression of the product. Compared with prior
studies which emphasize that advertising value affects the formation of advertising attitude
(Zha et al., 2015), this study extends the original web-type advertising value model to explore the
formation of advertising attitude in the social media context as well as its facilitating role in
consumers’ advertising clicks and purchase intention behavior, which is largely overlooked.
Fishbein and Ajzen (1975) believe that attitude is the positive or negative feeling of
individuals towards people or things, and the concept of advertising attitude refers to the
consumer’s preference for mobile advertising. Tsang et al. (2004) pointed out that advertising
attitude not only affects consumers’ willingness to receive information but also further affects
their purchase intentions. Yu et al. (2020) and Zhang and Mao (2016) pointed out that
advertising click is not only driven by consumers’ reactions to specific advertisements but
also influenced by their attitudes toward social media advertising. As for advertising,
consumers can only obtain some cues such as informativeness, entertainment and
advertising attitude (Zhang and Mao, 2016) before they click the advertisements; so we
used these three constructs as measurement indicators to predict the ad clicks of consumers.
After clicking advertisements, consumers will evaluate brands and products before deciding
whether to promote purchase intention (Zhang and Mao, 2016). In addition, Varnali et al.
(2012) pointed out that incentives are often used to attract consumers to accept or pay
attention to advertisements. Customers are interested in tangible benefits and give more
attention to an advertising message for financial advantage; when customers see free
coupons or discounted prices in the message through their smartphone, they search for ways
how to obtain these incentives (Kim and Han, 2014). Gao and Zang (2016) and Wang and Genç
(2019) found in mobile advertising research that advertising with financial benefits
(e.g. discounts) can better guide consumers to generate purchase intentions.
Based on the mentioned-above statement, this study draws on the advertising value
model, emotional appeal, advertising credibility, advertising clicks, product evaluation and
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incentive to explore consumer purchase intention. This study applied a structural equation
model (SEM) to assess the empirical strength of the relationships in the proposed model. This
work contributes to the literature on social media advertising theory in four ways: (1) we
extend the original web-based advertising value model proposed by Ducoffe (1996) to add
emotional appeal and advertising credibility to exploring the impacts on social media
advertising attitude; (2) by identifying informativeness, entertainment and advertising
attitude as antecedents of advertising clicks; (3) by taking advertising clicks as a predictor of
product evaluation in social media advertising context and (4) by determining that
advertising attitude, product evaluation and incentive play key roles as predictors of
consumers’ purchase intention. This study also contributes to developing guidelines for
marketers to use when performing social media advertising. Marketers can use the research
model proposed in this study to carry out marketing activities to get attention and
understand consumers’ online behavior because social media has a unique user-to-user
ecosystem. As a result, as investigated in this study, marketers can implement an advertising
model that encourages consumers to make purchases through ad clicks.
The rest of the paper is organized as follows: we present brief literature of the major study
constructs and hypothesis development following the introduction. Thereafter, the study
methodology and major findings are presented, the discussions, the managerial implications
and limitations and scope for future research in the following subsections.
2. Theoretical background and hypotheses
2.1 Advertising value model
Previous studies have indicated that web-based advertising value model is concerned with
three factors-informativeness, entertainment and irritation, which in turn influence
advertising attitude (Ducoffe, 1996). Furthermore, informativeness and entertainment are
evaluated from the positive aspect of web advertising, while irritation reflects consumers’
negative reactions to improperly delivered web advertising. Informativeness means that
advertisements provide consumers with real-time, convenient and, relevant information (Lee
et al., 2011) and its main purpose is to provide consumers with good information about new
products or services (Kotler and Keller, 2006), including accuracy, convenience and relevance.
In other words, advertising needs to provide consumers with the most correct and relevant
information in the shortest possible time. Entertainment aims to make consumers feel
interested and pleasant in advertisements (Ducoffe, 1996), good mobile advertising needs to
be entertaining (Liu et al., 2012). Irritation refers to the fact that advertisements make people
feel harassed, offended or manipulated excessively, such as the excessive number of
messages (Liu et al., 2012). Kim and Han (2014) found that when advertisements intrude on
consumers, they produce negative attitudes towards mobile advertising (Zha et al., 2015). In
addition, advertising attitude refers to the degree of likes and dislikes expressed by
consumers towards advertising content. Zabadi et al. (2012) argued that mobile advertising
attitude refers to the degree to which mobile advertising is liked and accepted by people.
