Exploring Predictors of Personalized Advertising Avoidance from the Perspective of Consumer Privacy and Control Agency

Exploring Predictors of Personalized Advertising Avoidance from the Perspective of Consumer Privacy and Control Agency | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Exploring Predictors of Personalized Advertising Avoidance from the Perspective of Consumer Privacy and Control Agency Yun Bo Chen, Yi Xiang Zhang, Jing Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5265590/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Drawing on advertising avoidance theory, control agency theory and privacy calculus theory, this study develops a model of factors affecting personalized advertising avoidance, which include consumers’ perceptions of privacy, encompassing their internal views, perception of the external privacy environment, perceived personalization of personalized advertising, and personal experiences of privacy. The study conducts an online survey, collecting 502 valid questionnaires. Following verification through structural equation modeling analysis, the research reveals that privacy concerns, privacy fatigue, the perceived effectiveness of privacy-protective technology, the perceived effectiveness of laws, perceived personalization, and prior negative experiences directly impact precision advertising avoidance. Perceived risk, plays a mediating role between privacy concern and advertising avoidance, between the perceived effectiveness of industry self-regulation and advertising avoidance, and between prior negative experiences and advertising avoidance. Lastly, this research proposes suggestions aimed at establishing a more secure network privacy protection environment and enhancing the appeal of personalized advertising content to reduce consumers’ avoidance and promote its future development. Business and commerce/Information systems and information technology Social science/Sociology Business and commerce/Business and management personalized advertising consumer’s privacy advertising avoidance control agency theory privacy fatigue Figures Figure 1 INTRODUCTION The Advancement of Precision Advertising In recent years, technologies such as 5G networks, big data, cloud computing, artificial intelligence, and machine learning have become pervasive in the realm of Internet advertising. These tools leverage extensive data on users’ Internet behavior and their tags, coupled with a sophisticated algorithm modeling system, to enable the creation of detailed profiles of target consumers. By modeling and analyzing users’ interests, needs, and environment, the advertising information they find interesting or relevant is regularly updated for personalized distribution, thereby enhancing the precision of advertising. Hence, precise advertising emerges as the result of the convergence of big data, algorithms, computing power, and advertising. The Pervasive Issue of Internet Privacy Breaches Frequent privacy breaches have fueled distrust among online users regarding network security. These breaches have also influenced public perception of algorithm technology and heightened awareness of privacy-related risks. The survey reveals that the public generally perceives digital interactions as insecure, including the potential for network system security and privacy breaches. Simultaneously, there is significant concern about the protection of personal information. The prevalence of privacy breaches has given rise to two distinct privacy perceptions among customers. On one hand, there is increased awareness of personal privacy. On the other hand, individuals feel increasingly powerless in the face of recurrent privacy breaches. Moreover, privacy protection measures have become more complex, leading to a sense of fatigue among consumers. This phenomenon is commonly referred to as privacy fatigue. Consumers’ Resistance to Precision Advertising The perception of privacy violations among Internet users has resulted in resistance to advertising. Precise advertising relies on tracking consumers’ online behavior and the utilization of their personal data. Given that most consumers are unaware of the specific operational models and rules governing the algorithmic mechanism behind this process, they begin to question whether their personal information and online privacy have been compromised, resulting in an avoidance of advertising. A Three-Pronged Approach to Protection In the current landscape of Internet privacy concerns, consumers’ private data has emerged as a crucial factor. Specifically, consumers’ data is now considered as valuable as oil or currency (Helberger et al. 2020 ). This data primarily comprises Internet users’ personal information and behavioral data, presenting a range of ethical issues and security vulnerabilities. In response to this development, many countries have introduced laws and regulations in recent years to fortify information data security and protect personal privacy, establishing robust institutional safeguards. The protection of privacy in precise advertising aligns closely with the supervision and regulation of the advertising industry. Consequently, industry self-regulation emerges as a primary governing force for ensuring the protection of privacy in precise advertising. It is anticipated that in the future, these regulatory efforts will also play a constructive role in advancing the protection of privacy in precise advertising. Influenced by legal and industry environments, major Internet platforms worldwide have implemented corresponding protection measures, ranging from research and development to the enhancement of privacy protection technologies, aiming to further safeguard user privacy. For example, in 2020, Apple took the lead by introducing the Identifier for Advertisers (IDFA) privacy policy. Another Internet giant, Google, has announced plans to gradually phase out third-party tracking cookies through its Privacy Sandbox technologies, aiming to prevent real-time tracking of users’ browsing activities on Google browsers. Research Objectives Given the current social environment and existing legislation, additional research is needed on the factors influencing the avoidance of precise advertising. Building on the background mentioned above, this study aims to examine the factors leading to consumers’ avoidance of precise advertising from the perspective of consumer privacy. From a macro perspective, or the customers’ perception of external factors, the study investigates whether the legal protection provided by the government, industry self-regulation measures, and platform privacy protection technologies can alleviate consumers’ avoidance of precise advertising. From a meso-level perspective, personalized precise advertising is a double-edged sword, and it is also worth exploring how this characteristic influences advertising avoidance. On a micro level, or consumers’ internal perception of their own privacy, they may hold different attitudes toward privacy in the current social privacy environment, specifically, either privacy concern or privacy fatigue. Coupled with past negative privacy experiences, it is imperative to investigate, through empirical research, how these three factors affect consumers’ avoidance of precise advertising. To date, there has been limited exploration of privacy factors in advertising avoidance research. Therefore, this paper combines advertising avoidance theory, control agency theory, and privacy calculus theory to explore the relationship between consumer privacy and advertising avoidance from four perspectives: consumers’ internal privacy perception, their perception of the external privacy environment, their perception of precise advertising, and their privacy experiences. Notably, control agency theory is seldom discussed in the existing literature on the impact of advertising, rendering this paper innovative in its approach. Privacy fatigue, as a new phenomenon of privacy perception, has garnered attention only since 2020. This paper aims to contribute meaningfully to the research on privacy fatigue by incorporating insights from the field of advertising. Meanwhile, among the relevant variables related to privacy, previous research has directed less attention toward privacy fatigue, perceived effectiveness of laws, industry self-regulation, and platform privacy technology. Therefore, this paper holds certain innovative value. Furthermore, the findings of this study can offer valuable recommendations for future legal regulations on consumer privacy, industry self-regulation, and platform privacy protection measures. LITERATURE REVIEW Advertising Avoidance Originating in the 1960s, the study of advertising avoidance has been significantly influenced by media development. The emergence of the Internet and other new media has also introduced new directions for research on advertising avoidance. When traditional media advertising was dominant, the primary factors affecting advertising avoidance were the audience’s personal attributes and advertising stimuli. Presently, with the advent of technologies such as big data, cloud computing, and artificial intelligence, advertising has become more precise and personalized. In this era, in addition to the factors influencing advertising avoidance in traditional media, two additional types emerge. The first pertains to the attributes of Internet advertising itself, including perceived personalization, perceived goal obstacles, and advertising clutter. The second involves factors related to emotions, such as prior negative experiences, privacy concerns, and time pressures. Consumers’ advertising avoidance currently comprises three dimensions: cognition, emotion, and behavior (Cho and Chen 2004). Factors such as privacy concerns, information sensitivity, and trust have become new research directions in the realm of advertising avoidance. Consumers are increasingly attentive to their privacy due to ongoing improvements in laws and regulations related to personal information protection. This study focuses on three variables highly relevant to consumer privacy: privacy concerns, perceived personalization, and prior negative experiences. Agency Theory In the process of collecting, using, and managing personal information, users may experience reduced privacy concern and disclose their information when they perceive a high level of privacy assurance (Xu et al. 2012 ). This phenomenon is referred to as control agency. Control agents can be categorized into two types: personal control and proxy control agents. Personal control involves individuals acting as controllers of their own privacy, whereas proxy control entails other entities, such as governments, industry regulatory agencies, businesses, or platforms serving as controllers and agents of privacy protection. The three primary approaches to information privacy protection typically include personal privacy protection, industry self-regulation, and government legislation. However, since agency entities typically possess abundant resources, individuals often turn to these entities to expand their control rights. Culnan ( 2003 ) and Xu et al. ( 2012 ) argue that, for privacy protection, the two key proxy control approaches are government regulation and industry self-regulation. Subsequently, considering the complementary features between the external and technological boundaries of the network, Gong ( 2019 ) adds that enterprise-oriented privacy policies could serve as another proxy control mechanism. At the level of personal control, factors such as privacy concern or risks can enhance individuals’ control and reflect consumers’ internal feelings toward the privacy environment. At the level of proxy control, three primary methods are involved in privacy protection: technological protection, industry self-regulation, and legal protection (Michelfelder 2001 ), with enterprises, industries, and governments, respectively, serving as the primary proxy entities. Privacy Calculus Theory The essence of privacy calculus theory lies in viewing individual privacy decision-making as a computational behavior and conducting a risk–benefit analysis from the user’s perspective (Dinev and Hart 2006 ). Perceived risk and perceived benefits are two crucial mediating variables in the privacy calculus model. Perceived risk often influences consumers’ final decision-making in the field of marketing and advertising. Particularly on social media or e-commerce platforms, perceived risk significantly impacts advertising avoidance and purchasing intentions. Studies show that the information consumers search for and review before making a decision is associated with perceived risk (Li and Huang 2009 ). Additionally, perceived risk is related to the manner or location where risks may occur (Hisrich et al. 1972 ). Regarding precise advertising on the Internet, perceived risk is even greater if consumers receive personalized advertising information (Um 2019 ). Undoubtedly, perceived risk has become a necessary and valuable variable for research in the context of precise advertising. Therefore, in this study, perceived risk and privacy concerns are selected as two variables to investigate their impact on precise advertising avoidance. RESEARCH MODEL AND HYPHOTHESIS DEVELOPMENT Privacy Concern Campbell (1977) defines privacy concerns as the subjective feelings individuals experience in response to specific privacy situations. It also refers to various aspects of awareness and perception related to specific information regarding privacy breaches and infringements (Rifon et al. 2005 ). In the context of the Internet, privacy concerns can be understood as individuals’ subjective evaluation of the disclosure of their personal information online. An individual’s level of concern about such disclosure is reflected within this context (Dinev and Hart 2006 ). Based on the widespread use of mobile Internet, scholars have proposed various dimensions of Mobile User Information Privacy Concern (MUIPC), including perceived surveillance, perceived intrusion, and the secondary use of personal information. These three dimensions are relevant to privacy concerns caused by precise advertising (Xu et al. 2012 ). Privacy Concern and Advertising Avoidance Personalized precise advertising often triggers privacy concerns in consumers. Baek and Morimoto (2020) verified that privacy concerns can heighten consumers’ vigilance toward advertising and its intentions, thereby having a direct impact on advertising avoidance. Other studies have also confirmed a positive correlation between privacy concerns and advertising avoidance. Consumers might not engage in thorough advertising avoidance if they lack a perception of privacy concern, even when they have sufficient knowledge for persuasion, considerations of the costs and benefits of online behavioral advertising, and self-efficacy (Ham 2016 ). Heightened privacy concern leads to enhanced advertising avoidance, and this influence may be further enhanced. Therefore, privacy concern is considered a major variable of consumers’ internal privacy perceptions in this study, and the following hypothesis is proposed: H1a: Privacy concern positively influences advertising avoidance. Privacy Concern and Perceived Risk Studies on the impact of perceived risk on privacy concern typically focus on e-commerce, with researchers often claiming that consumers’ privacy concerns might impede the development of e-commerce. Studies have found that consumers’ privacy concerns regarding the use of the Internet can diminish their trust and heighten their perception of risk (Fortes et al. 2017 ). This is particularly evident in online purchasing behavior, where consumers with higher levels of privacy concern may perceive risk even when dealing with well-known merchants (Van Slyke et al. 2006 ). Therefore, the following hypothesis is proposed: H1b: Privacy concern positively influences perceived risk. Privacy Concern and Privacy Protection Intention Numerous studies have revealed a positive correlation between consumers’ privacy concern and their intention to protect their privacy. Research conducted on information disclosure among American and German participants on Facebook revealed that privacy concern can positively impact users’ intention to safeguard their private information (Nosko et al. 2010 ). When encountering targeted advertising, users’ privacy concerns may lead to a corresponding intention to protect themselves. Therefore, the following hypothesis is proposed: H1c: Privacy concern positively influences privacy protection intention. Privacy Fatigue Due to the complexity and opacity of online privacy protection, privacy fatigue is fundamentally rooted in the