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The influence of perceived ad relevance on social media advertising: An empirical examination of a mediating role of privacy concern
Accepted Manuscript The Influence of Perceived Ad Relevance on Social Media Advertising: An Empirical Examination of a Mediating Role of Privacy Concern
A-Reum Jung PII:
S0747-5632(17)30008-0
DOI:
10.1016/j.chb.2017.01.008
Reference:
CHB 4703
To appear in:
Computers in Human Behavior
Received Date:
28 October 2016
Revised Date:
03 January 2017
Accepted Date:
05 January 2017
Please cite this article as: A-Reum Jung, The Influence of Perceived Ad Relevance on Social Media Advertising: An Empirical Examination of a Mediating Role of Privacy Concern, Computers in Human Behavior (2017), doi: 10.1016/j.chb.2017.01.008
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Highlights ∙ Perceived ad relevance increases privacy concern and ad attention, but decrease ad avoidance. ∙ Privacy concern increases ad avoidance. ∙ Privacy concern mediates perceived ad relevance and ad avoidance.
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The Influence of Perceived Ad Relevance on Social Media Advertising: An Empirical Examination of a Mediating Role of Privacy Concern
Author: A-Reum Jung, Louisiana State University, 252 Hodges Hall, Baton Rouge, LA 70803, [email protected]
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The Influence of Perceived Ad Relevance on Social Media Advertising: An Empirical Examination of a Mediating Role of Privacy Concern
ABSTRACT Todays, in order to break out of cluttered ad environment, advertisers provide customized ad messages for an individual consumer based on personal information. The targeting technique is a successful way for advertisers to increase advertising effectiveness, but it also causes privacy concern. Using an online survey, the current study examines the influence of perceived ad relevance and privacy concern on social media ads. The results confirmed that perceived ad relevance influences advertising effectiveness such as increased attention to ads and decreased ad avoidance. However, perceived relevance also increases privacy concern which ultimately raises ad avoidance in social media. Theoretical and practical implications of findings are discussed.
Keywords: Perceived ad relevance, privacy concern, advertising effectiveness
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1. Introduction Even though every media source is saturated by advertising messages, consumers only notice a very tiny amount of advertising. In 2014, a person was exposed to more than 5,000 ads and brands per day on average, but recognized 86 and had an impression of only 12 (Johnson, 2014). Not surprisingly, in order to break out of this cluttered ad environment, advertisers have tried to find noticeable and unavoidable ad strategies with minimal cost. Thanks to continuously developing data collecting and analyzing technologies, advertisers have achieved their goal by providing customized ad messages for individual customers. This environment has accelerated along with the development of social media. Social media platforms build a convenient interface in which users share their personal interests, experiences, and everyday life. This information enables advertisers to target their consumers with highly relevant ad messages in terms of demographical, geographical, and psychographic elements. Customizing ads to individual consumers is a successful way for advertisers to get consumers attention, but it also creates concern for potential personal information infringement. Along with the growth of social media as a major advertising vehicle, advertising scholars have attempted to examine the value of social media in studies of targeting techniques (Curran, Graham, & Temple, 2011), antecedents of ad effectiveness (e.g., Chellappa & Sin, 2005; Howard & Kerin, 2004; White, Zahay, Thorbjornsen, & Shavitt, 2008) and comparison of ad effectiveness by media (Baek & Morimoto, 2012). An especially highlighted topic is the influence of ad relevance and privacy concern on consumers’ responses to social media ads. In general, perceived ad relevance is positively related to ad effectiveness (Pechmann & Steward, 1990; Trampe, Stapel, Siero, & Mulder, 2010; Xia & Bechwati, 2008), but privacy concern is negatively related (e.g., Milne & Boza, 1999; Phelps, D’Souza, & Nowak, 2001). In addition,
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there is lots of anecdotal evidence that highly personalized ad messages increase privacy concern, but only a few researchers have attempted to figure out the relationship between perceived ad relevance and privacy concern (e.g., White et al., 2008; Zhu & Chang, 2016). Thus, this study aims to examine consumers’ responses to social media advertising focusing on perceived ad relevance and privacy concern. In particular, this study attempts to understand how perceived relevance is associated with privacy concern in generating social media ad effects. This study provides useful implications not only for researchers who are interested in social media ads, but also for ad practitioners who are trying to optimize social media ad effects between personalization and privacy concern. 2. Literature review 2.1. Situational background The exponential increase in use of social media makes it one of the most important advertising media. In 2015, the number of social media users worldwide was more than 2.04 billion, and it is expected to reach 2.72 billion in 2019 (eMarketer, 2016a). In the U.S., more than 180 million people used social media in 2015 (eMarketer, 2016b). Accordingly, marketers have tried to implement their advertising in social media. About 88% of U.S. marketers used social media for their marketing in 2015 (eMarketer, 2015). In 2015, social media generated $10.87 billion in ad revenue, and it is projected to reach $23.6 billion in 2018 (Statista, 2016). The main reason for rapidly increased use of social media advertising is its highly relevant targeting techniques based on users’ personal information. Social media platforms provide advertisers with users’ personal information such as gender, age, schools that users are currently attending or graduated from, language, job title, living and working places, interests, and friends. This demographic, geographic, and psychographic information are basically
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provided by users when they create their profiles. Even people who do not want to provide their information are targeted by friends’ information under the assumption that target users share their interest with their friends. This target technique is called the second-degree targeting or inferential targeting (Curran, Graham, & Temple, 2011). Social media also profiles actions taken by people in order to provide customized advertising such as Facebook tracks users who click “Like” or visit certain brand pages (Curran, Graham, & Temple, 2011). In addition, social media mobile applications allow marketers to provide location-based promotions such as nearby shops and special deals (Curran, Graham, & Temple, 2011). Although Facebook claims that personal information is only used for providing better services (Facebook, 2015), it is not easy for them to avoid the criticism that personal information is used for marketing purposes. In particular, providing Relevance Score for Ads starting from early 2015 (Facebook, n.d.) definitely shows that users’ information is related to targeting consumers which is the directly opponent view of Facebook claim. Concern about personal information infringement has become a major issue related to social media use. For instance, privacy concern was highlighted after a security problem in 2010 caused the accidental release of Facebook users’ personal information (Worthan, 2010). Despite increased attention on privacy concern, results related to the effects of privacy concern on users’ responses to personalized advertising have been inconclusive. A dominant view is that privacy concern increases reactions such as ad avoidance and negative attitude toward the ad and the brand (Baek & Morimoto, 2012; Phelps, D’Souza, & Nowak, 2001, Smit, Noort, &Voorveld, 2014), but a poll initiated by the Digital Advertising Alliance found that about 70% of respondents among 1000 people had positive attitude toward personalized ads, and only 4% of them worried about behavioral tracking (Bachman, 2013).
