Understanding The Effects of Privacy Policy and Government Regulation on Privacy Protection Behavior

Understanding The Effects of Privacy Policy and Government Regulation on Privacy Protection Behavior | 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 Research Article Understanding The Effects of Privacy Policy and Government Regulation on Privacy Protection Behavior Shanji Yao, Kai Gao, Hongyu Li, Ziyi Wei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6742139/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 Objective While consumers' reluctance to disclose personal information threatens the operational efficacy of e-commerce platforms, existing research fails to systematically explain the psychological and institutional drivers of privacy protection behavior. The study addresses this gap by proposing an integrative framework to examine three dimensions of influencing factors: (1) institutional governance mechanisms, (2) cognitive mediators, and (3) individual boundary conditions. The study aims to reveal how these multilevel elements collectively shape consumers' behavioral responses in digital transactions. Methods A survey of 398 e-commerce platform users was conducted, employing a seven-point Likert scale questionnaire. Structural equation modeling (SEM) using Amos 24.0 analyzed direct and mediated relationships, while moderated mediation effects were tested via the Process macro. Results Our findings indicate that privacy policy, corporate digital responsibility, and government regulation have a significant negative impact on privacy protection behavior. Furthermore, privacy concerns and digital trust act as mediators between privacy policy, corporate digital responsibility, government regulation, and privacy protection behavior. Additionally, information analysis capabilities and information sensitivity moderate the relationships between privacy policy, corporate digital responsibility, government regulation, privacy concerns, and digital trust. Conclusion This paper presents a comprehensive framework for understanding the adoption of privacy protection behaviors by consumers, offering valuable insights for e-commerce platforms seeking to enhance their consumer privacy protection practices. privacy policy government regulation privacy protection behavior Figures Figure 1 Figure 2 Figure 3 1. Introduction Consumer data has become an essential resource for e-commerce companies, driving strategic objectives and fostering business innovation. As businesses increasingly rely on consumer information for personalized services and targeted marketing, the improper collection and use of personal data raise significant privacy and security concerns. These issues not only expose consumers to potential risks but also undermine the long-term ability of platforms to leverage consumer data effectively. To address these risks and protect consumer rights, governments and leading e-commerce platforms have implemented various privacy protection measures. Notable examples include regulatory frameworks such as the American Data Privacy Protection Act, the California Consumer Privacy Act, and the European Union's General Data Protection Regulation (GDPR). In parallel, companies like Apple, Amazon, Alibaba, and TikTok have positioned themselves in the market by prioritizing transparency and consumer privacy protection in their data handling practices(Dorfleitner et al., 2023 ; Peukert et al., 2022 ). Despite these efforts, privacy breaches and data misuse persist, prompting a rise in consumer-driven privacy protection behaviors. These behaviors include reducing platform engagement, adjusting privacy settings, filing complaints, providing inaccurate data, or even abandoning platforms altogether (Yuniar, 2024 ). Research has shown that consumers with heightened privacy concerns are more likely to engage in these protective measures when interacting with online platforms (Maseeh et al., 2021 ). Conversely, a higher level of digital trust in platforms correlates with a greater willingness to disclose personal information (Mubarak & Petraite, 2020 ; Tunkevichus & Rebiazina, 2021 ). One of the most significant determinants of privacy-related behavior is the transparency and clarity of a platform's privacy policy. Studies indicate that detailed and clear privacy policies, particularly those that address the collection, processing, and use of personal information, enable consumers to better assess the risks and benefits of disclosure, thereby promoting greater privacy disclosure behavior (Gouthier et al., 2022 ; Zeng et al., 2022 ). In contrast, corporate digital responsibility, through collaborative efforts, mitigates the negative impact on users' privacy by steering digital enterprises toward more sustainable practices (Arora & Jain, 2024 ; Chaudhuri et al., 2023 ; Elliott et al., 2021 ). Furthermore, individual factors such as information analysis capability and privacy sensitivity significantly influence consumers' privacy protection behaviors. Consumers with stronger information analysis skills are better equipped to evaluate platform privacy policies and mechanisms, which in turn influences their privacy protection decisions (Guo et al., 2022 ; Yuniar, 2024 ). Additionally, the type of privacy at risk—whether related to physical, personal attributes, communication, or location—also plays a crucial role in how individuals respond to privacy concerns (Jiang & Yang, 2023 ). Despite the growing body of literature on consumer privacy protection behaviors, significant gaps remain in understanding the mechanisms behind these behaviors. Most existing studies have primarily focused on psychological and personality factors that influence privacy decisions. However, research exploring how external factors, such as privacy policies and government regulations interact with individual characteristics to shape consumer behavior is scarce. Notably, the role of government regulations (e.g., GDPR) in influencing these behaviors is still under-explored, as is the interaction between privacy policy design and regulatory frameworks in shaping user trust and privacy protection actions. To address these gaps, we propose a novel research framework that integrates both internal e-commerce platform operations (i.e., privacy policies) and external regulatory environments (i.e., government regulations) as key drivers of privacy protection behavior. Specifically, we develop a comprehensive customer privacy protection decision-making model that incorporates both psychological and environmental factors affecting consumer privacy behavior. Our study makes three key contributions to the literature: (1)Extended Framework: While prior research has predominantly focused on psychological and personality-driven factors, this paper integrates both internal factors (such as platform privacy policies) and external regulatory factors (such as government regulations) as primary determinants of user behavior. This approach allows for a more comprehensive prediction of privacy protection behaviors, providing valuable insights for both e-commerce platforms and regulators. (2) Mediating Role of Privacy Concerns and Digital Trust: By conceptualizing privacy concerns and digital trust as mediating variables, this paper examines how privacy policy design and government regulations influence consumers’ privacy protection behaviors. This exploration reveals the underlying processes and mechanisms through which external regulations and internal policies shape consumer behavior, thus contributing to the development of a more robust framework for understanding user information behavior. (3) Moderating Influence of Information Sensitivity and Analytical Capability: Additionally, we consider how information sensitivity and analytical capability act as moderating variables that influence the relationship between privacy policy design, government regulation, and privacy protection behavior. This perspective enhances our understanding of how different consumer profiles may respond to varying policy and regulatory environments, offering e-commerce platforms a more nuanced strategy for data protection management. 2. Theoretical background 2.1 SOR model The Stimulus-Organism-Response (SOR) model, proposed by (Falender & Mehrabian, 1979), is a widely recognized framework used to study the relationship between external environmental stimuli and individual decision-making. Falender and Mehrabian (1979) identified that in the SOR model, the stimulus serves as the antecedent variable, consisting primarily of external environmental factors that influence an individual’s psychological state. These factors include the shopping environment, information technology, user reviews, and information quality. The organism, as the mediating variable in the model, represents the internal processes and structures through which an individual’s psychological state changes—such as emotional responses or cognitive reactions. The response is the outcome variable, examining the effects of external stimuli on individual behavior, which may be positive or negative, often expressed as approach or avoidance tendencies. The SOR model has been extensively applied in environmental psychology and consumer behavior studies, demonstrating strong explanatory power, particularly in the context of e-commerce platform users (Jiang et al., 2024 ; Phamthi et al., 2024 ; Yadav et al., 2024 ). In this study, we argue that the government and e-commerce companies have collectively shaped the ecological environment in which consumers engage in online shopping. Specifically, government regulations, privacy protection policies established by businesses, and the digital responsibility levels of e-commerce platforms serve as stimuli that influence consumer information behavior. These stimuli primarily affect the organism (consumers), manifested in privacy concerns and digital trust, which ultimately result in a response—namely, privacy protection behavior. 2.2 Privacy policy and corporate digital responsibility A privacy policy is a critical governance tool employed by service providers to exercise industry self-regulation and oversight, providing consumers with transparency regarding how their personal data is collected, utilized, and safeguarded. Such policies outline the measures and strategies aimed at preventing data loss, misuse, and unauthorized access (Schindler & Bickart, 2012 ). An effective privacy policy can significantly enhance users' sense of security, thereby encouraging positive information disclosure behavior, as evidenced by multiple studies (Chang et al., 2018 ; Zeng et al., 2022 ). Further research by Guo et al. ( 2022 ) demonstrates that the three core dimensions of a privacy policy—transparency, control, and protection—both directly and indirectly affect users' perceptions of its effectiveness. In particular, individuals who place a high value on privacy are more influenced by the content of the privacy policy than those who are less privacy-conscious. This highlights the importance of policy content in shaping consumer behavior in digital contexts. In domains such as mobile payments and online shopping, where personal data transactions are frequent, consumers are especially attentive to any changes in the platform's privacy policies. Willis et al. ( 2021 ), applying commitment-trust theory, found that online retailers who voluntarily implement GDPR data rights significantly enhance user trust, thereby reducing negative privacy protection behavior among users. Similarly, Yuniar ( 2024 ) argue that the more comprehensive the privacy policy, the less likely users are to engage in proactive privacy protection behaviors. In essence, well-structured and transparent privacy policies can mitigate concerns and reduce users' defensive privacy behaviors, such as excessive information withholding. On the other hand, corporate digital responsibility refers to a set of values, norms, and principles that guide organizations in managing the digitalization of their operations, with a particular focus on data management and technology use. Liyanaarachchi et al. ( 2021 ) suggest that online retailers should consider corporate digital responsibility as a fundamental operational principle and prioritize the protection of vulnerable customers as key stakeholders. This concept is increasingly being explored as companies face growing expectations not only to ensure the safety of user data but also to contribute positively to the digital ecosystem. Lobschat et al. ( 2021 ) propose distinguishing into two dimensions: corporate digitized responsibility, which focuses on the digitization of operational processes, and corporate digitalized responsibility, which emphasizes broader social and ethical considerations in the digital realm. Their research establishes a positive correlation between corporate digital responsibility and improved digital performance, suggesting that organizations that uphold strong ethical standards in their digital practices benefit from enhanced operational outcomes. Wirtz et al. ( 2023 ) further refine this framework by identifying seven distinct dimensions of corporate digital responsibility: data privacy and security, product safety and responsibility, information transparency, education and awareness, access, economic benefits, and dispute resolution. Their findings indicate that an individual’s perception of an organization’s digital responsibility significantly influences their decision-making, reinforcing the idea that corporate digital responsibility extends beyond compliance to influence consumer behavior. Additionally, Doerr and Lautermann ( 2024 ) broaden the scope of corporate digital responsibility by considering its impact on societal stakeholders and institutions, moving the focus from direct business-consumer interactions to a more expansive, social perspective. In the digital marketing context, higher levels of corporate commitment to digital social responsibility contribute to increased consumer confidence, thereby influencing privacy protection behavior. Given these insights, it is evident that corporate digital responsibility plays an integral role in shaping consumer trust and behavior in digital environments. However, despite the growing body of literature, a gap remains in understanding the specific mechanisms by which privacy policies and corporate digital responsibility collectively influence users' privacy protection behavior. Further research is needed to investigate the interplay between these factors and how businesses can optimize both their privacy policies and their digital responsibility practices to foster a more secure and transparent digital marketplace. Based on these findings, this paper proposes the following hypotheses: H1: Privacy policy has a significant negative impact on privacy protection behavior. H2: Corporate digital responsibility has a significant negative impact on privacy protection behavior. 2.4 Government regulation Government regulation plays a crucial role in shaping the privacy protection behaviors of individuals by guiding or overseeing the collection, use, and dissemination of consumer data. These regulations, developed by governmental agencies and industry bodies, seek to ensure that companies operate within legal frameworks, preventing illegal data collection and misuse while protecting consumer rights. The existing literature on privacy protection behavior presents a variety of findings on the relationship between government regulation and privacy protection behavior. A key argument is that government regulation can alleviate concerns about data privacy, reducing the necessity for consumers to engage in protective behaviors such as limiting their interactions with platforms or providing false information. According to Skrinjaric et al. ( 2019 ), an increase in consumers’ perception of the effectiveness of government regulation can reduce Privacy concerns, which, in turn, diminishes the likelihood of negative privacy protection behaviors. This study implies that regulatory efforts, by addressing privacy risks, can foster trust and reduce the need for consumers to take protective actions. On the other hand, Otto and Jarke ( 2019 ) suggest that the implementation of certain privacy regulations, such as the GDPR, might lead to unintended consequences that could indirectly exacerbate Privacy concerns. They highlight that the readability and transparency of privacy policies have decreased post-GDPR, as companies adopt more standardized language to comply with the regulatory requirements. This shift could reduce consumer comprehension, leading to higher levels of uncertainty and, potentially, more protective behaviors, as consumers are unable to fully understand the privacy policies in place. This finding challenges the notion that government regulation always leads to positive outcomes in terms of reducing privacy protection behavior. Further complicating the picture is the mimicking behavior observed among companies, particularly in the context of FinTech firms studied by Dorfleitner et al. ( 2023 ). These firms, following the implementation of the GDPR, engaged in similar data privacy practices as their competitors, often leading to more standardized and less transparent privacy statements. This mimicry, while fostering some degree of uniformity, does not necessarily enhance consumer trust or reduce Privacy concerns, as it may limit the differentiation between companies and obscure critical privacy risks. Moreover, Easley et al. ( 2018 ) discuss net neutrality and data neutrality as other forms of regulation affecting how data is used and shared online. These regulatory frameworks, like GDPR, are intended to ensure that platforms are not abusing their control over user data. However, they argue that, much like in the case of net neutrality, the regulation itself could unintentionally create barriers or inequities in how users interact with platforms. This could further amplify Privacy concerns, as users may feel that regulatory frameworks don’t necessarily address their immediate privacy needs and risks. Another important issue in the literature is the market dynamics created by government regulation. Studies by Peukert et al. ( 2022 ) and Johnson et al. ( 2023 ) document how GDPR induced changes in web technology usage have contributed to the market concentration among major vendors such as Google and Facebook. These changes imply that larger companies with more resources to comply with regulations may dominate the market, leaving smaller players and users at a disadvantage. This dynamic could potentially lead to more concentrated power over consumer data, increasing privacy risks in the long run. As a result, consumers may engage in more privacy protection behaviors, further intensifying the privacy paradox—where users act to protect their data, but the larger system of regulations and corporate interests might not fully mitigate those concerns. The lack of clear, comprehensive guidelines on privacy protection in regulatory frameworks often leaves gaps in the protection offered to consumers. For instance, Kokshagina et al. ( 2023 ) highlight how the development of regulation around algorithmic control is still ongoing, with institutional tussles between governments, digital platforms, and third parties. These regulatory gaps contribute to inconsistent enforcement and potentially reduced user trust, compelling consumers to engage in behaviors that are intended to mitigate perceived privacy risks. Despite the significant body of work on government regulation and its effects on privacy protection behaviors, several key gaps remain. One of the most critical gaps is the unintended negative consequences of government regulation, which may inadvertently increase consumer anxiety about privacy. As the studies reviewed, while regulations like the GDPR aim to improve transparency and data security, they also risk complicating privacy statements, fostering mistrust, and potentially distracting consumers from addressing actual risks. Furthermore, most studies focus on the perceived effectiveness of regulation in terms of enhancing trust and privacy disclosure, but there is insufficient research exploring the differentiated impact of such regulations on consumer behavior in various contextual settings. The literature does not adequately address the dynamic interplay between regulation, market power, and privacy protection behavior. To address these gaps, we propose the following hypothesis:: H3: Government regulation has a significant negative impact on privacy protection behavior. 2.5 Privacy concerns Privacy concerns refer to the worries and apprehensions that users experience when websites collect and use their personal information (Bansal et al., 2016 ). These concerns can prompt consumers to engage in behaviors such as providing false information, spreading negative word-of-mouth, and switching to alternative platforms, thereby negatively impacting the marketing performance of businesses (Martin et al., 2017 ). Empirical research has demonstrated a significant relationship between privacy policies and privacy concerns. Specifically, the more comprehensive the privacy policy, the lower the level of privacy concerns consumers have regarding their personal information. Furthermore, trust in the website increases, making users less likely to engage in privacy protection behaviors (Belanger & Crossler, 2019 ), a trend particularly evident on social e-commerce platforms (Maseeh et al., 2021 ). Corporate digital responsibility and government regulation are also key antecedents influencing privacy concerns (Chaudhuri et al., 2023 ; Elliott et al., 2021 ; Hartley et al., 2024 ; Wirtz et al., 2023 ). In the realm of digital marketing, Wirtz et al. ( 2023 ) argue that the lack of corporate responsibility in digital security and privacy protection contributes to the formation of consumer privacy concerns. Strengthening corporate digital responsibility, therefore, can effectively reduce these concerns. Similarly, government actions—such as enacting laws and regulations and penalizing illegal activities—can constrain platforms, thereby fostering a stronger sense of privacy protection among consumers and alleviating their concerns about personal information (Lia et al., 2024 ). A comprehensive and effective regulatory framework encourages users to share personal information with platforms, while the absence of regulation heightens users' attention to data privacy, increasing their intent to protect their information (Butori & Miltgen, 2023 ). Based on these insights, we hypothesize the following: H4a: Privacy policy is negatively correlated with user privacy concerns. H4b: Corporate digital responsibility is negatively correlated with user privacy concerns. H4c: Government regulation is negatively correlated with user privacy concerns. Privacy policies, corporate digital responsibility, and government regulation are critical antecedents that influence privacy concerns. Furthermore, privacy concerns subsequently affect users' privacy protection behaviors. Studies have shown that social network users with higher levels of privacy concerns are more likely to adopt protective measures for their personal privacy information during online interactions (Gouthier et al., 2022 ; Maseeh et al., 2021 ). When consumers believe that companies have comprehensive privacy policies and that laws and regulations exist to protect personal privacy information, they exhibit lower levels of privacy concern and are less likely to engage in privacy protection behaviors. Conversely, when consumers perceive privacy policies as ineffective, corporate digital responsibility as inadequate, and government regulation as weak, their privacy concerns intensify, leading to a greater likelihood of adopting privacy protection behaviors. Therefore, we propose the following hypotheses: H4d: Privacy concerns mediate the relationship between privacy policy and privacy protection behavior. H4e: Privacy concerns mediate the relationship between corporate digital responsibility and privacy protection behavior. H4f: Privacy concerns mediate the relationship between government regulation and privacy protection behavior. 