passive approach consumers adopt toward privacy protection. Consumers face a significant information gap compared to other entities responsible for implementing protection measures, as the specific methods used to obtain their personal information and how that information is ultimately used are unclear. Consequently, two dimensions of privacy fatigue emerge—emotional exhaustion and cynicism—which lead to a reduction in individuals’ decision-making efforts related to privacy protection (Choi et al. 2018 ). Privacy Fatigue and Advertising Avoidance Advertising avoidance behavior becomes more likely when individuals perceive that excessive or irrelevant information weakens their sense of control over the outcomes of their online activities. Lengthy privacy regulations, laws, and other overwhelming information may appear complex and challenging to consumers. When attempting to digest and understand this information, consumers may experience cognitive overwhelm, diminishing their sense of control over privacy protection. Consequently, this may lead to privacy fatigue and, subsequently, the avoidance of precise advertising (Chen et al. 2021 ). Researchers have found that behaviors such as tolerance, neglect, or exit can result from users’ experience of privacy fatigue. When confronted with precise advertising, consumers may exhibit neglect behavior, avoiding advertising altogether if they are fatigued by the associated privacy concerns. Therefore, this study proposes the following hypothesis: H2a: Privacy fatigue positively influences advertising avoidance. Privacy Fatigue and Privacy Protection Intention Individuals are inclined to reduce their efforts in making privacy protection decisions when they experience fatigue resulting from privacy disclosure. Consequently, their intention to protect their privacy decreases as they feel a sense of powerlessness regarding privacy protection. This signifies that, rather than actively trying, consumers begin to compromise and feel resigned. Relevant research has indicated that users’ intention to protect their privacy tends to weaken when they exhibit signs of fatigue. Thus, it is reasonable to assert that privacy fatigue directly impacts individuals’ privacy concerns and protection intentions (Keith et al. 2014 ). Therefore, this study proposes the following hypothesis: H2b: Privacy fatigue negatively influences privacy protection intention. Prior Negative Experiences Prior Negative Experiences and Advertising Avoidance Individuals typically make decisions based on heuristic judgment, drawing on prior experiences. Unpleasant experiences can lead users to develop negative perceptions of advertising and avoid irrelevant advertisements on social media (Kelly et al. 2010 ). An empirical study targeting Chinese consumers further proved that the most significant influencing factor for personalized advertising avoidance is prior negative experiences (Song and Jiang 2017 ). If consumers have encountered negative experiences or harbor privacy concerns related to precise advertising, they are inclined to exhibit avoidance behavior when faced with precise advertising. Thus, this study proposes: H3a: Prior negative experiences positively influence advertising avoidance. Prior Negative Experiences and Perceived Risk The perceived risk level of individuals can be influenced by their personal environment, knowledge level, and life experiences. Consumers’ online experiences can heighten their perceived risk. Furthermore, negative experiences from external sources or personal experiences, such as being a victim of information infringement or identity theft (Chen 2020 ), can elevate perceived risk. Thus, this study proposes the following hypothesis: H3b: Prior negative experiences positively influence perceived risk. Prior Negative Experiences and Privacy Protection Intention Consumers base their assessment of their perceived ability to control their personal information on their own experiences or those of others, particularly in the context of privacy protection and infringements. Prior experiences are likely to influence their perceived control behavior regarding privacy protection. Prior negative experiences of similar privacy infringements may heighten consumers’ perception of privacy risks, potentially enhancing their intention to protect their privacy (Chen et al. 2016 ). Therefore, this study proposes the following hypothesis: H3c: Prior negative experiences positively influence privacy protection intention. Perceived Personalization Perceived Personalization and Advertising Avoidance Although advertisers typically favor targeted advertisements with precise effects, consumers’ attitudes toward such advertisements remain ambivalent (Johnson 2013 ). Studies have shown that when platforms track individual behaviors and generate personalized advertisements accordingly, audiences may feel uncomfortable and respond with advertising avoidance (Ruckenstein and Granroth 2019 ). Personalized advertising can make people feel a loss of choice, control, and ownership, leading to negative attitudes and reactions (Aguirre et al. 2015 ). However, some research has found that highly personalized advertisements can enable consumers to find products more easily, causing them to perceive the ads as useful, attractive, and motivational. Consequently, they encounter a diminished level of negative experience and exhibit a decreased inclination to avoid advertisements (Li and Huang 2016 ). Given the unclear impact of perceived personalization on advertising avoidance, the following research question is proposed: RQ1: Does perceived personalization significantly affect advertising avoidance? Perceived Personalization and Perceived Risk A positive correlation between personalization and consumers’ perception of risk has been established through empirical research on mobile apps (Kang and Namkung 2019 ). Additional research on location-based marketing has revealed that the positive correlation of personalized advertising with the perception of risk becomes more significant when it is exhibited more subtly (Xu et al. 2011 ). To achieve personalized advertising, advertisers employ extremely complex data technology, which may be exploited for the improper collection, use, or disclosure of personal information. This results in an increased risk of personal information disclosure, a circumstance that causes users’ feelings toward the risk of potential loss to escalate accordingly. Therefore, the following hypothesis is proposed: H4: Perceived personalization positively influences perceived risk. Perceived Personalization and Privacy Protection Intention In their analysis of extensive consumer data, Yuan and Niu ( 2020 ) revealed that precise advertising can heighten consumers’ concerns about privacy disclosure and their intention to self-protect due to their perceived personalization of precise advertising. However, the existing literature does not mention or validate the relationship between perceived personalization and privacy protection intention. Therefore, this study proposes the third research question: RQ2: Does perceived personalization affect privacy protection intention? Perceived Effectiveness of Privacy Protective Technology Undoubtedly, a critical battleground for precise advertising is the realm of Internet platforms. However, numerous recent disclosure incidents may have alerted these platforms to the importance of protecting users’ privacy. In response, major platforms have implemented various technologies that either restrict and track unauthorized software or anonymize users’ information to protect their private data. For instance, Apple has implemented default measures to disable IDFA tracking, and Google has introduced Privacy Sandbox (Zhang 2022 ). According to control agency theory, it is imperative to ensure that these platforms, acting as controlling agents, can provide privacy protection technologies for consumers. Perceived Effectiveness of Privacy Protective Technology and Advertising Avoidance Skovsgaard and Andersen ( 2020 ) identified a form of unintentional avoidance related to news. Due to technological reasons and concerns about the distribution of algorithmic recommendations, consumers may unconsciously avoid or miss news. Similarly, consumers may also exhibit avoidance due to concerns about algorithmic technology employed by precise advertising for content distribution. Thus, the technology employed by Internet platforms may be related to consumers’ advertising avoidance. To a certain extent, the effectiveness of platforms in utilizing privacy protection technologies may influence consumers’ perceptions of them and, consequently, their advertising avoidance. Therefore, this study proposes the following hypothesis: H5a: The perceived effectiveness of privacy protection technologies negatively influences advertising avoidance. Perceived Effectiveness of Privacy Protective Technologies and Perceived Risks For the owners of major Internet platforms, the technical aspects of privacy protection serve as important manifestations of their technological capabilities. These measures can instill a positive attitude toward privacy protection among users, effectively reducing consumers’ sense of privacy risk (Jutla et al. 2004 ). However, if consumers become aware of technological deficiencies, such as the inability to guarantee the security of their personal information, their trust in the platform’s technology may remain at its previous level while their perceptions of risk increase. Thus, the study proposes the following hypothesis: H5b: The perceived effectiveness of privacy technology negatively influences perceived risk. Perceived Effectiveness of Privacy Protection Technology and Privacy Protection Intention Research has indicated that users’ sense of control over their private information can be enhanced by privacy preference settings and privacy feedback technologies, leading to an increased willingness to disclose their private information. Other studies have suggested that privacy protection technologies positively influence the willingness of mobile e-commerce users to submit their personal information (Xiang 2018 ). In reference to users, the terms “privacy disclosure intention” and “personal information submission intention” refer to the voluntary act of proactively sharing personal information with others, which runs counter to privacy protection intention in terms of information control. Therefore, it is evident that consumers’ intention for privacy protection is diminished by the perceived effectiveness of privacy protection technologies. Therefore, the following research question is proposed: RQ3: Does the perceived effectiveness of privacy technology affect privacy protection intention? Perceived Effectiveness of Industry Self-Regulation Industry self-regulation plays a vital role in effectively penalizing and regulating companies that violate user privacy, thereby enhancing users’ confidence in the ability of companies to safeguard their personal information. When users perceive that functional third-party entities, such as service providers, industry self-regulation organizations, and government agencies have the ability to eliminate unethical behaviors and protect their privacy, it is referred to as the perceived effectiveness of industry self-regulation. Research has demonstrated a negative correlation between the perceived effectiveness of industry self-regulation and privacy concern. Culnan et al.’s (2003) study also affirmed that users may exert more control over their private information when informed about effective supervision from industry organizations, consequently reducing their awareness of privacy risks. It is evident that the perceived effectiveness of industry self-regulation negatively impacts consumers’ perceived risk and their privacy protection intention. Accordingly, the following research questions and hypotheses are proposed: RQ4: Is advertising avoidance influenced by the perceived effectiveness of industry self-regulation? H6a: The perceived effectiveness of industry self-regulation negatively influences perceived risk. H6b: The perceived effectiveness of industry self-regulation negatively influences privacy protection intention. Perceived Effectiveness of Laws When the government enacts laws and regulations to ensure the security of consumers’ privacy and penalizes those who unlawfully exploit users’ personal information, consumers’ perception of the effectiveness of laws and regulations is correspondingly enhanced, leading to a reduction in their concern about the privacy environment. In this context, the extent to which users trust the law’s protection of their privacy is defined as the perceived effectiveness of laws and regulations (Xu et al. 2012 ). This perception is likely to diminish users’ perceived risk, privacy protection intention, and advertising avoidance. However, current studies have only addressed its relationship with perceived risk and privacy protection intention to a limited extent. Therefore, the following hypothesis and research questions are proposed: H7: Advertising avoidance is negatively affected by the perceived effectiveness of laws. RQ5: Does the perceived effectiveness of laws influence perceived risk? RQ6: Does the perceived effectiveness of laws influence privacy protection intention? Perceived Risk and Advertising Avoidance Within privacy calculus theory, perceived risk is a necessary variable because users often conduct a risk–benefit analysis when assessing the risks associated with privacy disclosure. They tend to avoid advertisements that may bring uncertainty or even losses when they perceive a certain level of risk in terms of privacy, economy, or time associated with interacting with such advertisements. Precise advertising, based on user data and location, consistently raises privacy concerns and perceived risks. This, in turn, leads to consumers’ avoidance of advertising and a sense of intrusion, negatively impacting the effectiveness of the advertising (Boerman and Smit 2022 ). Therefore, this study proposes the following hypothesis: H8a: Perceived risk positively influences advertising avoidance. Perceived Risk and Privacy Protection Intention According to the protection motivation theory, individuals typically assess a situation before developing appropriate protection intentions based on the assessment of risks (Rogers, 1975 ). Consumers are likely to develop protection intentions and behaviors when they perceive a higher probability of potential risks to privacy and the potential harm resulting from those risks (Larose et al., 2005 ). Users’ positive attitudes toward privacy risks are reflected in their active privacy protection intention, demonstrating a positive relationship with perceived risks. Consumers are more inclined to show proactive intention toward online privacy protection when they become aware of greater risks associated with precise advertising (Youn ,2009). Therefore, the following hypothesis is proposed: H8b: Perceived risk positively influences privacy protection intention. Privacy Protection Intention and Advertising Avoidance In studies combining privacy protection and advertising, some researchers have considered advertising avoidance a method of privacy protection (Chen and Wen 2019 ). According to planned behavior theory, behaviors may be directly and positively influenced by behavioral intention. Therefore, advertising avoidance may be positively impacted by privacy protection intention. A study on online behavioral advertising confirmed that self-efficacy of privacy protection has a positive and predictive effect on precise advertising avoidance (Ham 2016 ). Therefore, the following hypothesis is proposed: H9: Privacy protection intention positively influences advertising avoidance. METHODOLOGY Sampling and Data Collection An online survey was conducted using snowball sampling from March to May 2023, with a total of 535 respondents participating in the study. After excluding incomplete and invalid questionnaires, 502 were included in the final analysis, resulting in an effective rate of 93.8%. Among the valid questionnaires, 170 were completed by male respondents and 332 were completed by female respondents. Sample characteristics are shown in Table 1. Table 1 Sample Demographics Items N Percentage(%) Gender Male 170 33.9 Female 332 66.1 Age 50 2 0.4 Research Measures This study employed seven-point Likert scales to measure 10 latent constructs: advertising avoidance, perceived risk, privacy protection intention, privacy concerns, privacy fatigue, perceived personalization, prior negative experiences, perceived effectiveness of privacy technology, perceived effectiveness