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Compared to the increased attention to ad relevance and privacy concern, empirical examinations of those two concepts are insufficient. In particular, people just assume that customized advertising increases privacy concern, but this assumption has not been examined. Thus, this study aims to address the less studied issues in social media advertising, by examining the relationship between perceived ad relevance and privacy concern and its’ effect on users’ responses to social media advertising. 2.2. Perceived ad relevance and consumers’ responses to advertising Personal relevance is a mental process stimulated by external sources in which people evaluate how much the source is self-related or how much it allows them to fulfill their needs, goals, and values (Celsi & Olson, 1988; Xia & Bechwail, 2008). Celsi and Olson (1988) defined two types of sources from which people feel personal relevance: situational or intrinsic sources. Situational sources come from the physical and sociological immediate environment and intrinsic sources is based on personal experience and knowledge. Regardless of types of sources, when the sources are related to personal needs and values, people perceive relevance (Celsi & Olson, 1988). Thus, perceived ad relevance means that advertised products or the situations in which product is located are related to personal needs and values. In general, perceived relevance of advertising messages plays an important role in generating positive impact on advertising effectiveness in cognitive, affective, and behavioral areas. For example, higher relevance attracts more attention (Celsi & Olson, 1988; Pechmann & Stewart, 1990). Similarly, people are more likely to show a positive attitude toward advertising when it includes personally relevant products compared to less relevant products (Trampe, Stapel, Siero, & Mulder, 2010). Perceived ad relevance also increases intention to purchase advertised products (Pavlou & Steward, 2000; Xia & Bechwati, 2008).
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The influence of perceived relevance on consumers’ responses to advertising has usually been examined in new media contexts such as online and mobile ads. The same pattern of results has been found. In online context, personal relevance generates a favorable attitude toward the repetitive online ad, the product and the website that the ad is implemented in (Campbell & Wright, 2008). Personalized product recommendations by online retailers are more effective for product choice than recommendations by other consumers or experts (Senecal & Nantel, 2004). Similarly, in the mobile context, when people perceive self-relevance to mobile advertising, they are more likely to show favorable attitude toward the mobile ad (Xu, 2006-2007), accept ad messages (Merisavo et al., 2007), text back or visit website (Rettie, Grandcolas, & Deakins, 2005). Other various aspects of advertising effectiveness were positively related to ad relevance such as message elaboration (Tam & Ho, 2005), recall (Watta, Zahay, Thobjornsen, & Shavitt, 2011), ad response rate (Howard & Kerin, 2004), and advertised service adoption (Chellappa & Sin, 2005). Advertising relevance decreases ad skepticism and ad avoidance in e-mail, direct mail, telemarketing, and text messaging (Baek & Morimoto, 2012). The relationship between ad relevance and consumers’ positive responses is also explained by self-referencing theory. Self-referencing refers to a cognitive process in which people are more likely to be persuaded by self-relevant messages (Rogers, Kuiper, & Kirker, 1977). Previous studies have indicated that self-referencing reinforces learning effects and capacity to recall messages (Klenin & Loftus, 1988; McCaul & Maki, 1984). The main explanation for these findings is that self-relevance stimulates people to elaborate incoming messages and, in turn, increase persuasive effects (Escalas, 2007; Markus, 1977). In advertising context, self-referencing has been applied to explain how ad contents including images and copies affect consumer persuasion. For example, when people have identical ethnicity and
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gender with advertising models, they are more likely to think that the products are self-related which, in turn, affects favorable responses to the ad, product, and models (Debevec & Iyer, 1988; Lee, Fernandez, & Martin, 2002; Peck & Loken, 2004). Successive studies have continuously supported self-referencing effects on persuasion in the advertising context. In general, the more the advertising generated self-related thought, the more positive responses toward advertising were produced in terms of ad message recall, attitude toward ad, brand, and purchase intention (Ahn & Bailenson, 2011; Burnkrant & Unnava, 1995; 1984; Debevec & Iyer, 1988; McCaul & Maki, 1984). 2.3. Privacy concern and consumers’ responses to advertising In general, customized ad contents are more effective than irrelevant ad messages. However, highly relevant ads do not always elicit favorable results, because consumers react to personalized messages (White et al., 2008). Meyers-Levy and Peracchio (1996) explained that messages that are too self-related make people process the messages critically which, in turn, generate reverse persuasive effects at a certain point. Related to the use of social media, the most plausible reactance is increased privacy concern because social media collect and track consumers’ behaviors on the Internet such as purchase histories and use the data to provide customized ad messages for consumers. When people are exposed to personalized ad messages, they recognize that someone knows their personal information and is using it for marketing purposes (Okazaki, Li, & Hirose, 2009) which, in turn, make people resist to the ad messages (Brehm & Brehm, 1981; Knowles & Linn, 2004; White et al., 2008). Privacy refers to “the ability to control and limit physical, interactional, psychological, and informational access to the self or one’s group” (Burgoon et al., 1989, p.132). In the online
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context, privacy is directly related to personal information access (e.g., Buchholz & Rosenthal, 2002; Charters 2002; Culnan & Bies 2003; Goodwin, 1991). When people perceive infringement of privacy by unauthorized people or loss of control of their personal information, privacy concern is activated (Debatin, Lovejoy, Horn, & Hughes, 2009; Gross & Acquisti 2005; Sieber, 1998). Surveys of public opinion reported that the majority of U.S. people worried that what kinds of personal information marketers have and how they acquire and use the information (Equifax-Harris, 1995; 1996). Different from the positive responses to relevant ad messages, privacy concern negatively affects advertising effectiveness. In the direct marketing context, people with high privacy concern showed a negative attitude toward direct marketing (Phelps, D’Souza, & Nowak, 2001), and lowered purchase intention and service adoption (Chellappa & Sin, 2005; Milne & Boza, 1999; Phelps, D’Souza, & Nowak, 2001). In addition, when people received unrequested email from the marketers, they avoid further emails, request to remove their information from email list, or provide incomplete information to the companies email sent. Similarly, as privacy concern increases, consumers are more likely to request removal of their information and support privacy protection policies (Dolnicar & Jordann, 2007). Privacy concern increases advertising skepticism and avoidance (Baek & Morimoto, 2012). Recently, Jeong and Coyle (2014) found that privacy concern about a marketer does not affect social media use such as posting, following or deleting existing posts. They explained that social media users do not limit their behaviors due to marketers, but form negative attitudes toward advertising. The studies that examined the influence of privacy concern on advertising focused on attitudinal and behavioral consequences, but did not address cognitive effects such as recall and attention. However, it is possible that privacy concern increases attention to ad messages. Lang
