An analysis of consumer’s trusting beliefs

Full text

Turn on search term navigation

Introduction

Consumers use different e-commerce platforms in order to buy a wide range of goods and services by conducting a variety of financial trades and transactions (Misra et al. 2022; Cavalinhos et al., 2021). These transactions necessitate the use of a user’s personal and financial information, which can be misused or employed for secondary purposes (Ermakova et al., 2014; Zhu et al., 2020). These types of misuse may affect consumer trust (Zhu et al., 2020). Reports suggest that these vulnerabilities are amplified in online businesses. According to Chawla and Kumar (2022), recent problems with e-commerce platforms include consumer fraud, unethical and unfair practices, and data exploitation. The identification of these concerns and their impact on the consumers’ trusting beliefs must be carefully investigated in order to develop consumer protection mechanisms. Consumer protection mechanisms could accordingly increase the user’s confidence and acceptance of e-commerce platforms (Choi et al., 2021). Establishing detailed security and privacy policies in an e-commerce environment can have an advantageous effect on customer trust (Rodríguez et al., 2020, Pagan et al., 2022). Implementing consumer protection mechanisms will enhance trust in e-commerce platforms, may create a competitive advantage, and influence consumers’ e-commerce intentions (Morimoto, 2022).

E-commerce has brought both convenience and the potential for fraud. According to a recent report, online purchase scams have increased to 78% in the year 2020, which is due to the surge of online purchases in India during the pandemic1. Researchers discovered numerous frauds, such as the loss of sales, the unethical use of data, copyright breaches, and money-back concerns, which impair the consumers’ trust (Chawla and Kumar, 2022). IT ethics explains the ethical use of information technology by practicing what is moral, right, and of value (Suh and Han, 2003). If sellers are proven to be engaging in unethical and impulsive actions, consumers are less likely to share their personal information on online platforms (Siciliani et al., 2019; Cuesta-Valiño et al., 2022). Studies that integrated ethics with technology usage indicated that personal information disclosure on online platforms might have consequences for both the consumer and the retailer, which has the potential to influence consumer purchasing behavior (Fue Zeng et al., 2022). There are incidents where consumers and retailers have experienced data breaches. The past research indicates that a lack of trust may be attributable to a breach of ethical principles, a lack of assurance, and transparency from vendors (Hajli, 2019; Rosillo-Díaz et al., 2020; Raza et al., 2022). However, consumers still found themselves frequently sharing their information despite their reservations about disclosure. (Pantano et al., 2021; Sheth, 2020). It is, therefore, critical to understand the factors that affect the consumers’ beliefs that lead to privacy paradoxes.

The concept of privacy implies that an individual’s confidentiality is protected when an information exchange complies with the privacy rules of a context. These norms comprise of the type of information, who will be able to see and utilize the information, and the information transmission principles (Nissenbaum and Daniel, 2009). According to the exploitation theory, consumers are more likely to become victims of online fraud due to their lack of comprehension and understanding of online commerce privacy mechanisms (Yoon and Occena, 2015). According to the research, numerous technologies, such as third-party certificates, digital signatures, and copyright policies have been utilized by firms to complete data or information integration. Studies discussed individuals’ security and privacy concerns using multi-perspective analyses and the necessity of a privacy framework with online digital communities, which are evolving with time (Suh and Han, 2003; Enaizan et al., 2020; Vieira et al., 2023). We developed a framework after reviewing the recent studies with multiple layers of security, such as awareness and confidentiality. Consumer knowledge and understanding of these dimensions are still in their nascent stages, which further slows down the adoption of e-commerce services (Siciliani et al., 2019; Alonso-Garcia et al., 2023).

The social contract and economic theories state that adoption can be improved by developing trusting beliefs (McCoubrey and White, 1999). According to Levine (2019), the social contract theory suggests that trust and cooperation between buyers and sellers are of the utmost significance, which should be stressed via the development of privacy standards and consumer protection mechanisms. A few studies also discussed how companies can increase the trusting belief, such as with the case of programmatic advertising, which involves dynamically putting adverts on websites based on user transactions. This type of advertising is growing, but it comes with the risk of exposing customer data to more sophisticated analyses (Núnez-Barriopedro et al., 2023; Gong et al., 2019). The studies suggested that companies can counteract the users’ negative perceptions by demonstrating the benefits of Internet advertising, which include personalized ads, better offers based on the users’ interests, and transparency in data processing, in addition to defining the role of legislation and policies on the programmatic ecosystem (Barretta and Fuat Fırat, 2022). The social contract theory also implies that when consumers trust an online business to handle their personal information, they regard the social contract as being more credible and are more willing to share their personal information (Kruikemeier et al., 2020). Understanding the consumers’ expectations and increasing their level of awareness regarding security concerns, privacy of information, copyrights, and data use in relation to online commerce is necessary for the development of these types of standards (Sfenrianto et al., 2018).

The economic theory similarly encourages system transparency and the necessity for consumer protection legislation in order to increase the consumers’ trust in e-commerce vendors and services (Chawla and Kumar, 2022). As a result, the theory implies that in order to conduct online transactions, buyers and sellers must enter into an ethical and legally binding agreement, which should be readable and verified by both parties (Degeling et al., 2018; Siciliani et al., 2019). As a result, the present study follows the social contract theory and the economic theory in order to influence consumer perception by creating trusting beliefs.

This study makes the following contributions, which are based on a survey of the relevant literature. First, the study evaluates consumer awareness regarding the protection of personal and financial data used on e-commerce platforms. Several studies examined privacy policies from the perspectives of policymakers, technological companies, and online business communities, and they provided an overview of these general considerations (Chawla and Kumar, 2022; Levine, 2019). How aware are consumers of these dimensions? Are their views sufficiently explored in the literature? The study deals with these questions. This study explains next the impact of the consumers’ perceptions towards various security dimensions, which include awareness about privacy protection, information confidentiality, preventing unauthorized secondary data usage, and perceived information integrity in e-commerce services on their trusting beliefs. Trust was treated in most of the past research as a single variable that included one or two security concerns along with technological or utilitarian factors (Suh and Han, 2003; Kim et al., 2008; Aeron et al., 2019). This study explored several aspects of trust, which may be influenced by numerous privacy and confidentiality aspects, which also discusses them in detail. Third, this study proposes the indirect influence of these variables on the users’ intention to use e-commerce platforms, which considers the mediating effect of trusting beliefs in the relationship. Finally, this study compares varied consumers' perceptions about privacy concerns based on their varied demographics by including the moderating effect of gender, age, and frequency of use. This study, therefore, proposes the following research objectives (RO), which are provided below.