of industry self-regulation, and perceived effectiveness of laws. To measure advertising avoidance, five items were adopted from two previous studies .Perceived risk used the scale proposed in Um’s ( 2019 ) study on cell phone advertising. Privacy protection intention was measured based on Liang’s ( 2010 ) items. Privacy concern used nine items from Xu (2012), with three each for “perceived surveillance,” “perceived intrusion,” and “secondary use of personal information.” Privacy fatigue was measured using the scale proposed by Choi (2018), incorporating two dimensions: emotional exhaustion and cynicism. Perceived personalization drew on Baek’s ( 2012 ) scale from his research on advertising avoidance. Prior negative experiences employed three items from Okazaki (2009). The perceived effectiveness of privacy protection technologies on Internet platforms used the scale from Wang’s (2020) study on the effects of privacy invasion on self-disclosure. The perceived effectiveness of industry self-regulation referred to the scale of Xu et al. ( 2011 ). The perceived effectiveness of laws was measured using three items from Qi and Li ( 2018 ). RESULTS We employed structural equation modeling (SEM) and used Amos 22 for data analysis to examine the proposed model. Measurement Model While the majority of the measures had been validated in prior studies, we conducted confirmatory factor analysis (CFA) to ensure that measures represented their latent constructs, assessing the reliability and validity of the latent variables in the model. Table 2–4 provides details on the items used for each construct, along with Cronbach’s alpha values, composite reliability (CR), and AVE scores. Table 2 Summary of Measures Measurement Item N Mean SD a CFL CR AVE Privacy concern .84 perceived surveillance 3 4.28 1.03 .85 .94 .81 .59 .74 .58 perceived intrusion 3 4.05 1.21 .83 .79 .75 .50 .69 .64 secondary use of personal information 3 4.48 1.08 .83 .80 .84 .65 .71 .89 Privacy fatigue .72 Emotional exhaustion 2 3.35 1.12 .75 .87 .81 .67 .77 Cynicism 3 3.10 1.02 .70 .87 .86 .67 .84 .75 Table 3 Summary of Measure(2) Measurement Item N Mean SD a CFL CR AVE Perceived effectiveness of privacy protective technologies .89 .91 .77 PEPPT1 1 3.01 1.14 .88 PEPPT2 1 2.63 1.32 .90 PEPPT3 1 2.73 1.27 .85 Perceived effectiveness of industry self-regulation .84 .90 .76 PEIS1 1 2.51 0.98 .85 PEIS2 1 2.33 1.01 .88 PEIS3 1 2.36 0.78 .88 Perceived Effectiveness of Laws .91 .94 .84 PEL1 1 2.89 0.85 .91 PEL2 1 2.68 0.89 .93 PEL3 1 2.77 1.11 .93 Perceived personalization .86 .81 .68 PP1 1 3.11 1.52 .80 PP2 1 3.00 1.31 .85 Table 4 Summary of Measure(3) Measurement Item N Mean SD a CFL CR AVE Prior negative experience .77 .86 .68 PNE1 1 3.89 1.23 .81 PNE2 1 3.68 0.78 .81 PNE3 1 3.98 1.12 .85 Perceived risk .78 .80 .58 PR1 1 4.11 0.95 .74 PR2 1 4.08 1.11 .77 PR3 1 4.02 1.25 .77 PR4 1 4.38 1.22 .81 Privacy protection intention .79 .76 .61 PPI1 1 4.20 1.11 .78 PPI2 1 4.15 1.42 .78 PPI3 1 4.16 1.23 .87 Advertising Avoidance .83 .89 .63 AA1 1 3.82 1.33 .74 AA2 1 3.66 1.24 .84 AA3 1 3.51 1.16 .85 AA4 1 4.02 1.09 .84 AA5 1 3.94 1.17 .68 The minimum value of factor loadings for the items used in each construct was 0.58, exceeding the threshold of 0.5 and demonstrating significance. Reliability measures, assessed through Cronbach’s alpha, ranged from .70 to .91, all meeting acceptable standards. Both CR scores (with a minimum value of 0.75) and AVE scores (with a minimum value of 0.50) indicated that the variables exhibited robust reliability and validity. Figure 1 presents the graphical model with the study’s constructs and their associated statistics. The standardized factors for privacy concern constructs ranged from .69 to .86, with secondary use of personal information (.69) being the lowest and perceived surveillance (.86) being the highest. These results suggest that, in terms of privacy concerns, perceived surveillance attracts the most attention. When considering privacy fatigue, cynicism (.92) emerged as a stronger factor than emotional exhaustion (.81). Figure 1 SEM model results. Structural Equation Model The SEM analysis demonstrated an acceptable goodness of fit: χ²=127.9, df = 76, χ²/ df = 1.67 (p < .01), GFI = .90, CFI = .94, NFI = .92, and RMSEA = .04. As indicated in Fig. 1, privacy concern was positively correlated with advertising avoidance (H1a), perceived risk (H1b), and privacy protection intention (H1c). Similarly, prior negative experiences also positively influenced these three variables (H3a, H3b, and H3c). However, privacy fatigue, perceived effectiveness of privacy-protective technologies, perceived effectiveness of laws, and perceived personalization only had a positive impact on advertising avoidance (H2a, H5a, H7, and RQ1). The perceived effectiveness of industry self-regulation did not directly influence advertising avoidance but did so indirectly through its impact on perceived risk (H6a) and privacy protection intention (H6b). Additionally, perceived risk and privacy protection intention both had a positive impact on advertising avoidance (H8a and H9). Lastly, the results revealed that perceived risk was positively correlated with privacy protection intention (H8b). Regarding the explained variance from predictors to outcomes, prior negative experiences exhibited the strongest predictive power (β = .38; p < .001), followed by perceived personalization (β=- .31; p < .001), perceived risk (β = .26; p < .001), and privacy fatigue (β = .14; p < .01). Perceived effectiveness of laws was the weakest predictor of advertising avoidance (β = .11; p < .01). CONCLUSION D iscussion Drawing on the theories of advertising avoidance, agency control, and privacy calculus, this study constructs a structural model to explore the factors influencing personalized advertising avoidance regarding consumers’ internal and external privacy perceptions. The research validates a total of 17 hypotheses and research questions. Consumer’s Internal Privacy Perception Regarding consumers’ internal privacy perception, this study proposes two contrasting attitudes; privacy concern and privacy fatigue. Despite their inherent conflict, these extreme attitudes toward privacy are prevalent in users’ online social behaviors in the era of big data. This research affirms that both privacy concern and privacy fatigue positively influence personalized advertising avoidance. In recent years, every part of consumers’ online experiences has been marked by the presence of personalized advertising, fueling ongoing discussions about online privacy. Frequent Internet privacy incidents have generated considerable concern among all sectors of society, including consumers. personalized advertising relies on the collection and analysis of consumers’ personal data for making personalized recommendations, raising suspicions about privacy breaches. Additionally, a growing number of reports on personalized advertising invasions has heightened consumers’ concerns about privacy protection, leading to an increasing aversion to personalized advertising, encompassing cognitive, emotional, and behavioral avoidance. This result aligns with prior research findings that link advertising avoidance with privacy concerns. Privacy fatigue represents another dimension of privacy perception. Research indicates that consumers’ avoidance of personalized advertising can be attributed to privacy fatigue. When measures for privacy protection become complex and cumbersome, consumers may perceive a loss of control, leading to increased privacy disclosures. This feeling of powerlessness can result in privacy fatigue and neglect of privacy protection, leading to an avoidance of information they perceive as negative. Due to the fatigue associated with privacy protection, consumers may instinctively avoid specific advertisements and disregard the marketing messages they convey, even if the content aligns with their interests and needs. Consumers’ Privacy Perception of the External Environment This study reveals that the avoidance of personalized advertising is directly influenced by the perceived effectiveness of privacy technologies, as well as the perceived effectiveness of laws and regulations. The hope is that governments can help mitigate the risk of privacy disclosures through the enactment of laws and regulations governing the collection, use, and management of personal data across various industries. Simultaneously, major Internet platforms strive to minimize or compensate for security vulnerabilities in processing users’ data through the implementation of privacy protection technologies, given their close association with big data utilization. As consumers become more informed about effective privacy technologies and legal regulations safeguarding their privacy, their inclination to avoid advertising decreases. While the avoidance of personalized advertising is not directly impacted by the perceived effectiveness of industry self-regulation, a negative impact is exerted through the mediating effect of perceived risk. This suggests that consumers are inclined to reduce their perceived risk and are less likely to avoid personalized advertising when they perceive the effectiveness of industry self-regulation measures. It is evident that the avoidance of personalized advertising can be influenced by the external privacy environment and the effectiveness of various control agents. However, average scores indicate that consumers currently hold a low perception of the effectiveness of the external privacy environment, reflecting its importance for the long-term development of personalized advertising. A favorable external privacy environment can only be created through collaborative efforts among governments, industries, platforms, and other key stakeholders. Through such efforts, consumers may genuinely recognize the effectiveness of privacy protection, subsequently diminishing their avoidance of personalized advertising. This, in turn, may provide more opportunities for the development of commercial advertising and digital marketing. Consumers’ Perception of personalized Advertising Attributes To some extent, consumers’ feelings and cognition toward the attributes of personalized advertising are reflected in their perceptions of personalization. Whether this perception has a positive impact on advertising avoidance remains inconclusive. Nevertheless, this study asserts that the overall impact of consumers’ perception of advertising personalization on ads avoidance is negative. personalized advertising can offer consumers more tailored recommendations when they have a higher perception of personalization in precise advertising, allowing their preferences and needs to be met. In other words, consumers may develop greater interest, pay more attention, and consequently make personalized advertising more effective, leading to a significant reduction in advertising avoidance. The Influence of Consumers’ Privacy Experience Consumers are likely to have encountered negative privacy experiences, significantly influencing their intention to avoid personalized advertising. The independent variable with a significant and direct impact on advertising avoidance is past negative experiences, which play a crucial role in consumers’ advertising avoidance behavior. In the realm of the Internet, consumers often encounter negative privacy experiences involving privacy disclosures, infringements, and fraudulent information. Due to the limited safeguards for data privacy and information security during the early days of the Internet, numerous vulnerabilities remained unaddressed, leading to a substantial number of Internet users experiencing privacy disclosures, infringements, and other related issues. These negative experiences, resulting from the collection of a large amount of personal information, have a detrimental effect on consumers, making them wary of personalized advertising and unwilling to receive such information. The Mediating Role of Perceived Risk In this study, perceived risk and privacy protection intention are conceptualized as two mediating variables within the hypothesized model. However, only perceived risk is observed to function as a mediator in the final structural model. Furthermore, consumers’ avoidance of personalized advertising is not influenced by privacy protection intention; instead, it is more influenced by consumers’ individual awareness of privacy risks. When consumers possess a high intention to protect their private information, they tend to directly avoid advertising when they perceive privacy risks. This suggests that their attitudes, emotions, or behaviors toward personalized advertising can be altered by these risks. In other words, for consumers, advertising avoidance is not considered a manifestation of privacy protection intention or behavior. The relationship between advertising avoidance and consumers’ perceptions of privacy is consistently mediated by perceived risk. Perceived risk also partially mediates the connection between advertising avoidance and negative experiences and fully mediates between advertising avoidance and the perceived effectiveness of industry self-regulation. Direct avoidance of personalized advertising occurs when consumers perceive a threat to their privacy, heightening their concern. Additionally, a higher perception of risk is formed alongside an increased level of privacy concern. Consumers are aware of the potential existence of privacy breaches, unknown risks, and unexpected losses associated with personalized advertising, recognizing that their protection of personal privacy may be negatively impacted. Consequently, consumers engage in more pronounced advertising avoidance as a strategy to minimize their exposure to personalized advertising and mitigate the misuse of their personal information. Consumers’ perception of the external privacy environment entails the perceived effectiveness of industry self-regulation. However, consumers’ avoidance of personalized advertising is not directly influenced by this perception. Instead, mediating the perceived risk ultimately determines the avoidance of advertising. Undoubtedly, consumers’ perceived privacy risk diminishes, and their willingness to accept personalized advertising increases, when they perceive effective industry self-regulation measures or mechanisms. Negative experiences enhance consumers’ perception of risk. These experiences occasionally serve as reminders of privacy concerns when consumers engage with personalized advertising. The prevalent mindset of “once bitten, twice shy” heightens their intention to avoid risk, leading to the avoidance of personalized advertising. The Influence of Consumer’s Privacy Perception on Privacy Protection Intention The results of the regression analysis demonstrate that consumers’ intention for privacy protection is directly influenced by their privacy concerns and indirectly affected when perceived risk acts as a mediating variable. Regarding consumers’ privacy concerns and their intention for privacy protection, perceived risk plays a partially mediating role. On one hand, consumers may directly develop an intention for protection due to their high regard for privacy, On the other hand, increased awareness of the threats posed by privacy issues may further influence perceived privacy risk. Overall, consumers are inclined to develop an intention to protect their privacy once they recognize the necessity of relying on their own efforts to protect their private information, thereby avoiding disclosures and misuse. Consumers’ intention for privacy protection is influenced both directly and indirectly (when mediated by perceived risk) by the perceived effectiveness of industry self-regulation. In various industries related to personalized advertising, consumers are often not fully cognizant of the perceived effectiveness of industry self-regulation measures. Despite this, consumers are prepared to develop a stronger intention to protect their privacy directly, especially when the perceived effectiveness of industry self-regulation is low. Furthermore, consumers may perceive certain risks in privacy protection, leading to an increased awareness of risks and a further strengthening of their intention for privacy protection. Negative experiences also have both direct and indirect positive effects on users’ intention to protect their privacy. For example, if consumers have negative privacy experiences, their concern about having similar experiences may make them more sensitive to privacy issues and fuel the development of privacy protection intention. This intention will be further strengthened through the mediating influence of perceived risk. P ractical Implications