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(2000) explained that when people perceive threat, they allocate greater cognitive efforts to the threat for selecting appropriate actions for managing the threat. Mogg and Bradley (1988) found that if perceived threat reaches a certain critical point, it makes people pay more attention to it. Thus, it can be assumed that people pay more attention to ads as privacy concern increases. 2.4. Hypotheses The literature reviews consumers’ responses to advertising influenced by perceived ad relevance and privacy concern in terms of cognitive, affective, and behavioral aspects. Previous studies demonstrated that perceived ad relevance increases attention to the ad (e.g., Celsi & Olson, 1988; Pechmann & Steward, 1990), generates favorable attitude toward the ad (e.g., Trampe, Stapel, Siero, & Mulder, 2010), heightens the intention to adopt the product or service (e.g., Pavlou & Steward, 2000; Xia & Bechwati, 2008), and decreases ad avoidance (Beak & Morimoto, 2012). Self-referencing also supports positive responses to relevant ad messages by stimulating people to elaborate incoming messages and, in turn, increase persuasive effects (Escalas, 2007; Markus, 1977). In addition, although the benefit of personalized ads is generally found, increased privacy concern as a reactance to customized ads has also been proposed. This is because messages that are too self-related make people worry about the misuse of their personal information. In addition, studies found that privacy concern is negatively related to adverting effects. Privacy concern leads people to negative attitude toward the ad (Phelps, D’Souza, & Nowak, 2001), decreased purchase intention (Chellappa & Sin, 2005; Milne & Boza, 1999) and increased ad skepticism, and ad avoidance (Baek & Morimoto, 2012). However, privacy concern also increases ad attention for appropriate management of the situation (Mogg & Bradley, 1988). To sum up, studies provided evidence that perceived ad relevance is positively related to, but privacy concern is negatively related to ad effectiveness. Moreover, lots of anecdotal 8
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evidence explained that ad relevance increases privacy concern. It is expected the mediating effects of privacy concern between perceived ad relevance and ad effectiveness. Based on the literatures, the current study attempts to examine how perceived ad relevance and privacy concern affect consumers’ responses to advertising. Among the various advertising effectiveness outcomes, the current study focuses on ad attention and ad avoidance which become the critical factors for indicating the effectiveness of adverting. This is because attention is the first step for ad message processing (Greenwald & Leavitt, 1984; MacInnis & Jaworski, 1989) and it is hard for an individual ad to get attention among the plentiful advertising. In addition, advertising avoidance is considered the greatest obstacles for advertisers (Baek & Morimoto, 2012), and technical development allows people to easily avoid advertising ever before. Thus, the following hypotheses are posed: H1: Perceived ad relevance is positively related to (a) attention to personalized ads, but negatively related to (b) ad avoidance. H2: Privacy concern is positively related to (a) attention to personalized ads and (b) ad avoidance. H3: Perceived ad relevance is positively related to privacy concern. H4: The effect of perceived ad relevance on (a) attention to personalized ads and (b) ad avoidance is mediated by privacy concern. ---------------------------------Figure 1. About Here ---------------------------------3. Method 3.1. Sample A total of 557 respondents recruited through Amazon Mechanical Turk were included in this analysis after excluding 92 respondents who did not complete the survey and 36 respondents 9
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who failed attention checks. Of the participants, 291 (52.2%) were male and 254 (46.6%) were female. The age of respondents ranges from 18 to 69 year-old (M = 34.16, SD = 10.26), and over 70% of the respondents were 20s and 30s. Table 1 shows the summarized demographic information of the respondents. ---------------------------------Table 1. About Here ---------------------------------3.2. Procedure After agreeing with the informed consent information, respondents read the study instructions and they were asked to complete online questionnaires related to social media advertising such as perceived ad relevance, privacy concern, attention to ads, and ad avoidance. Upon completing those questions, respondents answered demographic questions. The study was finished with a debriefing messages. 3.3. Measures All scales employed in this study were selected from previous studies and measured by 7point Likert scale items, ranging from strongly disagree (1) to strongly agree (7). Respondents were asked to answer questions related to general perceptions of advertising and privacy concern on social media. Perceived ad relevance was measured by four items. The original items adopted from Laczniak and Muehling (1993) were modified for the purposes of the current study. Items asked respondents to answer the question “When I saw advertising on SNS, I felt that it might be … “of value to me,” “relevant to my needs,” and “created just for me.” Privacy concern was measured by three items adopted from Dolnicat and Jordaan (2007). Among the 24 original items, items related to marketers were selected. Items include
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“companies’ information collection,” “companies’ use of information,” and “companies’ storing of information. Attention to advertising was measured by five items adopted from Laczniak and Muehling (1993), such as “How much attention do you pay to advertising on SNS,” “How much do you notice the advertising on SNS,” and “How much though did you put into evaluate advertising on SNS.” Advertising avoidance was measured by two items adopted from Baek and Morimoto (2012). Cho and Cheon (2004) divided ad avoidance into cognitive, affective and behavioral areas. However, cognitive and affective avoidance are similar to ad attention and attitude toward advertising. Therefore, this study adopted only the items related to behavioral avoidance. Among the five original items, only two items which were directly related to behavioral avoidance were selected. The items included “I discard (throw away, hang up) advertising on SNS,” “I have asked marketers to take me off their email (mailing, telephone) list.” 4. Results A two-step approach recommended by Anderson and Gerbing (1988) was applied to test the proposed reseach model. In the first step, a confirmatory factor analysis was condcuted to confirm the validity and reliability of measuremt model. In the second step, structural equation modeling (SEM) was conducted to analyze the proposed hypotheses by using Amos 23. 4.1. Measurment model Table 2, 3, and 4 show that there was no validity and reliability concern for all measurement. First, all measuremt items had satisfactory loading ( > .07) and AVE ( > .50) indicating good convergent vaility. Second, Square root of AVE for all measurmets were greater than the correlation of variables, and all ASVs were less than MSVs and AVEs. In addition,
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descriptive statistics indicated that the constructes ranged in normal distribution. These showed good discriminant validity. Third, Cronbach’s a and CR of each variable ( >.70) revealed that all measurement items were internally reliabile. Measurement model fit was also checked: χ2 (70) = 156.28, p < .001, χ2/df = 2.23, GFI = .96, CFI = .99, TLI = .99, RMSEA = .05, SRMR = .02. These indices showed that the measurement model had a good fit to the data. ---------------------------------Table 2. About Here ------------------------------------------------------------------Table 3. About Here ------------------------------------------------------------------Table 4. About Here ---------------------------------4.2. SEM results Table 5 indicates the hypothesized direct and indirect results. To examine the significance of the indirect effects, 5,000 bootstrap procedure was conducted. Statistical indices suggested that the model fits the data well: χ2 (71) = 188.83, p < .001, χ2/df = 2.66, GFI = .96, CFI = .98, TLI = .98, RMSEA = .06 and SRMR = .04. Hypothesis 1 posited that perceived ad relevance is positively related to ad attention, but negatively related to ad avoidance. As expected, people are more likely to pay attention to advertising (β = .80, p < .001) and less likely to avoid advertising (β = -.56, p < .001) as perceived ad relevance increases. Thus, hypothesis 1 were supported. Hypothesis 2 posited that privacy concern is positively associated with ad attention (H3a) and ad avoidance (H3b). As expected, people are more likely to avoid advertising as privacy
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concern increases, β = .12, p < .01. However, privacy concern has no effect on ad attention (β = -.03, p >.05). Thus, H3a was not supported, but H3b was supported. Hypothesis 3 assumed the positive relationship between ad relevance and privacy concern which was also supported, β = .11, p < .05. The result showed that people may feel that marketers use their personal information to target them when they are exposed to advertising that is highly relevant to their needs. Hypothesis 4 addressed the mediating effects of privacy concern on the relationship between ad relevance and ad attention (H4a) and the relationship between ad relevance and ad avoidance (H4b). The indirect effect of ad relevance on ad attention through privacy concern was not statistically significant, β = .00, p >.05. On the other hand, the indirect effect of ad relevance on ad avoidance through privacy concern was statistically significant, β = .01, p <.05. The results showed privacy concern as a mediating variable decreased the influence of ad relevance on ad avoidance, but not attention. Thus, H4a was not supported, but H4b was supported. ---------------------------------Table 5. About Here ---------------------------------5. Discussion The purpose of this study is to examine how perceived ad relevance and privacy concern influence the effectiveness of social media ads in terms of attention and avoidance. The results showed that perceived ad relevance was positively associated with attention to ads, but negatively related to ad avoidance. This means that people who think social media ads are personally relevant (e.g., the ads satisfied with my needs and helpful to achieve goals) are more likely to pay attention to, but less likely to avoid the ads. This result is consistent with previous findings that address the positive relationship between perceived ad relevance and ad