RO1: Investigate the connection between the consumers’ perceptions of security and how those perceptions may affect their behavioral intention to use e-commerce platforms.

RO2: Examine the mediating effect of trusting beliefs between various security measures and behavioral intention to use e-commerce platforms.

RO3: Evaluate the moderating effects of the consumers’ gender, age, and frequency of e-commerce platform usage on their trusting beliefs and subsequent behavioral intention to use e-commerce platforms.

This study used structural equation modeling (SEM) for the analysis, and it found a strong impact of the consumers’ perceptions of security on their trusting beliefs by eventually driving their adoption of e-commerce services in India. This study offers a few managerial and policy implications for online commerce communities, e-commerce firms, and policymakers. It implies that e-commerce companies should focus more on protecting consumers’ privacy, educating them, and offering them control. This might be accomplished by adding a new section on its website that is devoted to data security and privacy in order to enforce trusting belief and transparency. The next section discusses the theoretical background and the gaps in the literature. Section “Theoretical background” includes the hypotheses development of the conceptual model. Sections “Methods” and “Results” explain the methodology, data analysis, and major findings, whereas sections “Discussion and implications” present the discussions, implications of the results, and the limitations of the study. Lastly, the section “Conclusion” provides the concluding remarks.

Theoretical background

The consumer’s trust formation process is complex (Hung et al., 2021). Several factors, such as consumer beliefs, attitudes, perceptions, and trust in someone or a particular trait play a significant role in regards to influencing consumers’ trusting beliefs (Kim et al., 2008). Various categories of trusting beliefs in this regard have been defined in the literature, which are related to the nature of trust (disposition to trust), trust in a situation or structure (institutional trust), and interactive trust, which is also known as interpersonal trust (Zhu et al., 2020). Recent research posits that trust is established through the act of willingly placing one’s faith in a trustee, be it an individual or an organization. To achieve this, one must take into account the trustee’s traits, accept their data usage strategies, and accept that one cannot completely control the outcomes (Zhu et al., 2020; Merhi et al., 2021; Ghouse et al., 2022). However, there are some recent studies that indicate the significant role of information clarity and inputs, such as textual descriptions and pictures created by sellers on their websites in influencing customer trust on e-commerce sites (Nghiêm-Phú and Bagul, 2020). This may indicate the seller’s willingness to make the process transparent and ethical.

Trust is not a new component when it comes to e-commerce (Bandara et al., 2020). Several theories and/or models show how different kinds of behavior affect the process of creating trusting beliefs (Leonard and Jones, 2021; Chawla and Kumar, 2022). Some studies outlined the notion of trust in critical theory that elaborates trust as a concept of fetishism by describing it as the trust that reaches the status of an independent entity that controls human perceptions. Another is reification, which is typically viewed as a set of widely accepted yet one-sided views that influence trust (Joseph-Vaidyan, 2008). The positive side of critical theory indicates rebuilding the consumers’ trust via reification or a particular ideology that influences the adoption of e-commerce platforms. The negative side subsequently summarizes the myopic approach to trust by focusing on one aspect while ignoring other aspects of trust in the process (Chen et al., 2013). Sharma and Lijuan (2014) propose that critical theory posits that the ethical performance of an e-commerce website will promote trust, leading to an improvement in consumer commitment. However, to do so, e-commerce sites must improve communication platforms that provide customers with the opportunity to share feedback, receive after-sales services, and receive support. This enhances trust. According to past researchers in the area of e-commerce platforms, trust is generally treated as a fetish (Sharma and Lijuan, 2014). Due to this, e-commerce platforms are losing their ethical properties, hindering their ability to reach their maximum potential due to a lack of confidence from end consumers (Stahl, 2006). Thus, past studies suggest focusing on various aspects of trust, considering the role of institutions (sellers), platforms, the ethical and moral nature of trust, the role of information, and its evaluation, which are important aspects of the trust formation process (Nghiêm-Phú and Bagul, 2020; Ghouse et al. 2022).

Moreover, studies that are based on a grounded theory approach draw various aspects of trust, which developed foundations of consumer trust in an e-commerce environment. For example, Kang et al. (2015) and Zhu et al. (2020) concluded that privacy protection is one of the major concerns of consumers distrusting e-commerce websites by using a grounded theory approach. It was further discussed by Merhi et al. (2021) in the context of e-commerce sites that have a high possibility of unauthorized access and secondary data concerns for end users due to the high control of e-commerce agents and a lack of transparency and awareness. This, in turn, may influence the user’s trust linked to payments and other transactions.

A lack of information on privacy guidelines and ineffective legal enforcement are the few other crucial concerns that influence a user’s behavioral intention (Kang et al., 2015; Zhu et al., 2020). This concern is enhanced by a lack of information and the readability of privacy policies, which develop the consumers’ distrust of websites (Ermakova et al., 2014). The results of the previous studies offer significant theoretical contributions, which are accordingly based on well-known theories. A few recent studies discussed the significant role of perceived security and perceived privacy in the context of e-commerce sites, indicating the importance of personal data consumption transparency on consumer perception (Dogra and Adil, 2024).