Based on the research conclusions, this paper proposes the following recommendations: Firstly, the creation of a secure online privacy protection environment should be prioritized to foster the advancement of personalized advertising. To establish a secure and reliable online environment for privacy protection, the collective efforts of multiple stakeholders are essential. According to the research findings, consumers’ avoidance of personalized advertising is directly influenced by the effectiveness of laws and regulations, industry self-regulation measures, and the privacy protection technologies employed by Internet advertising platforms. Therefore, for the future development of a conducive environment for personalized advertising, all stakeholders must consistently enhance their efforts to safeguard consumer privacy. Secondly, relevant legislation in the field of privacy protection should be improved to ensure a secure advertising environment. Research indicates that continued legislative efforts are crucial in the field of privacy protection due to consumers having a low perception of the effectiveness of existing laws and regulations. Although legal regulations are lagging behind somewhat compared to the rapid development of personalized advertising, proactive considerations for legislation are essential. Laws and regulations serve as the primary means of consumer privacy control. Their enhancement, at both the macro and micro levels, serves to motivate all social entities to prioritize consumer privacy protection. The optimization of the entire Internet advertising environment relies on the reinforcement of laws and regulations, fostering a safer environment that may reduce consumers’ avoidance of personalized advertising. Thirdly, the industry should implement industry norms and management practices to enhance the effectiveness of industry self-regulation. Platforms and consumer technologies related to privacy currently have a relatively low perception of the effectiveness of industry self-regulation compared to that of laws and regulations. This suggests that consumers may have to wait for an extended period before their expectations are met. The industry, playing a crucial role as an entity for state enforcement and corporate ethical self-supervision, wields both external enforcement power and internal flexibility. Therefore, industries must collaborate with the state and society to enhance the protection of consumer privacy. In the personalized advertising industry, this collaboration is crucial for enhancing privacy protection and the governance of data security. Furthermore, improving data security capabilities can prevent personalized advertising from becoming a vulnerability for network privacy disclosures. Fourthly, advertising platforms should enhance privacy protection technology and avoid an “absolute black boxing” approach. Currently, advertising platforms struggle to instill a strong perception of the effectiveness of privacy protection technologies among consumers. One reason for this is that the associated principles and operational mechanisms are challenging for most consumers to comprehend. When consumers struggle to make sense of complex and lengthy privacy policies, they may experience privacy fatigue. Therefore, platforms should implement measures, such as technological safeguards or policy guarantees, to protect users’ information and manage data. This ensures the provision of reliable solutions in data security technology and enhances platforms’ data security and risk management, creating a secure advertising environment. Simultaneously, the practice of absolute black boxing should be avoided by applying privacy protection technologies. Platforms have a responsibility to use technological means to obtain consumers’ consent regarding the usage and processing of their private data. Furthermore, the language used to disclose their purposes, methods, and scope of data processing should be as clear and concise as possible. This approach benefits consumers by enhancing their understanding of privacy protection technologies, reducing their privacy concerns and fatigue, minimizing advertising avoidance, and fostering a positive perception of the effectiveness of privacy technology protections. Lastly, there is a need to enhance the accuracy and creativity of advertising to generate personalized advertising that resonates with consumers. Studies have revealed that perceived personalization has a negative impact on advertising avoidance. This suggests that when an advertisement aligns with consumers’ interests and hobbies, its content is perceived as more personalized, thereby more effectively reducing consumers’ avoidance. Therefore, advertising platforms should prioritize enhancing creativity while focusing on refining precision matching through the use of computational power and algorithmic technology. Many platforms now have the capability to automatically generate creative content, including advertising videos, images, headlines, and copywriting. However, a lingering question remains regarding whether creative yet mass-produced content can be fresh and entertaining enough to consistently attract consumer favor. Before implementing personalized delivery, advertising platforms must carefully consider this aspect. Advertisers and creative agencies should devote attention to enhancing their creativity, as advertisements that combine creativity with personalized matching algorithmic technology are more likely to capture consumers’ attention. Limitations and Future Research Directions This study is not without limitations. A notable concern is the reliance on self-reported data regarding personalized advertising avoidance, lacking actual observations of avoidance behaviors. The validity of the self-reported data may be questioned, as it may not accurately reflect respondents’ authentic reactions. Therefore, conducting well-designed and controlled experimental studies would be beneficial, manipulating the nine factors identified in this study to directly measure real-time personalized advertising avoidance behaviors. Privacy fatigue, the perceived effectiveness of privacy-protective technologies, as well as industry self-regulation and laws, are included in this study as new elements, building on previous research. However, due to limitations in sample size and research methods, further investigation is needed to determine the universal applicability of these elements. Additionally, cultural and regulatory differences may impact the generalizability of the study’s findings. The manner in which users interact with media continues to evolve, and while this study aimed for a comprehensive approach, it does not claim to be exhaustive. Declarations Acknowledgements This research was funded by XXX Project and National Social Science Fund of XXX. Author Contributions Y: data curation and software;Y and J: formal analysis and project administration; Y:investigation and writing—original draft; Y and Y: methodology;Yand J: writing— review and editing. Ethical approval This research project has been reviewed and approved by the Ethical Review Committee at XXX on May 2 nd . Our research team confirms that all procedures involving human participants were duly performed in rigorous accordance with the ethical standards stipulated by the Declaration of Helsinki. This includes, but is not limited to, obtaining informed consent from all human participants, ensuring their privacy and confidentiality, and minimizing any potential harm or discomfort. The approval ID for this research project is XXX. The scope of approval covers all aspects of the research, including but not limited to participant recruitment, data collection, analysis, and dissemination of results. Any amendments or additional protocols related to the research have reviewed and approved by the Ethical Review Committee at XXX under the same approval number or anew one issued accordingly. Informed consent In compliance with ethical guidelines, instructor (the first author of this study) collected consent form from all the participants, which stating the details of the study’s objectives, their involvement, potential risks and benefits, and assurances of confidentiality and anonymity. By signing the consent form, participants were giving their consent for the following: participation in the study, which may include surveys; use of the data collected for research purposes, which may include analysis and publication; publication of the results of the study, with no personal identifiers used to maintain confidentiality; all information collected will be kept confidential, data will be stored securely and will only be accessible to the research team, participants’ identity will not be disclosed in any reports or publications resulting from the study. No vulnerable individuals and payment or incentives were involved in this study. Competing of Interest The authors declare no competing interests. Data Availability Statement The raw data supporting the conclusions of this article can be found in the supplementary file. References Aguirre, E., Mahr, D., Grewal, D., Ruyter, K.D., and Wetzels, M. (2015). 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J . Consum . Aff . 43, 389–418. doi: 10.1111/j.1745-6606.2009.01146.x Yuan, X., and Niu, J. (2020). Research on young people's weibo privacy management based on social algorithm recom-mendation—the serial mediation model of personalized information acceptance and privacy concerns. J . Mass . Commun . (12), 58–70. doi: 10.15897/j.cnki.cn51-1046/g2.2020.12.002 Zhang, Y. (2022). Privacy protection in the algorithmic era: paradoxical dilemmas, path directions and future challenges. Media Forum . 5, 4–10. doi: 10.3969/j.issn.2096-5079.2022.16.001 Additional Declarations No competing interests reported. Supplementary Files datacyb.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5265590","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":375668649,"identity":"cd035c1c-3f28-4caa-a8f1-b107e096781f","order_by":0,"name":"Yun Bo Chen","email":"","orcid":"","institution":"Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"Bo","lastName":"Chen","suffix":""},{"id":375668650,"identity":"cb375bcb-6c9e-4eff-a27d-3c641893c1d5","order_by":1,"name":"Yi Xiang Zhang","email":"","orcid":"","institution":"Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"Xiang","lastName":"Zhang","suffix":""},{"id":375668651,"identity":"474136dd-8f5a-445e-a13a-951b53b3c2d3","order_by":2,"name":"Jing Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYFCCgw0MjA0gBvMBA7DAAQIaeBBa2BKI1QIEEC08EB0EtdgzHm57+HXHYXlz/jUfigvbGOT4biQwfi7A77B2Y9kzhw13zni7wXhmG4Ox5I0EZukZ+LW0SUu2HWbccOPsBmPeNobEDTcS2Jh5iNBiv+HGmQcgLfVEaZH82HY4ccP5HgaQlgQDgloOAG1hbEtP3nCDzcCY55yE4cwzD5ul8Wlhn3H8meTPNmvbDecPPzPmKbOR5zuefPAzPi0MEgcYIM6QSGADRowEAyyacAN+YOT/ADMOMD/Ar3QUjIJRMApGKgAAVbtSucqgSp8AAAAASUVORK5CYII=","orcid":"","institution":"Jinan University","correspondingAuthor":true,"prefix":"","firstName":"Jing","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-10-15 05:50:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5265590/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5265590/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68994475,"identity":"f4fea1b7-bfdc-4923-adcc-ca8188d524f9","added_by":"auto","created_at":"2024-11-14 10:13:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":94787,"visible":true,"origin":"","legend":"\u003cp\u003eSEM model results.\u003c/p\u003e","description":"","filename":"Figure.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5265590/v1/036aea65855903dbcbe4d850.jpg"},{"id":71228601,"identity":"4e7d3ebf-70e1-44c2-8ef4-1df45a375cd9","added_by":"auto","created_at":"2024-12-12 10:24:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":916345,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5265590/v1/a537edf5-f84d-4839-bd75-68c2a8b66bb4.pdf"},{"id":68994476,"identity":"d5a33a8d-3708-496a-97ea-30712db22199","added_by":"auto","created_at":"2024-11-14 10:13:22","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":235989,"visible":true,"origin":"","legend":"","description":"","filename":"datacyb.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5265590/v1/44396f98358934628f0b32fc.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring Predictors of Personalized Advertising Avoidance from the Perspective of Consumer Privacy and Control Agency","fulltext":[{"header":"INTRODUCTION","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eThe Advancement of Precision Advertising\u003c/h2\u003e \u003cp\u003eIn recent years, technologies such as 5G networks, big data, cloud computing, artificial intelligence, and machine learning have become pervasive in the realm of Internet advertising. These tools leverage extensive data on users\u0026rsquo; Internet behavior and their tags, coupled with a sophisticated algorithm modeling system, to enable the creation of detailed profiles of target consumers. By modeling and analyzing users\u0026rsquo; interests, needs, and environment, the advertising information they find interesting or relevant is regularly updated for personalized distribution, thereby enhancing the precision of advertising. Hence, precise advertising emerges as the result of the convergence of big data, algorithms, computing power, and advertising.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eThe Pervasive Issue of Internet Privacy Breaches\u003c/h2\u003e \u003cp\u003eFrequent privacy breaches have fueled distrust among online users regarding network security. These breaches have also influenced public perception of algorithm technology and heightened awareness of privacy-related risks. The survey reveals that the public generally perceives digital interactions as insecure, including the potential for network system security and privacy breaches. Simultaneously, there is significant concern about the protection of personal information.\u003c/p\u003e \u003cp\u003eThe prevalence of privacy breaches has given rise to two distinct privacy perceptions among customers. On one hand, there is increased awareness of personal privacy. On the other hand, individuals feel increasingly powerless in the face of recurrent privacy breaches. Moreover, privacy protection measures have become more complex, leading to a sense of fatigue among consumers. This phenomenon is commonly referred to as privacy fatigue.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConsumers’ Resistance to Precision Advertising\u003c/h3\u003e\n\u003cp\u003eThe perception of privacy violations among Internet users has resulted in resistance to advertising. Precise advertising relies on tracking consumers\u0026rsquo; online behavior and the utilization of their personal data. Given that most consumers are unaware of the specific operational models and rules governing the algorithmic mechanism behind this process, they begin to question whether their personal information and online privacy have been compromised, resulting in an avoidance of advertising.\u003c/p\u003e\n\u003ch3\u003eA Three-Pronged Approach to Protection\u003c/h3\u003e\n\u003cp\u003eIn the current landscape of Internet privacy concerns, consumers\u0026rsquo; private data has emerged as a crucial factor. Specifically, consumers\u0026rsquo; data is now considered as valuable as oil or currency (Helberger et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This data primarily comprises Internet users\u0026rsquo; personal information and behavioral data, presenting a range of ethical issues and security vulnerabilities. In response to this development, many countries have introduced laws and regulations in recent years to fortify information data security and protect personal privacy, establishing robust institutional safeguards.\u003c/p\u003e \u003cp\u003eThe protection of privacy in precise advertising aligns closely with the supervision and regulation of the advertising industry. Consequently, industry self-regulation emerges as a primary governing force for ensuring the protection of privacy in precise advertising. It is anticipated that in the future, these regulatory efforts will also play a constructive role in advancing the protection of privacy in precise advertising.\u003c/p\u003e \u003cp\u003eInfluenced by legal and industry environments, major Internet platforms worldwide have implemented corresponding protection measures, ranging from research and development to the enhancement of privacy protection technologies, aiming to further safeguard user privacy. For example, in 2020, Apple took the lead by introducing the Identifier for Advertisers (IDFA) privacy policy. Another Internet giant, Google, has announced plans to gradually phase out third-party tracking cookies through its Privacy Sandbox technologies, aiming to prevent real-time tracking of users\u0026rsquo; browsing activities on Google browsers.