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effectiveness (e.g., Beak & Morimoto, 2012; Celsi & Olson, 1988; Pechmann & Steward, 1990). The positive relationship also confirms that self-related information motivates people to elaborate the ad messages. When consumers are exposed to relevant ad messages, the messages’ saliency activates information processing system which ultimately generates strong persuasion effects (Escalas, 2007; Markus, 1977). This study also found that privacy concern is positively related to ad avoidance. In other words, consumers who know and worry that advertisers collect their personal information for marketing purposes are more likely to take actions to avoid social media ads such as scrolling down Internet pages, closing windows, or not clicking ads. This result confirms previous studies suggesting that privacy concern decreases ad effects (e.g., Beak & Morimoto, 2012; Chellappa & Sin, 2005; Milne & Boza, 1999; Phelps, D’Souza, & Nowak, 2001). However, different from the expectation, privacy concern has no effect on ad attention. This means that the amount of attention to ads is consistent whether people have high privacy concern or not. This observation is inconsistent with previous studies in which participants paid more attention to ads as perceived threat over a certain critical point for appropriate responses (Lang, 2006; Mogg & Bradley, 1988). The alternative explanations are that privacy concern may not be critical for consumers to do subsequent behaviors to protect their privacy, consumers just ignore ads because they have no option to get rid of social media ads, or consumers may do not pay attention to any ads regardless of privacy concern (i.e., ad blindness). One of the contributions the current study makes is that it empirically confirmed the positive relationship between perceived ad relevance and privacy concern. Previously, articles proposed an anecdotal evidence that customized ad messages increase privacy concern because the ads are created based on personal information, but it is hard to find studies that empirically
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examine the relationship. The empirical finding of this study indicated that when people are exposed to highly personalized ads on social media, they think that marketers track their information and use it for their marketing purposes. In turn, consumers have increased privacy concern. This finding supports the explanation that highly self-related messages make people process the messages critically, in turn, adverse persuasive effects can be generated (MeyersLevy & Peracchio, 1996). However, the result is opposed to the view that relevance decreases privacy concern if perceived benefit from personalized ads is over the risk (Zhu & Chang, 2016). Additional examinations will be necessary to better explain the relationship between perceived ad relevance and privacy concern. Another contribution of this paper is that it found a mediating effect of privacy concern between perceived relevance and ad avoidance, but not between perceived relevance and ad attention. These findings would be explained by the possibility to track additional personal information. If consumers do not avoid social media ads (e.g., they click on them), this behavior enables advertisers to obtain consumers’ other interests or buying behaviors, which will be used for future targeting information. If consumers are already aware of someone tracking their behavior on the Internet, they are more likely to avoid ads even if the ads include personally relevant messages. On the other hand, it is hard for advertisers to obtain additional consumers interests without certain actions. In other words, advertisers do not recognize consumers’ interests when the consumers simply see social media ads. Thus, consumers pay attention to personalized ads without worries about additional privacy infringement that may result from interacting with the ads. 5.1. Future research and conclusion
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The findings of this study promise to contribute to both academia and industry. The current study found a predominant influence of perceived relevance on consumers’ responses to social media ads. This means that currently used personalized advertising on social media works effectively. However, this study also found that perceived relevance enhances privacy concern which is negatively related to advertising effectiveness. This result indicates that too much personalization does not guarantee satisfactory ad effects. Thus, finding the optimal level which is enough for consumers to notice personal relevance but does not make them scared is an assignment for both advertisers and researchers. If advertisers have to use highly personalize messages, providing justifications explaining how consumer information is relevant to the personalized offer is a way to decrease privacy concern (White et al., 2008). This study has limitations. This study is based on self-reporting data which are limited to know behaviors in real situations. Employing various research methods is helpful to obtain realistic consumers’ responses to personalized ads. In particular, using eye-tracking to get realistic behavioral patterns will significantly compensate for self-reporting data. In addition, there are lots of variables which explain advertising effects such as attitude and memory. Including other variables in a study provides a more powerful explanation of ad effectiveness. Overall, personalized ads will be more popular in the future, but there are still numerous areas left uninvestigated. Thus, further examinations including those suggested above will provide a better understanding of how privacy concern and perceived relevance work to predict personalized ad effectiveness.
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Ad Attention
Ad Relevance
Privacy Concern Ad Avoidance
Fig ure 1. Res earc h mod el
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Table 1 Demographic Information of Respondents Gender Age
Income
Male Female Prefer not to answer < 19 20 – 29 30 – 39 40 – 49 > 50 Prefer not to answer > 10,000 10,000 – 24,999 25,000 – 49,999 50,000 – 99,999 < or = 100,000 Prefer not to answer
n 291 254 12 5 218 197 71 54 12 132 132 105 122 54 12
% 52.2 46.6 2.2 0.9 39.1 35.4 12.7 9.7 2.2 23.7 23.7 18.9 21.9 9.7 2.2
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Table 2 Factor loading, Cronbach’s a, CR, AVE, and ASV of Variables Loading Cronbach’s a CR AVE Relevance .93 .93 .77 Item 1 .92 Item 2 .88 Item 3 .93 Item 4 .92 Privacy concern .88 .88 .72 Item 1 .88 Item 2 .90 Item 3 .92 Attention .97 .97 .85 Item 1 .95 Item 2 .90 Item 3 .96 Item 4 .94 Item 5 .94 Avoidance .77 .78 .64 Item 1 .90 Item 2 .90
MSV .62
ASV .31
.01
.01
.62
.32
.34
.21
Note: CR (composite reliability), AVE (average variance extracted), MSV (maximum shared variance), and ASV (average shared variance)
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Table 3 Descriptive Statistics of Variables Variable Mean Relevance 4.48 Privacy concern 5.39 Attention 3.92 Avoidance 4.52
S.D 1.43 1.18 1.75 1.61
Min 1 1 1 1
Max 7 7 7 7
Kurtosis -.28 1.86 -1.13 -.63
Skewness -.46 -1.04 .03 -.31
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Table 4 Correlation Matrix of Variables Relevance 1 .88 2 .11 3 .79 4 -.53 Note: Diagrams are square root of AVE.
Privacy concern
Attention
Avoidance
.85 .07 .06
.92 -.58
.80
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Table 5 The Results of Hypotheses Testing
H1a H1b H2a H2b H3 H4a H4b
Path Rele Att Rele Avo PC Att PC Avo Rele PC Rele PC Att Rele PC Avo
Estimate .80*** -.56*** -.03𝑛.𝑠. .12 ** .11* -.00𝑛.𝑠. .01* 𝑛.𝑠.
SE .04 .05 .05 .06 .04 .00 .01
BC 95% CI Lower Upper .76 .83 -.63 -.48 .03 -.09 .03 .21 .02 .20 .00 -.01 .00 .04
Note1: * p <.05; ** p <.01; *** p <.001; = not significant Note2: Rele = perceived ad relevance; PC = privacy concern; Att = ad attention; Avo = ad avoidance, BC = bias corrected; CI = confidence interval; SE = standard error Note 3: χ2 (71) = 188.83, p < .001, χ2/df = 2.66, GFI = .96, CFI = .98, TLI = .98, RMSEA = .06, SRMR = .04. GFI = goodness-of-fit index; CFI = comparative fit index; TLI = Tucker-Lewis index, RMSEA = root mean square error of approximation; SRMR = standardized root mean residual.