When studying mobile advertising, Drossos et al. (2013) pointed out that when consumers
have a positive attitude towards advertising, they think that mobile advertising is helpful to
them, thus affecting their subsequent purchase intention.
2.1.1 Informativeness, entertainment and advertising attitude. Lee and Hong (2016), Chang
et al. (2020) and Ozcelik and Varnali (2019) found that informativeness has a positive impact
on consumer’s attitude toward mobile advertising. Gao and Zang (2016) pointed out that
interesting and informative advertisements can increase consumers’ favorable impression of
advertisements. Entertainment is considered to be an emotional factor contributing to the
formation of consumers’ attitudes towards advertising (Martı Pareo et al., 2013). In the mobile
advertising study, Cho et al. (2016), Wang and Genç (2019) and Ozcelik and Varnali (2019)
Click social
media ads and
purchase
intention
found that there is a positive relationship between entertainment and consumers’ attitudes.
Therefore, this study proposes the following hypothesis:
H1. Perceived informativeness positively affects advertising attitude.
H2. Perceived entertainment positively affects advertising attitude.
2.1.2 Relationship between irritation and advertising attitude. Irritation of advertisements has
a negative impact on the attitude of advertisements, thus reducing the effect of
advertisements (Ducoffe, 1996). Ozcelik and Varnali (2019) pointed out that if
advertisements appear at inappropriate times, they will generate a negative attitude
toward advertisements. Wang and Genç (2019) suggested that mobile advertising irritation
hurts advertising attitudes. Therefore, this study proposes the following hypotheses:
H3. Perceived irritation negatively affects advertising attitude.
2.2 Relationship between emotional appeal, advertising credibility and advertising attitude
Emotional appeal refers to clearly expressing certain interests or reasons, attracting consumers’
attention to advertising products or services and trying to persuade consumers to pay attention
to or purchase such products or services (Kotler, 2000). The emotional appeal aims at arousing
consumers’ emotions, and mainly uses persuasive methods to persuade consumers to agree
with their advertisements. Advertising credibility refers to consumers’ perception of the
believability and truthfulness of advertising content (MacKenzie and Lutz, 1989). Aslam et al.
(2016) and Lee and Hong (2016) suggested that effective emotional appeal has a positive impact
on consumers’ advertising attitudes. Drossos et al. (2013) pointed out that advertising appeal
has an impact on consumers’ positive attitudes. In addition, Choi et al. (2008) indicated that
credibility is an important factor in forming a positive attitude toward advertising. Gao and
Zang (2016), and Wang and Genç (2019) found from the research on mobile advertising that
credible advertising makes consumers feel safe and will improve the positive impact on
advertising attitude. Therefore, this study proposes the following hypothesis:
H4. Perceived emotional appeal positively affects advertising attitude.
H5. Perceived credibility positively affects advertising attitude.
2.3 Relationship between informativeness, entertainment and advertising click
Ad click refers to the user’s behavior of clicking on the advertisement after seeing the
advertisement, which can be used as a measure of the individual’s behavioral response after
seeing the advertisement (Zhang and Mao, 2016). If advertisers provide more complete
information about advertisements, consumers may click on advertisements just to meet their
own needs. Cochrane and Quester (2005) proposed that if advertisements can make customers
feel involved and enjoy entertainment, there will be a higher click rate. If consumers feel the
informativeness and entertainment in the advertisement and accept it, they also improve
the click on the advertisement (Zhang and Mao, 2016). Therefore, this study proposes
the following hypothesis:
H6. Perceived informativeness positively affects advertising clicks.
H7. Perceived entertainment positively affects advertising clicks.
2.4 Relationship between advertising attitude and advertising clicks
Zhang and Mao (2016) pointed out that consumers’ preference for social media
advertisements will be transferred to their attitudes and behaviors towards specific forms
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of advertisements. The research of Chatterjee et al. (2003) found that consumers have a
positive advertising attitude and then click more on mobile advertising. According to Mao
and Zhang (2017) and Hamouda (2018) research, advertising attitudes positively affect
advertising clicks. Therefore, this study proposes the following hypotheses:
H8. Advertising attitude positively affects advertising clicks.
2.5 Relationship between ad click and product evaluation
Zhang and Mao (2016) argued that the studies on mobile advertising that rich content and
interesting advertisements meet the needs of consumers generate positive advertising
attitudes, help to attract the number of clicks of consumers, obtain sufficient product
information through advertising descriptions and consumers form purchase decisions by
relying on product evaluation. Compared to unclicked, the clicking behavior represents a
positive response to the product; when the intention or frequency of ad clicks is higher, it will
affect the evaluation of the advertised product (Mao and Zhang, 2017). Therefore, this study
proposes the following hypotheses:
H9. Ad clicks positively affect product evaluation.