The social contract theory (SCT) draws attention towards a trust-risk framework that influences perceptions towards e-commerce platforms (Featherman and Hajli, 2016). The theory has been used in offline services and recently with respect to online services in order to understand the moral and ethical behavior in digital communities or platforms (Levine, 2019). According to SCT, there is both a positive and a negative side to the digital revolution and the rapid distribution of data on e-commerce platforms. On the one hand, it promotes a strong digital environment and an empowered, hyper-information-connected environment to boost the sharing of economic concepts. However, there are major concerns related to compromising sensitive information and disrupting the privacy of end users at the cost of sharing knowledge and information. These platforms, which were created for the benefit of users, have now been used for selfish motives (Liaropoulos, 2020). The theory suggests that to develop trusting beliefs, trustworthiness, and social cooperation, digital communities need to develop norms that are related to privacy, information integrity, and confidentiality, while displaying the use of hyper-norms, which include regularizing policies, in order to build consumer trust. Since e-commerce includes a higher level of privacy or confidentiality risk (Dogra and Adil, 2024), customers pay close attention to these aspects and regulations. In other words, according to the SCT, trusting beliefs among customers can be developed by the implementation of certain privacy or legal norms. E-commerce websites should stress privacy guidelines and display their data usage policies and legal initiatives that are in line with the customers’ beliefs and expectations, which is the opinion of Gulati et al. (2012) and Liaropoulos (2020). This increases customer intent in regards to utilizing these sites. The social contract theory is relevant in the present study, because it can be used in order to assess the users’ digital trust, which influences their choices and preferences. Moreover, the findings from the previous studies showed that SCT significantly explains a number of different types of ethical and normative beliefs of the consumers towards e-commerce privacy norms, user’s confidentiality norms, data usage norms, and other requirements (Featherman and Hajli, 2016; Levine, 2019). This provides an understanding of the digital businesses’ responsibility and cooperation that are required for building consumer trust on these types of platforms.

Security issues in e-commerce platforms were also outlined in several of the previous studies (Hou, 2019; Hajli, 2019; Chawla and Kumar, 2022). It was accordingly observed that consumers are often exploited by their contracting partners, which are e-commerce firms in this case, and there, therefore, is a need for consumer protection norms (Hou, 2019). Furthermore, previous studies suggested that consumers are considered to be the weaker party according to the exploitation theory (Poon, 2008). The argument goes that while corporations can take advantage of complexities and conditions to their advantage, customers are required to abide by the contractual terms set forth by e-commerce platforms (Ghouse et al., 2022). The prevalence of these issues is greater in online markets compared to face-to-face purchases. Furthermore, a lack of information and discrepancies in knowledge also affect the consumers’ bargaining power in e-commerce platforms (Chawla and Kumar, 2022). Consumer privacy and personal data are in need of certain security. However, the massive growth of e-commerce businesses and increased competition in recent years reject the claims of the exploitation theory, which indicates the high negotiating power of consumers in today’s digital world (Alonso-Garcia et al., 2021). According to Siciliani et al. (2019), consumers expect transparency in online processes and a strong protection framework by e-commerce firms. This is explained by ET, which promotes online market transactions and suggests strong protection and privacy laws for consumers in order to develop trust in e-commerce (Poon, 2008). Therefore, it is imperative for e-commerce companies to establish robust commercial and legal contracts and further communicate them to their customers for their awareness, which is crucial in fostering confidence between buyers and sellers (Nghiêm-Phú and Bagul, 2020; Chawla and Kumar, 2022). The current investigation discovered that consumer trusting beliefs are based on the fundamental principles of social contract theory (SCT) and ET.

Theoretical gaps related to trust in e-commerce platforms

There is an abundance of literature available on the use and adoption of e-commerce platforms or websites (Sfenrianto et al., 2018; Wei et al., 2018; Misra et al., 2020, 2022). A large variety of dimensions and antecedents of adoption were developed, and their predictive potential was evaluated in order to explain the behavioral intentions of consumers who use e-commerce sites (Suh and Han, 2003; Wei et al., 2018). The previous studies show that trust is one of the most important dimensions that affect how individuals perceive things and conduct online transactions (Sfenrianto et al., 2018; Yu et al., 2022). The activities and behavior of suppliers in an online market are highly unexpected, and they are beyond the control of consumers, which makes it extremely risky for consumers to purchase from or engage in any trade with a supplier. This leads to the avoidance of online markets for shopping (Chawla and Kumar, 2022). However, trust is found to be the most effective way of eliminating the consumers’ doubts and develop loyalty towards e-commerce (Hou, 2019; Siciliani et al. 2019). According to Zhao et al. (2020), trust is more necessary in online trades than in physical transactions, because both parties, which include buyers and suppliers, are uncertain about trades, contracts, and assurances. It is, therefore, essential to comprehend trust and the factors that influence it for the development of e-commerce. The technology acceptance model (TAM), the unified theory of acceptance and the use of technology (UTAUT), the theory of planned behavior (TPB), the theory of reasoned actions (TRA), and the expectation confirmation theory were used in pursuit of understanding how well technological and sociological components work in regards to developing trusting beliefs in an e-commerce setting the previous studies theoretical frameworks (Zhao et al., 2020; Misra et al. 2022). Despite the availability of academic literature on understanding the impact of trust on consumers’ beliefs and attitudes (Singh et al., 2020), there is a lack of this type of academic literature in the context of e-commerce (Lin and Lu, 2000; Suh and Han, 2003; Levine, 2019).

