messages are perceived to be justified. Yu & Brenda (2009) about via email, letter and telephone call Online and offline ads No Yes BT Consumers generally have a negative attitude towards personalized advertisements, both online and offline ones. They feel that their privacy was violated. Yan, Wang & Chen (2009) How much can targeting help online Other online ads No No BT (1) The users who clicked the same ad will be more similar than the users who clicked different ads; (2) ads CTR increase significantly by BT; and (3) tracking the short term user search behavior can perform better than tracking the long term user browsing behavior. Taylor, Lewin & Strutton (2011) Do ads work on social networks? How gender and age shape SNA No Yes SNT When SNA delivered content or impressions that provided entertainment or informational value or offered social value, consumers appeared more likely to respond favorably toward the ad stimuli themselves. ’
Tucker (2011) Social networks, and privacy control SNA No Yes SNT If social networks giving users more control over their private information can benefit advertising-supported media and advertisers on such sites. Bright & (2012) Does impact Other online ads Yes Yes BT Low control consumers in non-customized conditions exhibited the most positive attitude toward the ad; high control consumers in non-customized conditions had the most negative attitude toward the ad. Furthermore, Subjects with a low desire for control will in fact experience a decreased ability to recognize media content when exposed to a customized online environment. This thesis (2013) between level of and privacy concerns and ad SNA Yes Yes Both effective
Attitude towards SNS was also collected because we think it’s interesting to run an additional analysis to see if our IVs would also have an impact on attitude towards SNSs themselves. A 7 point scale with 4 items was adopted to measure attitude towards SNS. The original scale contains six items. We excluded three less relative items out of our study (“I like YouTube videos created by customs/fans of the product or brand”, “I like Twitter feeds for the product or brand” , “I like YouTube videos created by the sponsor company of the product or brand”) to keep the survey short. These questions were measured on a 7-point scale ranging from “strongly disagree” to “strongly agree”. The left items are: I like banner product and brand advertising on SNS profiles I like SNS profiles created by the sponsor or company of the product of brand. I like SNS profiles created by customer/fans of the product or brand. Furthermore, we add one more question to get deeper and more direct perception of subjects’ attitude toward SNS: I think Facebook is a safety platform. Privacy concerns (PC) A three-item scale about privacy concern by Culnan (1993) is widely used by scholars. But this scale is more likely to measure general privacy concerns instead of privacy concerns of online environment, not mentioning SNS environment. We made a little change upon the questions. These questions were measured on a 7-point scale ranging from “strongly disagree” to “strongly agree”. PC1: I am concerned about treats to my personal privacy after seeing this ad. PC2: Facebook users have lost all control over how personal information is used. PC3: Citizens begin surrendering their privacy the day they open their Facebook accounts or other SNS accounts. Intrusiveness (I) To measure the intrusiveness aroused by the ad on the “Facebook page”, we adopted the 7 point scale with 10 items which Doorn & Hoekstra (2013) used in their research. These questions were measured on a 7-point scale ranging from “strongly disagree” to “strongly agree”.
3.6 Hypothesis testing Although in experimental research ANOVA analysis is commonly preferred, logistic analysis and multiple regression analysis will be used in this research. Logistic analysis will be conducted to test H1. Multiple regression analysis will be conducted in order to find out if the independent variables have any effect on the dependent variables for H2, H3 and H7. For the independent variable, degree of personalization, which was manipulated during our quasi-experiment, we create four dummies to test the difference among these treatments: Ad1: dummy variable for contextual targeting: 1 – contextual, 0 – control Ad2: dummy variable for connection targeting: 1 – connection, 0 – control Ad3: dummy variable for search engine targeting: 1 – search engine, 0 – control Ad4: dummy variable for profile-based targeting: 1 – profile, 0 – control Furthermore, the independent variable, degree of personalization was coded by the result of the pretest, thus control group = 0, contextual group = 1, connection group = 2, search engine group = 3, profile-based group = 4. Analysis1: Testing H1 A logistic regression was conducted to test this hypothesis, because the dependent variable, brand recall, is a categorical variable divided into two groups (0-no, 1-yes), meanwhile, the independent variable (DP) is also a categorical variable. Brand recall was collected by asking our subjects whether they remember the brand of the ad they just saw on the “Facebook page”. For the subjects who answered yes but cannot tell anything related with the brand, we also value them with “0”. The goal of logistic regression is to predict an ‘event’ which may or may not occur, so a logistic regression was suitable to be conducted. Brand recall =β0 + β1DP+error term (1) The significance and sign of the coefficient of β1 will be checked for H1. Analysis2: Testing H2 and H6
Table 13: Mediating test of PC between DP and Aad Standardized Coefficients, Beta Sig. R-square DP => PC .603 *(p=.000) .369 PC => Aad -.368 *(p=.000) .147 DP => Aad -.070 *(p=.359) .006 DP+ PC => Aad .205; -.493 *(p=.019); *(p=.000) .186 Figure 17: Regression analysis Regression analysis (DV: Aad; IV: PC) SPSS output Figure 18: Regression analysis Regression analysis (DV: Aad; IV: DP) SPSS output Before we add privacy concern into our model, the relationship between degree of personalization and Aad was insignificant (P=0.359>0.05). Then we included PC in this model, the standardized coefficient beta of DP changed (from -0.078 to 0.231), and the coefficient also become significant (P=0.019<0.05). This means that privacy concern acted as a mediator between the relationship of DP and Aad. Furthermore, degree of personalization was statistically insignificant to attitude towards brand without the existence of PC, which means the relationship between DP and Aad was an indirect one. But the direction was opposite with we expected. The possible reason was that the negative impact that bring by PC was not strong enough to eliminate the positive impact bring by DP. The specific possible reason will be discussed in the next section. Table 14: Hypothesis summary H1: The higher the degree of personalization on SNA is, the higher the brand recall rate will be. Confirmed H2: Higher the degree of personalization on SNA, higher the level of users’ privacy concerns. Confirmed H3: As users’ privacy concern increase, their attitude towards advertisers’ brand will get more negative. Confirmed H4: Privacy concerns mediate the relationship between degree of personalization on SNA and attitude towards Not confirmed (significant, but in
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