2.6 Digital trust Digital trust refers to the confidence consumers have in the ability of e-commerce companies to safeguard personal information when they engage with digital products or services provided by these companies (Mubarak & Petraite, 2020 ; Tunkevichus & Rebiazina, 2021 ). Zhghenti and Chkareuli ( 2021 ) argue that, during digital transformation, companies should actively fulfill their digital responsibilities, enabling consumers to recognize the company’s technical capabilities in privacy and security protection. This, in turn, can effectively enhance consumer digital trust. In practice, leading e-commerce platforms (e.g., Amazon, Alibaba) are continuously improving and clarifying their privacy policies to boost user trust. These efforts effectively mitigate consumer concerns about privacy security, thereby strengthening their confidence in the platform (Kluiters et al., 2023 ). Moreover, government regulation can positively influence user digital trust. The implementation of privacy laws and regulations plays a direct or indirect role in enhancing users' trust in the digital security capabilities of e-commerce enterprises (Rodriguez-Priego et al., 2023 ). Based on these insights, we propose the following hypotheses: H5a: Privacy policy is positively correlated with user digital trust. H5b: Corporate digital responsibility is positively correlated with user digital trust. H5c: Government regulation is positively correlated with user digital trust. Privacy policy, corporate digital responsibility, and government regulation are key antecedent variables influencing user digital trust. Additionally, digital trust further impacts users' privacy protection behaviors. Miltgen and Smith ( 2019 ) developed a decision-making model to predict behaviors such as fabricating and concealing information, demonstrating through online experiments that there is a significant correlation between users' trust in a platform and their privacy protection behavior. Thus, we propose the following hypotheses: H5d: Digital trust mediates the relationship between privacy policy and privacy protection behavior. H5e: Digital trust mediates the relationship between corporate digital responsibility and privacy protection behavior. H5f: Digital trust mediates the relationship between government regulation and privacy protection behavior. 2.7 Information analysis capability Information analysis capability refers to an individual’s ability to critically evaluate and analyze persuasive information. This capability is influenced by both the individual’s relevant knowledge and the time available for processing the information. Bansal et al. ( 2016 ) found that consumers with higher information analysis capabilities are more adept at assessing the privacy protection levels of online shopping platforms. They evaluate privacy policies and guarantees, reducing concerns about personal data security, fostering trust, and ultimately influencing their privacy disclosure behaviors. In contrast, consumers with lower information analysis capabilities often form trust based on the platform’s reputation, which can also influence their privacy disclosure behavior. Moreover, research suggests that information analysis capability can moderate user information behavior (Chang et al., 2018 ; Skrinjaric et al., 2019 ). Specifically, consumers with higher information analysis capabilities are more likely to assess the effectiveness of an e-commerce platform’s corporate digital responsibility. A higher fulfillment of corporate digital responsibility is correlated with greater consumer trust, reduced privacy concerns, and a stronger inclination to engage in privacy protection behaviors (Wirtz et al., 2023 ). Additionally, Chaudhuri et al. ( 2023 ) argue that consumers with better information analysis capabilities perceive more comprehensive regulations protecting their privacy, enhancing their trust in the platform and decreasing concerns about privacy leakage. Consequently, these consumers are less likely to engage in excessive privacy protection behaviors. We propose the following hypotheses: H6a: Information analysis capability negatively moderates the impact of privacy policy on privacy concerns. H6b: Information analysis capability positively moderates the impact of privacy policy on digital trust. H6c: Information analysis capability negatively moderates the impact of corporate digital responsibility on privacy concerns. H6d: Information analysis capability positively moderates the impact of corporate digital responsibility on digital trust. H6e: Information analysis capability negatively moderates the impact of government regulation on privacy concerns. H6f: Information analysis capability positively moderates the impact of government regulation on digital trust. Privacy policy, corporate digital responsibility, and government regulation play critical roles in alleviating privacy concerns and enhancing digital trust, thus reducing the likelihood of privacy protection behaviors (Lobschat (Jiang & Yang, 2023 ; Lobschat et al., 2021 ). However, when consumers possess strong information analysis capabilities, this effect is further moderated: it reduces the positive impact of privacy concerns on privacy protection behavior and strengthens the negative impact of digital trust on such behaviors. Therefore, we hypothesize that information analysis capability moderates the mediating effects of privacy concerns and digital trust in the relationship between privacy policy, corporate digital responsibility, government regulation, and privacy protection behavior. Specifically, we propose the following hypotheses: H7a: Information analysis capability negatively moderates the mediating effect of privacy concerns between privacy policy and privacy protection behavior. H7b: Information analysis capability positively moderates the mediating effect of digital trust between privacy policy and privacy protection behavior. H7c: Information analysis capability negatively moderates the mediating effect of privacy concerns between corporate digital responsibility and privacy protection behavior. H7d: Information analysis capability positively moderates the mediating effect of digital trust between corporate digital responsibility and privacy protection behavior. H7e: Information analysis capability negatively moderates the mediating effect of privacy concerns between government regulation and privacy protection behavior. H7f: Information analysis capability positively moderates the mediating effect of digital trust between government regulation and privacy protection behavior. 2.8 information sensitivity Information sensitivity refers to an individual's awareness of the potential risks and negative consequences associated with the leakage of personal information (Wirth et al., 2019 ). Bansal et al. ( 2016 ) found that in environments where privacy risks are perceived to be higher, users with greater information sensitivity are more likely to engage in privacy protection behaviors. Additionally, a user's trust in a company's digital capabilities is influenced by their information sensitivity. When platforms require users to disclose more sensitive information, their level of trust in the platform tends to decrease, prompting a greater likelihood of engaging in privacy protection behaviors (Liu et al., 2022 ). Tao et al. ( 2024 ) conducted a study on Chinese consumers' willingness to disclose personal information to e-commerce platforms. The results revealed that the sensitivity of the information, especially with regard to medium and high-sensitivity types of personal data, significantly influences consumer privacy concerns. Furthermore, information sensitivity was found to mediate the impact of individual and situational factors on privacy concerns (Gouthier et al., 2022 ). Based on these insights, this study proposes the following hypotheses: H8a: Information sensitivity positively moderates the impact of privacy concerns on privacy protection behavior. H8b: Information sensitivity negatively moderates the impact of digital trust on privacy protection behavior. Privacy policies, corporate digital responsibility, and government regulation play a crucial role in alleviating privacy concerns and enhancing digital trust, which, in turn, reduces consumers' likelihood of engaging in privacy protection behaviors. However, when information sensitivity is high, it further amplifies the positive impact of privacy concerns on privacy protection behavior, while weakening the negative impact of digital trust on such behavior. Therefore, we hypothesize that information sensitivity may moderate the mediating effects of privacy concerns and digital trust in the relationships between privacy policy, corporate digital responsibility, government regulation, and privacy protection behavior. Specifically, we propose the following hypotheses: H9a: Information sensitivity positively moderates the mediating effect of privacy concerns between privacy policy and privacy protection behavior. H9b: Information sensitivity negatively moderates the mediating effect of digital trust between privacy policy and privacy protection behavior. H9c: Information sensitivity positively moderates the mediating effect of privacy concerns between corporate digital responsibility and privacy protection behavior. H9d: Information sensitivity negatively moderates the mediating effect of digital trust between corporate digital responsibility and privacy protection behavior. H9e: Information sensitivity positively moderates the mediating effect of privacy concerns between government regulation and privacy protection behavior. H9f: Information sensitivity negatively moderates the mediating effect of digital trust between government regulation and privacy protection behavior. Integrating the above analysis, this study adopts the Stimulus-Organism-Response (SOR) model as its overarching framework. In this model, privacy policy, corporate digital responsibility, and government regulation serve as independent variables, reflecting consumers' cognitive responses to environmental stimuli. Privacy concerns and digital trust act as mediating variables, representing the internal organismic responses that emerge within consumers as a result of these external stimuli. Privacy protection behavior is the dependent variable, reflecting consumers' behavioral responses under the combined influence of environmental stimuli and organismic reactions. By focusing on the e-commerce platform's privacy policies, corporate digital responsibility, and government regulation, and incorporating information analysis capability and information sensitivity as moderating variables, this study explores the mechanisms through which privacy protection practices at the policy level—implemented by enterprises and governments—affect consumers' privacy concerns, digital trust, and privacy protection behavior. The theoretical model is shown in Fig. 1 . 3. Research methods 3.1 Instrument development and subjects This study focuses on consumers of e-commerce platforms and utilizes a survey questionnaire to collect data. Prior to the formal investigation, a pilot test was conducted to assess the reliability and validity of the collected data. Based on this analysis, the questionnaire was revised, and the final version was developed. The formal questionnaire consists of two sections: the first section captures the demographic characteristics of the participants, while the second addresses the factors influencing e-commerce platform privacy protection behavior. This section includes eight latent variables: privacy policy, corporate digital responsibility, government regulation, privacy concerns, digital trust, privacy protection behavior, information analysis capability, and information sensitivity. A seven-point Likert scale was employed to score the questionnaire items, with responses ranging from 1 ("strongly disagree") to 7 ("strongly agree"). The survey was distributed through various channels, including social networking platforms, professional data collection agencies, and traditional paper formats. In total, 500 paired responses were collected. After excluding questionnaires with anomalous data, unusually short or long completion times, and other problematic entries, 398 valid paired questionnaires were retained, yielding a valid response rate of 79.6%. 3.2 Measurement development The measurement scale for privacy policy was adapted from the studies of Guo et al. ( 2022 ) and Yuniar ( 2024 ), comprising a total of 4 items. The corporate digital responsibility scale was derived from the works of Lobschat et al. ( 2021 ) and Hartley et al. ( 2024 ), consisting of 9 items. The government regulation measurement scale was based on the framework developed by Bandara et al. ( 2020 ), with 5 items in total. Privacy concerns were assessed using the scale designed by Miltgen and Smith ( 2019 ), which includes 5 items. The digital trust scale referenced the studies of Mubarak and Petraite ( 2020 ) and Kluiters et al. ( 2023 ), with a total of 5 items. Information analysis capability was measured using the scale developed by Shao et al. ( 2020 ), comprising 3 items. Information sensitivity was assessed using the scale by Dinev et al. ( 2013 ), also consisting of 3 items. Finally, privacy protection behavior was measured using the scales developed by Alkire et al. ( 2019 ) and Rodriguez-Priego et al. ( 2023 ), totaling 9 items. The latent variables and measurement items of this study are shown in Table 1 . To account for potential influences on privacy protection behavior, the study includes several control variables, including gender, age, education level, monthly disposable income, and platform usage duration. These factors, along with individual characteristics, platform purchase experience, and frequency of privacy infringements, may impact users' privacy protection behaviors. Table 1 Variables and measurement items of the study Variable Number Items privacy policy PP1 The e-commerce platform has taken sufficient measures to protect my personal information security. PP2 The privacy terms of the e-commerce platform give a clear explanation of how my personal information provided during registration will be used. PP3 The e-commerce platform can protect my personal information from being tampered with or destroyed. PP4 The privacy terms of the e-commerce platform are an effective way to show its commitment to privacy. corporate digital responsibility CDR1 I believe that enterprises uphold the principle of consumer voluntarism in data collection. CDR2 I believe that enterprises disclose the scope and purpose of data collection to consumers when collecting data. CDR3 I believe that enterprises publicly display the information involved in digital technologies to consumers. CDR4 I believe that enterprises publicly disclose not only the achievements but also the failure information in the use and operation of digital technologies to consumers. CDR5 I believe that enterprises possess the technology to quickly identify abnormal network accesses. CDR6 I believe that enterprises have a professional team for data and digital technologies operation. CDR7 I believe that enterprises have the technology to ensure consumers' data is not obtained by third parties. CDR8 I believe the algorithm security level of corporate digital activities is very high. CDR9 I believe that enterprises have relevant plans for handling consumer data leakage. government regulation GR1 Existing laws in Australia are sufficient to protect my online privacy. GR2 The government is doing enough to ensure consumers are protected against online privacy violations. GR3 The law is capable of governing practices of how online sellers collect, use and protect my information. GR4 There are strong international laws to protect personal information of individuals on the internet. GR5 Third party seals and certificates (e.g. TrustMark) are able to ensure my online privacy Privacy concerns PC1 I am concerned that my online behaviour and activities can be monitored/tracked without my permission. PC2 I am concerned that online sellers are collecting personally identifiable information without my permission. PC3 I am concerned that online sellers could use my personal information for other purposes without my authorization. PC4 I am concerned that online sellers share my personal information with different parties without my agreement. PC5 I am concerned that online sellers could store my personal information for years without my permission. PC6 I am concerned that online sellers could create a detailed profile about me using personal data from various sources without my knowledge. digital trust DT1 I believe that the e-commerce platform has the professional knowledge and skills to provide high-quality products and services. DT2 I believe that the product information provided by the e-commerce platform is truthful. DT3 I believe that the e-commerce platform will fulfill the agreements it has reached with consumers. DT4 I believe that the e-commerce platform has customers' interests at heart. DT5 I believe that the e-commerce platform will make efforts to solve any problems I encountered during shopping. consumer information analysis capability CIAA1 I know accurately about the benefit of using this information security management standard. CIAA2 I know accurately what benefit we can get from using this information security management standard. CIAA3 My predictions for the benefit of using information security management standards are usually accurate. information sensitivity IS1 I do not feel comfortable with the type of information these Web sites request from me. IS2 I feel that these Web sites gather highly personal information about me. IS3 The information I provide to these Web sites is very sensitive to me. consumer privacy protection behavior CPPB1 I would consider making up fictitious responses to avoid giving the Web site real information about myself. CPPB2 I would resort to using another name or Web/e-mail address when registering with this Web site so I can have full access and benefits as a registered user without divulging my real identity. CPPB3 When registering with this Web site, I would only fill up data partially. CPPB4 I would like to make use of software so that the recipient cannot track the origin of my mail (e.g., re-mailers). CPPB5 I would use software to eliminate Cookies that track my Web-browsing behavior (e.g., JunkBuster, WRQ AtGuard). CPPB6 I would like to make use of software to disguise my identity. CPPB7 I would be reluctant to register with this Web site. CPPB8 I would refuse to provide personal information to this Web site. CPPB9 I would avoid visiting this Web site. 4. Data analysis and results 4.1 Reliability and validity To ensure the rationality and scientific rigor of the data, this study first assessed the reliability and validity of the variables before evaluating the quality of the structural model. As shown in Table 2 , all variables demonstrate Cronbach's Alpha (α) and Composite Reliability (CR) values exceeding the 0.7 threshold, indicating a satisfactory level of internal consistency within the scales. Additionally, the Average Variance Extracted (AVE) for each variable is greater than 0.5, confirming robust construct validity. Furthermore, the assessment of discriminant validity, also presented in Table 2 , reveals that the square root of the AVE for each variable is greater than the correlation coefficients between variables, thereby supporting the discriminant validity of the constructs. Table 2 Reliability and internal consistency coefficient Variable Cronbach’s α AVE CR privacy policy 0.893 0.627 0.894 corporate digital responsibility 0.888 0.613 0.888 government regulation 0.88 0.595 0.88 Privacy concerns 0.882 0.599 0.882 digital trust 0.888 0.615 0.889 consumer information analysis capability 0.819 0.601 0.819 information sensitivity 0.893 0.625 0.893 consumer privacy protection behavior 0.877 0.589 0.877 To assess the discriminant validity among privacy policy, corporate digital responsibility, government regulation, consumer privacy concerns, digital trust, information analysis capability, information sensitivity, and privacy protection behavior, this study conducted a series of confirmatory factor analyses. The results are presented in Table 3 . The fit indices reveal that the ratio of the model's chi-square value to degrees of freedom (χ²/df) is 1.152, which is below the threshold of 3, indicating adequate fit. The Incremental Fit Index (IFI), Comparative Fit Index (CFI), and Tucker-Lewis Index (TLI) are 0.988, 0.988, and 0.987, respectively, all exceeding the benchmark of 0.9, thus satisfying the required standards. Additionally, the Root Mean Square Error of Approximation (RMSEA) is 0.020, which is below the 0.1 threshold, confirming that the model demonstrates a good overall fit. Consequently, further hypothesis testing can proceed. Table 3 CFA fitting indicators Index χ2/df IFI CFI SRMR TLI RMSEA Value 1.152 0.988 0.988 0.032 0.987 0.020 Standard 0.9 > 0.9 0.9 < 0.05 4.2 Common method bias examination This study uses Harman's single-factor analysis method to assess potential common method bias. All items related to privacy policy, corporate digital responsibility, government regulation, privacy concerns, digital trust, and privacy protection behavior were included in the common factor analysis. The results show that the highest variance explained by a single factor is 30.271%, which is below the 40% threshold. Therefore, common method bias in this study is deemed to be within an acceptable range. 4.3 Structural model estimation This study employs Amos 24.0 software to perform a path analysis on the structural equation model, with the corresponding data results from the path analysis provided in Table 4 . Table 4 Structural Equation Model and Path Analysis Path Beta (Non standardized) SE z (CR value) p Beta(standardized) H1:privacy policy→privacy protection behavior -0.238 0.046 -5.211 0.000 -0.245 H2:corporate digital responsibility→privacy protection behavior -0.218 0.048 -4.585 0.000 -0.224 H3:government regulation→privacy protection behavior -0.193 0.050 -3.826 0.000 -0.189 H4a:privacy policy→Privacy concerns -0.212 0.046 -4.572 0.000 -0.218 H5a:privacy policy→digital trust 0.215 0.048 4.513 0.000 0.217 H4b:corporate digital responsibility→Privacy concerns -0.165 0.048 -3.409 0.001 -0.169 H5b:corporate digital responsibility→digital trust 0.203 0.050 4.084 0.000 0.204 H4c:government regulation→Privacy concerns -0.247 0.051 -4.833 0.000 -0.241 H5c:government regulation→digital trust 0.201 0.052 3.832 0.000 0.193 * p < 0.05; ** p < 0.01 The results presented in Table 4 reveal several significant findings. First, regarding the relationship between privacy policy and privacy protection behavior, the standardized path coefficient is β = -0.245, indicating a negative effect. This path is statistically significant at the 0.01 level (z = -5.211, p = 0.000 < 0.01), suggesting that privacy policy has a significant negative impact on privacy protection behavior, thus supporting Hypothesis H1. Similarly, the standardized path coefficient for the effect of corporate digital responsibility on privacy protection behavior is β = -0.224, also negative, and significant at the 0.01 level (z = -4.585, p = 0.000 < 0.01). This finding confirms that corporate digital responsibility negatively influences privacy protection behavior, supporting Hypothesis H2. Furthermore, the standardized path coefficient for the impact of government regulation on privacy protection behavior is β = -0.189, which is negative and significant at the 0.01 level (z = -3.826, p = 0.009 < 0.01), indicating that government regulation also has a significant negative effect on privacy protection behavior, thereby supporting Hypothesis H3. In terms of privacy concerns, privacy policy has a significant negative impact (β = -0.218, p < 0.01), supporting Hypothesis H4a. Conversely, privacy policy significantly positively affects digital trust (β = 0.217, p < 0.01), supporting Hypothesis H5a. Corporate digital responsibility significantly reduces privacy concerns (β = -0.169, p < 0.01), supporting Hypothesis H4b, while it also significantly enhances digital trust (β = 0.204, p < 0.01), supporting Hypothesis H5b. Finally, government regulation significantly reduces privacy concerns (β = -0.241, p < 0.01), supporting Hypothesis H4c, and positively influences digital trust (β = 0.193, p < 0.01), supporting Hypothesis H5c. These findings collectively highlight the significant impacts of privacy policy, corporate digital responsibility, and government regulation on privacy protection behavior, privacy concerns, and digital trust. 