A-Reum Jung PII:
S0747-5632(17)30008-0
DOI:
10.1016/j.chb.2017.01.008
Reference:
CHB 4703
To appear in:
Computers in Human Behavior
Received Date:
28 October 2016
Revised Date:
03 January 2017
Accepted Date:
05 January 2017
Please cite this article as: A-Reum Jung, The Influence of Perceived Ad Relevance on Social Media Advertising: An Empirical Examination of a Mediating Role of Privacy Concern, Computers in Human Behavior (2017), doi: 10.1016/j.chb.2017.01.008
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Highlights ∙ Perceived ad relevance increases privacy concern and ad attention, but decrease ad avoidance. ∙ Privacy concern increases ad avoidance. ∙ Privacy concern mediates perceived ad relevance and ad avoidance.
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The Influence of Perceived Ad Relevance on Social Media Advertising: An Empirical Examination of a Mediating Role of Privacy Concern
Author: A-Reum Jung, Louisiana State University, 252 Hodges Hall, Baton Rouge, LA 70803, [email protected]
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The Influence of Perceived Ad Relevance on Social Media Advertising: An Empirical Examination of a Mediating Role of Privacy Concern
ABSTRACT Todays, in order to break out of cluttered ad environment, advertisers provide customized ad messages for an individual consumer based on personal information. The targeting technique is a successful way for advertisers to increase advertising effectiveness, but it also causes privacy concern. Using an online survey, the current study examines the influence of perceived ad relevance and privacy concern on social media ads. The results confirmed that perceived ad relevance influences advertising effectiveness such as increased attention to ads and decreased ad avoidance. However, perceived relevance also increases privacy concern which ultimately raises ad avoidance in social media. Theoretical and practical implications of findings are discussed.
Keywords: Perceived ad relevance, privacy concern, advertising effectiveness
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1. Introduction Even though every media source is saturated by advertising messages, consumers only notice a very tiny amount of advertising. In 2014, a person was exposed to more than 5,000 ads and brands per day on average, but recognized 86 and had an impression of only 12 (Johnson, 2014). Not surprisingly, in order to break out of this cluttered ad environment, advertisers have tried to find noticeable and unavoidable ad strategies with minimal cost. Thanks to continuously developing data collecting and analyzing technologies, advertisers have achieved their goal by providing customized ad messages for individual customers. This environment has accelerated along with the development of social media. Social media platforms build a convenient interface in which users share their personal interests, experiences, and everyday life. This information enables advertisers to target their consumers with highly relevant ad messages in terms of demographical, geographical, and psychographic elements. Customizing ads to individual consumers is a successful way for advertisers to get consumers attention, but it also creates concern for potential personal information infringement. Along with the growth of social media as a major advertising vehicle, advertising scholars have attempted to examine the value of social media in studies of targeting techniques (Curran, Graham, & Temple, 2011), antecedents of ad effectiveness (e.g., Chellappa & Sin, 2005; Howard & Kerin, 2004; White, Zahay, Thorbjornsen, & Shavitt, 2008) and comparison of ad effectiveness by media (Baek & Morimoto, 2012). An especially highlighted topic is the influence of ad relevance and privacy concern on consumers’ responses to social media ads. In general, perceived ad relevance is positively related to ad effectiveness (Pechmann & Steward, 1990; Trampe, Stapel, Siero, & Mulder, 2010; Xia & Bechwati, 2008), but privacy concern is negatively related (e.g., Milne & Boza, 1999; Phelps, D’Souza, & Nowak, 2001). In addition,
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there is lots of anecdotal evidence that highly personalized ad messages increase privacy concern, but only a few researchers have attempted to figure out the relationship between perceived ad relevance and privacy concern (e.g., White et al., 2008; Zhu & Chang, 2016). Thus, this study aims to examine consumers’ responses to social media advertising focusing on perceived ad relevance and privacy concern. In particular, this study attempts to understand how perceived relevance is associated with privacy concern in generating social media ad effects. This study provides useful implications not only for researchers who are interested in social media ads, but also for ad practitioners who are trying to optimize social media ad effects between personalization and privacy concern. 2. Literature review 2.1. Situational background The exponential increase in use of social media makes it one of the most important advertising media. In 2015, the number of social media users worldwide was more than 2.04 billion, and it is expected to reach 2.72 billion in 2019 (eMarketer, 2016a). In the U.S., more than 180 million people used social media in 2015 (eMarketer, 2016b). Accordingly, marketers have tried to implement their advertising in social media. About 88% of U.S. marketers used social media for their marketing in 2015 (eMarketer, 2015). In 2015, social media generated $10.87 billion in ad revenue, and it is projected to reach $23.6 billion in 2018 (Statista, 2016). The main reason for rapidly increased use of social media advertising is its highly relevant targeting techniques based on users’ personal information. Social media platforms provide advertisers with users’ personal information such as gender, age, schools that users are currently attending or graduated from, language, job title, living and working places, interests, and friends. This demographic, geographic, and psychographic information are basically
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provided by users when they create their profiles. Even people who do not want to provide their information are targeted by friends’ information under the assumption that target users share their interest with their friends. This target technique is called the second-degree targeting or inferential targeting (Curran, Graham, & Temple, 2011). Social media also profiles actions taken by people in order to provide customized advertising such as Facebook tracks users who click “Like” or visit certain brand pages (Curran, Graham, & Temple, 2011). In addition, social media mobile applications allow marketers to provide location-based promotions such as nearby shops and special deals (Curran, Graham, & Temple, 2011). Although Facebook claims that personal information is only used for providing better services (Facebook, 2015), it is not easy for them to avoid the criticism that personal information is used for marketing purposes. In particular, providing Relevance Score for Ads starting from early 2015 (Facebook, n.d.) definitely shows that users’ information is related to targeting consumers which is the directly opponent view of Facebook claim. Concern about personal information infringement has become a major issue related to social media use. For instance, privacy concern was highlighted after a security problem in 2010 caused the accidental release of Facebook users’ personal information (Worthan, 2010). Despite increased attention on privacy concern, results related to the effects of privacy concern on users’ responses to personalized advertising have been inconclusive. A dominant view is that privacy concern increases reactions such as ad avoidance and negative attitude toward the ad and the brand (Baek & Morimoto, 2012; Phelps, D’Souza, & Nowak, 2001, Smit, Noort, &Voorveld, 2014), but a poll initiated by the Digital Advertising Alliance found that about 70% of respondents among 1000 people had positive attitude toward personalized ads, and only 4% of them worried about behavioral tracking (Bachman, 2013).