A few gaps were accordingly identified in order to streamline the current research, which are based on the current academic literature on the impact of trust on consumers’ beliefs in the context of e-commerce. First, past studies considered trust as one of the antecedents of a user’s perception along with other technological, behavioral, and social factors (Han and Li, 2020; Misra et al., 2022). The researchers included perceived trust in order to explain a single aspect of a consumer’s trust perception due to this. For example, few studies only discussed privacy concerns while using a new service or technology (Hajli, 2019; Sinha and Singh, 2019). Others discussed the ethical and moral issues that may influence trust (Aeron et al., 2019; Chawla and Kumar, 2022). Furthermore, some studies explained the consumers’ views on data misuse that affected their intention to use e-commerce (Bandara et al., 2020; Zhu et al., 2020; Agrebi et al., 2022). There are only a few studies, to the best of our knowledge, that included trust as a main variable and elaborated on various dimensions of trust, particularly in an e-commerce context. Second, most of the findings among the previous trust-based studies were found to be descriptive in nature, which generally discussed the researchers’ views, privacy policies, and awareness level, or they were literature reviews that assessed the nature and dimensions of trust (Levine, 2019; Bandara et al., 2020; Chawla and Kumar, 2022). However, a few studies suggested certain antecedents of trust or distrust, which included information quality, word of mouth, perceived readability, consumers’ regulatory expectations, transparency, and security protection (Ermakova et al., 2014; Featherman and Hajli, 2016. Moreover, these types of studies are scattered and limited to one or few dimensions. Nevertheless, the authors of the present study found the study by Suh and Han (2003) to be significant, because the authors outlined various elements of the consumers’ perceptions and awareness about security controls, such as authenticity, confidentiality, privacy, and information integrity, which influence the trust and usage of e-commerce sites. The present study is influenced by Suh and Han's (2003) (Enaizan et al., 2020) findings, and this research extends their study by adding the consumers’ perceptions about other aspects that are related to awareness about privacy and secondary data usage in an Indian context. Third, our study further contributes to the academic literature by comparing the trusting beliefs of varied consumers based on their demographics, which include gender, age, and frequency of use of e-commerce platforms. The present study offers important theoretical and practical implications because there are very few studies in this specific context (Yoon and Occena, 2015; Han and Li, 2020).

Conceptual model

Figure 1 describes the conceptual model of the study. The study proposes the direct effect of awareness about privacy protection, information confidentiality, preventing unauthorized secondary data usage, and perceived information integrity on the consumer’s trusting beliefs. The study further examines the mediating effect of trusting beliefs and the moderating effect of gender, age, and frequency of use of consumers on the proposed relationships.

Fig. 1 [Images not available. See PDF.]

Determinants of consumer privacy concerns on e-commerce following social contract theory and ET.

Awareness about privacy protection: privacy policies guided by e-commerce sites are clear and understood by the customers; perceived information confidentiality: consumers belief their personal data to be protected by e commerce website from unauthorised access, storage, or manipulation; preventive unauthorised secondary data usage: customers concern about secondary usage of their personal data without their consent; information integrity: buyer confidence that an e-service provider is competent, trustworthy, and will keep the transaction's commitment; trusting belief: trusting belief is an understanding of privacy protection policies led by the company and self-regulatory mechanisms by the customer; behavioural intention: Behavioural intention refers to a customer's inclination or readiness to engage in a specific online shopping behaviour.

Awareness about privacy protection (Awareness) and trusting beliefs (Trust)

The research on privacy has recently become an important topic in the field of information systems as a result of the expansion of personal data collecting by Internet corporations and numerous data breaches (Harborth and Pape, 2020). According to the theoretical framework of the SCT, the study posits that any misuse of the users’ information constitutes a breach of the psychological agreement between the users and the service (Hoffman et al., 1999). This breach has significant consequences because it undermines the users’ trust and influences their behavior (Malhotra and Kubowicz Malhotra, 2011). This leads to the need for privacy protection standards that ensure that the users’ personal information that is collected from online interactions can’t be shared without their knowledge and/or approval, which indicates virtual agreement among both parties (Kim et al., 2021; Alonso-Garcia et al., 2023). Studies following ET observed that privacy protection statements are one of the significant factors that influence consumer trust with websites (Esmaeilzadeh, 2020). According to the ET, a clear and transparent privacy policy increases consumers’ trust, which in turn reduces privacy concerns and significantly influences decisions. Callanan et al. (2016) stated that users with high awareness about privacy policies are found to be more inclined towards using online services. If privacy policies are clear and understood, customers feel secure entering into a virtual agreement with high trust (Bandara et al., 2020). However, privacy concerns due to a limited understanding of privacy policies might, on the other hand, have a negative impact due to a breach of the social contract. This causes individuals to respond negatively as a result of diminished levels of trust when asked for personal information in an online setting (Adhikari and Panda, 2018). The following hypothesis is proposed, which is based on the arguments above.

H1: Awareness about privacy protection has a positive impact on trusting beliefs.

Perceived information confidentiality (Confidentiality) and trusting beliefs

Perceived information confidentiality is defined as the subjective probability that consumers believe that their personal information will not be viewed, stored, or manipulated during transit or storage by inappropriate parties (Sarkar et al., 2020). According to the SCT, e-commerce platforms enable a virtual agreement between the platform and users, which has implicit and explicit terms and conditions (Malhotra and Kubowicz Malhotra, 2011). There are social and moral obligations to protect the users’ privacy and data confidentiality, which affect the users’ trust and usage of a service if breached (Callanan et al., 2016). Confidentiality breaches occur when someone controls the dissemination of information that another person wants to keep private. (Adhikari and Panda, 2018). It relates to the security aspect of private information that is stored in databases, which “restricts the information flow in terms of what is externalized and who gets to see it” (Tawalbeh et al., 2020). Research indicates that the assessment of users’ trust is significantly influenced by the level of information confidentiality (Sarkar et al., 2020). The economic theory indicates that enhancing consumer trust is widely recognized as a crucial aspect, whereas the implementation of technical protections and a heightened level of authentication assurance can contribute to its improvement (Kim et al., 2010).

Concerns for confidentiality usually occur at the stage where private data has been disclosed and stored in a database. Hattingh et al. (2015) observed a lack of perceived confidentiality as a barrier to using the service. The level of access to the consumers’ sensitive private data may influence the users’ actions. Full access may have a negative effect from a confidentiality point of view (Malhotra and Kubowicz Malhotra, 2011). On the other hand, an additional layer of security and regulatory frameworks of confidentiality may positively influence trust and enhance the users’ purchases from these websites (Tawalbeh et al., 2020). The existing literature on SCT and ET suggests that customers tend to have a greater sense of trust in the security of their personal information when measures are in place in order to prevent unauthorized access and secure sensitive data (Zhu et al., 2020). Secure information sharing is expected to have a positive impact on trust in a collaborative environment (Ghondaghsaz et al., 2022). The following hypothesis is proposed, which is based on the arguments above.