4.4 Mediation effect analysis As shown in Table 5 , the standardized coefficient for the indirect effect of privacy policy on privacy protection behavior through privacy concerns is -0.033, with a 95% confidence interval of [-0.061, -0.012]. Since the confidence interval does not include zero, the result is significant, supporting Hypothesis H4d. Similarly, the standardized coefficient for the indirect effect of corporate digital responsibility on privacy protection behavior through privacy concerns is -0.027, with a 95% confidence interval of [-0.053, -0.008]. This result is also significant and does not include zero, supporting Hypothesis H4e. The standardized coefficient for the indirect effect of government regulation on privacy protection behavior through privacy concerns is -0.039, with a 95% confidence interval of [-0.068, -0.013]. This effect is significant and does not include zero, thus supporting Hypothesis H4f. In terms of digital trust, the standardized coefficient for the indirect effect of privacy policy on privacy protection behavior is -0.029, with a 95% confidence interval of [-0.058, -0.008]. Since the confidence interval does not include zero, this result is significant, supporting Hypothesis H5d. The standardized coefficient for the indirect effect of corporate digital responsibility on privacy protection behavior through digital trust is -0.029, with a 95% confidence interval of [-0.057, -0.008]. This finding is significant and supports Hypothesis H5e. Finally, the standardized coefficient for the indirect effect of government regulation on privacy protection behavior through digital trust is -0.029, with a 95% confidence interval of [-0.055, -0.008]. This result is also significant, supporting Hypothesis H5f. These findings collectively suggest that both privacy concerns and digital trust mediate the effects of privacy policy, corporate digital responsibility, and government regulation on privacy protection behavior. Table 5 Mediating effect test Path Beta (standardized) Lower limit of 95% Upper limit of 95% p Conclusion Mediating effect of Privacy concerns H4d:privacy policy = > Privacy concerns = > privacy protection behavior -0.033 -0.061 -0.012 0.009 partial mediation H4e:corporate digital responsibility = > Privacy concerns = > privacy protection behavior -0.027 -0.053 -0.008 0.019 partial mediation H4f:government regulation = > Privacy concerns = > privacy protection behavior -0.039 -0.068 -0.013 0.005 partial mediation Mediating effect of digital trust H5d:privacy policy = > digital trust = > privacy protection behavior -0.029 -0.058 -0.008 0.021 partial mediation H5e:corporate digital responsibility = > digital trust = > privacy protection behavior -0.029 -0.057 -0.008 0.024 partial mediation H5f:government regulation = > digital trust = > privacy protection behavior -0.029 -0.055 -0.008 0.016 partial mediation 4.5 Moderating effect test The interaction term between privacy policy and information analysis capability is significant (t = -3.635, p = 0.000 < 0.05). This suggests that the impact of privacy policy on privacy concerns varies significantly depending on the level of information analysis capability. Similarly, the interaction term between privacy policy and information analysis capability is also significant (t = 5.183, p = 0.000 < 0.05). This indicates that the effect of privacy policy on digital trust is moderated by information analysis capability, with varying impact magnitudes across different levels, thus supporting Hypotheses H6a and H6b. The interaction term between corporate digital responsibility and information analysis capability is significant (t = -3.070, p = 0.002 < 0.05), meaning that when corporate digital responsibility influences privacy concerns, its impact magnitude changes significantly at different levels of information analysis capability. Likewise, the interaction term between corporate digital responsibility and information analysis capability is significant (t = 3.873, p = 0.000 < 0.05), suggesting that when corporate digital responsibility affects digital trust, information analysis capability significantly moderates the magnitude of this effect. These results support Hypotheses H6c and H6d. The interaction term between government regulation and information analysis capability is significant (t = -3.291, p = 0.001 < 0.05), indicating that government regulation's influence on privacy concerns is significantly moderated by information analysis capability, leading to varying impact magnitudes at different levels. Furthermore, the interaction term between government regulation and information analysis capability is significant (t = 3.184, p = 0.002 < 0.05), suggesting that the effect of government regulation on digital trust also varies significantly depending on the level of information analysis capability, thus supporting Hypotheses H6e and H6f. For further details, see Fig. 2. The interaction term between privacy concerns and information sensitivity is significant (t = -2.667, p = 0.008 < 0.05), suggesting that the impact of privacy concerns on privacy protection behavior varies significantly depending on the level of information sensitivity. Therefore, Hypothesis H8a is supported. Similarly, the interaction term between digital trust and information sensitivity is significant (t = 4.615, p = 0.000 < 0.05), indicating that the effect of digital trust on privacy protection behavior also varies significantly across different levels of information sensitivity. Consequently, Hypothesis H8b is supported. For further details, see Fig. 3. 4.6 Moderated mediation effect test This paper employs the Process macro's Model 7 to test for moderated mediation effects. As shown in Table 6 , for the mediating variable of privacy concerns, when information analysis capability is low, the indirect effect of privacy policy on privacy protection behavior through privacy concerns is significant (Effect = -0.087, 95% CI = [-0.134, -0.047]). At an average level of information analysis capability, the indirect effect is also significant (Effect = -0.053, 95% CI = [-0.083, -0.027]). However, when information analysis capability is high, the indirect effect of privacy policy on privacy protection behavior through privacy concerns is not significant (Effect = -0.020, 95% CI = [-0.049, 0.010]). This finding suggests that as information analysis capability increases, the indirect effect of privacy policy on privacy protection behavior through privacy concerns weakens. Therefore, the moderated mediation effect is significant, supporting Hypothesis H7a. For the mediating variable of digital trust, when information analysis capability is low, the indirect effect of privacy policy on privacy protection behavior through digital trust is not significant (Effect = 0.001, 95% CI = [-0.024, 0.029]). At an average level, the indirect effect is significant (Effect = -0.041, 95% CI = [-0.069, -0.018]), and when information analysis capability is high, the indirect effect remains significant (Effect = -0.084, 95% CI = [-0.132, -0.040]). This suggests that higher levels of information analysis capability lead to a weaker indirect effect of privacy policy on privacy protection behavior through digital trust, thus confirming the significance of the moderated mediation effect and supporting Hypothesis H7b. Table 6 Moderated mediation effect test (privacy policy*information analysis capability) Mediator information analysis capability Value Effect BootSE BootLLCI BootULCI Privacy concerns low level(-1SD) 2.932 -0.087 0.023 -0.134 -0.047 average 4.322 -0.053 0.015 -0.083 -0.027 high level(+ 1SD) 5.711 -0.020 0.015 -0.049 0.010 digital trust low level(-1SD) 2.932 0.001 0.013 -0.024 0.029 average 4.322 -0.041 0.013 -0.069 -0.018 high level(+ 1SD) 5.711 -0.084 0.023 -0.132 -0.040 * BootLLCI refers to the lower limit of the 95% interval for Bootstrap sampling, while BootULCI refers to the upper limit of the 95% interval for Bootstrap sampling. Bootstrap type: percentile bootstrap method As shown in Table 7 , when information analysis capability is at a low level, the indirect effect of corporate digital responsibility on privacy protection behavior through privacy concerns is significant (Effect = -0.084, 95% CI = [-0.133, -0.045]). At an average level of information analysis capability, the indirect effect remains significant (Effect = -0.053, 95% CI = [-0.084, -0.027]). However, when information analysis capability is high, the indirect effect of corporate digital responsibility on privacy protection behavior through privacy concerns is not significant (Effect = -0.021, 95% CI = [-0.054, 0.011]). This indicates that as information analysis capability increases, the indirect effect of corporate digital responsibility on privacy protection behavior through privacy concerns weakens, confirming the significance of the moderated mediation effect and supporting Hypothesis H7c. For the mediating variable of digital trust, when information analysis capability is low, the indirect effect of corporate digital responsibility on privacy protection behavior through digital trust is not significant (Effect = -0.010, 95% CI = [-0.038, 0.016]). At an average level, the indirect effect is significant (Effect = -0.044, 95% CI = [-0.075, -0.019]), and when information analysis capability is high, the indirect effect is also significant (Effect = -0.079, 95% CI = [-0.130, -0.037]). This suggests that as information analysis capability increases, the indirect effect of corporate digital responsibility on privacy protection behavior through digital trust strengthens, supporting the significance of the moderated mediation effect and confirming Hypothesis H7d. Table 7 Moderated mediation effect test (corporate digital responsibility*information analysis capability) Mediator information analysis capability Value Effect BootSE BootLLCI BootULCI Privacy concerns low level(-1SD) 5.711 -0.084 0.022 -0.133 -0.045 average 4.322 -0.053 0.015 -0.084 -0.027 high level(+ 1SD) 2.932 -0.021 0.016 -0.054 0.011 digital trust low level(-1SD) 2.932 -0.010 0.013 -0.038 0.016 average 4.322 -0.044 0.014 -0.075 -0.019 high level(+ 1SD) 5.711 -0.079 0.024 -0.130 -0.037 As presented in Table 8 , when information analysis capability is at a low level, the indirect effect of government regulation on privacy protection behavior through privacy concerns is significant (Effect = -0.088, 95% CI = [-0.129, -0.051]). At an average level of information analysis capability, the indirect effect of government regulation on privacy protection behavior through privacy concerns remains significant (Effect = -0.054, 95% CI = [-0.086, -0.026]). However, at a high level, the indirect effect of government regulation on privacy protection behavior through privacy concerns is no longer significant (Effect = -0.029, 95% CI = [-0.063, 0.004]). This indicates that as information analysis capability increases, the indirect effect of government regulation on privacy protection behavior through privacy concerns weakens. Therefore, the moderated mediation effect is significant, supporting Hypothesis H7e. When examining the mediating role of digital trust, at a low level of information analysis capability, the indirect effect of government regulation on privacy protection behavior through digital trust is not significant (Effect = -0.025, 95% CI = [-0.053, 0.002]). At an average level, the indirect effect is significant (Effect = -0.054, 95% CI = [-0.086, -0.026]), and at a high level, it is also significant (Effect = -0.083, 95% CI = [-0.132, -0.041]). This suggests that as information analysis capability increases, the indirect effect of government regulation on privacy protection behavior through digital trust strengthens. Thus, the moderated mediation effect is significant, supporting Hypothesis H7f. Table 8 Moderated mediation effect test(government regulation*information analysis capability) Mediator information analysis capability Value Effect BootSE BootLLCI BootULCI Privacy concerns low level(-1SD) 2.932 -0.088 0.020 -0.129 -0.051 average 4.322 -0.059 0.014 -0.089 -0.033 high level(+ 1SD) 5.711 -0.029 0.017 -0.063 0.004 digital trust low level(-1SD) 2.932 -0.025 0.014 -0.053 0.002 average 4.322 -0.054 0.016 -0.086 -0.026 high level(+ 1SD) 5.711 -0.083 0.023 -0.132 -0.041 As shown in Table 9 , when information sensitivity is low, the indirect effect of privacy policy on privacy protection behavior through privacy concerns is not significant (Effect = -0.026, 95% CI = [-0.058, -0.001]). At an average level of information sensitivity, the indirect effect is significant (Effect = -0.050, 95% CI = [-0.080, -0.025]), and at a high level, the indirect effect is also significant (Effect = -0.073, 95% CI = [-0.115, -0.037]). This suggests that as information sensitivity increases, the indirect effect of privacy policy on privacy protection behavior through privacy concerns strengthens. Consequently, the moderated mediation effect is significant, supporting Hypothesis H9a. Regarding the mediating role of digital trust, when information sensitivity is low, the indirect effect of privacy policy on privacy protection behavior through digital trust is significant (Effect = -0.097, 95% CI = [-0.152, -0.048]). At an average level of information sensitivity, the indirect effect remains significant (Effect = -0.051, 95% CI = [-0.089, -0.017]). However, at a high level of information sensitivity, the indirect effect is not significant (Effect = -0.006, 95% CI = [-0.052, 0.043]). This indicates that as information sensitivity increases, the indirect effect of privacy policy on privacy protection behavior through digital trust weakens. Therefore, the moderated mediation effect is significant, supporting Hypothesis H9b. Table 9 Moderated mediation effect test (privacy policy and information sensitivity*Privacy concerns and digital trust) Mediator information sensitivity Value Effect BootSE BootLLCI BootULCI Privacy concerns low level(-1SD) 4.973 -0.026 0.015 -0.058 0.001 average 3.594 -0.050 0.014 -0.080 -0.025 high level(+ 1SD) 2.215 -0.073 0.020 -0.115 -0.037 digital trust low level(-1SD) 2.215 -0.097 0.027 -0.152 -0.048 average 3.594 -0.051 0.018 -0.089 -0.017 high level(+ 1SD) 4.973 -0.006 0.024 -0.052 0.043 In Table 10 , when information sensitivity is low, the indirect effect of corporate digital responsibility on privacy protection behavior through privacy concerns is not significant (Effect = -0.034, 95% CI = [-0.077, -0.009]). However, at an average level of information sensitivity, the indirect effect becomes significant (Effect = -0.071, 95% CI = [-0.109, -0.038]), and when information sensitivity is high, the indirect effect is even stronger (Effect = -0.108, 95% CI = [-0.163, -0.060]). These results suggest that as information sensitivity increases, the indirect effect of corporate digital responsibility on privacy protection behavior through privacy concerns becomes more pronounced, thereby supporting the moderated mediation effect and confirming Hypothesis H9c. Regarding digital trust, when information sensitivity is low, the indirect effect of corporate digital responsibility on privacy protection behavior through digital trust is significant (Effect = -0.100, 95% CI = [-0.159, -0.049]). However, at an average level of information sensitivity, the indirect effect is not significant (Effect = -0.052, 95% CI = [-0.092, -0.017]), and when information sensitivity is high, the indirect effect remains insignificant (Effect = -0.004, 95% CI = [-0.053, 0.047]). This indicates that as information sensitivity increases, the indirect effect of corporate digital responsibility on privacy protection behavior through digital trust diminishes. Thus, the moderated mediation effect holds, supporting Hypothesis H9d. Table 10 Moderated mediation effect test (corporate digital responsibility and information sensitivity*Privacy concerns and digital trust) Mediator information sensitivity Value Effect BootSE BootLLCI BootULCI Privacy concerns low level(-1SD) 1.834 -0.034 0.022 -0.077 0.009 average 3.515 -0.071 0.018 -0.109 -0.038 high level(+ 1SD) 5.196 -0.108 0.026 -0.163 -0.060 digital trust low level(-1SD) 2.215 -0.100 0.028 -0.159 -0.049 average 3.594 -0.052 0.019 -0.092 -0.017 high level(+ 1SD) 4.973 -0.004 0.025 -0.053 0.047 In Table 11 , when information sensitivity is low, the indirect effect of government regulation on privacy protection behavior through privacy concerns is not significant (Effect = -0.036, 95% CI = [-0.086, 0.010]). At an average level of information sensitivity, the indirect effect becomes significant (Effect = -0.082, 95% CI = [-0.124, -0.044]), and at a high level of information sensitivity, the indirect effect is even stronger (Effect = -0.128, 95% CI = [-0.186, -0.073]). These findings indicate that as information sensitivity increases, the indirect effect of government regulation on privacy protection behavior through privacy concerns intensifies, thereby supporting the moderated mediation effect and confirming Hypothesis H9e. Regarding digital trust, when information analysis capability is low, the indirect effect of government regulation on privacy protection behavior through digital trust is significant (Effect = -0.112, 95% CI = [-0.172, -0.058]). At an average level of information analysis capability, the indirect effect remains significant (Effect = -0.055, 95% CI = [-0.095, -0.018]), but when information analysis capability is high, the indirect effect is no longer significant (Effect = 0.002, 95% CI = [-0.048, 0.054]). This suggests that as information analysis capability increases, the indirect effect of government regulation on privacy protection behavior through digital trust weakens, thus supporting the moderated mediation effect and confirming Hypothesis H9f. Table 11 Moderated mediation effect test (government regulation and information sensitivity*Privacy concerns and digital trust) Mediator information sensitivity Value Effect BootSE BootLLCI BootULCI Privacy concerns low level(-1SD) 1.834 -0.036 0.025 -0.086 0.010 average 3.515 -0.082 0.020 -0.124 -0.044 high level(+ 1SD) 5.196 -0.128 0.029 -0.186 -0.073 digital trust low level(-1SD) 2.215 -0.112 0.029 -0.172 -0.058 average 3.594 -0.055 0.019 -0.095 -0.018 high level(+ 1SD) 4.973 0.002 0.026 -0.048 0.054 5. Discussion and conclusions Our research constructs a decision-making model for consumer privacy protection behavior and draws three key conclusions based on data analysis and hypothesis testing. Each of these findings not only aligns with existing research but also offers deeper insights into the intricate mechanisms that govern consumer privacy protection in the digital age. First Conclusion is about the negative impact of Privacy Policy, Corporate Digital Responsibility, and Government Regulation. The findings suggest that privacy policies, corporate digital responsibility, and government regulation have a negative impact on privacy protection behavior. This initially counter-intuitive result challenges traditional assumptions, which often position these factors as the primary enablers of improved privacy protection. Supporting the research of Jahari et al. ( 2022 ) and Yuniar ( 2024 ), the study argues that although e-commerce companies and governments are crucial in shaping privacy landscapes, their involvement may unintentionally contribute to consumer hesitance regarding privacy protection. One possible explanation for this negative relationship lies in the complexity and ambiguity of privacy policies. Consumers may find the fine print of privacy agreements too overwhelming, leading to confusion and increased concern rather than confidence. Additionally, when privacy policies are not clearly communicated or perceived as overly intrusive, they may lead to a sense of mistrust among consumers. Similarly, government regulations, while necessary to ensure privacy standards, can sometimes create the perception of an invasive or overly regulated environment. This finding compels e-commerce companies to strike a delicate balance between transparency and consumer empowerment, ensuring that privacy policies not only exist but are clear, accessible, and user-friendly. The role of corporate digital responsibility also warrants further discussion. While companies that demonstrate a strong commitment to digital responsibility can alleviate some privacy concerns, it is crucial to note that the mere presence of such policies does not guarantee an increase in privacy protection behavior. Consumers may remain skeptical about whether these policies are genuinely followed or merely a facade. As such, this paper underscores the importance of consistently demonstrating digital responsibility through actions rather than just words. E-commerce platforms must not only enact policies but also build trust through authentic privacy practices. The second conclusion of this study highlights the significant role of privacy concerns and digital trust as antecedents of privacy protection behavior, aligning with findings from Chaudhuri et al. ( 2023 ), Kluiters et al. ( 2023 ), and Lia et al. ( 2024 ). These antecedents are fundamental to understanding consumer behavior in the context of privacy protection. However, this research contributes additional insights by demonstrating that privacy concerns and digital trust do not merely influence privacy protection behavior directly but also mediate the relationships between privacy policy, corporate digital responsibility, government regulation, and privacy protection behavior. This mediating effect provides a more nuanced understanding of the decision-making process. It suggests that even if consumers are exposed to comprehensive privacy policies or stringent regulations, their ultimate behavior is shaped by how they perceive the threat of privacy breaches and the level of trust they place in the digital environment. This finding emphasizes the importance of addressing privacy concerns and building trust through consumer-focused communication strategies. By acknowledging and validating consumer fears, companies and governments can influence the effectiveness of privacy policies and regulatory frameworks. In practice, this implies that e-commerce platforms and governments must go beyond merely implementing privacy policies and focus on building a robust, trust-based relationship with