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Compared to the increased attention to ad relevance and privacy concern, empirical examinations of those two concepts are insufficient. In particular, people just assume that customized advertising increases privacy concern, but this assumption has not been examined. Thus, this study aims to address the less studied issues in social media advertising, by examining the relationship between perceived ad relevance and privacy concern and its’ effect on users’ responses to social media advertising. 2.2. Perceived ad relevance and consumers’ responses to advertising Personal relevance is a mental process stimulated by external sources in which people evaluate how much the source is self-related or how much it allows them to fulfill their needs, goals, and values (Celsi & Olson, 1988; Xia & Bechwail, 2008). Celsi and Olson (1988) defined two types of sources from which people feel personal relevance: situational or intrinsic sources. Situational sources come from the physical and sociological immediate environment and intrinsic sources is based on personal experience and knowledge. Regardless of types of sources, when the sources are related to personal needs and values, people perceive relevance (Celsi & Olson, 1988). Thus, perceived ad relevance means that advertised products or the situations in which product is located are related to personal needs and values. In general, perceived relevance of advertising messages plays an important role in generating positive impact on advertising effectiveness in cognitive, affective, and behavioral areas. For example, higher relevance attracts more attention (Celsi & Olson, 1988; Pechmann & Stewart, 1990). Similarly, people are more likely to show a positive attitude toward advertising when it includes personally relevant products compared to less relevant products (Trampe, Stapel, Siero, & Mulder, 2010). Perceived ad relevance also increases intention to purchase advertised products (Pavlou & Steward, 2000; Xia & Bechwati, 2008).
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The influence of perceived relevance on consumers’ responses to advertising has usually been examined in new media contexts such as online and mobile ads. The same pattern of results has been found. In online context, personal relevance generates a favorable attitude toward the repetitive online ad, the product and the website that the ad is implemented in (Campbell & Wright, 2008). Personalized product recommendations by online retailers are more effective for product choice than recommendations by other consumers or experts (Senecal & Nantel, 2004). Similarly, in the mobile context, when people perceive self-relevance to mobile advertising, they are more likely to show favorable attitude toward the mobile ad (Xu, 2006-2007), accept ad messages (Merisavo et al., 2007), text back or visit website (Rettie, Grandcolas, & Deakins, 2005). Other various aspects of advertising effectiveness were positively related to ad relevance such as message elaboration (Tam & Ho, 2005), recall (Watta, Zahay, Thobjornsen, & Shavitt, 2011), ad response rate (Howard & Kerin, 2004), and advertised service adoption (Chellappa & Sin, 2005). Advertising relevance decreases ad skepticism and ad avoidance in e-mail, direct mail, telemarketing, and text messaging (Baek & Morimoto, 2012). The relationship between ad relevance and consumers’ positive responses is also explained by self-referencing theory. Self-referencing refers to a cognitive process in which people are more likely to be persuaded by self-relevant messages (Rogers, Kuiper, & Kirker, 1977). Previous studies have indicated that self-referencing reinforces learning effects and capacity to recall messages (Klenin & Loftus, 1988; McCaul & Maki, 1984). The main explanation for these findings is that self-relevance stimulates people to elaborate incoming messages and, in turn, increase persuasive effects (Escalas, 2007; Markus, 1977). In advertising context, self-referencing has been applied to explain how ad contents including images and copies affect consumer persuasion. For example, when people have identical ethnicity and
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gender with advertising models, they are more likely to think that the products are self-related which, in turn, affects favorable responses to the ad, product, and models (Debevec & Iyer, 1988; Lee, Fernandez, & Martin, 2002; Peck & Loken, 2004). Successive studies have continuously supported self-referencing effects on persuasion in the advertising context. In general, the more the advertising generated self-related thought, the more positive responses toward advertising were produced in terms of ad message recall, attitude toward ad, brand, and purchase intention (Ahn & Bailenson, 2011; Burnkrant & Unnava, 1995; 1984; Debevec & Iyer, 1988; McCaul & Maki, 1984). 2.3. Privacy concern and consumers’ responses to advertising In general, customized ad contents are more effective than irrelevant ad messages. However, highly relevant ads do not always elicit favorable results, because consumers react to personalized messages (White et al., 2008). Meyers-Levy and Peracchio (1996) explained that messages that are too self-related make people process the messages critically which, in turn, generate reverse persuasive effects at a certain point. Related to the use of social media, the most plausible reactance is increased privacy concern because social media collect and track consumers’ behaviors on the Internet such as purchase histories and use the data to provide customized ad messages for consumers. When people are exposed to personalized ad messages, they recognize that someone knows their personal information and is using it for marketing purposes (Okazaki, Li, & Hirose, 2009) which, in turn, make people resist to the ad messages (Brehm & Brehm, 1981; Knowles & Linn, 2004; White et al., 2008). Privacy refers to “the ability to control and limit physical, interactional, psychological, and informational access to the self or one’s group” (Burgoon et al., 1989, p.132). In the online
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context, privacy is directly related to personal information access (e.g., Buchholz & Rosenthal, 2002; Charters 2002; Culnan & Bies 2003; Goodwin, 1991). When people perceive infringement of privacy by unauthorized people or loss of control of their personal information, privacy concern is activated (Debatin, Lovejoy, Horn, & Hughes, 2009; Gross & Acquisti 2005; Sieber, 1998). Surveys of public opinion reported that the majority of U.S. people worried that what kinds of personal information marketers have and how they acquire and use the information (Equifax-Harris, 1995; 1996). Different from the positive responses to relevant ad messages, privacy concern negatively affects advertising effectiveness. In the direct marketing context, people with high privacy concern showed a negative attitude toward direct marketing (Phelps, D’Souza, & Nowak, 2001), and lowered purchase intention and service adoption (Chellappa & Sin, 2005; Milne & Boza, 1999; Phelps, D’Souza, & Nowak, 2001). In addition, when people received unrequested email from the marketers, they avoid further emails, request to remove their information from email list, or provide incomplete information to the companies email sent. Similarly, as privacy concern increases, consumers are more likely to request removal of their information and support privacy protection policies (Dolnicar & Jordann, 2007). Privacy concern increases advertising skepticism and avoidance (Baek & Morimoto, 2012). Recently, Jeong and Coyle (2014) found that privacy concern about a marketer does not affect social media use such as posting, following or deleting existing posts. They explained that social media users do not limit their behaviors due to marketers, but form negative attitudes toward advertising. The studies that examined the influence of privacy concern on advertising focused on attitudinal and behavioral consequences, but did not address cognitive effects such as recall and attention. However, it is possible that privacy concern increases attention to ad messages. Lang