H2: Perceived information confidentiality has a positive impact on trusting beliefs

Preventing unauthorized secondary data usage (PUSDU) and trusting beliefs

Users are concerned about unauthorized access to personal information due to security vulnerabilities or a lack of data security policies (Eastin et al., 2016). The threat of secondary use or the reuse of their personal information for unrelated purposes without their knowledge is the issue that agitates customers (Chen and Liu, 2015). Secondary data usage (SDU) includes disclosing to third parties what belongs to different parties/businesses when the customer provides their personal information. An SDU also involves combining a consumer’s transaction data and other personal information in order to build a profile (Khan, 2016). The literature proves both the unauthorized sale of their private information to third parties and the misuse of their personal data as main barriers to the users’ using the service (Tawalbeh et al., 2020; Zhu et al., 2020). Studies confirmed the negative association between ambiguous privacy policy dimensions and the users’ trust, which indicates the need for a transparent policy framework in order to enhance trust in the system (Sarkar et al., 2020). According to Esmaeilzadeh (2020), a clear policy on preventing secondary data and third-party usage is likely to convince users to share the right amount of information in order to meet the needs of the providers. This is possible when the users’ trust the system to offer transparent privacy policies (ET) and agree to the terms (Kim et al., 2021).

Studies that follow the SCT explain that when users experience dishonesty, which includes the unauthorized or illegal secondary use of their personal and sensitive information, they may predict that relying on the system is not wise (Markos et al., 2023). This reduces their trust, which will lead them to remain dishonest in regards to exchanging the correct information in the future, or it may cause them to discontinue the usage of the services (Meinert et al., 2006). The customers’ decisions to transact in the world of e-commerce are influenced by data vulnerability, which provides the key mechanism in regard to preventing and eliminating potential unauthorized secondary data usage that could increase their trust (Chen and Liu, 2015). The following hypothesis is proposed based on these arguments.

H3: Preventing unauthorized secondary data usage will have a positive impact on trusting beliefs.

Information integrity and trusting beliefs

Another key concept in e-commerce security is information integrity. Integrity reflects “the buyer’s confidence that an e-service provider is knowledgeable and trustworthy and will uphold the terms of the transaction’s commitment” (Hang and Kim, 2019; Alonso-Garcia et al., 2023). Credibility, consistency, dependability, and reliability are the elements of integrity belief (Bandara et al., 2020; Özbölük and Akdoğan, 2022). SCT, which is based on a core ethical principle of privacy and security (Rinta-Kahila and Soliman, 2017), explains it by associating it with information security and commitment towards a contract. Information security is based on the three fundamental dimensions of information quality, which include integrity, availability, and confidentiality. Integrity ensures information authenticity among these (Loch et al., 1992). ET explains the concept of information integrity in a business context from a formal and legal standpoint, which pertains to the protection against unauthorized modifications or the deletion of information with a primary focus on guaranteeing the authenticity of the information (Markos et al., 2023). If firms fail with this, the ramifications of this may have a detrimental impact on the integrity and trustworthiness of the online service (Meinert et al., 2006). According to previous studies, users are more willing to trust online businesses that have strong data integrity policies. (Bandara et al., 2020; Markos et al., 2023). Information integrity, which includes information that is accurate, current, and relevant, helps in order to lower the levels of perceived ambiguity and risk that are associated with an e-commerce transaction (Meinert et al., 2006; Misra et al., 2022). According to Özbölük and Akdoğan (2022), the reliability of online sources has a significant role in regard to shaping customers’ trusting beliefs. We contend, in view of this, that information integrity and trust beliefs have a positive relationship.

H4: Information integrity has a positive impact on trusting beliefs.

Trusting beliefs and behavioral intention to use (BI)

Online trust has drawn a lot of attention in numerous consumer behavior studies, particularly in the context of SCT and ET (Siciliani et al., 2019; Markos et al., 2023). It is the product of several privacy and security aspects of service. It is imperative to comprehend the concerns that arise between service providers and users regarding privacy and to address these concerns by ensuring integrity and confidentiality, which is the social contract theory. It is additionally crucial to promote transparency within the system by establishing and validating privacy rules, which is the economic theory. This helps in regards to building trust (Chawla and Kumar, 2022), which in turn enhances consumer usage of a service (Palvia, 2009).

Trust is crucial in e-commerce because it minimizes the perceptions of risk and uncertainty and increases the likelihood that people will make purchases (Chang and Chen, 2008; Lăzăroiu et al., 2020; Özbölük and Akdoğan, 2022). Consumers are generally more vulnerable to product or service uncertainty, specifically in an online environment (Yap et al., 2021). According to prior research, trust has a negative impact on perceived risk (Kim et al., 2009; Lăzăroiu et al., 2020), but it has a favorable impact on attitudes toward online buying (Palvia, 2009). The unauthorized use of personal data, the compromise of data integrity, and the violation of information confidentiality are regarded as violations of the social contract (Xu et al., 2005). These breaches consequently have a detrimental impact on trust, which result in a state of skepticism and distrust among customers. This state of distrust poses challenges in regards to establishing online trust, and it indicates a lack of willingness to participate in the process (Head and Hassanein, 2002; Santo and Marques, 2021).

On the other hand, privacy protection policies and self-regulatory mechanisms build consumers’ trust beliefs (Xu et al., 2005), which reduce uncertainty, foster loyalty through word-of-mouth, and positively affect the consumers’ attitudes and behavioral intentions towards making online purchases (Santo and Marques, 2021). Özbölük and Akdoğan (2022) believe that the perceived trustworthiness of consumers towards source credibility has a favorable impact on consumer purchasing decisions. Creating trust in e-commerce might increase a user’s attitude and behavioral intention toward using a specific website for purchases (Raman, 2019). The customers’ trust and favorable attitude toward a certain web shop are positively correlated with their intention to use this type of web store (Raman, 2019). The following hypothesis is proposed based on these arguments.

H5: Trusting beliefs have a positive impact on behavioral intention to use.