consumers. Engaging in transparent dialogue and ensuring that privacy protections are genuinely effective can mitigate privacy concerns and reinforce digital trust. As this study demonstrates, privacy protection behavior hinges on a complex interplay of perceived risk and trust, which mediates the effect of external privacy mechanisms. The third key finding concerns the moderating effects of information analysis capability and information sensitivity on the relationships between privacy policies, corporate digital responsibility, government regulation, privacy concerns, and digital trust. The moderating role of information analysis capability reinforces earlier studies by Chaudhuri et al. ( 2023 ), Jiang and Yang ( 2023 ), highlighting the crucial importance of how consumers process and analyze privacy-related information. A strong capability in analyzing information allows consumers to better assess the risks and benefits associated with sharing personal data. Therefore, the presence of clear, comprehensible privacy policies and strong corporate digital responsibility can empower consumers with the information they need to make more informed decisions. Additionally, information sensitivity further moderates the mediating effects between these variables and privacy protection behavior. Consumers who perceive their data as highly sensitive are more likely to be cautious in their privacy-related actions, making them more responsive to privacy policies, corporate responsibility, and government regulation. This finding suggests that companies must tailor their privacy strategies to account for the varying degrees of sensitivity that different consumers attach to their personal data. For example, platforms might need to offer more granular privacy controls or heightened transparency for users dealing with particularly sensitive data. This deeper understanding of information sensitivity opens avenues for future research into consumer segmentation based on data sensitivity. By distinguishing consumers based on how they assess the risk of data exposure, companies can better design privacy practices that resonate with specific user groups, ultimately fostering a more privacy-conscious environment. The findings have important practical implications for both e-commerce platforms and government agencies. For e-commerce companies, it is essential to enhance their privacy policies and fulfill their digital responsibilities. A well-constructed privacy policy that clearly outlines the collection, usage, and sharing of personal data can significantly reduce consumer anxiety regarding privacy risks. E-commerce companies should invest in making privacy settings more accessible and transparent, ensuring that consumers feel in control of their data. This proactive approach not only builds consumer trust but also encourages data-sharing behavior that benefits both the platform and its users. Moreover, companies should engage in ongoing education and outreach efforts, helping consumers better understand their privacy rights and providing easily accessible tools to manage privacy settings. By establishing a feedback mechanism, platforms can continuously improve their privacy practices and address concerns in real-time, reinforcing their commitment to protecting consumer privacy. From a governmental perspective, the role of legal and regulatory frameworks cannot be overstated. As evidenced by the findings, government oversight is essential in ensuring that e-commerce platforms adhere to privacy protection standards. Strengthening privacy protection laws and improving enforcement will encourage companies to adopt privacy practices that prioritize consumer interests. Additionally, the government should consider incentivizing self-regulation within the industry to promote a collaborative approach to privacy protection. By providing clear guidelines and penalties for non-compliance, governments can ensure that e-commerce platforms remain accountable and that consumers' privacy rights are upheld. Furthermore, governments should focus on improving consumer education regarding digital privacy, ensuring that users understand their rights and the measures in place to protect them. This will help build trust in both the regulatory framework and the companies that operate within it, ultimately contributing to a more secure and trustworthy e-commerce environment. 6. Limitations and future research directions This study is limited to the context of online shopping on e-commerce platforms. Although this group is closely related to the research background of this paper, it is still unclear whether the conclusions of this study are applicable to other groups. Future research subjects can be continuously expanded to examine the privacy protection behavior on various types of platforms, such as social platforms, self-media platforms, and government service platforms, in order to enhance the applicability of the research findings. This paper explores the influencing factors of consumer privacy protection from the perspectives of enterprises and governments. Future research can continuously change the research entry points, for example, by starting from different cultural backgrounds to investigate a variety of factors affecting privacy protection behavior. Declarations Acknowledgements Not applicable. Author contributions Conceptualization, Yao and Gao; methodology, Gao and Li; formal analysis, Yao and Gao; data curation, Wei; writing original draft preparation, Yao and Gao; writing—review and editing, Gao. All authors have read and agreed to the published version of the manuscript. Funding This research was funded by the key projects of National Social Science Foundation of China (Grant:22AGL022), Scientific Research Projects of Jiangsu Vocational College of Agriculture and Forestry (Grant:XCZX202202, 2023kj20). Data availability The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate The research strictly adhered to the principles of the Helsinki Declaration and obtained ethical approval from the Research Ethics Committee at the School of Economics & Management, Nanjing Tech University. The participants provided their online informed consent for participation in the research. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author details 1 Nanjing Tech University, 30 Puzhu South Road, Nanjing, Jiangsu, China 2 Jiangsu Vocational College of Agriculture and Forestry,19 Wenchang Road, Jurong, Zhenjiang, Jiangsu, China 3 Nanjing Tech University, 30 Puzhu South Road, Nanjing, Jiangsu, China 4 Nanjing Tech University, 30 Puzhu South Road, Nanjing, Jiangsu, China References Alkire, L., Pohlmann, J., & Barnett, W. (2019). Triggers and motivators of privacy protection behavior on Facebook. Journal of Services Marketing , 33 (1), 57-72. https://doi.org/10.1108/jsm-10-2018-0287 Arora, A., & Jain, T. (2024). Data Sharing Between Firms and Social Planners: An Economic Analysis of Regulation, Privacy, and Competition. Service Science , 16 (3). https://doi.org/10.1287/serv.2022.0052 Bandara, R., Fernando, M., & Akter, S. (2020). Addressing privacy predicaments in the digital marketplace: A power-relations perspective. 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Journal of Consumer Behaviour , 21 (3), 625-638. https://doi.org/10.1002/cb.2029 Jiang, G., & Yang, W. (2023). Signal effect of government regulations on ride-hailing drivers' intention to mobile-based transportation platform governance: Evidence from China. Transport Policy , 139 , 63-78. https://doi.org/10.1016/j.tranpol.2023.05.009 Jiang, H., Cai, J., Lin, Y., & Wang, Q. (2024). Understanding the effect of TikTok marketing on user purchase behavior: a mixed-methods approach [; Early Access]. Electronic Commerce Research . https://doi.org/10.1007/s10660-024-09882-x Johnson, G. A., Shriver, S. K., & Goldberg, S. G. (2023). Privacy and Market Concentration: Intended and Unintended Consequences of the GDPR. Management Science , 69 (10), 5695-5721. https://doi.org/10.1287/mnsc.2023.4709 Kluiters, L., Srivastava, M., & Tyll, L. (2023). The impact of digital trust on firm value and governance: an empirical investigation of US firms. Society and Business Review , 18 (1), 71-103. https://doi.org/10.1108/sbr-07-2021-0119 Kokshagina, O., Reinecke, P. C., & Karanasios, S. (2023). To regulate or not to regulate: Unravelling government institutional work towards AI regulation. Journal of Information Technology , 38 (2), 160-179. https://doi.org/10.1177/02683962221114408 Lia, Z., Lee, G., Raghu, T. S., & Shic, Z. (2024). Impact of the General Data Protection Regulation on the Global Mobile App Market: Digital Trade Implications of Data Protection and Privacy Regulations [; Early Access]. Information Systems Research . https://doi.org/10.1287/isre.2022.0421 Liu, B. L., Pavlou, P. A., & Cheng, X. F. (2022). Achieving a Balance Between Privacy Protection and Data Collection: A Field Experimental Examination of a Theory-Driven Information Technology Solution. Information Systems Research , 33 (1), 203-223. https://doi.org/10.1287/isre.2021.1045 Liyanaarachchi, G., Deshpande, S., & Weaven, S. (2021). Market-oriented corporate digital responsibility to manage data vulnerability in online banking. International Journal of Bank Marketing , 39 (4), 571-591. https://doi.org/10.1108/ijbm-06-2020-0313 Lobschat, L., Mueller, B., Eggers, F., Brandimarte, L., Diefenbach, S., Kroschke, M., & Wirtz, J. (2021). Corporate digital responsibility. Journal of Business Research , 122 , 875-888. https://doi.org/10.1016/j.jbusres.2019.10.006 Martin, K. D., Borah, A., & Palmatier, R. W. (2017). Data Privacy: Effects on Customer and Firm Performance. Journal of Marketing , 81 (1), 36-58. https://doi.org/10.1509/jm.15.0497 Maseeh, H. I., Jebarajakirthy, C., Pentecost, R., Arli, D., Weaven, S., & Ashaduzzaman, M. (2021). Privacy concerns in e-commerce: A multilevel meta-analysis [Review]. Psychology & Marketing , 38 (10), 1779-1798. https://doi.org/10.1002/mar.21493 Miltgen, C. L., & Smith, H. J. (2019). Falsifying and withholding: exploring individuals' contextual privacy-related decision-making. Information & Management , 56 (5), 696-717. https://doi.org/10.1016/j.im.2018.11.004 Mubarak, M. F., & Petraite, M. (2020). Industry 4.0 technologies, digital trust and technological orientation: What matters in open innovation? Technological Forecasting and Social Change , 161 , Article 120332. https://doi.org/10.1016/j.techfore.2020.120332 Otto, B., & Jarke, M. (2019). Designing a multi-sided data platform: findings from the International Data Spaces case. Electronic Markets , 29 (4), 561-580. https://doi.org/10.1007/s12525-019-00362-x Peukert, C., Bechtold, S., Batikas, M., & Kretschmer, T. (2022). Regulatory Spillovers and Data Governance: Evidence from the GDPR. Marketing Science , 41 (4), 318-340. https://doi.org/10.1287/mksc.2021.1339 Phamthi, V. A., Nagy, A., & Ngo, T. M. (2024). The influence of perceived risk on purchase intention in e-commerce-Systematic review and research agenda [Review]. International Journal of Consumer Studies , 48 (4), Article e13067. https://doi.org/10.1111/ijcs.13067 Rodriguez-Priego, N., Porcu, L., Pena, M. B. P., & Almendros, E. C. (2023). Perceived customer care and privacy protection behavior: The mediating role of trust in self-disclosure. Journal of Retailing and Consumer Services , 72 , Article 103284. https://doi.org/10.1016/j.jretconser.2023.103284 Schindler, R. M., & Bickart, B. (2012). Perceived helpfulness of online consumer reviews: The role of message content and style. Journal of Consumer Behaviour , 11 (3), 234-243. https://doi.org/10.1002/cb.1372 Shao, X., Siponen, M., & Liu, F. (2020). Shall we follow? Impact of reputation concern on information security managers' investment decisions. Computers & Security , 97 , Article 101961. https://doi.org/10.1016/j.cose.2020.101961 Skrinjaric, B., Budak, J., & Rajh, E. (2019). Perceived quality of privacy protection regulations and online privacy concern. Economic Research-Ekonomska Istrazivanja , 32 (1), 982-1000. https://doi.org/10.1080/1331677x.2019.1585272 Tao, S., Liu, Y., & Sun, C. (2024). Understanding information sensitivity perceptions and its impact on information privacy concerns in e-commerce services: Insights from China. Computers & Security , 138 , Article 103646. https://doi.org/10.1016/j.cose.2023.103646 Tunkevichus, E. O., & Rebiazina, V. A. (2021). CONSUMER DIGITAL TRUST: THE MAIN TRENDS AND RESEARCH DIRECTIONS. Rossiiskii Zhurnal Menedzhmenta-Russian Management Journal , 19 (4), 429-450. https://doi.org/10.21638/spbu18.2021.403 Willis, B., Jai, T., & Lauderdale, M. (2021). Trust and commitment: Effect of applying consumer data rights on US Consumers' attitudes toward online retailers in big data era. Journal of Consumer Behaviour , 20 (6), 1575-1590. https://doi.org/10.1002/cb.1968 Wirth, J., Maier, C., Laumer, S., & Weitzel, T. (2019). Perceived information sensitivity and interdependent privacy protection: a quantitative study. Electronic Markets , 29 (3), 359-378. https://doi.org/10.1007/s12525-019-00335-0 Wirtz, J., Kunz, W. H., Hartley, N., & Tarbit, J. (2023). Corporate Digital Responsibility in Service Firms and Their Ecosystems. Journal of Service Research , 26 (2), 173-190, Article 10946705221130467. https://doi.org/10.1177/10946705221130467 Yadav, R., Giri, A., & Alzeiby, E. A. (2024). Analyzing the motivators and barriers associated with buying green apparel: Digging deep into retail consumers' behavior. Journal of Retailing and Consumer Services , 81 , Article 103983. https://doi.org/10.1016/j.jretconser.2024.103983 Yuniar, A. D. (2024). Thin privacy boundaries: proximity and accessibility of E-commerce privacy policy in young consumers of Indonesia. International Journal of Social Economics , 51 (9), 1182-1194. https://doi.org/10.1108/ijse-11-2022-0740 Zeng, F., Ye, Q., Yang, Z., Li, J., & Song, Y. A. (2022). Which Privacy Policy Works, Privacy Assurance or Personalization Declaration? An Investigation of Privacy Policies and Privacy Concerns. Journal of Business Ethics , 176 (4), 781-798. https://doi.org/10.1007/s10551-020-04626-x Zhghenti, T., & Chkareuli, V. (2021). ENHANCING ONLINE BUSINESS SECTOR: DIGITAL TRUST FORMATION PROCESS. Marketing and Management of Innovations (2), 87-93. https://doi.org/10.21272/mmi.2021.2-07 Additional Declarations No competing interests reported. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6742139","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482853357,"identity":"09471dd8-d8fe-4e26-9f9f-91a45fca7af0","order_by":0,"name":"Shanji Yao","email":"","orcid":"","institution":"Nanjing Tech University","correspondingAuthor":false,"prefix":"","firstName":"Shanji","middleName":"","lastName":"Yao","suffix":""},{"id":482853358,"identity":"b5ca6714-fcfe-4439-a602-72852f7219dd","order_by":1,"name":"Kai Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIie2NsQrCMBCGLwTaJQ8QQekrnGRUuvoaLQe6FHV0cMiki9BXqgQ6VV0FF0FwcujoIGK0e1s3wXyQ/y5wHz+Aw/GbcAAc2mDn6tNSGdvg+I0CBtorwcgYvM8PfrA2rITFINb+LqtV+sWY4g2eOBbEJRSTWItpVK/oRGXircAsB7YysZYC65X0prYP3PMgvXjAni2UQCaKBGYcjmQV3UJBeSXVReJ4vHAZ5RO1EklDS0qmc3uEZBdWlstBL/WLhpasmvTJyD6v9v7doqsZNh06HA7HH/MCns0/I07hiHsAAAAASUVORK5CYII=","orcid":"","institution":"Jiangsu Vocational College of Agriculture and Forestry","correspondingAuthor":true,"prefix":"","firstName":"Kai","middleName":"","lastName":"Gao","suffix":""},{"id":482853359,"identity":"ca10d4a9-6f3f-4e22-b27b-720161bcc0f7","order_by":2,"name":"Hongyu Li","email":"","orcid":"","institution":"Nanjing Tech University","correspondingAuthor":false,"prefix":"","firstName":"Hongyu","middleName":"","lastName":"Li","suffix":""},{"id":482853360,"identity":"8ea5834a-88d0-4186-895a-d251782cd116","order_by":3,"name":"Ziyi Wei","email":"","orcid":"","institution":"Nanjing Tech University","correspondingAuthor":false,"prefix":"","firstName":"Ziyi","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2025-05-25 06:53:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6742139/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6742139/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86429348,"identity":"be1f0ea0-4011-4442-aa49-355cb62cd2ff","added_by":"auto","created_at":"2025-07-10 14:10:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":78382,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTheoretical model of the study\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6742139/v1/3579ff821a33c2736eff87ad.png"},{"id":86429349,"identity":"0190354a-5ea2-4dfa-a3d9-9ec86d5caa08","added_by":"auto","created_at":"2025-07-10 14:10:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":142503,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModeration effect of information analysis capability\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6742139/v1/fd2ae73495d6b82a8b064209.png"},{"id":86429631,"identity":"03bfd205-53ac-4c28-a107-88e934409723","added_by":"auto","created_at":"2025-07-10 14:18:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59729,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModeration effect of information sensitivity\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6742139/v1/a493997b63ad138e8a7fe681.png"},{"id":109168213,"identity":"dd33b4e1-c6f4-4cba-a701-c765d06b1af5","added_by":"auto","created_at":"2026-05-13 08:32:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":966897,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6742139/v1/7737823a-3463-4257-af2e-c569b53c9785.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Understanding The Effects of Privacy Policy and Government Regulation on Privacy Protection Behavior","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eConsumer data has become an essential resource for e-commerce companies, driving strategic objectives and fostering business innovation. As businesses increasingly rely on consumer information for personalized services and targeted marketing, the improper collection and use of personal data raise significant privacy and security concerns. These issues not only expose consumers to potential risks but also undermine the long-term ability of platforms to leverage consumer data effectively. To address these risks and protect consumer rights, governments and leading e-commerce platforms have implemented various privacy protection measures. Notable examples include regulatory frameworks such as the American Data Privacy Protection Act, the California Consumer Privacy Act, and the European Union's General Data Protection Regulation (GDPR). In parallel, companies like Apple, Amazon, Alibaba, and TikTok have positioned themselves in the market by prioritizing transparency and consumer privacy protection in their data handling practices(Dorfleitner et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Peukert et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Despite these efforts, privacy breaches and data misuse persist, prompting a rise in consumer-driven privacy protection behaviors. These behaviors include reducing platform engagement, adjusting privacy settings, filing complaints, providing inaccurate data, or even abandoning platforms altogether (Yuniar, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eResearch has shown that consumers with heightened privacy concerns are more likely to engage in these protective measures when interacting with online platforms (Maseeh et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Conversely, a higher level of digital trust in platforms correlates with a greater willingness to disclose personal information (Mubarak \u0026amp; Petraite, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tunkevichus \u0026amp; Rebiazina, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). One of the most significant determinants of privacy-related behavior is the transparency and clarity of a platform's privacy policy. Studies indicate that detailed and clear privacy policies, particularly those that address the collection, processing, and use of personal information, enable consumers to better assess the risks and benefits of disclosure, thereby promoting greater privacy disclosure behavior (Gouthier et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zeng et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In contrast, corporate digital responsibility, through collaborative efforts, mitigates the negative impact on users' privacy by steering digital enterprises toward more sustainable practices (Arora \u0026amp; Jain, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chaudhuri et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Elliott et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, individual factors such as information analysis capability and privacy sensitivity significantly influence consumers' privacy protection behaviors. Consumers with stronger information analysis skills are better equipped to evaluate platform privacy policies and mechanisms, which in turn influences their privacy protection decisions (Guo et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yuniar, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, the type of privacy at risk\u0026mdash;whether related to physical, personal attributes, communication, or location\u0026mdash;also plays a crucial role in how individuals respond to privacy concerns (Jiang \u0026amp; Yang, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite the growing body of literature on consumer privacy protection behaviors, significant gaps remain in understanding the mechanisms behind these behaviors. Most existing studies have primarily focused on psychological and personality factors that influence privacy decisions. However, research exploring how external factors, such as privacy policies and government regulations interact with individual characteristics to shape consumer behavior is scarce. Notably, the role of government regulations (e.g., GDPR) in influencing these behaviors is still under-explored, as is the interaction between privacy policy design and regulatory frameworks in shaping user trust and privacy protection actions. To address these gaps, we propose a novel research framework that integrates both internal e-commerce platform operations (i.e., privacy policies) and external regulatory environments (i.e., government regulations) as key drivers of privacy protection behavior. Specifically, we develop a comprehensive customer privacy protection decision-making model that incorporates both psychological and environmental factors affecting consumer privacy behavior. Our study makes three key contributions to the literature: (1)Extended Framework: While prior research has predominantly focused on psychological and personality-driven factors, this paper integrates both internal factors (such as platform privacy policies) and external regulatory factors (such as government regulations) as primary determinants of user behavior. This approach allows for a more comprehensive prediction of privacy protection behaviors, providing valuable insights for both e-commerce platforms and regulators. (2) Mediating Role of Privacy Concerns and Digital Trust: By conceptualizing privacy concerns and digital trust as mediating variables, this paper examines how privacy policy design and government regulations influence consumers\u0026rsquo; privacy protection behaviors. This exploration reveals the underlying processes and mechanisms through which external regulations and internal policies shape consumer behavior, thus contributing to the development of a more robust framework for understanding user information behavior. (3) Moderating Influence of Information Sensitivity and Analytical Capability: Additionally, we consider how information sensitivity and analytical capability act as moderating variables that influence the relationship between privacy policy design, government regulation, and privacy protection behavior. This perspective enhances our understanding of how different consumer profiles may respond to varying policy and regulatory environments, offering e-commerce platforms a more nuanced strategy for data protection management.