713 ADS AVOIDANCE AND ATTITUDE TOWARDS ONLINE ADVERTISING AMONG NET-GENERATION IN JAKARTA Volume: 3 Number: 3 Page: 713 - 728 1 Henilia YULITA, 2 Pinckey TRIPUTRA, 3Udi RUSADI, 4Titi WIDANIGSIH 1Doctoral Student of Communication Program of Sahid University, Jakarta; Lecturer of Communication Studies, Bunda Mulia University, Jakarta 2 Senior Lecturer of Indonesia University; Guest Lecturer of Communication Doctoral Program of Sahid University, Jakarta. 3Senior Lecturer of Institut Ilmu Sosial dan Ilmu Politik Jakarta, Guest Lecturer of Communication Doctoral Program of Sahid University, Jakarta 4Vice Rector II and Lecturer of Communication Doctoral Program of Sahid University, Jakarta Corresponding author: Henilia Yulita E-mail: Henilia.yulita@gmail.com Article History: Received: 2022-07-18 Revised: 2022-08-15 Accepted: 2022-11-11 Abstract: Advertising is a form of non-personal presentation consisting of promoting ideas, goods, and services by certain sponsors and being paid. However, the trend of ad blocker users continues to increase and is expected to penetrate mobile devices. With this ad blocker, the advertisements carried out by the brand will not be able to reach the intended target market. This study examines factors influencing the Internet generation's avoidance of advertising on social media. This type of research is quantitative, taking all net-generation in Jakarta as the population and 490 net-generation as a sample. Sampling in this study is non-probability sampling. The results of this study indicate that the attitude towards online advertising is directly and significantly influenced by the variables of entertainment, informativeness, credibility, personalization, and incentives. In contrast, the irritation variable has no effect. The advertising avoidance variable is also directly and significantly influenced by the attitude towards online advertising and irritation. However, it is not influenced by entertainment, Informativeness, credibility, Personalization, and incentive variables. Meanwhile, advertising avoidance is influenced indirectly and significantly by the variables of entertainment, Informativeness, credibility, Personalization, and incentives through the attitude towards online advertising as a moderating variable. At the same time, the advertising avoidance variable is not affected indirectly and significantly by the irritation variable through the attitude towards online advertising as a moderating variable. Keywords: Entertainment, Informativeness, Irritation, Credibility, Personalization, Incentives, Attitude Towards Online Advertising, Advertising Avoidance. Cite this as: YULITA, H., TRIPUTRA, P., RUDASI, U., WIDANINGSIH T. (2022) “Ads Avoidance and Attitude Towards Online Advertising among Net- Generation in Jakarta”. International Journal of Environmental, Sustainability, and Social Sciences, 3 (3), 713 - 728. INTRODUCTION Advertising is a form of non-personal presentation consisting of promoting ideas, goods, and services by certain sponsors and being paid. However, the trend of ad blocker users continues to increase and is expected to penetrate mobile devices. With this ad blocker, the advertisements carried out by the brand will not be able to reach the intended target market. This study examines factors influencing the Internet generation's avoidance of advertising on social media. This type of research is quantitative, taking all net-generation in Jakarta as the population and 490 net- generation as a sample. Sampling in this study is non-probability sampling. The results of this mailto:Henilia.yulita@gmail.com 714 study indicate that the attitude towards online advertising is directly and significantly influenced by the variables of entertainment, Informativeness, credibility, Personalization, and incentives. In contrast, the irritation variable has no effect. The advertising avoidance variable is also directly and significantly influenced by the attitude towards online advertising and irritation. However, it is not influenced by entertainment, Informativeness, credibility, Personalization, and incentive variables. Meanwhile, advertising avoidance is influenced indirectly and significantly by the variables of entertainment, Informativeness, credibility, Personalization, and incentives through the attitude towards online advertising as a moderating variable. At the same time, the advertising avoidance variable is not affected indirectly and significantly by the irritation variable through the attitude towards online advertising as a moderating variable. Advertising is a form of non-personal presentation consisting of promoting ideas, goods, and services by certain sponsors and being paid. Advertising is one of the practical tools to create a corporate image in the long term. The growing role of information technology, especially in online media, has made the shift in advertising media increasingly visible. Online advertising has more implications that it can be widely spread along with the rapid growth of Internet media and smartphones at this time. Almost all ages already have smartphones, life is all digitized, and even the dissemination of information is made easier through social media. Social media is one of the most promising tools in digital advertising. Data from ComScore shows that more than 93 million unique viewers in Indonesia (over the age of 18) have watched videos on YouTube every month over the past year. This number was recorded to increase by 10 million compared to the previous year (kompas.com, 2021). The company invested over $3 billion in YouTube ads in the US alone in 2017. A survey of 11,000 people in America states that 65% of people skip online video ads. The numbers are increasing when people watch ads on the go on smartphones. Namely, 84% of YouTube viewers miss ads (Lifia Mawaddah Putri, 2022). The trend of ad blocker users continues to increase and is expected to penetrate mobile devices, especially after Apple released iOS 9. Through iOS, Apple allows users to block ads by using third-party