Moderating impact of gender

The previous research witnessed that gender significantly influences the behavior of users towards privacy and security concerns that influence their usage patterns, preferences, and general web accessibility (Correa, 2016; Molinillo et al., 2021). According to the research, male users are more comfortable using ICT and managing privacy and security-related issues (Meng et al., 2021). Further, the concerns related to privacy and data confidentiality are found to be more prevalent among female users in the context of digital and electronic services (Molinillo et al., 2021). Women are shown to be less inclined than men in regard to divulge personal information, such as phone numbers and addresses (Chi and Han, 2021; Meng et al., 2021). Further, women are found to be more concerned with privacy protection behavior and the sharing of personal information than men, which indicates low awareness and low trust among women consumers towards privacy norms with online services (Hoy and Milne, 2010; Shao et al., 2019). A recent study demonstrated that both genders are willing to share the same amount of information, however, males were shown to be substantially more concerned with cyberattacks (Zwilling et al., 2022).

According to Merhi et al. (2021), trust is an important consideration in the context of electronic markets due to the inherent risk and privacy concerns associated with these platforms. User’s trust may get affected by the data breach or unauthorized issues linked with payments and other transactions. According to previous studies, male and female buyers perceive and behave differently when assessed on personal information control, which is due to the growing relevance of e-commerce and privacy protection behaviors (Ghouse et al., 2022). This may have a different influence on their trusting beliefs. According to past studies, women read privacy policies more than men, and they can have better controlled behavior (Sheehan and Hoy, 1999). In contrast, a recent investigation found that men exhibit a larger predisposition towards accepting surveillance and engaging in privacy protection measures than women, hence having high trust in more sensitive online applications like banking (Balapour et al., 2020; Merhi et al., 2021). It is further suggested that male customers possess a heightened need in regards to monitoring and place greater trust in these types of practices (Ioannou and Tussyadiah, 2021). The following hypotheses are proposed, which are based on these arguments.

H6a: The relationship between awareness about privacy protection and trusting beliefs is significantly different by gender.

H6b: The relationship between perceived information confidentiality and trusting beliefs is significantly different by gender.

H6c: The relationship between preventing unauthorized secondary data usage and trusting beliefs is significantly different by gender.

H6d: The relationship between information integrity and trusting beliefs is significantly different by gender.

H6e: The relationship between trusting beliefs and behavioral intention to use is significantly different by gender.

Moderating impact of age

Since the younger generation grew up in the digital era, they might have greater levels of faith in online resources and technology. They frequently have more experience with conducting digital transactions and disclosing personal information online (Herrando et al., 2019). As per recent studies young people’s digital interaction is increasing year after year. Social networking sites play an important part in their personal and professional interactions, and they have a high percentage of digital abilities (Herrando et al., 2019; Meng et al., 2021; Nghiêm-Phú, 2022). Younger and older age groups differ significantly in their perceptions, motivations, interests, and attitudes (Shin and Kang, 2016; Manosuthi et al., 2020; Meng et al., 2021; Untaru and Han, 2021) towards digital technology. According to Meng et al. (2021), individuals belonging to the younger demographic exhibit a notable level of self-assurance in their ability to effectively navigate a diverse range of information and communications technology (ICT) duties. Younger cohorts are found to be more proficient in various computer and internet-related tasks like office applications, active involvement in several brand communities, and a comprehensive comprehension of the complexities that are associated with online shopping (Castillo de Mesa et al., 2020). Certain research suggests that older cohorts exhibit lower levels of confidence when it comes to disseminating content and safeguarding the privacy of the content that they publish (Poon, 2008; Malik et al., 2016). The older generation is more concerned about information sharing, particularly in the context of e-banking services (Poon, 2008). Ethical principles and legal frameworks are critical in protecting information confidentiality for older individuals (Chung et al., 2016). Younger persons, on the other hand, demonstrate lower levels of anxiety in terms of privacy-related concerns, and they exhibit an elevated level of confidence with respect to posting content on social media as well as making payments in online shopping (Lissitsa and Kol, 2021). Previous studies also reported that increased Internet use and active participation in a variety of online activities, like online shopping, result in positive attitudes and higher levels of trustworthiness toward the Internet (Santo and Marques, 2021; Castillo‐Abdul et al., 2022). This is most likely due to young users being typically more engaged and active in a variety of Internet activities, such as sharing photos and videos, playing online games, sharing online shopping experiences, and participating in various forms of social networking and online forms of communication (Muscanell and Guadagno, 2012; Ghazali et al., 2016). The previous studies additionally show that younger users are more knowledgeable about privacy-related issues (Lissitsa and Kol, 2021), and they also discuss ICT-related issues and subjects with their peers (Malik et al., 2016). The following hypotheses are built on the preceding arguments:

H7a: The relationship between awareness about privacy protection and trusting beliefs is significantly different by age group.

H7b: The relationship between perceived information confidentiality and trusting beliefs is significantly different by age group.

H7c: The relationship between preventing unauthorized secondary data usage and trusting beliefs is significantly different by age group.

H7d: The relationship between perceived information integrity and trusting beliefs is significantly different by age group.

H7e: The relationship between trusting beliefs and behavioral intention to use is significantly different by age group.

Moderating impact of frequency of use

The three factors that influence the adoption of security and privacy technologies are knowledge and experience derived from frequent usage, awareness of security threats, and motivation to use security tools (Kang et al., 2015). Individuals who use digital platforms frequently are more conversant and at ease with digital technologies. The frequency of engagement in digital transactions might aid in a sense of trusting belief (Merhi et al., 2021).

The frequency of use is regarded as a behavioral variable that can be influenced by the characteristics of a platform and its perceived value (Molinillo et al., 2021). However, further studies also indicated that users’ views may evolve with time due to high use and interactions with the service (Amin et al., 2008; Aldas-Manzano et al., 2011). As people get more Internet expertise, their concerns about online information privacy may lessen. This is due to growing awareness about online privacy practices (Wu et al., 2020).