2.6 Relationship between advertising attitude and purchase intention
Purchase intention indicates the possibility that consumers will purchase a certain product or
service in the future (Martins et al., 2019). Past research has demonstrated that an increase in
purchase intention reflects an increase in the chance of purchasing. Tsang et al. (2004) found
that the advertising attitude not only affects the consumer’s willingness to receive
information but also further affects their purchase intention. Liu et al. (2012) argued that if the
advertising attitude tends to be positive, consumers will feel that advertising is helpful to
their own lives and will naturally be more receptive to mobile advertising. Lee et al. (2017) and
Chang et al. (2020) also confirmed that a positive advertising attitude will affect consumers’
purchase intention. Therefore, this study proposes the following hypotheses:
H10. Advertising attitude positively affects purchase intention.
2.7 Relationship between product evaluation and purchase intention
Product evaluation and purchase intention have been studied in consumer behavior for a long
time, helping scholars and businesses understand the important factors that finally induce
consumers to purchase (Zhang and Mao, 2016). Product evaluation is defined as the
consumer’s attitude towards the product. Lam and Mukherjee (2005) pointed out that positive
evaluation of products by consumers will positively affect their purchase intention. When
consumers realize the value of products, they have a positive impact on consumers’ purchase
intention (Grewal et al., 1998). Therefore, this study proposes the following hypotheses:
H11. Product evaluation positively affects purchase intention.
2.8 Relationship between incentives and purchase intention
Incentives are major predictors of consumers’responses and entail monetary benefits such as
discounts, coupons, gifts and non-monetary benefits (Varnali et al., 2012). Incentives are
considered to have an impact on consumer intentions to receive mobile advertising and
provide specific financial rewards to consumers who agree to receive an advertisement
(Martins et al., 2019). Gao and Zang (2016) and Wang and Genç (2019) indicated that incentive
advertising information containing monetary benefits can better ensure consumers to accept
Click social
media ads and
purchase
intention
advertisements and guide consumers to purchase. Therefore, this study proposes the
following hypotheses:
H12. Incentives positively affect purchase intention.
3. Research methodology
3.1 Measurement development
The study developed a questionnaire to collect the data through an online survey. The
measurement scales of the research model constructs were adopted from previous, related
studies. A group of four experts (with information management professionals) reviewed the
methodology and the measurement scales to ensure content validity and the appropriateness
of the wording of the questions. A preliminary list of measurement items was prepared
through a review of the previous literature (see Appendix 1). The respondents were requested
to answer each questionnaire item using a seven-point Likert scale to measure the construct
items. The Likert scale is widely used to allow the individual to express how much they agree
or disagree (1 5 strongly disagree; 7 5 strongly agree) with a questionnaire item.
3.2 Data collection
Before conducting the main survey, the instrument was validated using both a pre-test and
pilot test. The pre-test involved three professors in the information management field who
were familiar with social media advertising. They were asked to provide comments on a list of
items related to the constructs, including the wording of the scales, the length of the
instrument and the questionnaire format. In the pilot test, we invited 50 respondents from the
population of the social media to participate. Several minor modifications of the content and
structure of the items were solicited before the formal survey. The survey period lasted for
four weeks, and participants with prior experience browsing social media advertising were
invited to complete an online questionnaire at their convenience. In the questionnaire, we
described the meaning of social media advertising and listed several examples on social
networking websites. To encourage participation, this study offered respondents who
completed the questionnaire a chance to enter into a drawing to receive one of twenty NT$280
(US$ 10) convenience store gift certificates. By the time the survey was concluded, 313 valid
questionnaires were collected for further analysis. From among the 313 valid questionnaires,
females comprised 175 of the respondents (55.9%), and 138 were males (44.1%). Most
respondents are less than 25 years old (147 of the respondents, 47.0%), followed by 26–
35 years old (100 of the respondents, 31.9%). The majority of respondents have a
bachelor’s degree (195 of the respondents, 62.3%), followed by a graduate school degree (95 of
the respondents, 30.7%). A total of 59.5% (186 of the respondents) of the respondents spend
2–4 h per day using social media, whereas 19.8% (62 of the respondents) spend more than5
hours per day using social media. The first four social media platforms frequently used by
participants include: Facebook (273 of the respondents, 22.1%), YouTube (276 of the
respondents, 22.3%), Line (281 of the respondents, 22.7%) and Instagram (234 of the
respondents, 18.9%) etc.