applications. With this ad blocker, the advertisements carried out by the brand will not be able to reach the intended target market, so the brand must be willing to get poor ad performance (Bagus Ramadhan, 2021). The avoidance of advertising that occurs to youtube viewers is terrible news for the video- sharing channel, a path done to maintain the comfort of the youtube site visitors. The middle ground is to allow users to filter information or avoid advertisements because they are not attractive and do not suit visitors' needs through the skip ads feature. This feature is the only challenge for ad producers in creating attractive advertising concepts so that Youtube users are willing to see ads until the end (Sukma Yona Asmara, 2020). Research Jong-Hyuok Jung (2017) states that ad avoidance behavior is significantly influenced by attitudes towards advertising (Attitude towards online advertisement). The results of a study through an online survey of 2,002 US adults showed that attitudes toward advertising, in general, are the strongest predictors of ad avoidance in four types of media (TV, Radio, Newspaper, and Magazine). Individuals with a more positive attitude towards advertising tend to avoid advertising in all four media. The results also showed a significant influence of age on TV and radio, but not on avoiding advertising in newspapers and magazines. Young respondents are more likely to avoid advertising on TV and radio than older ones. Another research on advertising value conducted by (Arora & Agarwal, 2019) from Amity University, India, provides a comprehensive advertising model, which examines the impact of identified predictors such as entertainment, informativeness, irritation, credibility, incentives, and personalization on advertising value. Social media and look further into its impact on the attitudes 715 of the Internet generation towards social media advertising. Murillo (2017) conducted a study entitled Attitudes toward mobile search ads: a study among Mexican millennials using the structural equation model estimated by Partial Least Squares. Responses were collected from 1,215 Internet smartphone owners in Mexico. This study proves that the level of consumer perception of advertising (Perceived Advertising Value), which consists of informativeness, entertainment, distraction and credibility, significantly affects consumer attitudes towards online advertising (Attitude Towards Online Advertising). Based on the background of the problem described previously, the problem formulation is: What factors influence the avoidance of advertising on social media in the Internet generation? Advertising avoidance. Advertising avoidance is an action carried out by users in various media that reduces exposure to advertising content. Thus, ad avoidance is influenced by the attitude to advertising. Hwang, Yoon and Park (2011) found that advertising is a form of negative attitude towards advertising. Furthermore, Schiffman and Kanuk (2007) state that an attitude towards negative advertising can affect the occurrence of ad avoidance. Speck & Elliott (1997) evaluates traditional media and proposes that ad avoidance is cognitive, mechanical/affective or behavioral (Sukma Yona Asmara, 2020). Attitude Towards Online Advertising. Attitudes towards YouTube advertising derived from Attitudes of Use are an aggregation of the evaluation of the cognitive and affectively perceived attributes and benefits of an online advertisement (Wang & Sun, 2010b, p. 334). Although research on attitudes towards advertising is new and rare because past studies have mainly focused on consumer attitudes towards traditional media, experts agree that attitudes towards advertising can significantly influence subsequent consumer behavior towards the advertised product or brand and their purchase intentions (Drossos, Giaglis, Vlachos, Zamani, & Lekakos, 2013; Wolin & Korgaonkar, 2003; Tsang et al., 2004) in (Ammarie & Nurfebiaraning, 2019). Attitude Towards Online Advertising. Consumer attitudes towards advertising can be understood through three components: cognition, influence and conation. Of these, cognition is considered the most important. Here, cognition refers to the mental status of consumers, which influences their acceptance of advertisements and, in turn, impacts their purchasing decisions. If the message is sufficiently informative, credible and entertaining, it will be a positively received message; otherwise, it can be counterproductive and disturbing to consumers (Aramendia-Muneta & Olarte-Pascual, 2019). Entertainment. In a Web 2.0-based digital social media environment, the value of entertainment lies in the ability to meet the audience's need for emotional release, diversion and enjoyment (Muntinga et al., 2011) by allowing users to exchange various types of experiences, information, video clips and music and many more again with their connections on social media sites (Kim, Sohn, & Choi, 2011). It was also confirmed in the mall interception study at Ducoffe, which reported a substantial, significant, and positive significant correlation, and positive correlation of 0.48 between multi-item entertainment measures and advertising value (Ducoffe, 1996) (Arora & Agarwal, 2019). Informativeness. According to Ducoffe (1996), informational advertising is the ability to inform consumers of alternative product information, which can create a balance between consumer needs and producer offers, thereby enabling a more efficient market (Pollay & Mittal, 1993). Furthermore, Schlosser, Shavitt, and Kanfer (1999) confirmed a positive relationship between consumer attitudes towards Internet advertising and informative characteristics. Thus, information plays a significant incentive in marketing because recipients are inclined toward advertisements that offer incentives (Andrews, 1989; Varshney, 2003) in (Arora & Agarwal, 2019). 