\u003c/p\u003e"},{"header":"2. Theoretical background","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 SOR model\u003c/h2\u003e\u003cp\u003eThe Stimulus-Organism-Response (SOR) model, proposed by (Falender \u0026amp; Mehrabian, 1979), is a widely recognized framework used to study the relationship between external environmental stimuli and individual decision-making. Falender and Mehrabian (1979) identified that in the SOR model, the stimulus serves as the antecedent variable, consisting primarily of external environmental factors that influence an individual\u0026rsquo;s psychological state. These factors include the shopping environment, information technology, user reviews, and information quality. The organism, as the mediating variable in the model, represents the internal processes and structures through which an individual\u0026rsquo;s psychological state changes\u0026mdash;such as emotional responses or cognitive reactions. The response is the outcome variable, examining the effects of external stimuli on individual behavior, which may be positive or negative, often expressed as approach or avoidance tendencies. The SOR model has been extensively applied in environmental psychology and consumer behavior studies, demonstrating strong explanatory power, particularly in the context of e-commerce platform users (Jiang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Phamthi et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yadav et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this study, we argue that the government and e-commerce companies have collectively shaped the ecological environment in which consumers engage in online shopping. Specifically, government regulations, privacy protection policies established by businesses, and the digital responsibility levels of e-commerce platforms serve as stimuli that influence consumer information behavior. These stimuli primarily affect the organism (consumers), manifested in privacy concerns and digital trust, which ultimately result in a response\u0026mdash;namely, privacy protection behavior.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Privacy policy and corporate digital responsibility\u003c/h2\u003e\u003cp\u003eA privacy policy is a critical governance tool employed by service providers to exercise industry self-regulation and oversight, providing consumers with transparency regarding how their personal data is collected, utilized, and safeguarded. Such policies outline the measures and strategies aimed at preventing data loss, misuse, and unauthorized access (Schindler \u0026amp; Bickart, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). An effective privacy policy can significantly enhance users' sense of security, thereby encouraging positive information disclosure behavior, as evidenced by multiple studies (Chang et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zeng et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Further research by Guo et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) demonstrates that the three core dimensions of a privacy policy\u0026mdash;transparency, control, and protection\u0026mdash;both directly and indirectly affect users' perceptions of its effectiveness. In particular, individuals who place a high value on privacy are more influenced by the content of the privacy policy than those who are less privacy-conscious. This highlights the importance of policy content in shaping consumer behavior in digital contexts. In domains such as mobile payments and online shopping, where personal data transactions are frequent, consumers are especially attentive to any changes in the platform's privacy policies. Willis et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), applying commitment-trust theory, found that online retailers who voluntarily implement GDPR data rights significantly enhance user trust, thereby reducing negative privacy protection behavior among users. Similarly, Yuniar (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) argue that the more comprehensive the privacy policy, the less likely users are to engage in proactive privacy protection behaviors. In essence, well-structured and transparent privacy policies can mitigate concerns and reduce users' defensive privacy behaviors, such as excessive information withholding.\u003c/p\u003e\u003cp\u003eOn the other hand, corporate digital responsibility refers to a set of values, norms, and principles that guide organizations in managing the digitalization of their operations, with a particular focus on data management and technology use. Liyanaarachchi et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) suggest that online retailers should consider corporate digital responsibility as a fundamental operational principle and prioritize the protection of vulnerable customers as key stakeholders. This concept is increasingly being explored as companies face growing expectations not only to ensure the safety of user data but also to contribute positively to the digital ecosystem. Lobschat et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) propose distinguishing into two dimensions: corporate digitized responsibility, which focuses on the digitization of operational processes, and corporate digitalized responsibility, which emphasizes broader social and ethical considerations in the digital realm. Their research establishes a positive correlation between corporate digital responsibility and improved digital performance, suggesting that organizations that uphold strong ethical standards in their digital practices benefit from enhanced operational outcomes. Wirtz et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) further refine this framework by identifying seven distinct dimensions of corporate digital responsibility: data privacy and security, product safety and responsibility, information transparency, education and awareness, access, economic benefits, and dispute resolution. Their findings indicate that an individual\u0026rsquo;s perception of an organization\u0026rsquo;s digital responsibility significantly influences their decision-making, reinforcing the idea that corporate digital responsibility extends beyond compliance to influence consumer behavior. Additionally, Doerr and Lautermann (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) broaden the scope of corporate digital responsibility by considering its impact on societal stakeholders and institutions, moving the focus from direct business-consumer interactions to a more expansive, social perspective. In the digital marketing context, higher levels of corporate commitment to digital social responsibility contribute to increased consumer confidence, thereby influencing privacy protection behavior. Given these insights, it is evident that corporate digital responsibility plays an integral role in shaping consumer trust and behavior in digital environments. However, despite the growing body of literature, a gap remains in understanding the specific mechanisms by which privacy policies and corporate digital responsibility collectively influence users' privacy protection behavior. Further research is needed to investigate the interplay between these factors and how businesses can optimize both their privacy policies and their digital responsibility practices to foster a more secure and transparent digital marketplace. Based on these findings, this paper proposes the following hypotheses:\u003c/p\u003e\u003cp\u003eH1: Privacy policy has a significant negative impact on privacy protection behavior.\u003c/p\u003e\u003cp\u003eH2: Corporate digital responsibility has a significant negative impact on privacy protection behavior.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Government regulation\u003c/h2\u003e\u003cp\u003eGovernment regulation plays a crucial role in shaping the privacy protection behaviors of individuals by guiding or overseeing the collection, use, and dissemination of consumer data. These regulations, developed by governmental agencies and industry bodies, seek to ensure that companies operate within legal frameworks, preventing illegal data collection and misuse while protecting consumer rights. The existing literature on privacy protection behavior presents a variety of findings on the relationship between government regulation and privacy protection behavior. A key argument is that government regulation can alleviate concerns about data privacy, reducing the necessity for consumers to engage in protective behaviors such as limiting their interactions with platforms or providing false information. According to Skrinjaric et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), an increase in consumers\u0026rsquo; perception of the effectiveness of government regulation can reduce Privacy concerns, which, in turn, diminishes the likelihood of negative privacy protection behaviors. This study implies that regulatory efforts, by addressing privacy risks, can foster trust and reduce the need for consumers to take protective actions. On the other hand, Otto and Jarke (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) suggest that the implementation of certain privacy regulations, such as the GDPR, might lead to unintended consequences that could indirectly exacerbate Privacy concerns. They highlight that the readability and transparency of privacy policies have decreased post-GDPR, as companies adopt more standardized language to comply with the regulatory requirements. This shift could reduce consumer comprehension, leading to higher levels of uncertainty and, potentially, more protective behaviors, as consumers are unable to fully understand the privacy policies in place. This finding challenges the notion that government regulation always leads to positive outcomes in terms of reducing privacy protection behavior. Further complicating the picture is the mimicking behavior observed among companies, particularly in the context of FinTech firms studied by Dorfleitner et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These firms, following the implementation of the GDPR, engaged in similar data privacy practices as their competitors, often leading to more standardized and less transparent privacy statements. This mimicry, while fostering some degree of uniformity, does not necessarily enhance consumer trust or reduce Privacy concerns, as it may limit the differentiation between companies and obscure critical privacy risks. Moreover, Easley et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) discuss net neutrality and data neutrality as other forms of regulation affecting how data is used and shared online. These regulatory frameworks, like GDPR, are intended to ensure that platforms are not abusing their control over user data. However, they argue that, much like in the case of net neutrality, the regulation itself could unintentionally create barriers or inequities in how users interact with platforms. This could further amplify Privacy concerns, as users may feel that regulatory frameworks don\u0026rsquo;t necessarily address their immediate privacy needs and risks. Another important issue in the literature is the market dynamics created by government regulation. Studies by Peukert et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Johnson et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) document how GDPR induced changes in web technology usage have contributed to the market concentration among major vendors such as Google and Facebook. These changes imply that larger companies with more resources to comply with regulations may dominate the market, leaving smaller players and users at a disadvantage. This dynamic could potentially lead to more concentrated power over consumer data, increasing privacy risks in the long run. As a result, consumers may engage in more privacy protection behaviors, further intensifying the privacy paradox\u0026mdash;where users act to protect their data, but the larger system of regulations and corporate interests might not fully mitigate those concerns. The lack of clear, comprehensive guidelines on privacy protection in regulatory frameworks often leaves gaps in the protection offered to consumers. For instance, Kokshagina et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) highlight how the development of regulation around algorithmic control is still ongoing, with institutional tussles between governments, digital platforms, and third parties. These regulatory gaps contribute to inconsistent enforcement and potentially reduced user trust, compelling consumers to engage in behaviors that are intended to mitigate perceived privacy risks. Despite the significant body of work on government regulation and its effects on privacy protection behaviors, several key gaps remain. One of the most critical gaps is the unintended negative consequences of government regulation, which may inadvertently increase consumer anxiety about privacy. As the studies reviewed, while regulations like the GDPR aim to improve transparency and data security, they also risk complicating privacy statements, fostering mistrust, and potentially distracting consumers from addressing actual risks. Furthermore, most studies focus on the perceived effectiveness of regulation in terms of enhancing trust and privacy disclosure, but there is insufficient research exploring the differentiated impact of such regulations on consumer behavior in various contextual settings. The literature does not adequately address the dynamic interplay between regulation, market power, and privacy protection behavior. To address these gaps, we propose the following hypothesis::\u003c/p\u003e\u003cp\u003eH3: Government regulation has a significant negative impact on privacy protection behavior.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Privacy concerns\u003c/h2\u003e\u003cp\u003ePrivacy concerns refer to the worries and apprehensions that users experience when websites collect and use their personal information (Bansal et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These concerns can prompt consumers to engage in behaviors such as providing false information, spreading negative word-of-mouth, and switching to alternative platforms, thereby negatively impacting the marketing performance of businesses (Martin et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Empirical research has demonstrated a significant relationship between privacy policies and privacy concerns. Specifically, the more comprehensive the privacy policy, the lower the level of privacy concerns consumers have regarding their personal information. Furthermore, trust in the website increases, making users less likely to engage in privacy protection behaviors (Belanger \u0026amp; Crossler, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), a trend particularly evident on social e-commerce platforms (Maseeh et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCorporate digital responsibility and government regulation are also key antecedents influencing privacy concerns (Chaudhuri et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Elliott et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hartley et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wirtz et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the realm of digital marketing, Wirtz et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) argue that the lack of corporate responsibility in digital security and privacy protection contributes to the formation of consumer privacy concerns. Strengthening corporate digital responsibility, therefore, can effectively reduce these concerns. Similarly, government actions\u0026mdash;such as enacting laws and regulations and penalizing illegal activities\u0026mdash;can constrain platforms, thereby fostering a stronger sense of privacy protection among consumers and alleviating their concerns about personal information (Lia et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A comprehensive and effective regulatory framework encourages users to share personal information with platforms, while the absence of regulation heightens users' attention to data privacy, increasing their intent to protect their information (Butori \u0026amp; Miltgen, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Based on these insights, we hypothesize the following:\u003c/p\u003e\u003cp\u003eH4a: Privacy policy is negatively correlated with user privacy concerns.\u003c/p\u003e\u003cp\u003eH4b: Corporate digital responsibility is negatively correlated with user privacy concerns.\u003c/p\u003e\u003cp\u003eH4c: Government regulation is negatively correlated with user privacy concerns.\u003c/p\u003e\u003cp\u003ePrivacy policies, corporate digital responsibility, and government regulation are critical antecedents that influence privacy concerns. Furthermore, privacy concerns subsequently affect users' privacy protection behaviors. Studies have shown that social network users with higher levels of privacy concerns are more likely to adopt protective measures for their personal privacy information during online interactions (Gouthier et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Maseeh et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). When consumers believe that companies have comprehensive privacy policies and that laws and regulations exist to protect personal privacy information, they exhibit lower levels of privacy concern and are less likely to engage in privacy protection behaviors. Conversely, when consumers perceive privacy policies as ineffective, corporate digital responsibility as inadequate, and government regulation as weak, their privacy concerns intensify, leading to a greater likelihood of adopting privacy protection behaviors. Therefore, we propose the following hypotheses:\u003c/p\u003e\u003cp\u003eH4d: Privacy concerns mediate the relationship between privacy policy and privacy protection behavior.\u003c/p\u003e\u003cp\u003eH4e: Privacy concerns mediate the relationship between corporate digital responsibility and privacy protection behavior.\u003c/p\u003e\u003cp\u003eH4f: Privacy concerns mediate the relationship between government regulation and privacy protection behavior.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Digital trust\u003c/h2\u003e\u003cp\u003eDigital trust refers to the confidence consumers have in the ability of e-commerce companies to safeguard personal information when they engage with digital products or services provided by these companies (Mubarak \u0026amp; Petraite, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tunkevichus \u0026amp; Rebiazina, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Zhghenti and Chkareuli (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) argue that, during digital transformation, companies should actively fulfill their digital responsibilities, enabling consumers to recognize the company\u0026rsquo;s technical capabilities in privacy and security protection. This, in turn, can effectively enhance consumer digital trust. In practice, leading e-commerce platforms (e.g., Amazon, Alibaba) are continuously improving and clarifying their privacy policies to boost user trust. These efforts effectively mitigate consumer concerns about privacy security, thereby strengthening their confidence in the platform (Kluiters et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, government regulation can positively influence user digital trust. The implementation of privacy laws and regulations plays a direct or indirect role in enhancing users' trust in the digital security capabilities of e-commerce enterprises (Rodriguez-Priego et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Based on these insights, we propose the following hypotheses:\u003c/p\u003e\u003cp\u003eH5a: Privacy policy is positively correlated with user digital trust.\u003c/p\u003e\u003cp\u003eH5b: Corporate digital responsibility is positively correlated with user digital trust.\u003c/p\u003e\u003cp\u003eH5c: Government regulation is positively correlated with user digital trust.\u003c/p\u003e\u003cp\u003ePrivacy policy, corporate digital responsibility, and government regulation are key antecedent variables influencing user digital trust. Additionally, digital trust further impacts users' privacy protection behaviors. Miltgen and Smith (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) developed a decision-making model to predict behaviors such as fabricating and concealing information, demonstrating through online experiments that there is a significant correlation between users' trust in a platform and their privacy protection behavior. Thus, we propose the following hypotheses:\u003c/p\u003e\u003cp\u003eH5d: Digital trust mediates the relationship between privacy policy and privacy protection behavior.\u003c/p\u003e\u003cp\u003eH5e: Digital trust mediates the relationship between corporate digital responsibility and privacy protection behavior.\u003c/p\u003e\u003cp\u003eH5f: Digital trust mediates the relationship between government regulation and privacy protection behavior.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Information analysis capability\u003c/h2\u003e\u003cp\u003eInformation analysis capability refers to an individual\u0026rsquo;s ability to critically evaluate and analyze persuasive information. This capability is influenced by both the individual\u0026rsquo;s relevant knowledge and the time available for processing the information. Bansal et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found that consumers with higher information analysis capabilities are more adept at assessing the privacy protection levels of online shopping platforms. They evaluate privacy policies and guarantees, reducing concerns about personal data security, fostering trust, and ultimately influencing their privacy disclosure behaviors. In contrast, consumers with lower information analysis capabilities often form trust based on the platform\u0026rsquo;s reputation, which can also influence their privacy disclosure behavior. Moreover, research suggests that information analysis capability can moderate user information behavior (Chang et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Skrinjaric et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Specifically, consumers with higher information analysis capabilities are more likely to assess the effectiveness of an e-commerce platform\u0026rsquo;s corporate digital responsibility. A higher fulfillment of corporate digital responsibility is correlated with greater consumer trust, reduced privacy concerns, and a stronger inclination to engage in privacy protection behaviors (Wirtz et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, Chaudhuri et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) argue that consumers with better information analysis capabilities perceive more comprehensive regulations protecting their privacy, enhancing their trust in the platform and decreasing concerns about privacy leakage. Consequently, these consumers are less likely to engage in excessive privacy protection behaviors. We propose the following hypotheses:\u003c/p\u003e\u003cp\u003eH6a: Information analysis capability negatively moderates the impact of privacy policy on privacy concerns.\u003c/p\u003e\u003cp\u003eH6b: Information analysis capability positively moderates the impact of privacy policy on digital trust.