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(2000) explained that when people perceive threat, they allocate greater cognitive efforts to the threat for selecting appropriate actions for managing the threat. Mogg and Bradley (1988) found that if perceived threat reaches a certain critical point, it makes people pay more attention to it. Thus, it can be assumed that people pay more attention to ads as privacy concern increases. 2.4. Hypotheses The literature reviews consumers’ responses to advertising influenced by perceived ad relevance and privacy concern in terms of cognitive, affective, and behavioral aspects. Previous studies demonstrated that perceived ad relevance increases attention to the ad (e.g., Celsi & Olson, 1988; Pechmann & Steward, 1990), generates favorable attitude toward the ad (e.g., Trampe, Stapel, Siero, & Mulder, 2010), heightens the intention to adopt the product or service (e.g., Pavlou & Steward, 2000; Xia & Bechwati, 2008), and decreases ad avoidance (Beak & Morimoto, 2012). Self-referencing also supports positive responses to relevant ad messages by stimulating people to elaborate incoming messages and, in turn, increase persuasive effects (Escalas, 2007; Markus, 1977). In addition, although the benefit of personalized ads is generally found, increased privacy concern as a reactance to customized ads has also been proposed. This is because messages that are too self-related make people worry about the misuse of their personal information. In addition, studies found that privacy concern is negatively related to adverting effects. Privacy concern leads people to negative attitude toward the ad (Phelps, D’Souza, & Nowak, 2001), decreased purchase intention (Chellappa & Sin, 2005; Milne & Boza, 1999) and increased ad skepticism, and ad avoidance (Baek & Morimoto, 2012). However, privacy concern also increases ad attention for appropriate management of the situation (Mogg & Bradley, 1988). To sum up, studies provided evidence that perceived ad relevance is positively related to, but privacy concern is negatively related to ad effectiveness. Moreover, lots of anecdotal 8
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evidence explained that ad relevance increases privacy concern. It is expected the mediating effects of privacy concern between perceived ad relevance and ad effectiveness. Based on the literatures, the current study attempts to examine how perceived ad relevance and privacy concern affect consumers’ responses to advertising. Among the various advertising effectiveness outcomes, the current study focuses on ad attention and ad avoidance which become the critical factors for indicating the effectiveness of adverting. This is because attention is the first step for ad message processing (Greenwald & Leavitt, 1984; MacInnis & Jaworski, 1989) and it is hard for an individual ad to get attention among the plentiful advertising. In addition, advertising avoidance is considered the greatest obstacles for advertisers (Baek & Morimoto, 2012), and technical development allows people to easily avoid advertising ever before. Thus, the following hypotheses are posed: H1: Perceived ad relevance is positively related to (a) attention to personalized ads, but negatively related to (b) ad avoidance. H2: Privacy concern is positively related to (a) attention to personalized ads and (b) ad avoidance. H3: Perceived ad relevance is positively related to privacy concern. H4: The effect of perceived ad relevance on (a) attention to personalized ads and (b) ad avoidance is mediated by privacy concern. ---------------------------------Figure 1. About Here ---------------------------------3. Method 3.1. Sample A total of 557 respondents recruited through Amazon Mechanical Turk were included in this analysis after excluding 92 respondents who did not complete the survey and 36 respondents 9
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who failed attention checks. Of the participants, 291 (52.2%) were male and 254 (46.6%) were female. The age of respondents ranges from 18 to 69 year-old (M = 34.16, SD = 10.26), and over 70% of the respondents were 20s and 30s. Table 1 shows the summarized demographic information of the respondents. ---------------------------------Table 1. About Here ---------------------------------3.2. Procedure After agreeing with the informed consent information, respondents read the study instructions and they were asked to complete online questionnaires related to social media advertising such as perceived ad relevance, privacy concern, attention to ads, and ad avoidance. Upon completing those questions, respondents answered demographic questions. The study was finished with a debriefing messages. 3.3. Measures All scales employed in this study were selected from previous studies and measured by 7point Likert scale items, ranging from strongly disagree (1) to strongly agree (7). Respondents were asked to answer questions related to general perceptions of advertising and privacy concern on social media. Perceived ad relevance was measured by four items. The original items adopted from Laczniak and Muehling (1993) were modified for the purposes of the current study. Items asked respondents to answer the question “When I saw advertising on SNS, I felt that it might be … “of value to me,” “relevant to my needs,” and “created just for me.” Privacy concern was measured by three items adopted from Dolnicat and Jordaan (2007). Among the 24 original items, items related to marketers were selected. Items include
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“companies’ information collection,” “companies’ use of information,” and “companies’ storing of information. Attention to advertising was measured by five items adopted from Laczniak and Muehling (1993), such as “How much attention do you pay to advertising on SNS,” “How much do you notice the advertising on SNS,” and “How much though did you put into evaluate advertising on SNS.” Advertising avoidance was measured by two items adopted from Baek and Morimoto (2012). Cho and Cheon (2004) divided ad avoidance into cognitive, affective and behavioral areas. However, cognitive and affective avoidance are similar to ad attention and attitude toward advertising. Therefore, this study adopted only the items related to behavioral avoidance. Among the five original items, only two items which were directly related to behavioral avoidance were selected. The items included “I discard (throw away, hang up) advertising on SNS,” “I have asked marketers to take me off their email (mailing, telephone) list.” 4. Results A two-step approach recommended by Anderson and Gerbing (1988) was applied to test the proposed reseach model. In the first step, a confirmatory factor analysis was condcuted to confirm the validity and reliability of measuremt model. In the second step, structural equation modeling (SEM) was conducted to analyze the proposed hypotheses by using Amos 23. 4.1. Measurment model Table 2, 3, and 4 show that there was no validity and reliability concern for all measurement. First, all measuremt items had satisfactory loading ( > .07) and AVE ( > .50) indicating good convergent vaility. Second, Square root of AVE for all measurmets were greater than the correlation of variables, and all ASVs were less than MSVs and AVEs. In addition,
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descriptive statistics indicated that the constructes ranged in normal distribution. These showed good discriminant validity. Third, Cronbach’s a and CR of each variable ( >.70) revealed that all measurement items were internally reliabile. Measurement model fit was also checked: χ2 (70) = 156.28, p < .001, χ2/df = 2.23, GFI = .96, CFI = .99, TLI = .99, RMSEA = .05, SRMR = .02. These indices showed that the measurement model had a good fit to the data. ---------------------------------Table 2. About Here ------------------------------------------------------------------Table 3. About Here ------------------------------------------------------------------Table 4. About Here ---------------------------------4.2. SEM results Table 5 indicates the hypothesized direct and indirect results. To examine the significance of the indirect effects, 5,000 bootstrap procedure was conducted. Statistical indices suggested that the model fits the data well: χ2 (71) = 188.83, p < .001, χ2/df = 2.66, GFI = .96, CFI = .98, TLI = .98, RMSEA = .06 and SRMR = .04. Hypothesis 1 posited that perceived ad relevance is positively related to ad attention, but negatively related to ad avoidance. As expected, people are more likely to pay attention to advertising (β = .80, p < .001) and less likely to avoid advertising (β = -.56, p < .001) as perceived ad relevance increases. Thus, hypothesis 1 were supported. Hypothesis 2 posited that privacy concern is positively associated with ad attention (H3a) and ad avoidance (H3b). As expected, people are more likely to avoid advertising as privacy
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concern increases, β = .12, p < .01. However, privacy concern has no effect on ad attention (β = -.03, p >.05). Thus, H3a was not supported, but H3b was supported. Hypothesis 3 assumed the positive relationship between ad relevance and privacy concern which was also supported, β = .11, p < .05. The result showed that people may feel that marketers use their personal information to target them when they are exposed to advertising that is highly relevant to their needs. Hypothesis 4 addressed the mediating effects of privacy concern on the relationship between ad relevance and ad attention (H4a) and the relationship between ad relevance and ad avoidance (H4b). The indirect effect of ad relevance on ad attention through privacy concern was not statistically significant, β = .00, p >.05. On the other hand, the indirect effect of ad relevance on ad avoidance through privacy concern was statistically significant, β = .01, p <.05. The results showed privacy concern as a mediating variable decreased the influence of ad relevance on ad avoidance, but not attention. Thus, H4a was not supported, but H4b was supported. ---------------------------------Table 5. About Here ---------------------------------5. Discussion The purpose of this study is to examine how perceived ad relevance and privacy concern influence the effectiveness of social media ads in terms of attention and avoidance. The results showed that perceived ad relevance was positively associated with attention to ads, but negatively related to ad avoidance. This means that people who think social media ads are personally relevant (e.g., the ads satisfied with my needs and helpful to achieve goals) are more likely to pay attention to, but less likely to avoid the ads. This result is consistent with previous findings that address the positive relationship between perceived ad relevance and ad