\u003c/p\u003e\n\u003ch3\u003eResearch Objectives\u003c/h3\u003e\n\u003cp\u003eGiven the current social environment and existing legislation, additional research is needed on the factors influencing the avoidance of precise advertising. Building on the background mentioned above, this study aims to examine the factors leading to consumers\u0026rsquo; avoidance of precise advertising from the perspective of consumer privacy. From a macro perspective, or the customers\u0026rsquo; perception of external factors, the study investigates whether the legal protection provided by the government, industry self-regulation measures, and platform privacy protection technologies can alleviate consumers\u0026rsquo; avoidance of precise advertising. From a meso-level perspective, personalized precise advertising is a double-edged sword, and it is also worth exploring how this characteristic influences advertising avoidance. On a micro level, or consumers\u0026rsquo; internal perception of their own privacy, they may hold different attitudes toward privacy in the current social privacy environment, specifically, either privacy concern or privacy fatigue. Coupled with past negative privacy experiences, it is imperative to investigate, through empirical research, how these three factors affect consumers\u0026rsquo; avoidance of precise advertising.\u003c/p\u003e \u003cp\u003eTo date, there has been limited exploration of privacy factors in advertising avoidance research. Therefore, this paper combines advertising avoidance theory, control agency theory, and privacy calculus theory to explore the relationship between consumer privacy and advertising avoidance from four perspectives: consumers\u0026rsquo; internal privacy perception, their perception of the external privacy environment, their perception of precise advertising, and their privacy experiences. Notably, control agency theory is seldom discussed in the existing literature on the impact of advertising, rendering this paper innovative in its approach. Privacy fatigue, as a new phenomenon of privacy perception, has garnered attention only since 2020. This paper aims to contribute meaningfully to the research on privacy fatigue by incorporating insights from the field of advertising. Meanwhile, among the relevant variables related to privacy, previous research has directed less attention toward privacy fatigue, perceived effectiveness of laws, industry self-regulation, and platform privacy technology. Therefore, this paper holds certain innovative value.\u003c/p\u003e \u003cp\u003eFurthermore, the findings of this study can offer valuable recommendations for future legal regulations on consumer privacy, industry self-regulation, and platform privacy protection measures.\u003c/p\u003e"},{"header":"LITERATURE REVIEW","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eAdvertising Avoidance\u003c/h2\u003e\n \u003cp\u003eOriginating in the 1960s, the study of advertising avoidance has been significantly influenced by media development. The emergence of the Internet and other new media has also introduced new directions for research on advertising avoidance.\u003c/p\u003e\n \u003cp\u003eWhen traditional media advertising was dominant, the primary factors affecting advertising avoidance were the audience\u0026rsquo;s personal attributes and advertising stimuli. Presently, with the advent of technologies such as big data, cloud computing, and artificial intelligence, advertising has become more precise and personalized. In this era, in addition to the factors influencing advertising avoidance in traditional media, two additional types emerge. The first pertains to the attributes of Internet advertising itself, including perceived personalization, perceived goal obstacles, and advertising clutter. The second involves factors related to emotions, such as prior negative experiences, privacy concerns, and time pressures. Consumers\u0026rsquo; advertising avoidance currently comprises three dimensions: cognition, emotion, and behavior (Cho and Chen 2004).\u003c/p\u003e\n \u003cp\u003eFactors such as privacy concerns, information sensitivity, and trust have become new research directions in the realm of advertising avoidance. Consumers are increasingly attentive to their privacy due to ongoing improvements in laws and regulations related to personal information protection. This study focuses on three variables highly relevant to consumer privacy: privacy concerns, perceived personalization, and prior negative experiences.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eAgency Theory\u003c/h3\u003e\n\u003cp\u003eIn the process of collecting, using, and managing personal information, users may experience reduced privacy concern and disclose their information when they perceive a high level of privacy assurance (Xu et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). This phenomenon is referred to as control agency. Control agents can be categorized into two types: personal control and proxy control agents. Personal control involves individuals acting as controllers of their own privacy, whereas proxy control entails other entities, such as governments, industry regulatory agencies, businesses, or platforms serving as controllers and agents of privacy protection.\u003c/p\u003e\n\u003cp\u003eThe three primary approaches to information privacy protection typically include personal privacy protection, industry self-regulation, and government legislation. However, since agency entities typically possess abundant resources, individuals often turn to these entities to expand their control rights. Culnan (\u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e) and Xu et al. (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) argue that, for privacy protection, the two key proxy control approaches are government regulation and industry self-regulation. Subsequently, considering the complementary features between the external and technological boundaries of the network, Gong (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) adds that enterprise-oriented privacy policies could serve as another proxy control mechanism.\u003c/p\u003e\n\u003cp\u003eAt the level of personal control, factors such as privacy concern or risks can enhance individuals\u0026rsquo; control and reflect consumers\u0026rsquo; internal feelings toward the privacy environment. At the level of proxy control, three primary methods are involved in privacy protection: technological protection, industry self-regulation, and legal protection (Michelfelder \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e), with enterprises, industries, and governments, respectively, serving as the primary proxy entities.\u003c/p\u003e\n\u003ch3\u003ePrivacy Calculus Theory\u003c/h3\u003e\n\u003cp\u003eThe essence of privacy calculus theory lies in viewing individual privacy decision-making as a computational behavior and conducting a risk\u0026ndash;benefit analysis from the user\u0026rsquo;s perspective (Dinev and Hart \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). Perceived risk and perceived benefits are two crucial mediating variables in the privacy calculus model.\u003c/p\u003e\n\u003cp\u003ePerceived risk often influences consumers\u0026rsquo; final decision-making in the field of marketing and advertising. Particularly on social media or e-commerce platforms, perceived risk significantly impacts advertising avoidance and purchasing intentions. Studies show that the information consumers search for and review before making a decision is associated with perceived risk (Li and Huang \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). Additionally, perceived risk is related to the manner or location where risks may occur (Hisrich et al. \u003cspan class=\"CitationRef\"\u003e1972\u003c/span\u003e). Regarding precise advertising on the Internet, perceived risk is even greater if consumers receive personalized advertising information (Um \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eUndoubtedly, perceived risk has become a necessary and valuable variable for research in the context of precise advertising. Therefore, in this study, perceived risk and privacy concerns are selected as two variables to investigate their impact on precise advertising avoidance.\u003c/p\u003e"},{"header":"RESEARCH MODEL AND HYPHOTHESIS DEVELOPMENT","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003ePrivacy Concern\u003c/h2\u003e\n \u003cp\u003eCampbell (1977) defines privacy concerns as the subjective feelings individuals experience in response to specific privacy situations. It also refers to various aspects of awareness and perception related to specific information regarding privacy breaches and infringements (Rifon et al. \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e). In the context of the Internet, privacy concerns can be understood as individuals\u0026rsquo; subjective evaluation of the disclosure of their personal information online. An individual\u0026rsquo;s level of concern about such disclosure is reflected within this context (Dinev and Hart \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). Based on the widespread use of mobile Internet, scholars have proposed various dimensions of Mobile User Information Privacy Concern (MUIPC), including perceived surveillance, perceived intrusion, and the secondary use of personal information. These three dimensions are relevant to privacy concerns caused by precise advertising (Xu et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003ePrivacy Concern and Advertising Avoidance\u003c/h2\u003e\n \u003cp\u003ePersonalized precise advertising often triggers privacy concerns in consumers. Baek and Morimoto (2020) verified that privacy concerns can heighten consumers\u0026rsquo; vigilance toward advertising and its intentions, thereby having a direct impact on advertising avoidance. Other studies have also confirmed a positive correlation between privacy concerns and advertising avoidance. Consumers might not engage in thorough advertising avoidance if they lack a perception of privacy concern, even when they have sufficient knowledge for persuasion, considerations of the costs and benefits of online behavioral advertising, and self-efficacy (Ham \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Heightened privacy concern leads to enhanced advertising avoidance, and this influence may be further enhanced. Therefore, privacy concern is considered a major variable of consumers\u0026rsquo; internal privacy perceptions in this study, and the following hypothesis is proposed:\u003c/p\u003e\n \u003cp\u003eH1a: Privacy concern positively influences advertising avoidance.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003ePrivacy Concern and Perceived Risk\u003c/h2\u003e\n \u003cp\u003eStudies on the impact of perceived risk on privacy concern typically focus on e-commerce, with researchers often claiming that consumers\u0026rsquo; privacy concerns might impede the development of e-commerce. Studies have found that consumers\u0026rsquo; privacy concerns regarding the use of the Internet can diminish their trust and heighten their perception of risk (Fortes et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). This is particularly evident in online purchasing behavior, where consumers with higher levels of privacy concern may perceive risk even when dealing with well-known merchants (Van Slyke et al. \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). Therefore, the following hypothesis is proposed:\u003c/p\u003e\n \u003cp\u003eH1b: Privacy concern positively influences perceived risk.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003ePrivacy Concern and Privacy Protection Intention\u003c/h2\u003e\n \u003cp\u003eNumerous studies have revealed a positive correlation between consumers\u0026rsquo; privacy concern and their intention to protect their privacy. Research conducted on information disclosure among American and German participants on Facebook revealed that privacy concern can positively impact users\u0026rsquo; intention to safeguard their private information (Nosko et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). When encountering targeted advertising, users\u0026rsquo; privacy concerns may lead to a corresponding intention to protect themselves. Therefore, the following hypothesis is proposed:\u003c/p\u003e\n \u003cp\u003eH1c: Privacy concern positively influences privacy protection intention.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003ePrivacy Fatigue\u003c/h2\u003e\n \u003cp\u003eDue to the complexity and opacity of online privacy protection, privacy fatigue is fundamentally rooted in the passive approach consumers adopt toward privacy protection. Consumers face a significant information gap compared to other entities responsible for implementing protection measures, as the specific methods used to obtain their personal information and how that information is ultimately used are unclear. Consequently, two dimensions of privacy fatigue emerge\u0026mdash;emotional exhaustion and cynicism\u0026mdash;which lead to a reduction in individuals\u0026rsquo; decision-making efforts related to privacy protection (Choi et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003ePrivacy Fatigue and Advertising Avoidance\u003c/h2\u003e\n \u003cp\u003eAdvertising avoidance behavior becomes more likely when individuals perceive that excessive or irrelevant information weakens their sense of control over the outcomes of their online activities. Lengthy privacy regulations, laws, and other overwhelming information may appear complex and challenging to consumers. When attempting to digest and understand this information, consumers may experience cognitive overwhelm, diminishing their sense of control over privacy protection. Consequently, this may lead to privacy fatigue and, subsequently, the avoidance of precise advertising (Chen et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eResearchers have found that behaviors such as tolerance, neglect, or exit can result from users\u0026rsquo; experience of privacy fatigue. When confronted with precise advertising, consumers may exhibit neglect behavior, avoiding advertising altogether if they are fatigued by the associated privacy concerns. Therefore, this study proposes the following hypothesis:\u003c/p\u003e\n \u003cp\u003eH2a: Privacy fatigue positively influences advertising avoidance.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003ePrivacy Fatigue and Privacy Protection Intention\u003c/h2\u003e\n \u003cp\u003eIndividuals are inclined to reduce their efforts in making privacy protection decisions when they experience fatigue resulting from privacy disclosure. Consequently, their intention to protect their privacy decreases as they feel a sense of powerlessness regarding privacy protection. This signifies that, rather than actively trying, consumers begin to compromise and feel resigned. Relevant research has indicated that users\u0026rsquo; intention to protect their privacy tends to weaken when they exhibit signs of fatigue. Thus, it is reasonable to assert that privacy fatigue directly impacts individuals\u0026rsquo; privacy concerns and protection intentions (Keith et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Therefore, this study proposes the following hypothesis:\u003c/p\u003e\n \u003cp\u003eH2b: Privacy fatigue negatively influences privacy protection intention.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003ePrior Negative Experiences\u003c/h2\u003e\n \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\n \u003ch2\u003ePrior Negative Experiences and Advertising Avoidance\u003c/h2\u003e\n \u003cp\u003eIndividuals typically make decisions based on heuristic judgment, drawing on prior experiences. Unpleasant experiences can lead users to develop negative perceptions of advertising and avoid irrelevant advertisements on social media (Kelly et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). An empirical study targeting Chinese consumers further proved that the most significant influencing factor for personalized advertising avoidance is prior negative experiences (Song and Jiang \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). If consumers have encountered negative experiences or harbor privacy concerns related to precise advertising, they are inclined to exhibit avoidance behavior when faced with precise advertising. Thus, this study proposes:\u003c/p\u003e\n \u003cp\u003eH3a: Prior negative experiences positively influence advertising avoidance.