\u003c/p\u003e\u003cp\u003eH6c: Information analysis capability negatively moderates the impact of corporate digital responsibility on privacy concerns.\u003c/p\u003e\u003cp\u003eH6d: Information analysis capability positively moderates the impact of corporate digital responsibility on digital trust.\u003c/p\u003e\u003cp\u003eH6e: Information analysis capability negatively moderates the impact of government regulation on privacy concerns.\u003c/p\u003e\u003cp\u003eH6f: Information analysis capability positively moderates the impact of government regulation on digital trust.\u003c/p\u003e\u003cp\u003ePrivacy policy, corporate digital responsibility, and government regulation play critical roles in alleviating privacy concerns and enhancing digital trust, thus reducing the likelihood of privacy protection behaviors (Lobschat (Jiang \u0026amp; Yang, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lobschat et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, when consumers possess strong information analysis capabilities, this effect is further moderated: it reduces the positive impact of privacy concerns on privacy protection behavior and strengthens the negative impact of digital trust on such behaviors. Therefore, we hypothesize that information analysis capability moderates the mediating effects of privacy concerns and digital trust in the relationship between privacy policy, corporate digital responsibility, government regulation, and privacy protection behavior. Specifically, we propose the following hypotheses:\u003c/p\u003e\u003cp\u003eH7a: Information analysis capability negatively moderates the mediating effect of privacy concerns between privacy policy and privacy protection behavior.\u003c/p\u003e\u003cp\u003eH7b: Information analysis capability positively moderates the mediating effect of digital trust between privacy policy and privacy protection behavior.\u003c/p\u003e\u003cp\u003eH7c: Information analysis capability negatively moderates the mediating effect of privacy concerns between corporate digital responsibility and privacy protection behavior.\u003c/p\u003e\u003cp\u003eH7d: Information analysis capability positively moderates the mediating effect of digital trust between corporate digital responsibility and privacy protection behavior.\u003c/p\u003e\u003cp\u003eH7e: Information analysis capability negatively moderates the mediating effect of privacy concerns between government regulation and privacy protection behavior.\u003c/p\u003e\u003cp\u003eH7f: Information analysis capability positively moderates the mediating effect of digital trust between government regulation and privacy protection behavior.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.8 information sensitivity\u003c/h2\u003e\u003cp\u003eInformation sensitivity refers to an individual's awareness of the potential risks and negative consequences associated with the leakage of personal information (Wirth et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Bansal et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found that in environments where privacy risks are perceived to be higher, users with greater information sensitivity are more likely to engage in privacy protection behaviors. Additionally, a user's trust in a company's digital capabilities is influenced by their information sensitivity. When platforms require users to disclose more sensitive information, their level of trust in the platform tends to decrease, prompting a greater likelihood of engaging in privacy protection behaviors (Liu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Tao et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) conducted a study on Chinese consumers' willingness to disclose personal information to e-commerce platforms. The results revealed that the sensitivity of the information, especially with regard to medium and high-sensitivity types of personal data, significantly influences consumer privacy concerns. Furthermore, information sensitivity was found to mediate the impact of individual and situational factors on privacy concerns (Gouthier et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Based on these insights, this study proposes the following hypotheses:\u003c/p\u003e\u003cp\u003eH8a: Information sensitivity positively moderates the impact of privacy concerns on privacy protection behavior.\u003c/p\u003e\u003cp\u003eH8b: Information sensitivity negatively moderates the impact of digital trust on privacy protection behavior.\u003c/p\u003e\u003cp\u003ePrivacy policies, corporate digital responsibility, and government regulation play a crucial role in alleviating privacy concerns and enhancing digital trust, which, in turn, reduces consumers' likelihood of engaging in privacy protection behaviors. However, when information sensitivity is high, it further amplifies the positive impact of privacy concerns on privacy protection behavior, while weakening the negative impact of digital trust on such behavior. Therefore, we hypothesize that information sensitivity may moderate the mediating effects of privacy concerns and digital trust in the relationships between privacy policy, corporate digital responsibility, government regulation, and privacy protection behavior. Specifically, we propose the following hypotheses:\u003c/p\u003e\u003cp\u003eH9a: Information sensitivity positively moderates the mediating effect of privacy concerns between privacy policy and privacy protection behavior.\u003c/p\u003e\u003cp\u003eH9b: Information sensitivity negatively moderates the mediating effect of digital trust between privacy policy and privacy protection behavior.\u003c/p\u003e\u003cp\u003eH9c: Information sensitivity positively moderates the mediating effect of privacy concerns between corporate digital responsibility and privacy protection behavior.\u003c/p\u003e\u003cp\u003eH9d: Information sensitivity negatively moderates the mediating effect of digital trust between corporate digital responsibility and privacy protection behavior.\u003c/p\u003e\u003cp\u003eH9e: Information sensitivity positively moderates the mediating effect of privacy concerns between government regulation and privacy protection behavior.\u003c/p\u003e\u003cp\u003eH9f: Information sensitivity negatively moderates the mediating effect of digital trust between government regulation and privacy protection behavior.\u003c/p\u003e\u003cp\u003eIntegrating the above analysis, this study adopts the Stimulus-Organism-Response (SOR) model as its overarching framework. In this model, privacy policy, corporate digital responsibility, and government regulation serve as independent variables, reflecting consumers' cognitive responses to environmental stimuli. Privacy concerns and digital trust act as mediating variables, representing the internal organismic responses that emerge within consumers as a result of these external stimuli. Privacy protection behavior is the dependent variable, reflecting consumers' behavioral responses under the combined influence of environmental stimuli and organismic reactions. By focusing on the e-commerce platform's privacy policies, corporate digital responsibility, and government regulation, and incorporating information analysis capability and information sensitivity as moderating variables, this study explores the mechanisms through which privacy protection practices at the policy level\u0026mdash;implemented by enterprises and governments\u0026mdash;affect consumers' privacy concerns, digital trust, and privacy protection behavior. The theoretical model is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Research methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Instrument development and subjects\u003c/h2\u003e\u003cp\u003eThis study focuses on consumers of e-commerce platforms and utilizes a survey questionnaire to collect data. Prior to the formal investigation, a pilot test was conducted to assess the reliability and validity of the collected data. Based on this analysis, the questionnaire was revised, and the final version was developed. The formal questionnaire consists of two sections: the first section captures the demographic characteristics of the participants, while the second addresses the factors influencing e-commerce platform privacy protection behavior. This section includes eight latent variables: privacy policy, corporate digital responsibility, government regulation, privacy concerns, digital trust, privacy protection behavior, information analysis capability, and information sensitivity. A seven-point Likert scale was employed to score the questionnaire items, with responses ranging from 1 (\"strongly disagree\") to 7 (\"strongly agree\"). The survey was distributed through various channels, including social networking platforms, professional data collection agencies, and traditional paper formats. In total, 500 paired responses were collected. After excluding questionnaires with anomalous data, unusually short or long completion times, and other problematic entries, 398 valid paired questionnaires were retained, yielding a valid response rate of 79.6%.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Measurement development\u003c/h2\u003e\u003cp\u003eThe measurement scale for privacy policy was adapted from the studies of Guo et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Yuniar (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), comprising a total of 4 items. The corporate digital responsibility scale was derived from the works of Lobschat et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Hartley et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), consisting of 9 items. The government regulation measurement scale was based on the framework developed by Bandara et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), with 5 items in total. Privacy concerns were assessed using the scale designed by Miltgen and Smith (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which includes 5 items. The digital trust scale referenced the studies of Mubarak and Petraite (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Kluiters et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), with a total of 5 items. Information analysis capability was measured using the scale developed by Shao et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), comprising 3 items. Information sensitivity was assessed using the scale by Dinev et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), also consisting of 3 items. Finally, privacy protection behavior was measured using the scales developed by Alkire et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Rodriguez-Priego et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), totaling 9 items. The latent variables and measurement items of this study are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. To account for potential influences on privacy protection behavior, the study includes several control variables, including gender, age, education level, monthly disposable income, and platform usage duration. These factors, along with individual characteristics, platform purchase experience, and frequency of privacy infringements, may impact users' privacy protection behaviors.\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\u003eVariables and measurement items of the study\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eItems\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eprivacy policy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePP1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe e-commerce platform has taken sufficient measures to protect my personal information security.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePP2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe privacy terms of the e-commerce platform give a clear explanation of how my personal information provided during registration will be used.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePP3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe e-commerce platform can protect my personal information from being tampered with or destroyed.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePP4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe privacy terms of the e-commerce platform are an effective way to show its commitment to privacy.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e\u003cp\u003ecorporate digital responsibility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCDR1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI believe that enterprises uphold the principle of consumer voluntarism in data collection.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCDR2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI believe that enterprises disclose the scope and purpose of data collection to consumers when collecting data.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCDR3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI believe that enterprises publicly display the information involved in digital technologies to consumers.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCDR4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI believe that enterprises publicly disclose not only the achievements but also the failure information in the use and operation of digital technologies to consumers.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCDR5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI believe that enterprises possess the technology to quickly identify abnormal network accesses.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCDR6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI believe that enterprises have a professional team for data and digital technologies operation.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCDR7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI believe that enterprises have the technology to ensure consumers' data is not obtained by third parties.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCDR8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI believe the algorithm security level of corporate digital activities is very high.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCDR9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI believe that enterprises have relevant plans for handling consumer data leakage.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003egovernment regulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGR1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExisting laws in Australia are sufficient to protect my online privacy.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGR2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe government is doing enough to ensure consumers are protected against online privacy violations.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGR3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe law is capable of governing practices of how online sellers collect, use and protect my information.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGR4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThere are strong international laws to protect personal information of individuals on the internet.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGR5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThird party seals and certificates (e.g. TrustMark) are able to ensure my online privacy\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003ePrivacy concerns\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI am concerned that my online behaviour and activities can be monitored/tracked without my permission.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI am concerned that online sellers are collecting personally identifiable information without my permission.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePC3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI am concerned that online sellers could use my personal information for other purposes without my authorization.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePC4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI am concerned that online sellers share my personal information with different parties without my agreement.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePC5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI am concerned that online sellers could store my personal information for years without my permission.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePC6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI am concerned that online sellers could create a detailed profile about me using personal data from various sources without my knowledge.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003edigital trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI believe that the e-commerce platform has the professional knowledge and skills to provide high-quality products and services.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI believe that the product information provided by the e-commerce platform is truthful.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI believe that the e-commerce platform will fulfill the agreements it has reached with consumers.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDT4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI believe that the e-commerce platform has customers' interests at heart.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDT5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI believe that the e-commerce platform will make efforts to solve any problems I encountered during shopping.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003econsumer information analysis capability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCIAA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI know accurately about the benefit of using this information security management standard.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCIAA2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI know accurately what benefit we can get from using this information security management standard.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCIAA3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMy predictions for the benefit of using information security management standards are usually accurate.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003einformation sensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIS1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI do not feel comfortable with the type of information these Web sites request from me.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIS2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI feel that these Web sites gather highly personal information about me.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIS3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe information I provide to these Web sites is very sensitive to me.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e\u003cp\u003econsumer privacy protection behavior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCPPB1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI would consider making up fictitious responses to avoid giving the Web site real information about myself.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCPPB2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI would resort to using another name or Web/e-mail address when registering with this Web site so I can have full\u003c/p\u003e\u003cp\u003eaccess and benefits as a registered user without divulging my real identity.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCPPB3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWhen registering with this Web site, I would only fill up data partially.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCPPB4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI would like to make use of software so that the recipient cannot track the origin of my mail (e.g., re-mailers).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCPPB5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI would use software to eliminate Cookies that track my Web-browsing behavior (e.g., JunkBuster, WRQ AtGuard).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCPPB6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI would like to make use of software to disguise my identity.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCPPB7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI would be reluctant to register with this Web site.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCPPB8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI would refuse to provide personal information to this Web site.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCPPB9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI would avoid visiting this Web site.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Data analysis and results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Reliability and validity\u003c/h2\u003e\u003cp\u003eTo ensure the rationality and scientific rigor of the data, this study first assessed the reliability and validity of the variables before evaluating the quality of the structural model. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, all variables demonstrate Cronbach's Alpha (α) and Composite Reliability (CR) values exceeding the 0.7 threshold, indicating a satisfactory level of internal consistency within the scales. Additionally, the Average Variance Extracted (AVE) for each variable is greater than 0.5, confirming robust construct validity. Furthermore, the assessment of discriminant validity, also presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, reveals that the square root of the AVE for each variable is greater than the correlation coefficients between variables, thereby supporting the discriminant validity of the constructs.\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\u003eReliability and internal consistency coefficient\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCronbach\u0026rsquo;s α\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAVE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eprivacy policy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.627\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.894\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecorporate digital responsibility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.888\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003egovernment regulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrivacy concerns\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.882\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edigital trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.889\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003econsumer information analysis capability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.819\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003einformation sensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.893\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003econsumer privacy protection behavior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.877\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\u003eTo assess the discriminant validity among privacy policy, corporate digital responsibility, government regulation, consumer privacy concerns, digital trust, information analysis capability, information sensitivity, and privacy protection behavior, this study conducted a series of confirmatory factor analyses. The results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The fit indices reveal that the ratio of the model's chi-square value to degrees of freedom (χ\u0026sup2;/df) is 1.152, which is below the threshold of 3, indicating adequate fit. The Incremental Fit Index (IFI), Comparative Fit Index (CFI), and Tucker-Lewis Index (TLI) are 0.988, 0.988, and 0.987, respectively, all exceeding the benchmark of 0.9, thus satisfying the required standards. Additionally, the Root Mean Square Error of Approximation (RMSEA) is 0.020, which is below the 0.1 threshold, confirming that the model demonstrates a good overall fit. Consequently, further hypothesis testing can proceed.