716 Irritation. Irritation is one of the tactics advertisers use when competing for consumer attention. When advertising uses distracting, unfriendly, or overly manipulative techniques, consumers tend to perceive it as an unwanted and annoying influence (Li & Huang, 2016). Credibility. Advertising credibility is the degree to which consumers accept claims about the brand in advertisements to be honest and trustworthy (MacKenzie & Lutz, 1989). Advertising credibility has always been one of the most studied concepts on the Internet in advertising literature (Arora & Agarwal, 2019). Personalization. Personalization is the content of advertising messages that are tailored to consumer interests. The personalization of mobile advertising can enable marketers to reach their potential customers in a highly individualized way, thereby enhancing the relationship with consumers (Jayasuriya, 2019). Incentive. This incentive makes consumers believe they will benefit from the advertising message (Kim and Han, 2014). Previous research has stated that price discounts effectively generate results and influence purchasing decisions (Shi, Cheung & Prendergast 2005). Customers are interested in benefits and pay more attention to advertising messages with financial benefits (Kim and Han, 2014); (Arora & Agarwal, 2019). Net Generation. Generational groupings in the world of work will emerge following the development of human resource management. Research on this generational difference was first conducted by Manheim (1952). According to Manheim, generation is a social construction in which a group of people of the same age and the same historical experience (Lase & Daeli, 2020). Source: Researchers (2022) Figure 1. Framework Research 717 Direct research hypothesis: Hypothesis 1 : Attitude Towards Online Advertising is significantly influenced by entertainment Hypothesis 2 : Attitude Towards Online Advertising is significantly influenced by informativeness Hypothesis 3 : Attitude Towards Online Advertising is significantly affected by irritation. Hypothesis 4 : Attitude Towards Online Advertising is significantly affected by credibility. Hypothesis 5 : Attitude Towards Online Advertising is significantly influenced by personalization Hypothesis 6 : Attitude Towards Online Advertising is significantly influenced by Incentives Hypothesis 7 : Advertising Avoidance on YouTube is significantly affected by Attitude Towards Online Advertising. Hypothesis 8 : Advertising Avoidance on YouTube is significantly affected by entertainment Hypothesis 9 : Advertising Avoidance on YouTube is significantly affected by informativeness. Hypothesis 10 : Advertising Avoidance on YouTube is significantly affected by irritation Hypothesis 11 : Advertising Avoidance on YouTube is significantly affected by credibility Hypothesis 12 : Advertising Avoidance on YouTube is significantly affected by personalization Hypothesis 13 : Advertising Avoidance on YouTube is significantly affected by Incentives Indirect research hypothesis: Hypothesis 14 : Advertising Avoidance on YouTube is significantly affected by Entertainment through Attitude Towards Online Advertising Hypothesis 15 : Advertising Avoidance on YouTube is significantly affected by Informativeness through Attitude Towards Online Advertising Hypothesis 16 : Advertising Avoidance on YouTube is significantly affected by Irritation through Attitude Towards Online Advertising Hypothesis 17 : Advertising Avoidance on YouTube is significantly affected by Credibility through Attitude Towards Online Advertising Hypothesis 18 : Advertising Avoidance on YouTube is significantly affected by Personalization through Attitude Towards Online Advertising Hypothesis 19: Advertising Avoidance on YouTube is significantly affected by Incentives through Attitude Towards Online Advertising METHODS This study uses quantitative research, which uses theory deductively and aims to test or verify a theory (Ghozali, 2014). This study uses a random sampling method to explain the relationship between assumptions and procedures for applying existing assumptions. This study uses multiple regression analysis with a measuring instrument that is PLS-SEM. Data collection techniques were used in this study through questionnaires, interviews and documentation methods. This study used a survey instrument in the form of a questionnaire. RESULT AND DISCUSSION Based on the study's results, it can be explained that this study consisted of 490 respondents, with 255 male respondents or 52.05% and 235 female respondents or 47.95%. The involvement of respondents with high school education is the most dominant group compared to other respondents' educational backgrounds. The respondents in this study are indeed the internet generation, most of whom continue their education. Only 2.44 internet generations are directly working. 718 Evaluation of Measurement Model (Outer Model). Evaluation of the outer model or measurement model is carried out to assess the validity and reliability of the model. A model can be known for validity and reliability by knowing the correlation. The outer model is evaluated through 3 criteria: convergent validity, discriminant validity and composite reliability. Table 1. Outer Loading (Measurement Model) Variable Laten Code Outer loading Description Entertainment Hib1 0.880 >0,7; Meet convergent validity standard Hib2 0.918 >0,7; Meet convergent validity standard Hib3 0,897 >0,7; Meet convergent validity standard Hib4 0.839 >0,7; Meet convergent validity standard Informativeness Inf1 0.836 >0,7; Meet convergent validity standard Inf 2 0.843 >0,7; Meet convergent validity standard Inf 3 0.870 >0,7; Meet convergent validity standard Inf 4 0.888 >0,7; Meet convergent validity standard Irritation Irr1 0.959 >0,7; Meet convergent validity standard Irr 2 0.962 >0,7; Meet convergent validity standard Credibility Cre1 0.749 >0,7; Meet convergent validity standard Cre2 0.871 >0,7; Meet convergent validity standard Cre 3 0.861 >0,7; Meet convergent validity standard Cre4 0.839 >0,7; Meet convergent validity standard Personalization Per1 0.859 >0,7; Meet convergent validity standard Per 2 0.9855 >0,7; Meet convergent validity standard Per 3 0.894 >0,7; Meet convergent validity standard Per4 0.910 >0,7; Meet convergent validity standard Incentives Inc1 0.894 >0,7; Meet convergent validity standard Inc2 0.917 >0,7; Meet convergent validity standard Attitude Towards Online Advertising Atoa 3 0.812 >0,7; Meet convergent validity standard Atoa 4 0.825 >0,7; Meet convergent validity standard Atoa 5 0.873 >0,7; Meet convergent validity standard Atoa 7 0.833 >0,7; Meet convergent validity standard Advertising Avoidance Avoid1 0.769 >0,7; Meet convergent validity standard Avoid3 0.848 >0,7; Meet convergent validity standard Avoid5 0.729 >0,7; Meet convergent validity standard Avoid8 0.847 >0,7; Meet convergent validity standard Source: Researchers, Smart PLS 03, n=490 (2022) Convergent validity of the measurement model can be seen from the correlation between the indicator score and the construct score (loading factor) with the criteria for the loading factor value of each indicator greater than 0.70 (Kock, 2018), which can be said to be valid. All indicators in this study have an outer loading value greater than 0.70, so it can be stated that all indicators are valid. Table 2. Discriminant Validity Discriminant Validity Advertising Avoidance Attitude Towards Online Advertising Credibility Entertainment Incentives Informativeness Irritation Personalization Cre1 -0,299 0,537 0,741 0,523 0,604 0,585 -0,181 0,632 Cre2 -0,15 0,529 0,87 0,538 0,452 0,533 -0,1 0,539 Cre3 -0,079 0,481 0,866 0,478 0,393 0,444 -0,086 0,527 719 Cre4 -0,085 0,504 0,845 0,457 0,41 0,454 -0,059 0,51 Hib1 -0,216 0,567 0,518 0,879 0,474 0,622 -0,226 0,511 Hib2 -0,228 0,605 0,548 0,917 0,485 0,628 -0,203 0,514 Hib3 -0,174 0,582 0,556 0,898 0,435 0,671 -0,203 0,501 Hib4 -0,147 0,559 0,509 0,841 0,52 0,669 -0,209 0,496 Inc1 -0,22 0,522 0,492 0,461 0,891 0,548 -0,178 0,587 Inc2 -0,206 0,603 0,528 0,516 0,92 0,537 -0,173 0,668 Inf1 -0,165 0,537 0,506 0,621 0,495 0,837 -0,225 0,463 Inf2 -0,167 0,527 0,52 0,571 0,464 0,844 -0,199 0,527 Inf3 -0,175 0,602 0,54 0,628 0,527 0,871 -0,18 0,544 Inf4 -0,219 0,61 0,534 0,691 0,565 0,887 -0,234 0,578 Per3 -0,164 0,551 0,555 0,462 0,541 0,469 -0,173 0,861 Per4 -0,243 0,638 0,624 0,512 0,697 0,599 -0,159 0,854 Pers1 -0,21 0,586 0,595 0,505 0,59 0,54 -0,168 0,894 Pers2 -0,228 0,587 0,568 0,528 0,607 0,547 -0,18 0,91 ATOA3 -0,177 0,812 0,461 0,531 0,436 0,534 -0,12 0,511 ATOA4 -0,184 0,825 0,516 0,508 0,457 0,562 -0,169 0,523 ATOA5 -0,267 0,873 0,551 0,601 0,56 0,565 -0,203 0,593 ATOA7 -0,229 0,833 0,538 0,544 0,615 0,559 -0,162 0,617 Avoid1 0,77 -0,193 -0,106 -0,151 -0,12 -0,135 0,295 -0,147 Avoid3 0,834 -0,176 -0,136 -0,176 -0,22 -0,171 0,291 -0,188 Avoid5 0,748 -0,209 -0,136 -0,185 -0,145 -0,18 0,272 -0,211 Avoid8 0,841 -0,238 -0,15 -0,18 -0,255 -0,186 0,353 -0,219 Source: Researchers, Smart PLS 03, n=490 (2022) Discriminant validity is assessed from cross-loading measurements with constructs. It can be seen by looking at the latent construct loading, which will predict the indicator better than other constructs. Suppose the correlation of the construct with the subject of measurement (each indicator) is more significant than the size of the other constructs. The discriminant validity is met (Jayasooriya et al., 2019). The cross-loading value of all indicators is more excellent than the other construct values, so all indicators can be said to be valid. Table 3. Average Variance Extracted (AVE) Laten Variable AVE Criteria Notes Entertainment 0,781 > 0,5 Meet standard Informativeness 0,739 > 0,5 Meet standard Irritation 0,922 > 0,5 Meet standard Credibility 0,692 > 0,5 Meet standard Personalization 0,774 > 0,5 Meet standard Incentives 0,820 > 0,5 Meet standard Attitude Towards Online Advertising 0,699 > 0,5 Meet standard Advertising Avoidance 0,639 > 0,5 Meet standard Source: Researchers, Smart PLS 03, n=490 (2022) Based on the results of table 4, eight constructs have met convergent validity. Entertainment with a value of 0.781> 0.5, Informativeness with a value of 0.922 also met a value of> 0.5, Irritation with a value of 0.922 also met a value of> 0.5, Credibility with a value of 0.692, Personalization with a value of 0.774> 0.5, Incentives with a value of 0.820> 0.5, Attitude Towards Online Advertising with a value of 0.699> 0.5, and Advertising Avoidance with a value of 0.639> 0.5. 720 Table 4. Composite Reliability & Cronbach’s Alpha Item Cronbach’s Alpha Composite Reliability Criteria Notes Entertainment 0,906 0,935 >0,7 Reliable Informativeness 0,882 0,919 >0,7 Reliable Irritation 0,915 0,959 >0,7 Reliable Credibility 0,850 0,900 >0,7 Reliable Personalization 0,903 0,932 >0,7 Reliable Incentives 0,781 0,901 >0,7 Reliable Attitude Towards Online Advertising 0,857 0,903 >0,7 Reliable Advertising Avoidance 0,811 0,876 >0,7 Reliable Source: Researchers, Smart PLS 03, n=490 (2022) Based on the results of the numbers in Cronbach's alpha table, which show numbers more significant than 0.5, it can be explained that all factors in the table meet the criteria and are reliable. In addition, all Composite Reliability factors have numbers above 0.7, so it can be concluded that all factors are reliable. Table 5. Coefficient of Determination R Square Source: Researchers, Smart PLS 03, n=490 (2022) According to Chin (1998), the value of R2 is considered weak, moderate, and vital if it shows around 0.19, 0.33, and 0.67 (Ghozali, 2014). In this research model, the Advertising Avoidance variable has an R Square value of 0.067, which is relatively weak because it is close to 0.19. Meanwhile, the Attitude Towards Online Advertising is quite strong because it is close to or above 0.67. Hypothesis Test Results. Provisions on the results of hypothesis testing in this study use P- values smaller than 0.05. Table 8 shows the summary results of hypothesis testing on the research model. Table 6. Hypothesis Testing Results on the Research Model Hypothesis Path P-Values Notes H1 ENT → ATOA 0,000 Significant H2 INF → ATOA 0,002 Significant H3 IRR → ATOA 0,697 Not Significant H4 CRE → ATOA 0,012 Significant H5 PER → ATOA 0,000 Significant