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003ePrior Negative Experiences and Perceived Risk\u003c/h2\u003e\n \u003cp\u003eThe perceived risk level of individuals can be influenced by their personal environment, knowledge level, and life experiences. Consumers\u0026rsquo; online experiences can heighten their perceived risk. Furthermore, negative experiences from external sources or personal experiences, such as being a victim of information infringement or identity theft (Chen \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), can elevate perceived risk. Thus, this study proposes the following hypothesis:\u003c/p\u003e\n \u003cp\u003eH3b: Prior negative experiences positively influence perceived risk.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003ePrior Negative Experiences and Privacy Protection Intention\u003c/h2\u003e\n \u003cp\u003eConsumers base their assessment of their perceived ability to control their personal information on their own experiences or those of others, particularly in the context of privacy protection and infringements. Prior experiences are likely to influence their perceived control behavior regarding privacy protection. Prior negative experiences of similar privacy infringements may heighten consumers\u0026rsquo; perception of privacy risks, potentially enhancing their intention to protect their privacy (Chen et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, this study proposes the following hypothesis:\u003c/p\u003e\n \u003cp\u003eH3c: Prior negative experiences positively influence privacy protection intention.\u003c/p\u003e\n \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n \u003ch2\u003ePerceived Personalization\u003c/h2\u003e\n \u003cdiv id=\"Sec24\" class=\"Section4\"\u003e\n \u003ch2\u003ePerceived Personalization and Advertising Avoidance\u003c/h2\u003e\n \u003cp\u003eAlthough advertisers typically favor targeted advertisements with precise effects, consumers\u0026rsquo; attitudes toward such advertisements remain ambivalent (Johnson \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). Studies have shown that when platforms track individual behaviors and generate personalized advertisements accordingly, audiences may feel uncomfortable and respond with advertising avoidance (Ruckenstein and Granroth \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Personalized advertising can make people feel a loss of choice, control, and ownership, leading to negative attitudes and reactions (Aguirre et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, some research has found that highly personalized advertisements can enable consumers to find products more easily, causing them to perceive the ads as useful, attractive, and motivational. Consequently, they encounter a diminished level of negative experience and exhibit a decreased inclination to avoid advertisements (Li and Huang \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Given the unclear impact of perceived personalization on advertising avoidance, the following research question is proposed:\u003c/p\u003e\n \u003cp\u003eRQ1: Does perceived personalization significantly affect advertising avoidance?\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\n \u003ch2\u003ePerceived Personalization and Perceived Risk\u003c/h2\u003e\n \u003cp\u003eA positive correlation between personalization and consumers\u0026rsquo; perception of risk has been established through empirical research on mobile apps (Kang and Namkung \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additional research on location-based marketing has revealed that the positive correlation of personalized advertising with the perception of risk becomes more significant when it is exhibited more subtly (Xu et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). To achieve personalized advertising, advertisers employ extremely complex data technology, which may be exploited for the improper collection, use, or disclosure of personal information. This results in an increased risk of personal information disclosure, a circumstance that causes users\u0026rsquo; feelings toward the risk of potential loss to escalate accordingly. Therefore, the following hypothesis is proposed:\u003c/p\u003e\n \u003cp\u003eH4: Perceived personalization positively influences perceived risk.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\n \u003ch2\u003ePerceived Personalization and Privacy Protection Intention\u003c/h2\u003e\n \u003cp\u003eIn their analysis of extensive consumer data, Yuan and Niu (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) revealed that precise advertising can heighten consumers\u0026rsquo; concerns about privacy disclosure and their intention to self-protect due to their perceived personalization of precise advertising. However, the existing literature does not mention or validate the relationship between perceived personalization and privacy protection intention. Therefore, this study proposes the third research question:\u003c/p\u003e\n \u003cp\u003eRQ2: Does perceived personalization affect privacy protection intention?\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\n \u003ch2\u003ePerceived Effectiveness of Privacy Protective Technology\u003c/h2\u003e\n \u003cp\u003eUndoubtedly, a critical battleground for precise advertising is the realm of Internet platforms. However, numerous recent disclosure incidents may have alerted these platforms to the importance of protecting users\u0026rsquo; privacy. In response, major platforms have implemented various technologies that either restrict and track unauthorized software or anonymize users\u0026rsquo; information to protect their private data. For instance, Apple has implemented default measures to disable IDFA tracking, and Google has introduced Privacy Sandbox (Zhang \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). According to control agency theory, it is imperative to ensure that these platforms, acting as controlling agents, can provide privacy protection technologies for consumers.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\n \u003ch2\u003ePerceived Effectiveness of Privacy Protective Technology and Advertising Avoidance\u003c/h2\u003e\n \u003cp\u003eSkovsgaard and Andersen (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) identified a form of unintentional avoidance related to news. Due to technological reasons and concerns about the distribution of algorithmic recommendations, consumers may unconsciously avoid or miss news. Similarly, consumers may also exhibit avoidance due to concerns about algorithmic technology employed by precise advertising for content distribution. Thus, the technology employed by Internet platforms may be related to consumers\u0026rsquo; advertising avoidance. To a certain extent, the effectiveness of platforms in utilizing privacy protection technologies may influence consumers\u0026rsquo; perceptions of them and, consequently, their advertising avoidance. Therefore, this study proposes the following hypothesis:\u003c/p\u003e\n \u003cp\u003eH5a: The perceived effectiveness of privacy protection technologies negatively influences advertising avoidance.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\n \u003ch2\u003ePerceived Effectiveness of Privacy Protective Technologies and Perceived Risks\u003c/h2\u003e\n \u003cp\u003eFor the owners of major Internet platforms, the technical aspects of privacy protection serve as important manifestations of their technological capabilities. These measures can instill a positive attitude toward privacy protection among users, effectively reducing consumers\u0026rsquo; sense of privacy risk (Jutla et al. \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e). However, if consumers become aware of technological deficiencies, such as the inability to guarantee the security of their personal information, their trust in the platform\u0026rsquo;s technology may remain at its previous level while their perceptions of risk increase. Thus, the study proposes the following hypothesis:\u003c/p\u003e\n \u003cp\u003eH5b: The perceived effectiveness of privacy technology negatively influences perceived risk.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003ePerceived Effectiveness of Privacy Protection Technology and Privacy Protection Intention\u003c/h3\u003e\n\u003cp\u003eResearch has indicated that users\u0026rsquo; sense of control over their private information can be enhanced by privacy preference settings and privacy feedback technologies, leading to an increased willingness to disclose their private information. Other studies have suggested that privacy protection technologies positively influence the willingness of mobile e-commerce users to submit their personal information (Xiang \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). In reference to users, the terms \u0026ldquo;privacy disclosure intention\u0026rdquo; and \u0026ldquo;personal information submission intention\u0026rdquo; refer to the voluntary act of proactively sharing personal information with others, which runs counter to privacy protection intention in terms of information control. Therefore, it is evident that consumers\u0026rsquo; intention for privacy protection is diminished by the perceived effectiveness of privacy protection technologies. Therefore, the following research question is proposed:\u003c/p\u003e\n\u003cp\u003eRQ3: Does the perceived effectiveness of privacy technology affect privacy protection intention?\u003c/p\u003e\n\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\n \u003ch2\u003ePerceived Effectiveness of Industry Self-Regulation\u003c/h2\u003e\n \u003cp\u003eIndustry self-regulation plays a vital role in effectively penalizing and regulating companies that violate user privacy, thereby enhancing users\u0026rsquo; confidence in the ability of companies to safeguard their personal information. When users perceive that functional third-party entities, such as service providers, industry self-regulation organizations, and government agencies have the ability to eliminate unethical behaviors and protect their privacy, it is referred to as the perceived effectiveness of industry self-regulation. Research has demonstrated a negative correlation between the perceived effectiveness of industry self-regulation and privacy concern. Culnan et al.\u0026rsquo;s (2003) study also affirmed that users may exert more control over their private information when informed about effective supervision from industry organizations, consequently reducing their awareness of privacy risks. It is evident that the perceived effectiveness of industry self-regulation negatively impacts consumers\u0026rsquo; perceived risk and their privacy protection intention. Accordingly, the following research questions and hypotheses are proposed:\u003c/p\u003e\n \u003cp\u003eRQ4: Is advertising avoidance influenced by the perceived effectiveness of industry self-regulation?\u003c/p\u003e\n \u003cp\u003eH6a: The perceived effectiveness of industry self-regulation negatively influences perceived risk.\u003c/p\u003e\n \u003cp\u003eH6b: The perceived effectiveness of industry self-regulation negatively influences privacy protection intention.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\n \u003ch2\u003ePerceived Effectiveness of Laws\u003c/h2\u003e\n \u003cp\u003eWhen the government enacts laws and regulations to ensure the security of consumers\u0026rsquo; privacy and penalizes those who unlawfully exploit users\u0026rsquo; personal information, consumers\u0026rsquo; perception of the effectiveness of laws and regulations is correspondingly enhanced, leading to a reduction in their concern about the privacy environment. In this context, the extent to which users trust the law\u0026rsquo;s protection of their privacy is defined as the perceived effectiveness of laws and regulations (Xu et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). This perception is likely to diminish users\u0026rsquo; perceived risk, privacy protection intention, and advertising avoidance. However, current studies have only addressed its relationship with perceived risk and privacy protection intention to a limited extent. Therefore, the following hypothesis and research questions are proposed:\u003c/p\u003e\n \u003cp\u003eH7: Advertising avoidance is negatively affected by the perceived effectiveness of laws.\u003c/p\u003e\n \u003cp\u003eRQ5: Does the perceived effectiveness of laws influence perceived risk?\u003c/p\u003e\n \u003cp\u003eRQ6: Does the perceived effectiveness of laws influence privacy protection intention?\u003c/p\u003e\n \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e\n \u003ch2\u003ePerceived Risk and Advertising Avoidance\u003c/h2\u003e\n \u003cp\u003eWithin privacy calculus theory, perceived risk is a necessary variable because users often conduct a risk\u0026ndash;benefit analysis when assessing the risks associated with privacy disclosure. They tend to avoid advertisements that may bring uncertainty or even losses when they perceive a certain level of risk in terms of privacy, economy, or time associated with interacting with such advertisements. Precise advertising, based on user data and location, consistently raises privacy concerns and perceived risks. This, in turn, leads to consumers\u0026rsquo; avoidance of advertising and a sense of intrusion, negatively impacting the effectiveness of the advertising (Boerman and Smit \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, this study proposes the following hypothesis:\u003c/p\u003e\n \u003cp\u003eH8a: Perceived risk positively influences advertising avoidance.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e\n \u003ch2\u003ePerceived Risk and Privacy Protection Intention\u003c/h2\u003e\n \u003cp\u003eAccording to the protection motivation theory, individuals typically assess a situation before developing appropriate protection intentions based on the assessment of risks (Rogers, \u003cspan class=\"CitationRef\"\u003e1975\u003c/span\u003e). Consumers are likely to develop protection intentions and behaviors when they perceive a higher probability of potential risks to privacy and the potential harm resulting from those risks (Larose et al., \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e). Users\u0026rsquo; positive attitudes toward privacy risks are reflected in their active privacy protection intention, demonstrating a positive relationship with perceived risks. Consumers are more inclined to show proactive intention toward online privacy protection when they become aware of greater risks associated with precise advertising (Youn ,2009). Therefore, the following hypothesis is proposed:\u003c/p\u003e\n \u003cp\u003eH8b: Perceived risk positively influences privacy protection intention.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003ePrivacy Protection Intention and Advertising Avoidance\u003c/h3\u003e\n\u003cp\u003eIn studies combining privacy protection and advertising, some researchers have considered advertising avoidance a method of privacy protection (Chen and Wen \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). According to planned behavior theory, behaviors may be directly and positively influenced by behavioral intention. Therefore, advertising avoidance may be positively impacted by privacy protection intention. A study on online behavioral advertising confirmed that self-efficacy of privacy protection has a positive and predictive effect on precise advertising avoidance (Ham \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, the following hypothesis is proposed:\u003c/p\u003e\n\u003cp\u003eH9: Privacy protection intention positively influences advertising avoidance.\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003eSampling and Data Collection\u003c/h2\u003e \u003cp\u003eAn online survey was conducted using snowball sampling from March to May 2023, with a total of 535 respondents participating in the study. After excluding incomplete and invalid questionnaires, 502 were included in the final analysis, resulting in an effective rate of 93.8%. Among the valid questionnaires, 170 were completed by male respondents and 332 were completed by female respondents. Sample characteristics are shown in Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eSample Demographics\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec38\" class=\"Section3\"\u003e \u003ch2\u003eResearch Measures\u003c/h2\u003e \u003cp\u003eThis study employed seven-point Likert scales to measure 10 latent constructs: advertising avoidance, perceived risk, privacy protection intention, privacy concerns, privacy fatigue, perceived personalization, prior negative experiences, perceived effectiveness of privacy technology, perceived effectiveness of industry self-regulation, and perceived effectiveness of laws.