The data used for this study was gathered based on the self-reported perceptions of the
respondents, common method variance (CMV) was deemed a potential concern in this study
(Podsakoff and Organ, 1986). For Harman’s single-factor test, all the variables’ data in our
model were entered into an unrotated exploratory factor analysis. The exploratory analysis
shows that five factors (with eigenvalue greater than one) can be derived, the first factor
explaining 27.61% of variance (<50%), which explained 68.16% of total variance. Second,
consistent with Liang et al. (2007), we included a common method factor in our hypothesized
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model and allowed the constructs’ indicators to reflectively associate with this factor. As
shown in Appendix 2, the average substantively explained variance for the indicator was
0.634, while the average common method based variance was 0.008. The ratio of substantive
variance to method variance was approximately 79.2:1. Third, as indicated by Bagozzi et al.
(1991), CMV is likely to be an issue if the correlations among the constructs are found to be
above 0.9. As presented in Table 1, all the correlations between variables are below the
threshold of 0.9. Thus, given the above assurance and analysis, CMV is not a serious problem
in our research.
3.3 Measurement model
This study utilized SmartPLS, a two-step approach recommended by Anderson and Gerbing
(1988), to verify the research hypotheses. In the first step, we measured reliability and
construct validity by using confirmatory factor analysis (CFA), followed by examining the
structural model. The measurement model was assessed to ensure reliability, convergent
validity and discriminant validity. The reliability of the constructs was evaluated using
Cronbach’s alpha and composite reliability (CR). The Cronbach’s alphas for the constructs
were between 0.819 and 0.938; and the CR values were above 0.851 (as shown in Table 1), all
exceeding the 0.70 thresholds proposed by Hairet al. (2014). Therefore, the constructs used in
this study represented acceptable reliability.
Next, convergent validity and discriminant validity were tested. First, convergent validity
was assessed by examining factor loadings of each observed variable. All factor loadings
were significant and exceeded the recommended 0.70 thresholds (between 0.749 and 0.928)
(Hair et al., 2014). Convergent validity was further assessed by examining the average
variances extracted (AVE) values. In Table 1, AVE values were above 0.50, indicating that
AVE values exceeded the corresponding cutoff criteria (Hair et al., 2014), AVE values ranged
from 0.603 to 0.803. This demonstrated fair convergent validity. Moreover, we examined this
using a criterion suggested by Fornell and Larcker (1981): the square roots of the AVE on the
diagonal are greater than the corresponding off-diagonal inter-construct correlations. Table 1
shows that the correlation between the pair of constructs was less than the corresponding
square root of AVEs (diagonal values). Therefore, the constructs used in this study
represented acceptable validity.
3.4 Structural model
We tested the research model with a bootstrapping procedure in SmartPLS. As illustrated in
Figure 1 and Table 2, informativeness, entertainment, irritation, emotional appeal and
advertising credibility affected advertising attitude (β 5 0.070, t 5 4.096; β 5 0.343,
t 5 15.311; β 5 0.052, t 5 4.704; β 5 0.116, t 5 4.963; β 5 0.540, t 5 3.2.931 respectively).
Thus, H1, H2, H3, H4 and H5 were all supported. Entertainment (β 5 0.239, t 5 9.587) and
advertising attitude (β 5 0.689, t 5 31.477) were both found to have a significant positive
effect on attitude to empathy expression. However, the effect of informativeness on
advertising clicks proved insignificant (β 5 0.022, t 5 1.437). Thus, H7 and H9 were
supported while H6 was not. As expected, advertising clicks had a significant positive impact
on product evaluation (β 5 0.816, t 5 106.533), supporting H10. In addition, advertising
attitude, product evaluation and incentive affected positively purchase intention (β 5 0.492,
t 5 20.278; β 5 0.301, t 5 13.246; β 5 0.159, t 5 9.997 respectively). Thus, H8, H11 and H12
were all supported.
Furthermore, we have examined of the variance inflation factor (VIF) scores. The highest
VIF values is 3.782(range from 1.841 to 3.782), which is well below the threshold value of 5 and
indicates that there are no instances of multicollinearity among any of the constructs (Hair
et al., 2014).