It is also demonstrated that a strong relationship exists between trust and Internet use as well as prior Internet experience (Molinillo et al., 2021). Studies also claim that the usage and familiarity with computers are both favorably correlated with their ICT self-efficacy, which in turn enhances trusting beliefs and more confidence towards privacy and data usage (Tondeur et al., 2011). Contrary to a previous study, Aldas-Manzano et al. (2011) discovered that frequent use can diminish the impact of trust on consumer behavior. This occurs because consumers may rely more on their own experiences rather than trust when perceiving benefits. According to Efe (2015), students who frequently utilize the Internet demonstrate enhanced levels of self-efficacy and comprehension of online security norms. According to Carlson and O’Cass (2011), a positive correlation exists between the frequency of Internet usage and confidence in privacy standards and the ability to use online services. More Internet-savvy users were more inclined to change their social media site privacy settings (Malik et al., 2016; Radic et al., 2022a). The more knowledgeable and frequent the Internet users are, the more concerned they are with privacy and security policy. Users who are knowledgeable about cyber and other data security challenges will devote more time to understanding data security strategies such as safeguarding personal information, refraining from visiting specific websites, and identifying fake information (Öğütçü et al., 2016). The following hypotheses are postulated based on these arguments.

H8a: The relationship between awareness about privacy protection and trusting beliefs is significantly different by frequency of use.

H8b: The relationship between perceived information confidentiality and trusting beliefs is significantly different by frequency of use.

H8c: The relationship between preventing unauthorized secondary data usage and trusting beliefs is significantly different by frequency of use.

H8d: The relationship between perceived information integrity and trusting beliefs is significantly different by frequency of use.

H8e: The relationship between trusting beliefs and behavioral intention to use is significantly different by frequency of use.

Methods

Research design

This study adopted a positivistic paradigm, which is where the researcher acts as an unbiased observer who is detached from distinguishing phenomena and is in pursuit in order to quantitatively measure reality in a society that is constantly developing and changing (Shabani, 2020). Positivism with its rigid approach is accordingly adequate for theory testing (Liyanage, 2022), because its’ tenets are grounded on the hypothetico-deductive method in order to demonstrate the truth of a priori hypotheses (Vos, 2023). Furthermore, the action research strategy was used in this research, because action research is a complex, analytical, comprehensive, contextual, and prospective synergetic approach towards problem-solving where problem solutions are respectful (Bowling, 2023). A cross-sectional time horizon was decided on because it is felicitous for studies that are attempting to comprehend the consumers’ behavior that is concealed by the ascendancy of various variables (Gupta and Singh, 2020) as a researcher's interest is related to the fixed image of a phenomenon that does not require multiple data collection (Fernandes et al., 2020). Lastly, covariance-based structural equation modeling (CB-SEM) was applied in order to evaluate the research model. CB-SEM is an augmentation of the conceptualization of the multiple regression method (Akgül, 2019), and it is an effective tool in order to assess and strengthen contemporary theories and contrasting divergent theories (Hair et al., 2017). Moreover, CB-SEM was picked over partial least squares SEM (PLS-SEM), because CB-SEM calculates model fit statistics, which is a more suitable technique for confirmatory research. Also, it provides the reliability of constructs because it does not inflate correlations between them, works well with larger sample sizes, and offers identical benefits in regards to handling non-normal data, such as PLS-SEM (Collier, 2020).

Measures for the study variables

The survey contained a mix of multi-item measures. The scale items for this study were accordingly adopted from previously validated measurement items, and they were anchored using a 7-point Likert scale, which ranged from (1) strongly disagree to (7) strongly agree. Moreover, the scale adoption that was utilized in this study meets the criteria that were set forward by Pillet et al. (2023), which includes where deliberate changes to the item phrasing were not performed. This study’s scale cognitive validity was more precisely not compromised by using the already validated scales, because the authors avoid any purposeful item wording adjustments. The scale ability in regards to generating unbiased responses was subsequently successful. Lastly, all the participants were fluent in the English language, so there were not any language issues with the questionnaire.

Awareness about privacy protection

It was proposed by Malhotra et al.’s (2004) and validated by Okazaki et al. (2020), and the three-item measure (AVE = 0.708 and CR = 0.878) evaluates the participants’ awareness about privacy protection. Furthermore, awareness about privacy protection was measured using companies seeking information online should disclose the way the data is collected, processed, and used (β = 0.924), a good consumer online privacy policy should have a clear and conspicuous disclosure (β = 0.858), and it is very important to me that I am aware and knowledgeable about how my personal information will be used (β = 0.731).

Perceived information confidentiality and risk of unauthorized secondary data usage

Perceived information confidentiality and preventing unauthorized secondary data were assessed using the eight loading items of Smith et al.’s (1996) and Malhotra et al.’s (2004) scales, which were validated by Okazaki et al. (2020) and Vimalkumar et al. (2021). Hence, perceived information confidentiality (AVE: 0.776, CR: 0.932) was measured by the following items. All communications with this site are restricted to the site and me (β = 0.884), I am convinced that this site respects the confidentiality of the transactions received from me (β = 0.919), this site uses some security controls for the confidentiality of transactions (β = 0.882), and this site checks all communications between the site and me for protection from wiretapping or eavesdropping (β = 0.836). Moreover, the risk of unauthorized secondary data usage (AVE = 0.874 and CR = 0.965) was assessed by online companies should not use personal information for any purpose unless it has been authorized by the individuals who provided information (β = 0.938) when people give personal information to an online company for some reason, the online company should never use the information for any other reason (β = 0.938), online companies should never sell the personal information in their computer databases to other companies (β = 0.940), and online companies should never share personal information with other companies unless it has been authorized by the individuals who provided the information (β = 0.924).

Information integrity and trusting beliefs

The three-item scale developed by Smith et al.’s (1996) and the five-item scale developed by Jarvenpaa et al.’s (1999) and Malhotra et al.’s (2004) were validated Merhi et al. (2019), Okazaki et al. (2020), and Vimalkumar et al. (2021), and they were utilized in this study. The participants were asked to rate their agreement with the items based on information integrity and trusting beliefs. The three-item scale that was used in order to measure information integrity (AVE = 0.809 and CR = 0.927) included online companies should take more steps to make sure that the personal information in their files is accurate (β = 0.882), online companies should have better procedures to correct errors in personal information (β = 0.889), and online companies should devote more time and effort to verifying the accuracy of the personal information in their databases (β = 0.918). Furthermore, the five items for measuring trusting beliefs (AVE = 0.760 and CR = 0.940) included online companies would be trustworthy in handling information” (β = 0.801), online companies would tell the truth and fulfill promises related to the information provided by me (β = 0.867), I trust that online companies would keep my best interests in mind when dealing with the information (β = 0.891), online companies are in general predictable and consistent regarding the usage of (the information (β = 0.903), and online companies are always honest with customers when it comes to using the information that I would provide (β = 0.892).