\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\u003eCFA fitting indicators\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eχ2/df\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSRMR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTLI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRMSEA\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStandard\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Common method bias examination\u003c/h2\u003e\u003cp\u003eThis study uses Harman's single-factor analysis method to assess potential common method bias. All items related to privacy policy, corporate digital responsibility, government regulation, privacy concerns, digital trust, and privacy protection behavior were included in the common factor analysis. The results show that the highest variance explained by a single factor is 30.271%, which is below the 40% threshold. Therefore, common method bias in this study is deemed to be within an acceptable range.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Structural model estimation\u003c/h2\u003e\u003cp\u003eThis study employs Amos 24.0 software to perform a path analysis on the structural equation model, with the corresponding data results from the path analysis provided in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\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\u003eStructural Equation Model and Path Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePath\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBeta (Non standardized)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ez\u003c/em\u003e\u0026nbsp;(CR value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBeta(standardized)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1:privacy policy\u0026rarr;privacy protection behavior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.238\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-5.211\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.245\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2:corporate digital responsibility\u0026rarr;privacy protection behavior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.218\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-4.585\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.224\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH3:government regulation\u0026rarr;privacy protection behavior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.193\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.826\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.189\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4a:privacy policy\u0026rarr;Privacy concerns\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.212\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-4.572\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.218\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH5a:privacy policy\u0026rarr;digital trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.513\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4b:corporate digital responsibility\u0026rarr;Privacy concerns\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.165\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.409\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.169\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH5b:corporate digital responsibility\u0026rarr;digital trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4c:government regulation\u0026rarr;Privacy concerns\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.247\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-4.833\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.241\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH5c:government regulation\u0026rarr;digital trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.832\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.193\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e* \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reveal several significant findings. First, regarding the relationship between privacy policy and privacy protection behavior, the standardized path coefficient is β = -0.245, indicating a negative effect. This path is statistically significant at the 0.01 level (z = -5.211, p\u0026thinsp;=\u0026thinsp;0.000\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting that privacy policy has a significant negative impact on privacy protection behavior, thus supporting Hypothesis H1. Similarly, the standardized path coefficient for the effect of corporate digital responsibility on privacy protection behavior is β = -0.224, also negative, and significant at the 0.01 level (z = -4.585, p\u0026thinsp;=\u0026thinsp;0.000\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This finding confirms that corporate digital responsibility negatively influences privacy protection behavior, supporting Hypothesis H2. Furthermore, the standardized path coefficient for the impact of government regulation on privacy protection behavior is β = -0.189, which is negative and significant at the 0.01 level (z = -3.826, p\u0026thinsp;=\u0026thinsp;0.009\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that government regulation also has a significant negative effect on privacy protection behavior, thereby supporting Hypothesis H3. In terms of privacy concerns, privacy policy has a significant negative impact (β = -0.218, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), supporting Hypothesis H4a. Conversely, privacy policy significantly positively affects digital trust (β\u0026thinsp;=\u0026thinsp;0.217, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), supporting Hypothesis H5a. Corporate digital responsibility significantly reduces privacy concerns (β = -0.169, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), supporting Hypothesis H4b, while it also significantly enhances digital trust (β\u0026thinsp;=\u0026thinsp;0.204, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), supporting Hypothesis H5b. Finally, government regulation significantly reduces privacy concerns (β = -0.241, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), supporting Hypothesis H4c, and positively influences digital trust (β\u0026thinsp;=\u0026thinsp;0.193, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), supporting Hypothesis H5c. These findings collectively highlight the significant impacts of privacy policy, corporate digital responsibility, and government regulation on privacy protection behavior, privacy concerns, and digital trust.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Mediation effect analysis\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the standardized coefficient for the indirect effect of privacy policy on privacy protection behavior through privacy concerns is -0.033, with a 95% confidence interval of [-0.061, -0.012]. Since the confidence interval does not include zero, the result is significant, supporting Hypothesis H4d. Similarly, the standardized coefficient for the indirect effect of corporate digital responsibility on privacy protection behavior through privacy concerns is -0.027, with a 95% confidence interval of [-0.053, -0.008]. This result is also significant and does not include zero, supporting Hypothesis H4e. The standardized coefficient for the indirect effect of government regulation on privacy protection behavior through privacy concerns is -0.039, with a 95% confidence interval of [-0.068, -0.013]. This effect is significant and does not include zero, thus supporting Hypothesis H4f. In terms of digital trust, the standardized coefficient for the indirect effect of privacy policy on privacy protection behavior is -0.029, with a 95% confidence interval of [-0.058, -0.008]. Since the confidence interval does not include zero, this result is significant, supporting Hypothesis H5d. The standardized coefficient for the indirect effect of corporate digital responsibility on privacy protection behavior through digital trust is -0.029, with a 95% confidence interval of [-0.057, -0.008]. This finding is significant and supports Hypothesis H5e. Finally, the standardized coefficient for the indirect effect of government regulation on privacy protection behavior through digital trust is -0.029, with a 95% confidence interval of [-0.055, -0.008]. This result is also significant, supporting Hypothesis H5f. These findings collectively suggest that both privacy concerns and digital trust mediate the effects of privacy policy, corporate digital responsibility, and government regulation on privacy protection behavior.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMediating effect test\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePath\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBeta\u003c/p\u003e\u003cp\u003e(standardized)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLower limit of 95%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUpper limit of 95%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eConclusion\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eMediating effect of Privacy concerns\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4d:privacy policy\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;Privacy concerns\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;privacy protection behavior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003epartial mediation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4e:corporate digital responsibility\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;Privacy concerns\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;privacy protection behavior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003epartial mediation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4f:government regulation\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;Privacy concerns\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;privacy protection behavior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003epartial mediation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMediating effect of digital trust\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH5d:privacy policy\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;digital trust\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;privacy protection behavior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003epartial mediation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH5e:corporate digital responsibility\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;digital trust\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;privacy protection behavior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003epartial mediation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH5f:government regulation\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;digital trust\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;privacy protection behavior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003epartial mediation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Moderating effect test\u003c/h2\u003e\u003cp\u003eThe interaction term between privacy policy and information analysis capability is significant (t = -3.635, p\u0026thinsp;=\u0026thinsp;0.000\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This suggests that the impact of privacy policy on privacy concerns varies significantly depending on the level of information analysis capability. Similarly, the interaction term between privacy policy and information analysis capability is also significant (t\u0026thinsp;=\u0026thinsp;5.183, p\u0026thinsp;=\u0026thinsp;0.000\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This indicates that the effect of privacy policy on digital trust is moderated by information analysis capability, with varying impact magnitudes across different levels, thus supporting Hypotheses H6a and H6b. The interaction term between corporate digital responsibility and information analysis capability is significant (t = -3.070, p\u0026thinsp;=\u0026thinsp;0.002\u0026thinsp;\u0026lt;\u0026thinsp;0.05), meaning that when corporate digital responsibility influences privacy concerns, its impact magnitude changes significantly at different levels of information analysis capability. Likewise, the interaction term between corporate digital responsibility and information analysis capability is significant (t\u0026thinsp;=\u0026thinsp;3.873, p\u0026thinsp;=\u0026thinsp;0.000\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that when corporate digital responsibility affects digital trust, information analysis capability significantly moderates the magnitude of this effect. These results support Hypotheses H6c and H6d. The interaction term between government regulation and information analysis capability is significant (t = -3.291, p\u0026thinsp;=\u0026thinsp;0.001\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that government regulation's influence on privacy concerns is significantly moderated by information analysis capability, leading to varying impact magnitudes at different levels. Furthermore, the interaction term between government regulation and information analysis capability is significant (t\u0026thinsp;=\u0026thinsp;3.184, p\u0026thinsp;=\u0026thinsp;0.002\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that the effect of government regulation on digital trust also varies significantly depending on the level of information analysis capability, thus supporting Hypotheses H6e and H6f. For further details, see Fig.\u0026nbsp;2.\u003c/p\u003e\u003cp\u003eThe interaction term between privacy concerns and information sensitivity is significant (t = -2.667, p\u0026thinsp;=\u0026thinsp;0.008\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that the impact of privacy concerns on privacy protection behavior varies significantly depending on the level of information sensitivity. Therefore, Hypothesis H8a is supported. Similarly, the interaction term between digital trust and information sensitivity is significant (t\u0026thinsp;=\u0026thinsp;4.615, p\u0026thinsp;=\u0026thinsp;0.000\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that the effect of digital trust on privacy protection behavior also varies significantly across different levels of information sensitivity. Consequently, Hypothesis H8b is supported. For further details, see Fig.\u0026nbsp;3.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.6 Moderated mediation effect test\u003c/h2\u003e\u003cp\u003eThis paper employs the Process macro's Model 7 to test for moderated mediation effects. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, for the mediating variable of privacy concerns, when information analysis capability is low, the indirect effect of privacy policy on privacy protection behavior through privacy concerns is significant (Effect = -0.087, 95% CI = [-0.134, -0.047]). At an average level of information analysis capability, the indirect effect is also significant (Effect = -0.053, 95% CI = [-0.083, -0.027]). However, when information analysis capability is high, the indirect effect of privacy policy on privacy protection behavior through privacy concerns is not significant (Effect = -0.020, 95% CI = [-0.049, 0.010]). This finding suggests that as information analysis capability increases, the indirect effect of privacy policy on privacy protection behavior through privacy concerns weakens. Therefore, the moderated mediation effect is significant, supporting Hypothesis H7a.\u003c/p\u003e\u003cp\u003eFor the mediating variable of digital trust, when information analysis capability is low, the indirect effect of privacy policy on privacy protection behavior through digital trust is not significant (Effect\u0026thinsp;=\u0026thinsp;0.001, 95% CI = [-0.024, 0.029]). At an average level, the indirect effect is significant (Effect = -0.041, 95% CI = [-0.069, -0.018]), and when information analysis capability is high, the indirect effect remains significant (Effect = -0.084, 95% CI = [-0.132, -0.040]). This suggests that higher levels of information analysis capability lead to a weaker indirect effect of privacy policy on privacy protection behavior through digital trust, thus confirming the significance of the moderated mediation effect and supporting Hypothesis H7b.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModerated mediation effect test (privacy policy*information analysis capability)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMediator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003einformation analysis capability\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEffect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBootSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBootLLCI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBootULCI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePrivacy concerns\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003elow level(-1SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.047\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eaverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehigh level(+\u0026thinsp;1SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003edigital trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003elow level(-1SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eaverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehigh level(+\u0026thinsp;1SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.040\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e* BootLLCI refers to the lower limit of the 95% interval for Bootstrap sampling, while BootULCI refers to the upper limit of the 95% interval for Bootstrap sampling. Bootstrap type: percentile bootstrap method\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\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, when information analysis capability is at a low level, the indirect effect of corporate digital responsibility on privacy protection behavior through privacy concerns is significant (Effect = -0.084, 95% CI = [-0.133, -0.045]). At an average level of information analysis capability, the indirect effect remains significant (Effect = -0.053, 95% CI = [-0.084, -0.027]). However, when information analysis capability is high, the indirect effect of corporate digital responsibility on privacy protection behavior through privacy concerns is not significant (Effect = -0.021, 95% CI = [-0.054, 0.011]). This indicates that as information analysis capability increases, the indirect effect of corporate digital responsibility on privacy protection behavior through privacy concerns weakens, confirming the significance of the moderated mediation effect and supporting Hypothesis H7c.\u003c/p\u003e\u003cp\u003eFor the mediating variable of digital trust, when information analysis capability is low, the indirect effect of corporate digital responsibility on privacy protection behavior through digital trust is not significant (Effect = -0.010, 95% CI = [-0.038, 0.016]). At an average level, the indirect effect is significant (Effect = -0.044, 95% CI = [-0.075, -0.019]), and when information analysis capability is high, the indirect effect is also significant (Effect = -0.079, 95% CI = [-0.130, -0.037]). This suggests that as information analysis capability increases, the indirect effect of corporate digital responsibility on privacy protection behavior through digital trust strengthens, supporting the significance of the moderated mediation effect and confirming Hypothesis H7d.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModerated mediation effect test (corporate digital responsibility*information analysis capability)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMediator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003einformation analysis capability\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEffect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBootSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBootLLCI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBootULCI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePrivacy concerns\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003elow level(-1SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.045\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eaverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehigh level(+\u0026thinsp;1SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003edigital trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003elow level(-1SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eaverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehigh level(+\u0026thinsp;1SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.037\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\u003eAs presented in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, when information analysis capability is at a low level, the indirect effect of government regulation on privacy protection behavior through privacy concerns is significant (Effect = -0.088, 95% CI = [-0.129, -0.051]). At an average level of information analysis capability, the indirect effect of government regulation on privacy protection behavior through privacy concerns remains significant (Effect = -0.054, 95% CI = [-0.086, -0.026]). However, at a high level, the indirect effect of government regulation on privacy protection behavior through privacy concerns is no longer significant (Effect = -0.029, 95% CI = [-0.063, 0.004]). This indicates that as information analysis capability increases, the indirect effect of government regulation on privacy protection behavior through privacy concerns weakens. Therefore, the moderated mediation effect is significant, supporting Hypothesis H7e.\u003c/p\u003e\u003cp\u003eWhen examining the mediating role of digital trust, at a low level of information analysis capability, the indirect effect of government regulation on privacy protection behavior through digital trust is not significant (Effect = -0.025, 95% CI = [-0.053, 0.002]). At an average level, the indirect effect is significant (Effect = -0.054, 95% CI = [-0.086, -0.026]), and at a high level, it is also significant (Effect = -0.083, 95% CI = [-0.132, -0.041]). This suggests that as information analysis capability increases, the indirect effect of government regulation on privacy protection behavior through digital trust strengthens. Thus, the moderated mediation effect is significant, supporting Hypothesis H7f.