Click social
media ads and
purchase
intention
CR AVE APL ATT CLK CRE ENT INC INF IRR PEV PIN
APL 0.918 0.692 0.832
ATT 0.940 0.758 0.432 0.871
CLK 0.947 0.780 0.705 0.520 0.883
CRE 0.931 0.731 0.411 0.497 0.402 0.855
ENT 0.922 0.704 0.684 0.547 0.578 0.588 0.839
INC 0.880 0.648 0.709 0.655 0.556 0.539 0.620 0.805
INF 0.851 0.690 0.639 0.684 0.629 0.692 0.575 0.629 0.831
IRR 0.918 0.790
0.600
0.653
0.658
0.646
0.616
0.535
0.448 0.889
PEV 0.942 0.802 0.484 0.586 0.572 0.573 0.570 0.517 0.671
0.563 0.896
PIN 0.953 0.803 0.583 0.536 0.470 0.631 0.602 0.633 0.602
0.629 0.636 0.896
Note(s): APL
5 emotional appeal; ATT
5 advertising attitude; CLK
5 advertising clicks; CRE
5 advertising credibility; ENT
5 entertainment; INC
5 incentive;
INF
5 informativeness; IRR
5 irritation; PEV
5 product evaluation; PIN
5 purchase intention
Table 1.
Reliability and validity
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In addition, R2 value refers to the percentage with which the exogenous variables explain the
variation in the endogenous variable; it is used as an indicator of the overall predictive power
of the model. However, for a multiple regression model, R2 increases or remains the same as
we add new predictors to the model, even if the newly added predictors are independent of the
target variable and do not add any value to the predicting power of the model. Adjusted R2
eliminates this drawback of R2
. It only increases if the newly added predictor improves the
model’s predicting power. Adding independent and irrelevant predictors to a regression
model results in a decrease in the adjusted R2
. As shown in Figure 1, the adjusted R2 values
for advertising attitude, advertising clicks, product evaluation and purchase intention were
0.881, 0.789, 0.665 and 0.818, respectively. The adjusted R2 value exceeds the recommended
level of 0.20 advocated by Chen et al. (2019). This study indicates that the goodness of fit of the
Advertising
Attitude
Adjusted R2 = 0.881
Q2 = 0.660
Informativeness
Incentive
Purchase Intention
Adjusted R2 = 0.818
Q2 = 0.653
0.159***
–0.052***
0.116**
0.492***
Entertainment
Irritation
0.070***
0.348***
Advertising Value Model
Product Evaluation
Adjusted R2 = 0.665
Q2 = 0.528
0.540***
–0.022ns
Ad Clicks
Adjusted R2 = 0.789
Q2 = 0.608
0.301***
Emotional
Appeal
Advertising
Credibility
0.816***
0.239***
0.689***
Note(s): * p < 0.05, ** p < 0.01, *** p < 0.001
Hypothesis Path coefficient t-value Decision
Informativeness→Advertising attitude 0.070 4.096*** H1 (supported)
Entertainment→Advertising attitude 0.348 15.311*** H2 (supported)
Irritation→Advertising attitude 0.052 4.704*** H3 (supported)
Emotional appeal→Advertising attitude 0.116 4.963*** H4 (supported)
Credibility→Advertising attitude 0.540 32.931*** H5 (supported)
Informativeness→advertising clicks 0.022 1.437 H6 (non-supported)
Entertainment→advertising clicks 0.239 9.587*** H7 (supported)
Advertising attitude→Purchase intention 0.492 20.278*** H8 (supported)
Advertising attitude→advertising clicks 0.689 31.477*** H9 (supported)
Ad clicks→Product evaluation 0.816 106.533*** H10 (supported)
Product evaluation→Purchase intention 0.301 13.246*** H11 (supported)
Incentive→Purchase intention 0.159 9.997*** H12 (supported)
Note(s): ***p < 0.001, **p < 0.01, *p < 0.05
Figure 1.
The results for
hypothesis
Table 2.
Tests of hypothesized
relationships
Click social
media ads and
purchase
intention
model is satisfactory and that the model is statistically viable. Moreover, we also adopted Q2
to evaluate the cross validated redundancy of the structural model. The Q2 of advertising
attitude, Ad clicks, production evaluation and purchase intention are 0.660, 0.608, 0.528 and
0.653 respectively, which are all above 0 and indicate the predictive relevance of factors
(Fornell and Cha, 1994).