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effectiveness (e.g., Beak & Morimoto, 2012; Celsi & Olson, 1988; Pechmann & Steward, 1990). The positive relationship also confirms that self-related information motivates people to elaborate the ad messages. When consumers are exposed to relevant ad messages, the messages’ saliency activates information processing system which ultimately generates strong persuasion effects (Escalas, 2007; Markus, 1977). This study also found that privacy concern is positively related to ad avoidance. In other words, consumers who know and worry that advertisers collect their personal information for marketing purposes are more likely to take actions to avoid social media ads such as scrolling down Internet pages, closing windows, or not clicking ads. This result confirms previous studies suggesting that privacy concern decreases ad effects (e.g., Beak & Morimoto, 2012; Chellappa & Sin, 2005; Milne & Boza, 1999; Phelps, D’Souza, & Nowak, 2001). However, different from the expectation, privacy concern has no effect on ad attention. This means that the amount of attention to ads is consistent whether people have high privacy concern or not. This observation is inconsistent with previous studies in which participants paid more attention to ads as perceived threat over a certain critical point for appropriate responses (Lang, 2006; Mogg & Bradley, 1988). The alternative explanations are that privacy concern may not be critical for consumers to do subsequent behaviors to protect their privacy, consumers just ignore ads because they have no option to get rid of social media ads, or consumers may do not pay attention to any ads regardless of privacy concern (i.e., ad blindness). One of the contributions the current study makes is that it empirically confirmed the positive relationship between perceived ad relevance and privacy concern. Previously, articles proposed an anecdotal evidence that customized ad messages increase privacy concern because the ads are created based on personal information, but it is hard to find studies that empirically
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examine the relationship. The empirical finding of this study indicated that when people are exposed to highly personalized ads on social media, they think that marketers track their information and use it for their marketing purposes. In turn, consumers have increased privacy concern. This finding supports the explanation that highly self-related messages make people process the messages critically, in turn, adverse persuasive effects can be generated (MeyersLevy & Peracchio, 1996). However, the result is opposed to the view that relevance decreases privacy concern if perceived benefit from personalized ads is over the risk (Zhu & Chang, 2016). Additional examinations will be necessary to better explain the relationship between perceived ad relevance and privacy concern. Another contribution of this paper is that it found a mediating effect of privacy concern between perceived relevance and ad avoidance, but not between perceived relevance and ad attention. These findings would be explained by the possibility to track additional personal information. If consumers do not avoid social media ads (e.g., they click on them), this behavior enables advertisers to obtain consumers’ other interests or buying behaviors, which will be used for future targeting information. If consumers are already aware of someone tracking their behavior on the Internet, they are more likely to avoid ads even if the ads include personally relevant messages. On the other hand, it is hard for advertisers to obtain additional consumers interests without certain actions. In other words, advertisers do not recognize consumers’ interests when the consumers simply see social media ads. Thus, consumers pay attention to personalized ads without worries about additional privacy infringement that may result from interacting with the ads. 5.1. Future research and conclusion
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The findings of this study promise to contribute to both academia and industry. The current study found a predominant influence of perceived relevance on consumers’ responses to social media ads. This means that currently used personalized advertising on social media works effectively. However, this study also found that perceived relevance enhances privacy concern which is negatively related to advertising effectiveness. This result indicates that too much personalization does not guarantee satisfactory ad effects. Thus, finding the optimal level which is enough for consumers to notice personal relevance but does not make them scared is an assignment for both advertisers and researchers. If advertisers have to use highly personalize messages, providing justifications explaining how consumer information is relevant to the personalized offer is a way to decrease privacy concern (White et al., 2008). This study has limitations. This study is based on self-reporting data which are limited to know behaviors in real situations. Employing various research methods is helpful to obtain realistic consumers’ responses to personalized ads. In particular, using eye-tracking to get realistic behavioral patterns will significantly compensate for self-reporting data. In addition, there are lots of variables which explain advertising effects such as attitude and memory. Including other variables in a study provides a more powerful explanation of ad effectiveness. Overall, personalized ads will be more popular in the future, but there are still numerous areas left uninvestigated. Thus, further examinations including those suggested above will provide a better understanding of how privacy concern and perceived relevance work to predict personalized ad effectiveness.
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Ad Attention
Ad Relevance
Privacy Concern Ad Avoidance
Fig ure 1. Res earc h mod el
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Table 1 Demographic Information of Respondents Gender Age
Income
Male Female Prefer not to answer < 19 20 – 29 30 – 39 40 – 49 > 50 Prefer not to answer > 10,000 10,000 – 24,999 25,000 – 49,999 50,000 – 99,999 < or = 100,000 Prefer not to answer
n 291 254 12 5 218 197 71 54 12 132 132 105 122 54 12
% 52.2 46.6 2.2 0.9 39.1 35.4 12.7 9.7 2.2 23.7 23.7 18.9 21.9 9.7 2.2
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Table 2 Factor loading, Cronbach’s a, CR, AVE, and ASV of Variables Loading Cronbach’s a CR AVE Relevance .93 .93 .77 Item 1 .92 Item 2 .88 Item 3 .93 Item 4 .92 Privacy concern .88 .88 .72 Item 1 .88 Item 2 .90 Item 3 .92 Attention .97 .97 .85 Item 1 .95 Item 2 .90 Item 3 .96 Item 4 .94 Item 5 .94 Avoidance .77 .78 .64 Item 1 .90 Item 2 .90
MSV .62
ASV .31
.01
.01
.62
.32
.34
.21
Note: CR (composite reliability), AVE (average variance extracted), MSV (maximum shared variance), and ASV (average shared variance)
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Table 3 Descriptive Statistics of Variables Variable Mean Relevance 4.48 Privacy concern 5.39 Attention 3.92 Avoidance 4.52
S.D 1.43 1.18 1.75 1.61
Min 1 1 1 1
Max 7 7 7 7
Kurtosis -.28 1.86 -1.13 -.63
Skewness -.46 -1.04 .03 -.31
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Table 4 Correlation Matrix of Variables Relevance 1 .88 2 .11 3 .79 4 -.53 Note: Diagrams are square root of AVE.
Privacy concern
Attention
Avoidance
.85 .07 .06
.92 -.58
.80
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Table 5 The Results of Hypotheses Testing
H1a H1b H2a H2b H3 H4a H4b
Path Rele Att Rele Avo PC Att PC Avo Rele PC Rele PC Att Rele PC Avo
Estimate .80*** -.56*** -.03𝑛.𝑠. .12 ** .11* -.00𝑛.𝑠. .01* 𝑛.𝑠.
SE .04 .05 .05 .06 .04 .00 .01
BC 95% CI Lower Upper .76 .83 -.63 -.48 .03 -.09 .03 .21 .02 .20 .00 -.01 .00 .04
Note1: * p <.05; ** p <.01; *** p <.001; = not significant Note2: Rele = perceived ad relevance; PC = privacy concern; Att = ad attention; Avo = ad avoidance, BC = bias corrected; CI = confidence interval; SE = standard error Note 3: χ2 (71) = 188.83, p < .001, χ2/df = 2.66, GFI = .96, CFI = .98, TLI = .98, RMSEA = .06, SRMR = .04. GFI = goodness-of-fit index; CFI = comparative fit index; TLI = Tucker-Lewis index, RMSEA = root mean square error of approximation; SRMR = standardized root mean residual.