\u003c/p\u003e \u003cp\u003eTo measure advertising avoidance, five items were adopted from two previous studies .Perceived risk used the scale proposed in Um\u0026rsquo;s (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) study on cell phone advertising. Privacy protection intention was measured based on Liang\u0026rsquo;s (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) items. Privacy concern used nine items from Xu (2012), with three each for \u0026ldquo;perceived surveillance,\u0026rdquo; \u0026ldquo;perceived intrusion,\u0026rdquo; and \u0026ldquo;secondary use of personal information.\u0026rdquo; Privacy fatigue was measured using the scale proposed by Choi (2018), incorporating two dimensions: emotional exhaustion and cynicism. Perceived personalization drew on Baek\u0026rsquo;s (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) scale from his research on advertising avoidance. Prior negative experiences employed three items from Okazaki (2009). The perceived effectiveness of privacy protection technologies on Internet platforms used the scale from Wang\u0026rsquo;s (2020) study on the effects of privacy invasion on self-disclosure. The perceived effectiveness of industry self-regulation referred to the scale of Xu et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The perceived effectiveness of laws was measured using three items from Qi and Li (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eWe employed structural equation modeling (SEM) and used Amos 22 for data analysis to examine the proposed model.\u003c/p\u003e \u003cdiv id=\"Sec40\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement Model\u003c/h2\u003e \u003cp\u003eWhile the majority of the measures had been validated in prior studies, we conducted confirmatory factor analysis (CFA) to ensure that measures represented their latent constructs, assessing the reliability and validity of the latent variables in the model. Table\u0026nbsp;2\u0026ndash;4 provides details on the items used for each construct, along with Cronbach\u0026rsquo;s alpha values, composite reliability (CR), and AVE scores.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eSummary of Measures\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasurement Item\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCFL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrivacy concern\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eperceived surveillance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eperceived intrusion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003esecondary use of personal information\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrivacy fatigue\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eEmotional exhaustion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eCynicism\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eSummary of Measure(2)\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasurement Item\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCFL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived effectiveness of privacy protective technologies\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEPPT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEPPT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEPPT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived effectiveness of industry self-regulation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEIS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEIS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEIS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived Effectiveness of Laws\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived personalization\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eSummary of Measure(3)\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasurement Item\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCFL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrior negative experience\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived risk\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrivacy protection intention\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPI3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdvertising Avoidance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe minimum value of factor loadings for the items used in each construct was 0.58, exceeding the threshold of 0.5 and demonstrating significance. Reliability measures, assessed through Cronbach\u0026rsquo;s alpha, ranged from .70 to .91, all meeting acceptable standards. Both CR scores (with a minimum value of 0.75) and AVE scores (with a minimum value of 0.50) indicated that the variables exhibited robust reliability and validity.\u003c/p\u003e \u003cp\u003eFigure 1 presents the graphical model with the study\u0026rsquo;s constructs and their associated statistics. The standardized factors for privacy concern constructs ranged from .69 to .86, with secondary use of personal information (.69) being the lowest and perceived surveillance (.86) being the highest. These results suggest that, in terms of privacy concerns, perceived surveillance attracts the most attention. When considering privacy fatigue, cynicism (.92) emerged as a stronger factor than emotional exhaustion (.81).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;1\u003c/b\u003e SEM model results.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStructural Equation Model\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe SEM analysis demonstrated an acceptable goodness of fit: χ\u0026sup2;=127.9, df\u0026thinsp;=\u0026thinsp;76, χ\u0026sup2;/ df\u0026thinsp;=\u0026thinsp;1.67 (p\u0026thinsp;\u0026lt;\u0026thinsp;.01), GFI\u0026thinsp;=\u0026thinsp;.90, CFI\u0026thinsp;=\u0026thinsp;.94, NFI\u0026thinsp;=\u0026thinsp;.92, and RMSEA\u0026thinsp;=\u0026thinsp;.04.\u003c/p\u003e \u003cp\u003eAs indicated in Fig.\u0026nbsp;1, privacy concern was positively correlated with advertising avoidance (H1a), perceived risk (H1b), and privacy protection intention (H1c). Similarly, prior negative experiences also positively influenced these three variables (H3a, H3b, and H3c). However, privacy fatigue, perceived effectiveness of privacy-protective technologies, perceived effectiveness of laws, and perceived personalization only had a positive impact on advertising avoidance (H2a, H5a, H7, and RQ1). The perceived effectiveness of industry self-regulation did not directly influence advertising avoidance but did so indirectly through its impact on perceived risk (H6a) and privacy protection intention (H6b). Additionally, perceived risk and privacy protection intention both had a positive impact on advertising avoidance (H8a and H9). Lastly, the results revealed that perceived risk was positively correlated with privacy protection intention (H8b).\u003c/p\u003e \u003cp\u003eRegarding the explained variance from predictors to outcomes, prior negative experiences exhibited the strongest predictive power (β\u0026thinsp;=\u0026thinsp;.38; p\u0026thinsp;\u0026lt;\u0026thinsp;.001), followed by perceived personalization (β=- .31; p\u0026thinsp;\u0026lt;\u0026thinsp;.001), perceived risk (β\u0026thinsp;=\u0026thinsp;.26; p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and privacy fatigue (β\u0026thinsp;=\u0026thinsp;.14; p\u0026thinsp;\u0026lt;\u0026thinsp;.01). Perceived effectiveness of laws was the weakest predictor of advertising avoidance (β\u0026thinsp;=\u0026thinsp;.11; p\u0026thinsp;\u0026lt;\u0026thinsp;.01).\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003ch2\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003cstrong\u003eiscussion\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eDrawing on the theories of advertising avoidance, agency control, and privacy calculus, this study constructs a structural model to explore the factors influencing\u0026nbsp;personalized\u0026nbsp;advertising avoidance regarding consumers\u0026rsquo; internal and external privacy perceptions. The research validates a total of 17 hypotheses and research questions.\u003c/p\u003e\n\u003ch3\u003eConsumer\u0026rsquo;s Internal Privacy Perception\u003c/h3\u003e\n\u003cp\u003eRegarding consumers\u0026rsquo; internal privacy perception, this study proposes two contrasting attitudes; privacy concern and privacy fatigue. Despite their inherent conflict, these extreme attitudes toward privacy are prevalent in users\u0026rsquo; online social behaviors in the era of big data. This research affirms that both privacy concern and privacy fatigue positively influence\u0026nbsp;personalized\u0026nbsp;advertising avoidance.\u003c/p\u003e\n\u003cp\u003eIn recent years, every part of consumers\u0026rsquo; online experiences has been marked by the presence of\u0026nbsp;personalized\u0026nbsp;advertising, fueling ongoing discussions about online privacy. Frequent Internet privacy incidents have generated considerable concern among all sectors of society, including consumers.\u0026nbsp;personalized\u0026nbsp;advertising relies on the collection and analysis of consumers\u0026rsquo; personal data for making personalized recommendations, raising suspicions about privacy breaches. Additionally, a growing number of reports on personalized advertising invasions has heightened consumers\u0026rsquo; concerns about privacy protection, leading to an increasing aversion to\u0026nbsp;personalized\u0026nbsp;advertising, encompassing cognitive, emotional, and behavioral avoidance. This result aligns with prior research findings that link advertising avoidance with privacy concerns.\u003c/p\u003e\n\u003cp\u003ePrivacy fatigue represents another dimension of privacy perception. Research indicates that consumers\u0026rsquo; avoidance of\u0026nbsp;personalized\u0026nbsp;advertising can be attributed to privacy fatigue. When measures for privacy protection become complex and cumbersome, consumers may perceive a loss of control, leading to increased privacy disclosures. This feeling of powerlessness can result in privacy fatigue and neglect of privacy protection, leading to an avoidance of information they perceive as negative. Due to the fatigue associated with privacy protection, consumers may instinctively avoid specific advertisements and disregard the marketing messages they convey, even if the content aligns with their interests and needs.\u003c/p\u003e\n\u003ch3\u003eConsumers\u0026rsquo; Privacy Perception of the External Environment\u003c/h3\u003e\n\u003cp\u003eThis study reveals that the avoidance of\u0026nbsp;personalized\u0026nbsp;advertising is directly influenced by the perceived effectiveness of privacy technologies, as well as the perceived effectiveness of laws and regulations. The hope is that governments can help mitigate the risk of privacy disclosures through the enactment of laws and regulations governing the collection, use, and management of personal data across various industries. Simultaneously, major Internet platforms strive to minimize or compensate for security vulnerabilities in processing users\u0026rsquo; data through the implementation of privacy protection technologies, given their close association with big data utilization. As consumers become more informed about effective privacy technologies and legal regulations safeguarding their privacy, their inclination to avoid advertising decreases. While the avoidance of\u0026nbsp;personalized\u0026nbsp;advertising is not directly impacted by the perceived effectiveness of industry self-regulation, a negative impact is exerted through the mediating effect of perceived risk. This suggests that consumers are inclined to reduce their perceived risk and are less likely to avoid\u0026nbsp;personalized\u0026nbsp;advertising when they perceive the effectiveness of industry self-regulation measures.\u003c/p\u003e\n\u003cp\u003eIt is evident that the avoidance of\u0026nbsp;personalized\u0026nbsp;advertising can be influenced by the external privacy environment and the effectiveness of various control agents. However, average scores indicate that consumers currently hold a low perception of the effectiveness of the external privacy environment, reflecting its importance for the long-term development of\u0026nbsp;personalized\u0026nbsp;advertising. A favorable external privacy environment can only be created through collaborative efforts among governments, industries, platforms, and other key stakeholders. Through such efforts, consumers may genuinely recognize the effectiveness of privacy protection, subsequently diminishing their avoidance of\u0026nbsp;personalized\u0026nbsp;advertising. This, in turn, may provide more opportunities for the development of commercial advertising and digital marketing.\u003c/p\u003e\n\u003ch3\u003eConsumers\u0026rsquo; Perception of\u0026nbsp;personalized\u0026nbsp;Advertising Attributes\u003c/h3\u003e\n\u003cp\u003eTo some extent, consumers\u0026rsquo; feelings and cognition toward the attributes of\u0026nbsp;personalized\u0026nbsp;advertising are reflected in their perceptions of personalization. Whether this perception has a positive impact on advertising\u0026nbsp;avoidance\u0026nbsp;remains inconclusive. Nevertheless, this study asserts that the overall impact of consumers\u0026rsquo; perception of advertising personalization\u0026nbsp;on ads avoidance\u0026nbsp;is negative.\u0026nbsp;personalized\u0026nbsp;advertising can offer consumers more tailored recommendations when they have a higher perception of personalization in precise advertising, allowing their preferences and needs to be met. In other words, consumers may develop greater interest, pay more attention, and consequently make\u0026nbsp;personalized\u0026nbsp;advertising more effective, leading to a significant reduction in advertising avoidance.\u003c/p\u003e\n\u003ch3\u003eThe Influence of Consumers\u0026rsquo; Privacy Experience\u003c/h3\u003e\n\u003cp\u003eConsumers are likely to have encountered negative privacy experiences, significantly influencing their intention to avoid\u0026nbsp;personalized\u0026nbsp;advertising. The independent variable with a significant and direct impact on advertising avoidance is past negative experiences, which play a crucial role in consumers\u0026rsquo; advertising avoidance behavior.\u003c/p\u003e\n\u003cp\u003eIn the realm of the Internet, consumers often encounter negative privacy experiences involving privacy disclosures, infringements, and fraudulent information. Due to the limited safeguards for data privacy and information security during the early days of the Internet, numerous vulnerabilities remained unaddressed, leading to a substantial number of Internet users experiencing privacy disclosures, infringements, and other related issues. These negative experiences, resulting from the collection of a large amount of personal information, have a detrimental effect on consumers, making them wary of\u0026nbsp;personalized\u0026nbsp;advertising and unwilling to receive such information.\u003c/p\u003e\n\u003ch3\u003eThe Mediating Role of Perceived Risk\u003c/h3\u003e\n\u003cp\u003eIn this study, perceived risk and privacy protection intention are conceptualized as two mediating variables within the hypothesized model. However, only perceived risk is observed to function as a mediator in the final structural model. Furthermore, consumers\u0026rsquo; avoidance of\u0026nbsp;personalized\u0026nbsp;advertising is not influenced by privacy protection intention; instead, it is more influenced by consumers\u0026rsquo; individual awareness of privacy risks. When consumers possess a high intention to protect their private information, they tend to directly avoid advertising when they perceive privacy risks. This suggests that their attitudes, emotions, or behaviors toward\u0026nbsp;personalized\u0026nbsp;advertising can be altered by these risks. In other words, for consumers, advertising avoidance is not considered a manifestation of privacy protection intention or behavior.\u003c/p\u003e\n\u003cp\u003eThe relationship between advertising avoidance and consumers\u0026rsquo; perceptions of privacy is consistently mediated by perceived risk. Perceived risk also partially mediates the connection between advertising avoidance and negative experiences and fully mediates between advertising avoidance and the perceived effectiveness of industry self-regulation.