4. Discussion and implications
4.1 Research findings
An extension of the original web-based advertising value model is used to examine
customers’responses (advertising clicks, product evaluation and purchase intention) to social
media websites in this study. The following conclusions were drawn based on empirical
analysis:
First, emotional appeal and credibility are added to the model to further explore how
consumer attitude toward social media advertisements are influenced by emotional aspects
and consider the credibility of ads. In turn, we will gain a greater understanding of how these
two factors influence advertising attitudes. According to the empirical results, advertising
credibility is the most important predictor of consumers’ attitude toward advertising.
Therefore, consumers are more likely to have favorable advertising attitudes when they
perceive social media advertising and sources to be trustworthy. The result is consistent with
previous research that has found that credibility has a positive influence on attitudes toward
advertising (e.g. Gao and Zang, 2016; Wang and Genç, 2019).
Second, entertaining is the second largest predictor factor of advertising attitude and
advertising clicks. This indicates that entertaining advertising could satisfy consumer
desires for enjoyment, thus positively impacting consumers’ advertising attitude and their
willingness to engage in advertising (Zhang and Mao, 2016). Consumers prefer to use social
media to satisfy their needs and prefer a more entertainment-oriented approach to their
leisure time in the era of advanced information.
Third, advertising credibility and emotional appeal have a higher impact on advertising
attitude than informativeness and irritation. This means that the addition of these two
constructs can effectively explain consumers’ attitudes towards social media advertising,
which is different from the original web-based advertising value model, which is just suitable
for discussing web-based advertising.
Fourth, informativeness does not have a significant effect on advertising clicks, this is
inconsistent with Zhang and Mao (2016). When consumers browse social media
advertisements, even if they are attracted to the product’s information, they may not
generate the intention to click the advertisement until they have a positive attitude toward
advertisements; when consumers feel a positive attitude toward advertisements, they should
generate a desire to click the advertisement. Even though informativeness does not appear to
have a significant impact on ad clicks, our study confirms that entertainment is a significant
influence on the behavior of ad clicks in social media context. In other words, if
advertisements provide customers with entertainment, a higher click-through rate will be
achieved.
Fifth, ad clicks have a significant effect on product evaluation, this result is consistent
with Zhang and Mao (2016). Zhang and Mao (2016) proposed that product evaluations, likes
or dislikes of a product, affect the likelihood of their product purchase. In other words,
consumers must go through the stage of product evaluation after clicking the advertisement
before they do a decision to purchase a product. For consumers who purchase a product
immediately after clicking an advertisement, it is another research issue, i.e. impulse buying.
Finally, advertising attitude was found to be a significant predictor of purchase intention,
the result is consistent with Lee et al. (2017). This study confirms that consumers are likely to
APJML
respond positively to social media ads, increasing the likelihood of a purchase. Additionally,
consumers have realized the value of the product in advertisements, resulting in a positive
impact on their purchasing decisions. In contrast, incentives played a significant role in
increasing purchase intentions. It appears that consumers are motivated to click ads on social
media when they see monetary benefits, such as discounts, coupons and gifts that can be used
to support their purchases (Varnali et al., 2012).
4.2 Managerial implications
We extended the web-based Advertising Value Model to investigate customer advertising
attitude and subsequent ad clicks, product evaluation, incentive and purchase intention to
synthesize the discussion of social media user behaviors and hoping to have better
understanding of customer decision process toward social media advertisement. The
findings serve as a reference for marketing strategy development by managers of social
media advertisement. On the basis of our results, several practical suggestions for the design
and operational policies of social media advertisement are presented as follows.
First, the evaluation of advertising credibility is based on the information provided by
other consumers’ comments on their shopping experience, so the advertisers provide
trustworthy product review content to improve consumers’ attitudes toward social media
advertising. If advertisers provide advertising with a high error rate, then low advertising
credibility will be obtained. Therefore, the advertisers must establish a good advertising
reputation to consumers to produce a favorable advertising attitude. When consumers
believe that advertisements are trustworthy, they will have positive advertising attitude, and
subsequently, increase their purchase intention.
Second, the temptation to offer monetary benefits (e.g. e-coupon and raffle) on behalf of
social media advertising helps to improve the purchase intention. Therefore, advertisers
should attach the latest preferential activities to social media advertising and highlight the
information about preferential activities as much as possible. When consumers browse social
media advertisements, discount messages and preferential schemes will automatically jump
out to promote consumers knowing the marketing to increase consumers’ purchase intention.