H6 INC → ATOA 0,000 Significant H7 ATOA → AVOID 0,000 Significant H8 ENT → AVOID 0,601 Not Significant H9 INF → AVOID 0,451 Not Significant H10 IRR → AVOID 0,000 Significant H11 CRE → AVOID 0,495 Not Significant H12 PER → AVOID 0,215 Not Significant H13 INC → AVOID 0,240 Not Significant H14 ENT → ATOA → AVOID 0,001 Significant H15 INF → ATOA → AVOID 0,006 Significant R Square R Square Adjusted Advertising Avoidance 0.067 0.065 Attitude Towards Online Advertising 0.605 0.600 721 H16 IRR → ATOA → AVOID 0,715 Not Significant H17 CRE → ATOA → AVOID 0,021 Significant H18 PER → ATOA → AVOID 0,001 Significant H19 INC → ATOA → AVOID 0,006 Significant Source: Researchers, Smart PLS 03, n=490 (2022) RESULT AND DISCUSSION Entertainment to Attitude Towards Online Advertising. Based on the results of hypothesis testing, the p-values are 0.000 (<0.05). This figure explains that the Entertainment factor significantly affects Attitude Towards Online Advertising. Based on these results, it can be stated that Hypothesis 1 is accepted. In entertainment, the indicator that has the most significant influence on Attitude Towards Online Advertising is that the display of advertisements on YouTube is quite entertaining. In this study, the internet generation felt that displaying YouTube advertisements was entertaining. Hence, the entertainment value of an advertisement influenced the internet generation who wanted to see advertisements on YouTube. Informativeness to Attitude Towards Online Advertising. Based on the results of hypothesis testing, the p-values are 0.002 (<0.05). This figure explains that the Informativeness factor significantly affects Attitude Towards Online Advertising. Based on these results, it can be stated that Hypothesis 2 is accepted. In the Informativeness display, the indicator with the most significant influence on the INF variable is INF4. This indicator emphasizes the benefits gained through advertising on YouTube. The internet generation feels that YouTube is an excellent source for finding new products, so they are willing to see ads if they are helpful. The internet generation feels that advertising on Youtube is a suitable source for finding new products. Irritation to Attitude Towards Online Advertising. Based on the results of the hypothesis, it is shown that the p-values are 0.679 (> 0.05). Figures explain that the Irritation factor in YouTube ads does not affect Attitude Towards Online Advertising. Based on these results, it can be stated that Hypothesis 3 is rejected. In irritation, the indicator that has the most significant influence on the IRR variable is Irr2. In the Irr2 indicator, the internet generation does not think the presence of advertisements on YouTube is bothersome. It is because the presence of disturbing advertisements does not affect the attitude of using the internet generation towards YouTube social media. The number of YouTube advertisements is not considered disturbing the internet generation using YouTube. Hence, the number of advertisements influences the attitude toward using YouTube's social media, as seen from the Path Coefficient (β) of 0.011. Credibility to Attitude Towards Online Advertising. Based on the results of the hypothesis, it is shown that the p-values are 0.012 (<0.05). Figures explain that the Credibility factor on YouTube's social media affects the Internet generation's Attitude Towards Online Advertising. Based on these results, it can be stated that Hypothesis 4 is accepted. In credibility, the indicator that has the most significant influence on the CRE variable is Cre3. This indicator emphasizes that the internet generation believes that the products provided by advertisements on Youtube can be trusted. The internet generation trusts the products advertised on YouTube. Hence, the internet generation believes that advertisements on Youtube can be used as a reference for purchases, not advertisements that deceive or mislead them. Personalization to Attitude Towards Online Advertising. Based on the results of the hypothesis, it is shown that the p-values are 0.000 (<0.05). Figures explain that the Personalization factor in ad impressions on YouTube affects Attitude Towards Online Advertising. Based on these results, it can be stated that Hypothesis 5 is accepted. In personalization, the indicator with the most significant influence on the PER variable is Per2. Per2 is an indicator that explains advertising on Youtube aimed at consumer needs. The internet generation, who were respondents in this study, 722 considered that advertising on YouTube was indeed provided for personal consumer needs. The internet generation views that advertising content on YouTube can recognize the nature of consumers. It is because the advertising content on YouTube is deemed by their interests and is also intended for their needs as internet generation. Incentives to Attitude Towards Online Advertising. Based on the results of the hypothesis, it is shown that the p-values are 0.000 (<0.05). The results on the direct effect where the number explains that the Incentives factor affects Attitude Towards Online Advertising. Based on these results, it can be stated that Hypothesis 6 is accepted. In Incentives, the indicator that has the most significant influence on the INC variable is Inc2. Inc2 is an indicator that explains how YouTube Ads influence consumers to buy more products. The internet generation, who were respondents in this study, considered that YouTube advertising offers many advantages. It is because the advertising content on YouTube is felt to make consumers sure to benefit from the advertising message and can influence their preferences in making purchasing decisions. Attitude Towards Online Advertising to Advertising Avoidance. Based on the results of the hypothesis on the direct effect, it is shown that the p-values are 0.000 (<0.05). Figures explain that the Attitude Towards Online Advertising factor significantly influences the Advertising Avoidance factor. Based on these results, it can be stated that