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModerated mediation effect test(government regulation*information analysis capability)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMediator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003einformation analysis capability\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEffect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBootSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBootLLCI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBootULCI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePrivacy concerns\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003elow level(-1SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eaverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehigh level(+\u0026thinsp;1SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003edigital trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003elow level(-1SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eaverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehigh level(+\u0026thinsp;1SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.041\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\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, when information sensitivity is low, the indirect effect of privacy policy on privacy protection behavior through privacy concerns is not significant (Effect = -0.026, 95% CI = [-0.058, -0.001]). At an average level of information sensitivity, the indirect effect is significant (Effect = -0.050, 95% CI = [-0.080, -0.025]), and at a high level, the indirect effect is also significant (Effect = -0.073, 95% CI = [-0.115, -0.037]). This suggests that as information sensitivity increases, the indirect effect of privacy policy on privacy protection behavior through privacy concerns strengthens. Consequently, the moderated mediation effect is significant, supporting Hypothesis H9a.\u003c/p\u003e\u003cp\u003eRegarding the mediating role of digital trust, when information sensitivity is low, the indirect effect of privacy policy on privacy protection behavior through digital trust is significant (Effect = -0.097, 95% CI = [-0.152, -0.048]). At an average level of information sensitivity, the indirect effect remains significant (Effect = -0.051, 95% CI = [-0.089, -0.017]). However, at a high level of information sensitivity, the indirect effect is not significant (Effect = -0.006, 95% CI = [-0.052, 0.043]). This indicates that as information sensitivity increases, the indirect effect of privacy policy on privacy protection behavior through digital trust weakens. Therefore, the moderated mediation effect is significant, supporting Hypothesis H9b.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModerated mediation effect test (privacy policy and information sensitivity*Privacy concerns and digital trust)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMediator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003einformation sensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEffect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBootSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBootLLCI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBootULCI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePrivacy concerns\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003elow level(-1SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eaverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehigh level(+\u0026thinsp;1SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003edigital trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003elow level(-1SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.048\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eaverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehigh level(+\u0026thinsp;1SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.043\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\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, when information sensitivity is low, the indirect effect of corporate digital responsibility on privacy protection behavior through privacy concerns is not significant (Effect = -0.034, 95% CI = [-0.077, -0.009]). However, at an average level of information sensitivity, the indirect effect becomes significant (Effect = -0.071, 95% CI = [-0.109, -0.038]), and when information sensitivity is high, the indirect effect is even stronger (Effect = -0.108, 95% CI = [-0.163, -0.060]). These results suggest that as information sensitivity increases, the indirect effect of corporate digital responsibility on privacy protection behavior through privacy concerns becomes more pronounced, thereby supporting the moderated mediation effect and confirming Hypothesis H9c.\u003c/p\u003e\u003cp\u003eRegarding digital trust, when information sensitivity is low, the indirect effect of corporate digital responsibility on privacy protection behavior through digital trust is significant (Effect = -0.100, 95% CI = [-0.159, -0.049]). However, at an average level of information sensitivity, the indirect effect is not significant (Effect = -0.052, 95% CI = [-0.092, -0.017]), and when information sensitivity is high, the indirect effect remains insignificant (Effect = -0.004, 95% CI = [-0.053, 0.047]). This indicates that as information sensitivity increases, the indirect effect of corporate digital responsibility on privacy protection behavior through digital trust diminishes. Thus, the moderated mediation effect holds, supporting Hypothesis H9d.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModerated mediation effect test (corporate digital responsibility and information sensitivity*Privacy concerns and digital trust)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMediator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003einformation sensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEffect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBootSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBootLLCI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBootULCI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePrivacy concerns\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003elow level(-1SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.834\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eaverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.038\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehigh level(+\u0026thinsp;1SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.060\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003edigital trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003elow level(-1SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eaverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehigh level(+\u0026thinsp;1SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.047\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\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e, when information sensitivity is low, the indirect effect of government regulation on privacy protection behavior through privacy concerns is not significant (Effect = -0.036, 95% CI = [-0.086, 0.010]). At an average level of information sensitivity, the indirect effect becomes significant (Effect = -0.082, 95% CI = [-0.124, -0.044]), and at a high level of information sensitivity, the indirect effect is even stronger (Effect = -0.128, 95% CI = [-0.186, -0.073]). These findings indicate that as information sensitivity increases, the indirect effect of government regulation on privacy protection behavior through privacy concerns intensifies, thereby supporting the moderated mediation effect and confirming Hypothesis H9e.\u003c/p\u003e\u003cp\u003eRegarding digital trust, when information analysis capability is low, the indirect effect of government regulation on privacy protection behavior through digital trust is significant (Effect = -0.112, 95% CI = [-0.172, -0.058]). At an average level of information analysis capability, the indirect effect remains significant (Effect = -0.055, 95% CI = [-0.095, -0.018]), but when information analysis capability is high, the indirect effect is no longer significant (Effect\u0026thinsp;=\u0026thinsp;0.002, 95% CI = [-0.048, 0.054]). This suggests that as information analysis capability increases, the indirect effect of government regulation on privacy protection behavior through digital trust weakens, thus supporting the moderated mediation effect and confirming Hypothesis H9f.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModerated mediation effect test (government regulation and information sensitivity*Privacy concerns and digital trust)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMediator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003einformation sensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEffect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBootSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBootLLCI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBootULCI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePrivacy concerns\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003elow level(-1SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.834\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eaverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.044\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehigh level(+\u0026thinsp;1SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.073\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003edigital trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003elow level(-1SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eaverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehigh level(+\u0026thinsp;1SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion and conclusions","content":"\u003cp\u003eOur research constructs a decision-making model for consumer privacy protection behavior and draws three key conclusions based on data analysis and hypothesis testing. Each of these findings not only aligns with existing research but also offers deeper insights into the intricate mechanisms that govern consumer privacy protection in the digital age.\u003c/p\u003e\u003cp\u003eFirst Conclusion is about the negative impact of Privacy Policy, Corporate Digital Responsibility, and Government Regulation. The findings suggest that privacy policies, corporate digital responsibility, and government regulation have a negative impact on privacy protection behavior. This initially counter-intuitive result challenges traditional assumptions, which often position these factors as the primary enablers of improved privacy protection. Supporting the research of Jahari et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Yuniar (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the study argues that although e-commerce companies and governments are crucial in shaping privacy landscapes, their involvement may unintentionally contribute to consumer hesitance regarding privacy protection. One possible explanation for this negative relationship lies in the complexity and ambiguity of privacy policies. Consumers may find the fine print of privacy agreements too overwhelming, leading to confusion and increased concern rather than confidence. Additionally, when privacy policies are not clearly communicated or perceived as overly intrusive, they may lead to a sense of mistrust among consumers. Similarly, government regulations, while necessary to ensure privacy standards, can sometimes create the perception of an invasive or overly regulated environment. This finding compels e-commerce companies to strike a delicate balance between transparency and consumer empowerment, ensuring that privacy policies not only exist but are clear, accessible, and user-friendly. The role of corporate digital responsibility also warrants further discussion. While companies that demonstrate a strong commitment to digital responsibility can alleviate some privacy concerns, it is crucial to note that the mere presence of such policies does not guarantee an increase in privacy protection behavior. Consumers may remain skeptical about whether these policies are genuinely followed or merely a facade. As such, this paper underscores the importance of consistently demonstrating digital responsibility through actions rather than just words. E-commerce platforms must not only enact policies but also build trust through authentic privacy practices.\u003c/p\u003e\u003cp\u003eThe second conclusion of this study highlights the significant role of privacy concerns and digital trust as antecedents of privacy protection behavior, aligning with findings from Chaudhuri et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Kluiters et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and Lia et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These antecedents are fundamental to understanding consumer behavior in the context of privacy protection. However, this research contributes additional insights by demonstrating that privacy concerns and digital trust do not merely influence privacy protection behavior directly but also mediate the relationships between privacy policy, corporate digital responsibility, government regulation, and privacy protection behavior. This mediating effect provides a more nuanced understanding of the decision-making process. It suggests that even if consumers are exposed to comprehensive privacy policies or stringent regulations, their ultimate behavior is shaped by how they perceive the threat of privacy breaches and the level of trust they place in the digital environment. This finding emphasizes the importance of addressing privacy concerns and building trust through consumer-focused communication strategies. By acknowledging and validating consumer fears, companies and governments can influence the effectiveness of privacy policies and regulatory frameworks. In practice, this implies that e-commerce platforms and governments must go beyond merely implementing privacy policies and focus on building a robust, trust-based relationship with consumers. Engaging in transparent dialogue and ensuring that privacy protections are genuinely effective can mitigate privacy concerns and reinforce digital trust. As this study demonstrates, privacy protection behavior hinges on a complex interplay of perceived risk and trust, which mediates the effect of external privacy mechanisms.\u003c/p\u003e\u003cp\u003eThe third key finding concerns the moderating effects of information analysis capability and information sensitivity on the relationships between privacy policies, corporate digital responsibility, government regulation, privacy concerns, and digital trust. The moderating role of information analysis capability reinforces earlier studies by Chaudhuri et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Jiang and Yang (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), highlighting the crucial importance of how consumers process and analyze privacy-related information. A strong capability in analyzing information allows consumers to better assess the risks and benefits associated with sharing personal data. Therefore, the presence of clear, comprehensible privacy policies and strong corporate digital responsibility can empower consumers with the information they need to make more informed decisions.\u003c/p\u003e\u003cp\u003eAdditionally, information sensitivity further moderates the mediating effects between these variables and privacy protection behavior. Consumers who perceive their data as highly sensitive are more likely to be cautious in their privacy-related actions, making them more responsive to privacy policies, corporate responsibility, and government regulation. This finding suggests that companies must tailor their privacy strategies to account for the varying degrees of sensitivity that different consumers attach to their personal data. For example, platforms might need to offer more granular privacy controls or heightened transparency for users dealing with particularly sensitive data. This deeper understanding of information sensitivity opens avenues for future research into consumer segmentation based on data sensitivity. By distinguishing consumers based on how they assess the risk of data exposure, companies can better design privacy practices that resonate with specific user groups, ultimately fostering a more privacy-conscious environment.\u003c/p\u003e\u003cp\u003eThe findings have important practical implications for both e-commerce platforms and government agencies. For e-commerce companies, it is essential to enhance their privacy policies and fulfill their digital responsibilities. A well-constructed privacy policy that clearly outlines the collection, usage, and sharing of personal data can significantly reduce consumer anxiety regarding privacy risks. E-commerce companies should invest in making privacy settings more accessible and transparent, ensuring that consumers feel in control of their data. This proactive approach not only builds consumer trust but also encourages data-sharing behavior that benefits both the platform and its users. Moreover, companies should engage in ongoing education and outreach efforts, helping consumers better understand their privacy rights and providing easily accessible tools to manage privacy settings. By establishing a feedback mechanism, platforms can continuously improve their privacy practices and address concerns in real-time, reinforcing their commitment to protecting consumer privacy.\u003c/p\u003e\u003cp\u003eFrom a governmental perspective, the role of legal and regulatory frameworks cannot be overstated. As evidenced by the findings, government oversight is essential in ensuring that e-commerce platforms adhere to privacy protection standards. Strengthening privacy protection laws and improving enforcement will encourage companies to adopt privacy practices that prioritize consumer interests. Additionally, the government should consider incentivizing self-regulation within the industry to promote a collaborative approach to privacy protection. By providing clear guidelines and penalties for non-compliance, governments can ensure that e-commerce platforms remain accountable and that consumers' privacy rights are upheld. Furthermore, governments should focus on improving consumer education regarding digital privacy, ensuring that users understand their rights and the measures in place to protect them. This will help build trust in both the regulatory framework and the companies that operate within it, ultimately contributing to a more secure and trustworthy e-commerce environment.\u003c/p\u003e"},{"header":"6. Limitations and future research directions","content":"\u003cp\u003eThis study is limited to the context of online shopping on e-commerce platforms. Although this group is closely related to the research background of this paper, it is still unclear whether the conclusions of this study are applicable to other groups. Future research subjects can be continuously expanded to examine the privacy protection behavior on various types of platforms, such as social platforms, self-media platforms, and government service platforms, in order to enhance the applicability of the research findings. This paper explores the influencing factors of consumer privacy protection from the perspectives of enterprises and governments. Future research can continuously change the research entry points, for example, by starting from different cultural backgrounds to investigate a variety of factors affecting privacy protection behavior.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Yao and Gao; methodology, Gao and Li; formal analysis, Yao and Gao; data curation, Wei; writing original draft preparation, Yao and Gao; writing\u0026mdash;review and editing, Gao. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the key projects of National Social Science Foundation of China (Grant:22AGL022), Scientific Research Projects of Jiangsu Vocational College of Agriculture and Forestry (Grant:XCZX202202, 2023kj20).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research strictly adhered to the principles of the Helsinki Declaration and obtained ethical approval from the Research Ethics Committee at the School of Economics \u0026amp; Management, Nanjing Tech University. The participants provided their online informed consent for participation in the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1 Nanjing Tech University, 30 Puzhu South Road, Nanjing, Jiangsu, China\u003c/p\u003e\n\u003cp\u003e2 Jiangsu Vocational College of Agriculture and Forestry,19 Wenchang Road, Jurong, Zhenjiang, Jiangsu, China\u003c/p\u003e\n\u003cp\u003e3 Nanjing Tech University, 30 Puzhu South Road, Nanjing, Jiangsu, China\u003c/p\u003e\n\u003cp\u003e4 Nanjing Tech University, 30 Puzhu South Road, Nanjing, Jiangsu, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlkire, L., Pohlmann, J., \u0026amp; Barnett, W. 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An Investigation of Privacy Policies and Privacy Concerns. \u003cem\u003eJournal of Business Ethics\u003c/em\u003e,\u003cem\u003e 176\u003c/em\u003e(4), 781-798. https://doi.org/10.1007/s10551-020-04626-x\u003c/li\u003e\n\u003cli\u003eZhghenti, T., \u0026amp; Chkareuli, V. (2021). ENHANCING ONLINE BUSINESS SECTOR: DIGITAL TRUST FORMATION PROCESS. \u003cem\u003eMarketing and Management of Innovations\u003c/em\u003e(2), 87-93. https://doi.org/10.21272/mmi.2021.2-07\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":"privacy policy, government regulation, privacy protection behavior","lastPublishedDoi":"10.21203/rs.3.rs-6742139/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6742139/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eWhile consumers' reluctance to disclose personal information threatens the operational efficacy of e-commerce platforms, existing research fails to systematically explain the psychological and institutional drivers of privacy protection behavior. The study addresses this gap by proposing an integrative framework to examine three dimensions of influencing factors: (1) institutional governance mechanisms, (2) cognitive mediators, and (3) individual boundary conditions. The study aims to reveal how these multilevel elements collectively shape consumers' behavioral responses in digital transactions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA survey of 398 e-commerce platform users was conducted, employing a seven-point Likert scale questionnaire. Structural equation modeling (SEM) using Amos 24.0 analyzed direct and mediated relationships, while moderated mediation effects were tested via the Process macro.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOur findings indicate that privacy policy, corporate digital responsibility, and government regulation have a significant negative impact on privacy protection behavior. Furthermore, privacy concerns and digital trust act as mediators between privacy policy, corporate digital responsibility, government regulation, and privacy protection behavior. Additionally, information analysis capabilities and information sensitivity moderate the relationships between privacy policy, corporate digital responsibility, government regulation, privacy concerns, and digital trust.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis paper presents a comprehensive framework for understanding the adoption of privacy protection behaviors by consumers, offering valuable insights for e-commerce platforms seeking to enhance their consumer privacy protection practices.\u003c/p\u003e","manuscriptTitle":"Understanding The Effects of Privacy Policy and Government Regulation on Privacy Protection Behavior","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-10 14:10:28","doi":"10.21203/rs.3.rs-6742139/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":"0c2f587f-e45a-41ca-88f4-2d907cbdb42a","owner":[],"postedDate":"July 10th, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Withdrawn","date":"2026-05-13T08:22:02+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"139518716515905928432793973925463649083","date":"2026-05-10T12:31:01+00:00","index":89,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T08:29:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-10 14:10:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6742139","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6742139","identity":"rs-6742139","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}