Finally, since the strongest predictor of advertising clicks was the advertising attitude
which is mainly driven by the credibility of the advertisement, if the social media advertisers
can offer credible advertisements on social media, then the consumer should be a positive
advertising attitude to promote the willingness to click advertisement.
4.3 Limitations and future research
This study had limitations. First, in this study, the young people (age 16–25) are major
subjects may limit the generalizability of the findings. Young people may be inherently more
technology-friendly, and hence more eager to accept new information technology efforts than
the average population. Future research may recommend collecting the respondents across
different age groups. For example, according to CREDIT SUCESS (2017) research on the
economic activity behavior of global silver-haired people, it is found that people over 65 spend
more money on online shopping than those aged 25–64. For the elderly over 65 years old in
the United States, online shopping consumption is 37% more than that of young people. It is
suggested that future research can be conducted on the elderly population.
Second, according to the research results of Mao and Zhang (2017), informativeness,
entertainment, involvement and security can all lead to ad clicks. This research only explored
the impact of informativeness and entertainment on advertising clicks. The results of the
research show that entertaining advertisement could fulfill consumer need for enjoyment,
thus make consumers curious and click on the ads. Consumers’ involvement with the display
ads signifies their personal interest in the ads. The more they are involved in the ads, the more
Click social
media ads and
purchase
intention
likely they will have a favorable reaction to the ads (e.g. clicking the ads). Therefore, further
investigations are advised to utilize involvement and security as additional variables to
impact ad clicks.
Finally, gender has been regarded as an important research topic in different research
fields, such as psychology, marketing and behavioral studies. Male shoppers may have
purchase intentions influenced by online advertising information, while female shoppers may
be influenced by text, images and emotional appeal advertisements (Shaouf et al., 2016). For
advertisers, it is necessary to understand gender differences and needs as much as possible to
design different advertisements that meet consumers (Shaouf et al., 2016). Therefore,
understanding these differences is a big challenge for advertisers and marketers. They can
adjust strategies between genders and make social media advertisements that meet the needs
of men and women according to gender differences. It is suggested that gender can be
included in research related to social media advertising in the future.
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Appendix 1
Construct Measure
INF 5 Informativeness (Kim and Han, 2014; Lee and Hong, 2016)
INF1 Social media ad provides the information I need
INF2 I think the information obtained from the social media ad would be helpful
INF3 Social media ad always can help me keep up to date about products
INF4 I can get new advertising information from the social media
ENT 5 Entertainment (Kim and Han, 2014; Lee and Hong, 2016)
ENT1 Social media ad is fun
ENT2 Social media ad is entertaining
ENT3 When I’m bored, I see social media ad
ENT4 I enjoy seeing different social media ad
ENT5 I feel that seeing social media ad is enjoyable
IRR 5 Irritation (Kim and Han, 2014; Lee and Hong, 2016)
IRR1 Social media ad is annoying
IRR2 I feel that social media ads are almost everywhere
IRR3 Social media ad is confusing
APL 5 Emotional Appeal (Aslam et al., 2016; Lee and Hong, 2016)
APL1 Social media ad pleasantly stimulates my senses
APL2 I was emotionally attracted by the key message of this social media ad
APL3 The emotional aspect of this ad leads me to like the ad
APL4 Social media shows me ads which are visually pleasing
APL5 After seeing this social media ad, I had intense feelings
CRE 5 Credibility (Drossos et al., 2013; Zha et al., 2015)
CRE1 I trust social media ads
CRE2 Social media ads are convincing
CRE3 Social media ads are credible
CRE4 Social media ads are believable
CRE5 I use social media ad as a reference for purchasing
ATT 5 Advertising attitude (Zha et al., 2015; Shaouf et al., 2016)
ATT1 Social media ads can provide pleasant experience for me
ATT2 I am favorable toward social media ad
ATT3 My overall attitude toward this ad is positive
ATT4 Using social media ad is a good idea
ATT5 Overall, I like social media ad
CLK 5 Ad clicks (Zhang and Mao, 2016)
CLK1 I often click on social media ads
CLK2 I am likely to click through social media ads again
(continued)
Table A1.