Hypothesis 7 is accepted. In Attitude Towards Online Advertising, the indicator with the most significant influence on the ATOA variable is Atoa5. Atoa5 indicates that YouTube advertising is a source of information for consumers in buying a product. The internet generation who were respondents in this study considered that YouTube ads were creative and cheerful. Because consumers felt the advertising content on YouTube could add the information needed, the internet generation liked informative YouTube ads. Entertainment to Advertising Avoidance. Based on the results of the hypothesis on the direct effect, it is shown that the p-values are 0.064 (> 0.05). Figures explain that the Entertainment factor does not affect Advertising Avoidance. Based on these results, it can be stated that Hypothesis 8 is rejected. Advertising Avoidance carried out by the internet generation is not due to entertainment factors such as advertisements on YouTube, which are considered entertaining. However, on the contrary, advertisements on YouTube are often annoying for the internet generation, so internet people deliberately ignore the advertisements that appear or intentionally do other things when advertisements appear. Such as reading What's App, viewing other e-commerce, or even intentionally clicking skip ads to avoid ads. Even though the ads on YouTube are entertaining, they do not make the internet generation interested in seeing these ads until they run out. Informativeness to Advertising Avoidance. Based on the results of the hypothesis, it is shown that the p-values are 0.451 (> 0.05). Figures explain that the Informativeness factor does not affect Advertising Avoidance. The results of this study also explain that the internet generation still carries out ad avoidance because the internet generation does not see the informativeness factor of advertising on YouTube. Based on these results, it can be stated that Hypothesis 9 is rejected. The internet generation's negative feelings toward advertisements drive them to avoid them. They do not feel the informational benefits of YouTube advertising for fulfilling their information-seeking needs. Irritation to Advertising Avoidance. Based on the results of the hypothesis, it is shown that the p-values are 0.000 (<0.05), with a path coefficient of 0.093. Figures explain that the Irritation factor has a significant influence on Advertising Avoidance. Based on these results, it can be stated that Hypothesis 10 is accepted. On the other hand, a positive attitude towards advertising can also create a transforming effect that shows the attitude of loyal consumers to a brand so that they cannot tell the difference between some brands. Negative attitudes toward advertising can create advertising irritation so that consumers' attention and retention of advertisements are reduced. 723 Credibility to Advertising Avoidance. Based on the results of the hypothesis, it is shown that the p-values are 0.495 (> 0.05). Figures explain that the Credibility factor does not affect Advertising Avoidance. Based on these results, it can be stated that Hypothesis 11 is rejected. In all indicators, the Credibility factor does not significantly influence the Advertising Avoidance variable, where the internet generation does not feel that advertising on Youtube is a reference for purchases. They do not believe in the advertisements delivered on YouTube, so the products or promotions displayed do not make the internet generation believe and immediately watch all ad impressions on YouTube. Personalization Against Advertising Avoidance. Based on the results of the hypothesis, it is shown that the p-values are 0.215 (> 0.05). Figures explain that the Personalization factor does not affect Advertising Avoidance. Based on these results, it can be stated that Hypothesis 12 is rejected. In the Personalization factor, all indicators do not significantly influence the Advertising Avoidance variable, where the internet generation does not feel that advertising on Youtube is by consumer interests. The internet generation will still avoid advertisements not because the advertising content on Youtube knows the nature of consumers or because advertisements on Youtube are adjusted to consumer preferences. The internet generation will still avoid ads on YouTube, not because of the Personalization factor of these ads. Incentives to Advertising Avoidance. Based on the results of the hypothesis, it is shown that the p-values are <0.240 (>0.05). Figures explain that the Incentive factor does not affect Advertising Avoidance. Based on these results, it can be stated that Hypothesis 13 is rejected. In the Incentives factor, all indicators do not have the most significant influence on the Advertising Avoidance variable, where the internet generation does not feel that advertising on Youtube is a reference for obtaining many monetary benefits such as discounts, gifts and coupons, and non-monetary benefits including their intrinsic value as level increases, status rewards, and right to priority service. Entertainment to Advertising Avoidance through Attitude Towards Online Advertising. Based on the results of the hypothesis, it is shown that the p-values are <0.001 (<0.05). Figures explain that the Entertainment factor significantly influences Advertising Avoidance through Attitude Towards Online Advertising. Based on these results, it can be stated that Hypothesis 14 is accepted. The perception of the internet generation (Attitude Towards Online Advertising) plays its role as a mediating variable. The ad avoidance factor can be reduced by a good perception of the entertainment factor in advertisements on YouTube. Informativeness to Advertising Avoidance through Attitude Towards Online Advertising. Based on the results of the hypothesis, it is shown that the p-values are 0.006 (<0.05). Figures explain an indirect effect of Informativeness on Advertising Avoidance through Attitude Towards Online Advertising. Based on these results, it can be stated that Hypothesis 15 is accepted. In the