\u003c/p\u003e\n\u003cp\u003eDirect avoidance of\u0026nbsp;personalized\u0026nbsp;advertising occurs when consumers perceive a threat to their privacy, heightening their concern. Additionally, a higher perception of risk is formed alongside an increased level of privacy concern. Consumers are aware of the potential existence of privacy breaches, unknown risks, and unexpected losses associated with\u0026nbsp;personalized\u0026nbsp;advertising, recognizing that their protection of personal privacy may be negatively impacted. Consequently, consumers engage in more pronounced advertising avoidance as a strategy to minimize their exposure to\u0026nbsp;personalized\u0026nbsp;advertising and mitigate the misuse of their personal information.\u003c/p\u003e\n\u003cp\u003eConsumers\u0026rsquo; perception of the external privacy environment entails the perceived effectiveness of industry self-regulation. However, consumers\u0026rsquo; avoidance of\u0026nbsp;personalized\u0026nbsp;advertising is not directly influenced by this perception. Instead, mediating the perceived risk ultimately determines the avoidance of advertising. Undoubtedly, consumers\u0026rsquo; perceived privacy risk diminishes, and their willingness to accept\u0026nbsp;personalized\u0026nbsp;advertising increases, when they perceive effective industry self-regulation measures or mechanisms.\u003c/p\u003e\n\u003cp\u003eNegative experiences enhance consumers\u0026rsquo; perception of risk. These experiences occasionally serve as reminders of privacy concerns when consumers engage with\u0026nbsp;personalized\u0026nbsp;advertising. The prevalent mindset of \u0026ldquo;once bitten, twice shy\u0026rdquo; heightens their intention to avoid risk, leading to the avoidance of\u0026nbsp;personalized\u0026nbsp;advertising.\u003c/p\u003e\n\u003ch3\u003eThe Influence of Consumer\u0026rsquo;s Privacy Perception on Privacy Protection Intention\u003c/h3\u003e\n\u003cp\u003eThe results of the regression analysis demonstrate that consumers\u0026rsquo; intention for privacy protection is directly influenced by their privacy concerns and indirectly affected when perceived risk acts as a mediating variable. Regarding consumers\u0026rsquo; privacy concerns and their intention for privacy protection, perceived risk plays a partially mediating role. On one hand, consumers may directly develop an intention for protection due to their high regard for privacy, On the other hand, increased awareness of the threats posed by privacy issues may further influence perceived privacy risk.\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Overall, consumers are inclined to develop an intention to protect their privacy once they recognize the necessity of relying on their own efforts to protect their private information, thereby avoiding disclosures and misuse.\u003c/p\u003e\n\u003cp\u003eConsumers\u0026rsquo; intention for privacy protection is influenced both directly and indirectly (when mediated by perceived risk) by the perceived effectiveness of industry self-regulation. In various industries related to\u0026nbsp;personalized\u0026nbsp;advertising, consumers are often not fully cognizant of the perceived effectiveness of industry self-regulation measures. Despite this, consumers are prepared to develop a stronger intention to protect their privacy directly, especially when the perceived effectiveness of industry self-regulation is low. Furthermore, consumers may perceive certain risks in privacy protection, leading to an increased awareness of risks and a further strengthening of their intention for privacy protection.\u003c/p\u003e\n\u003cp\u003eNegative experiences also have both direct and indirect positive effects on users\u0026rsquo; intention to protect their privacy. For example, if consumers have negative privacy experiences, their concern about having similar experiences may make them more sensitive to privacy issues and fuel the development of privacy protection intention. This intention will be further strengthened through the mediating influence of perceived risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003cstrong\u003eractical Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the research conclusions, this paper proposes the following recommendations:\u003c/p\u003e\n\u003cp\u003eFirstly, the creation of a secure online privacy protection environment should be prioritized to foster the advancement of\u0026nbsp;personalized\u0026nbsp;advertising. To establish a secure and reliable online environment for privacy protection, the collective efforts of multiple stakeholders are essential. According to the research findings, consumers\u0026rsquo; avoidance of\u0026nbsp;personalized\u0026nbsp;advertising is directly influenced by the effectiveness of laws and regulations, industry self-regulation measures, and the privacy protection technologies employed by Internet advertising platforms. Therefore, for the future development of a conducive environment for\u0026nbsp;personalized\u0026nbsp;advertising, all stakeholders must consistently enhance their efforts to safeguard consumer privacy.\u003c/p\u003e\n\u003cp\u003eSecondly, relevant legislation in the field of privacy protection should be improved to ensure a secure advertising environment. Research indicates that continued legislative efforts are crucial in the field of privacy protection due to consumers having a low perception of the effectiveness of existing laws and regulations. Although legal regulations are lagging behind somewhat compared to the rapid development of\u0026nbsp;personalized\u0026nbsp;advertising, proactive considerations for legislation are essential. Laws and regulations serve as the primary means of consumer privacy control. Their enhancement, at both the macro and micro levels, serves to motivate all social entities to prioritize consumer privacy protection. The optimization of the entire Internet advertising environment relies on the reinforcement of laws and regulations, fostering a safer environment that may reduce consumers\u0026rsquo; avoidance of\u0026nbsp;personalized\u0026nbsp;advertising.\u003c/p\u003e\n\u003cp\u003eThirdly, the industry should implement industry norms and management practices to enhance the effectiveness of industry self-regulation. Platforms and consumer technologies related to privacy currently have a relatively low perception of the effectiveness of industry self-regulation compared to that of laws and regulations. This suggests that consumers may have to wait for an extended period before their expectations are met.\u003c/p\u003e\n\u003cp\u003eThe industry, playing a crucial role as an entity for state enforcement and corporate ethical self-supervision, wields both external enforcement power and internal flexibility. Therefore, industries must collaborate with the state and society to enhance the protection of consumer privacy. In the\u0026nbsp;personalized\u0026nbsp;advertising industry, this collaboration is crucial for enhancing privacy protection and the governance of data security. Furthermore, improving data security capabilities can prevent\u0026nbsp;personalized\u0026nbsp;advertising from becoming a vulnerability for network privacy disclosures.\u003c/p\u003e\n\u003cp\u003eFourthly, advertising platforms should enhance privacy protection technology and avoid an \u0026ldquo;absolute black boxing\u0026rdquo; approach. Currently, advertising platforms struggle to instill a strong perception of the effectiveness of privacy protection technologies among consumers. One reason for this is that the associated principles and operational mechanisms are challenging for most consumers to comprehend. When consumers struggle to make sense of complex and lengthy privacy policies, they may experience privacy fatigue. Therefore, platforms should implement measures, such as technological safeguards or policy guarantees, to protect users\u0026rsquo; information and manage data. This ensures the provision of reliable solutions in data security technology and enhances platforms\u0026rsquo; data security and risk management, creating a secure advertising environment. Simultaneously, the practice of absolute black boxing should be avoided by applying privacy protection technologies. Platforms have a responsibility to use technological means to obtain consumers\u0026rsquo; consent regarding the usage and processing of their private data. Furthermore, the language used to disclose their purposes, methods, and scope of data processing should be as clear and concise as possible. This approach benefits consumers by enhancing their understanding of privacy protection technologies, reducing their privacy concerns and fatigue, minimizing advertising avoidance, and fostering a positive perception of the effectiveness of privacy technology protections.\u003c/p\u003e\n\u003cp\u003eLastly, there is a need to enhance the accuracy and creativity of advertising to generate\u0026nbsp;personalized\u0026nbsp;advertising that resonates with consumers. Studies have revealed that perceived personalization has a negative impact on advertising avoidance. This suggests that when an advertisement aligns with consumers\u0026rsquo; interests and hobbies, its content is perceived as more personalized, thereby more effectively reducing consumers\u0026rsquo; avoidance. Therefore, advertising platforms should prioritize enhancing creativity while focusing on refining precision matching through the use of computational power and algorithmic technology. Many platforms now have the capability to automatically generate creative content, including advertising videos, images, headlines, and copywriting. However, a lingering question remains regarding whether creative yet mass-produced content can be fresh and entertaining enough to consistently attract consumer favor. Before implementing personalized delivery, advertising platforms must carefully consider this aspect. Advertisers and creative agencies should devote attention to enhancing their creativity, as advertisements that combine creativity with\u0026nbsp;personalized\u0026nbsp;matching algorithmic technology are more likely to capture consumers\u0026rsquo; attention.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eLimitations and Future Research Directions\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis study is not without limitations. A notable concern is the reliance on self-reported data regarding\u0026nbsp;personalized\u0026nbsp;advertising avoidance, lacking actual observations of avoidance behaviors. The validity of the self-reported data may be questioned, as it may not accurately reflect respondents\u0026rsquo; authentic reactions. Therefore, conducting well-designed and controlled experimental studies would be beneficial, manipulating the nine factors identified in this study to directly measure real-time\u0026nbsp;personalized\u0026nbsp;advertising avoidance behaviors.\u003c/p\u003e\n\u003cp\u003ePrivacy fatigue, the perceived effectiveness of privacy-protective technologies, as well as industry self-regulation and laws, are included in this study as new elements, building on previous research. However, due to limitations in sample size and research methods, further investigation is needed to determine the universal applicability of these elements. Additionally, cultural and regulatory differences may impact the generalizability of the study\u0026rsquo;s findings. The manner in which users interact with media continues to evolve, and while this study aimed for a comprehensive approach, it does not claim to be exhaustive.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by\u0026nbsp;XXX\u0026nbsp;Project\u0026nbsp;and National Social Science Fund of XXX.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY: data curation and software;Y and J: formal analysis and project administration; Y:investigation and writing\u0026mdash;original draft; Y and Y: methodology;Yand J: writing\u0026mdash; review and editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research project has been reviewed and approved by the Ethical Review Committee at XXX on \u0026nbsp;May 2\u003csup\u003end\u003c/sup\u003e. Our research team confirms that all procedures involving human participants were duly performed in rigorous accordance with the ethical standards stipulated by the Declaration of Helsinki. This includes, but is not limited to, obtaining informed consent from all human participants, ensuring their privacy and confidentiality, and minimizing any potential harm or discomfort. The approval ID for this research project is XXX. The scope of approval covers all aspects of the research, including but not limited to participant recruitment, data collection, analysis, and dissemination of results. Any amendments or additional protocols related to the research have reviewed and approved by the Ethical Review Committee at XXX under the same approval number or anew one issued accordingly.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn compliance with ethical guidelines, instructor (the first author of this study) collected consent form from all the participants, which stating the details of the study\u0026rsquo;s objectives, their involvement, potential risks and benefits, and assurances of confidentiality and anonymity. By signing the consent form, participants were giving their consent for the following: participation in the study, which may include surveys; use of the data collected for research purposes, which may include analysis and publication; publication of the results of the study, with no personal identifiers used to maintain confidentiality; all information collected will be kept confidential, data will be stored securely and will only be accessible to the research team, participants\u0026rsquo; identity will not be disclosed in any reports or publications resulting from the study. No vulnerable individuals and payment or incentives were involved in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article can be found in the supplementary file.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAguirre, E., Mahr, D., Grewal, D., Ruyter, K.D., and Wetzels, M. (2015). 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Privacy protection in the algorithmic era: paradoxical dilemmas, path directions and future challenges. \u003cem\u003eMedia Forum\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e \u003c/em\u003e5, 4\u0026ndash;10. doi: 10.3969/j.issn.2096-5079.2022.16.001\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"personalized advertising, consumer’s privacy, advertising avoidance, control agency theory, privacy fatigue","lastPublishedDoi":"10.21203/rs.3.rs-5265590/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5265590/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDrawing on advertising avoidance theory, control agency theory and privacy calculus theory, this study develops a model of factors affecting personalized advertising avoidance, which include consumers\u0026rsquo; perceptions of privacy, encompassing their internal views, perception of the external privacy environment, perceived personalization of personalized advertising, and personal experiences of privacy. The study conducts an online survey, collecting 502 valid questionnaires. Following verification through structural equation modeling analysis, the research reveals that privacy concerns, privacy fatigue, the perceived effectiveness of privacy-protective technology, the perceived effectiveness of laws, perceived personalization, and prior negative experiences directly impact precision advertising avoidance. Perceived risk, plays a mediating role between privacy concern and advertising avoidance, between the perceived effectiveness of industry self-regulation and advertising avoidance, and between prior negative experiences and advertising avoidance. Lastly, this research proposes suggestions aimed at establishing a more secure network privacy protection environment and enhancing the appeal of personalized advertising content to reduce consumers\u0026rsquo; avoidance and promote its future development.\u003c/p\u003e","manuscriptTitle":"Exploring Predictors of Personalized Advertising Avoidance from the Perspective of Consumer Privacy and Control Agency","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-14 10:13:18","doi":"10.21203/rs.3.rs-5265590/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c3051912-0864-46b2-870f-fd3fe2ce7741","owner":[],"postedDate":"November 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":39994339,"name":"Business and commerce/Information systems and information technology"},{"id":39994340,"name":"Social science/Sociology"},{"id":39994341,"name":"Business and commerce/Business and management"}],"tags":[],"updatedAt":"2025-01-06T12:11:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-14 10:13:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5265590","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5265590","identity":"rs-5265590","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}