Measurement scales
Click social
media ads and
purchase
intention
Appendix 2
Construct Measure
CLK3 I click on the display ads on social media to make a purchase
CLK4 I click on the display ads on social media to get more information about the products
CLK5 I click on the display ads on social media to understand more about the products
PEV 5 Product evaluation (Zhang and Mao, 2016)
PEV1 I like the brands shown in the display ads on social media
PEV2 In my opinion, the brands shown in the display ads on social media are good
PEV3 In my opinion, the brands shown in the display ads on social media are positive
PEV4 I think favorably about the brands shown in the display ads on social media
INC 5 Incentive (Huang and Ge, 2019; Wang and Genç, 2019)
INC1 I like to receive social media ad which gives me a chance to take part in raffle for receiving message
INC2 If the social media ads contain mobile coupons, I would like to receive them
INC3 If the social media ads do not induce any charges, I would like to receive them
INC4 I like to receive social media ad which is informative about raffles and discounts
PIN 5 purchase intention (Lee and Hong, 2016; Shaouf et al., 2016)
PIN1 After viewing the social media ad, I became interested in making a purchase
PIN2 After viewing the social media ad, I will probably purchase the product being advertised
PIN3 After viewing the social media ad, I am willing to purchase the product being advertised
PIN4 I will purchase the product next time I need a product
Table A1. PIN5 I intend to continue using social media ad for purchasing in the future
Indicator
Substantive loading
(R1)
Substantive variance
(R2
1)
Method loading
(R2)
Method variance
(R2
2)
INF 5 Informativeness
INF1 0.686** 0.471 0.075 0.006
INF2 0.799** 0.638 0.031 0.001
INF3 0.882** 0.778 0.071 0.005
INF4 0.810** 0.656 0.087 0.008
ENT 5 Entertainment
ENT1 0.721** 0.519 0.093 0.009
ENT2 0.815** 0.664 0.023 0.001
ENT3 0.754** 0.568 0.101 0.010
ENT4 0.764** 0.584 0.031 0.001
ENT5 0.849** 0.722 0.156* 0.024
IRR 5 Irritation
IRR1 0.658** 0.432 0.067 0.004
IRR2 0.081 0.007 0.089 0.008
IRR3 0.716** 0.513 0.308 0.095
APL 5 Emotional Appeal
APL1 0.720** 0.518 0.153* 0.023
APL2 0.872** 0.760 0.029 0.001
APL3 0.804** 0.646 0.054 0.003
APL4 0.773** 0.598 0.094 0.009
APL5 0.836** 0.699 0.015 0.000
(continued)
Table A2.
Common method
variance analysis
APJML
Corresponding author
Chien-Wen Chen can be contacted at: <PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>
For instructions on how to order reprints of this article, please visit our website:
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Indicator
Substantive loading
(R1)
Substantive variance
(R2
1)
Method loading
(R2)
Method variance
(R2
2)
CRE 5 Credibility
CRE1 0.742** 0.551 0.101 0.010
CRE2 0.775** 0.600 0.075 0.006
CRE3 0.809** 0.655 0.044 0.002
CRE4 0.852** 0.725 0.047 0.002
CRE5 0.823** 0.677 0.064 0.004
ATT 5 Advertising attitude
ATT1 0.836** 0.698 0.081 0.007
ATT2 0.884** 0.781 0.071 0.005
ATT3 0.821** 0.674 0.091 0.008
ATT4 0.735** 0.541 0.036 0.001
ATT5 0.895** 0.801 0.027 0.001
CLK 5 Ad clicks
CLK1 0.806** 0.650 0.041 0.002
CLK2 0.813** 0.660 0.045 0.002
CLK3 0.816** 0.665 0.048 0.002
CLK4 0.837** 0.701 0.087 0.008
CLK5 0.816** 0.666 0.099 0.010
PEV 5 Product evaluation
PEV1 0.794** 0.630 0.085 0.007
PEV2 0.855** 0.730 0.096 0.009
PEV3 0.841** 0.707 0.067 0.004
PEV4 0.780** 0.609 0.074 0.005
INC 5 Incentive
INC1 0.826** 0.682 0.062 0.004
INC2 0.725** 0.526 0.031 0.001
INC3 0.862** 0.743 0.052 0.003
INC4 0.686** 0.471 0.071 0.005
PIN 5 Purchase intention
PIN1 0.818** 0.668 0.028 0.001
PIN2 0.851** 0.724 0.182* 0.033
PIN3 0.864** 0.746 0.024 0.001
PIN4 0.852** 0.726 0.084 0.007
PIN5 0.864** 0.746 0.067 0.004
0.818** 0.668 0.028 0.001
Average
variance
0.634 0.008
Note(s): **p < 0.001, *p < 0.01 Table A2.
Click social
media ads and
purchase
intention
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please refer this document and give me a independent , dependent and mediating variables under the dimensions and inticators.