Informativeness factor, all indicators have the most significant influence on the advertising avoidance variable (Advertising Avoidance) through the attitude of use (Attitude Towards Online Advertising), where the internet generation feels that advertising on Youtube is a reference for purchases if they already have a good perception of advertising on YouTube. If they have a good perception of the information in the advertisements on YouTube as a reference, then the chances of them avoiding the ad are lower. Irritation to Advertising Avoidance through Attitude Towards Online Advertising. Based on the results of the hypothesis, it is shown that the p-values are <0.715 (>0.05). This figure explains that the Irritation factor does not affect Advertising Avoidance through Attitude Towards Online Advertising. Based on these results, it can be stated that Hypothesis 16 is rejected. In this study, all Irritation factor indicators do not have the most significant influence on the Advertising Avoidance variable through Attitude Towards Online Advertising through which the internet generation does 724 not have a positive attitude towards advertising on YouTube so that they avoid advertising on YouTube not influenced by the presence of fish on YouTube as a detrimental. Credibility to Advertising Avoidance through Attitude Towards Online Advertising. Based on the indirect hypothesis, the Credibility factor significantly influences Advertising Avoidance through Attitude Towards Online Advertising, which shows that the p-values are <0.021 (<0.05). Based on these results, it can be stated that Hypothesis 17 is accepted. In the results of this study, all Credibility factor indicators have the most significant influence on the Advertising Avoidance variable through the Attitude Towards Online Advertising variable, where for the internet generation, attitude is a significant factor in making them avoid or not avoid advertisements on YouTube. Personalization to Advertising Avoidance through Attitude Towards Online Advertising. Based on the results of the hypothesis, it is shown that the p-values are <0.01 (<0.05). Figures explain that the Personalization factor significantly influences Advertising Avoidance through Attitude Towards Online Advertising. Based on these results, it can be stated that Hypothesis 18 is accepted. On the Personalization factor, all indicators significantly influence the Advertising Avoidance variable through Attitude Towards Online Advertising, where the internet generation feels that advertising on Youtube is generally acceptable if their perception is good that the personalization of the advertisements delivered brings benefits. Incentives Against Advertising Avoidance through Attitude Towards Online Advertising. Based on the results of the hypothesis, it is shown that the p-values are 0.006 (<0.05). This figure explains that the Incentive factor significantly influences Advertising Avoidance through Attitude Towards Online Advertising. Based on these results, it can be stated that Hypothesis 19 is accepted. In the Incentives factor, all indicators have the most significant influence on the Advertising Avoidance variable through Attitude Towards Online Advertising, where the internet generation will pay attention to advertisements on YouTube if they have a perception or attitude towards YouTube advertising (Attitude Towards Online Advertising) providing benefits or benefits for them. Regression Equation. The path coefficient value shows how strong the influence of a variable is on other variables. The higher the path coefficient value, the stronger the effect. The following figure shows the regression equation model based on the path coefficient value calculation results. 725 Source: Researcher, Smart PLS 03, n=490 (2022) Figure 1. Path Coefficient In the Advertising Avoidance equation, the Attitude Towards Online Advertising and Irritation variables have significant effects. Furthermore, the variables Entertainment, Informativeness, Credibility, Personalization, and incentive did not have significant effects. Therefore, the Advertising Avoidance regression equation only consists of two path coefficients: the ATOA and AVOID variables. Based on Figure 2. it can be explained that the equations of structure 1 and structure 2 in the model are as follows: Structural equation 1: ATOA = 0,097X1 + 0,223X2 - 0,011X3 + 0,124X4 + 0,243X5 + 0,154X6 + e Structural equation 2: AVOID = -0,036X1 + 0,046X2 + 0,337X3 + 0,038X4 - 0,075X5 - 0,069X6 - 0,128X6 + e From the structural equation, it can be explained that the most dominant variable in equation 1 is the Personalization Factor, which is 0.243. Then the dominant variable in the second equation is irritation, which is 0.337. CONCLUSION Based on the research that has been done, the conclusions obtained from the results of the analysis of YouTube ad avoidance analysis on the internet generation using the Structural Equation Modeling (SEM) method are: 1. Entertainment factor has a significant effect on Attitude Towards Online Advertising. The results of this study support the results of previous research conducted by (Le, 2014); supported by (Murillo, 2017); (Jayasooriya, 2019); (Muneta, 2019); and (Arora, 2019). 2. Informativeness factor has a significant effect on Attitude Towards Online Advertising. The results of this study support the results of previous research conducted by Chia-Ling 'Eunice' Liu (2012); research (Le, 2014); (Murillo, 2017); (Humbani, 2017). 726 3. The Irritation factor in YouTube advertising does not affect Attitude Towards Online Advertising. The results of this study contradict the results of previous research conducted by (Jayasooriya, 2019), which stated that advertising on social media could provide a variety of information that confuses and distracts recipients and floods consumers with information (Xu, 2007). 4. The Credibility factor on YouTube's social media affects the Internet generation's Attitude Towards Online Advertising. The results of this study support the results of previous research conducted by (Saadeghvaziri & Hosseini, 2011; Tsang et al., 2004) in (Gaber 2019), which states that advertising credibility appears in the advertising literature as one of the essential elements of advertising value. 5. Personalization factor on ad impressions on YouTube affects Attitude Towards Online Advertising. The results of this study support the results of previous research conducted by Raoand et al. (2003), which argues that it is essential for marketing techniques based on knowledge of customer profiles, history, and needs (Jayasooriya, 2019). 6. Factors Incentives (Incentives) affect the Attitude of Use (Attitude Towards Online Advertising). The results of this study are under the research conducted by (Elliott P. S. 1997) and (Le, 2014) and (Gregorio, 2017). 7. The Attitude Towards Online Advertising factor significantly influences the Advertising Avoidance factor. The results of this study follow research conducted by Incentives (Kim and Han, 2014); (Taanika Arora, 2019). 8. The entertainment factor does not affect Advertising Avoidance. As a direct effect, the results of this study are not in line with the research results (Tito, 2019). The advertising avoidance carried out by the internet generation is not due to entertainment factors such as advertisements on YouTube, which are considered entertaining. 9. The informativeness factor does not affect Advertising Avoidance. The results of this study are not in line with research conducted by (Indah Dwi Pratama, 2016), which shows the significance of the relationship between advertising avoidance and the availability of information (Informativeness) about products that affect it. 10. Irritation factor has a significant influence on Advertising Avoidance. The results of this study support the results of previous studies conducted by (Titin Ekowati, 2020), (Elliott P. S. 1997) and (Le, 2014) and (Gregorio, 2017), which state that avoidance of advertising is significantly affected by irritation. ). 11. The credibility factor does not affect Advertising Avoidance. The results of this study do not follow research conducted by research (Wijayanto, 2018) which states that entertainment, informativeness, and credibility negatively influence advertising avoidance. 12. The personalization factor does not affect Advertising Avoidance. The results of this study do not follow the research conducted by (Adisasmita, 2021), where research states that avoidance of advertisements can be reduced or even avoided if the advertisements displayed are by the personality/personalization of each audience. 13. Incentives do not affect Advertising Avoidance. This study's results do not follow research conducted by (Christian Michael (2017), where the attractiveness of advertisements, such as incentives, can shape the public's desire to be willing to see advertisements. 14. The entertainment factor significantly influences Advertising Avoidance through Attitude Towards Online Advertising. The results of this study support the results of research conducted by (Arora, 2019) and (Ivan De Battista, 2021). 15. There is an indirect effect of Informativeness (Informativeness) on the avoidance of advertising (Advertising Avoidance) through the attitude of use (Attitude Towards Online Advertising). 727 This study's results align with the results of research conducted (Dalaal Maheasy, 2019), where the value of information in advertisements on social media has a positive and significant influence on the attitudes of young consumers. 16. Irritation factor does not affect Advertising Avoidance through Attitude Towards Online Advertising. The results of this study contradict the results of previous studies conducted by (Dalaal Mahmudah, 2019) and (Adisasmita, 2021), where the result is that the irritation value contained in advertisements on social media does not affect avoidance of advertisements through Attitude Towards Online Advertising. 17. The credibility factor does not affect Advertising Avoidance through Attitude Towards Online Advertising. The results of this study are under research that has been carried out by (Mikael, 2012), which states that credibility affects the attitude toward using advertising on social media and attitude of use affects avoidance of advertising in the Internet generation. 18. Personalization factor significantly influences Advertising Avoidance through Attitude Towards Online Advertising. The results of this study are under research conducted by (Adisasmita, 2021), which concluded that the avoidance of advertisements in the millennial generation could be reduced or even avoided if the advertisements displayed are by the personality/personalization of each audience. 19. Incentives have a significant influence on Advertising Avoidance through Attitude Towards Online Advertising. This study's results align with previous research conducted by (Ho, 2021), which concluded that incentives from advertising on social media significantly influenced attitudes toward online advertising. Based on these conclusions, the suggestions that researchers can convey are as follows: 1. For companies that will promote their products through advertisements on YouTube, the results of this study can be used as a consideration so that advertisements on YouTube are not avoided or skipped by the internet generation. 2. For academics, the results of this research can be used as a development of the theory of Computer-Mediated Communication regarding the behavior of avoiding advertising on YouTube by the internet generation. 3. For further researchers, the results of this study can be used as a reference regarding the factors that influence the avoidance of advertisements on YouTube in the internet generation. REFERENCES Ammarie, R. H., & Nurfebiaraning, S. (2019). Pengaruh Iklan Pop-Up Bukalapak Versi Pahlawan Pada Youtube Terhadap Sikap Khalayak. Jurnal Manajemen Komunikasi, 2(2), 78. https://doi.org/10.24198/jmk.v2i2.12871 Aramendia-Muneta, M. E., & Olarte-Pascual, C. (2019). 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