Behavioral intention

The four-item scale that was developed and validated by Lee et al.’s (2017) was used in this study in order to measure behavioral intention. The four items scale for measuring behavioral intention (AVE = 0.840 and CR = 0.955) included I intend to continue using this site in the future (β = 0.897), I expect my use of this site to continue in the future (β = 0.927), I will frequently use this site in the future (β = 0.923), and I will strongly recommend others to use this site (β = 0.919).

The minimization of a common method bias (CMB) of the self-administrated questionnaires was achieved by following the procedural and statistical strategies of Jordan and Troth (2020). All the respondents were knowledgeable with respect to the goal of the research and how the results would be used by looking at the procedural strategies. The questionnaire that was used was not substantial, the measurements did not extend over, the phrasing of the items was leveled cautiously, measures were adopted from divergent sources, and the questions presented were transparent and unambiguous (Jordan and Troth, 2020; Radic et al., 2022a). The aforementioned set of procedural remedies was confirmed in the recent studies by Radic et al. (2022b, 2022c) and Calder et al. (2022), so the issue of common method bias is unlikely to be of concern in this research. Furthermore, Harman’s one-factor test was applied from the statistical strategy, since Jordan and Troth (2020) highlight Harman’s one-factor test as one of the most common statistical approaches to test for CMB. Harman’s one-factor test points out problematic CMBs, which is due to the method (Fuller et al., 2016).

Data collection process and characteristics of the samples

A small pool of 50 participants, the initial respondents was selected using the personal administration method for pilot testing. The purposive sampling approach was used in combination with snowball sampling for the final sample collection. The purposive sampling is a non-probability sampling technique that provides rich data on specific phenomena within particular settings (Adu and Miles, 2023). The purposive sampling in this study accordingly provided a primary set of research participants because it permits the universality of the research population (Berget and Kvikne, 2022). The selection criterion for finding the most suitable applicants was online shoppers. The respondents were requested to send the invitation forward by using the snowball effect. Snowball sampling is another non-probability sampling technique that is often employed in context-specific studies where the participants are hard to find (Sharma, 2022). This study subsequently investigated specific consumers’ perceptions of IT ethics, which was followed by online commerce websites, so online surveys were considered appropriate. Moreover, we met the criteria for representativeness of the survey in relation to the variables that are outlined by Ochsner (2021) by adopting the purposive sampling technique because representativeness of online purposive sampling provides confidence that the researcher's findings are externally valid (Barratt and Lenton, 2015). A total of 780 responses were collected via online surveys using Google Forms links, which were sent via email, a WhatsApp group, and social media. The sample consisted of students, university staff, managers, consultants, and engineers coming from academic backgrounds that are diverse. The data was collected between August 2021 and September 2021. We removed the missing values and dishonest responses in order to ensure the authenticity of the data. Moreover, we determined whether there was a common method bias in the data via Harman’s single-factor test in order to ensure that the quality of the data was not misleading or biased. We conducted Harman’s single-factor test via SPSS 26.0. We defined all the variables as 1 single factor, and the result showed that the total variance of this factor was 36.882%, which was below the threshold criterion of 40% (Zhonglin, 2020), so the issue of a common method bias was not present in the dataset that was collected for this study. Finally, a total of 780 valid responses were used for the empirical analysis.

We used a frequency analysis in order to produce the following results, which are in response to the collected samples. 50.1% of the 780 participants who participated in the survey were male, and females accounted for 49.9%. The largest number of participants was below the age of 25, which included 56.7%. This was followed by the respondents between 25–34 years of age with 36.4%. 3.2% of participants were between 45–54 years of age, 2.9% of the participants were between 35–44 years of age, and the least number of participants were between 55 and 64 years of age, which totalled 0.89%. We found that graduates were the most numerous with 59.7% in regard to the education level, which was followed by post-graduates at 29.5%. The participants with other degrees totalled 9.4%, and the participants with a doctorate were the least with 1.4%. We found that the largest number of participants with more than 3 years of experience using the Internet totalled 82.4% in regards to the online experience of the participants. The proportion of participants with 2–3 years was 9.4%, and it was 4.4% for 1–2 years. The participants with less than one year had the least share with 3.8%. According to a Rakuten Insight survey (Statista, 2023) on online buying behavior in India that was conducted during June 2022, over 84% of consumers aged 16–44 years opted for e-commerce marketplaces, such as Shopee, Amazon, and AliExpress in order to shop online. Approximately 90% of the sample in our study is in the 18–44-year-old age category, which is somewhat close to the population. According to the Statista (2022) report, 72% of urban Internet users use the Internet on a daily basis. Nine out of ten users access the Internet at least once a week in metropolitan areas, and young users aged 16–29 are the most frequent users. This indicates that urban Indian strata have sufficient Internet exposure, which is also evident in our findings.

Results

Data quality testing using a confirmatory factor analysis

This study conducted a statistical analysis of the data sample by using both SPSS 26.0 and AMOS 26.0 programs and following Anderson and Gerbing’s (1988) two-step method. The measurement model was first developed by conducting a confirmatory factor analysis for the measurement items in this study. The measurement models were used next as the basis for developing structural equation models and testing the goodness-of-fit of the model. In addition, the variability of gender, age, and frequency of use were evaluated in the structural model for each path. The results are shown in Tables 1 and 2, and the fit indices of the measurement models exhibited satisfactory indices according to the confirmatory factor analysis (χ² = 602.645, df = 215, χ²/df = 2.803, p < 0.01, NFI = 0.955, IFI = 0.964, CFI = 0.963, and RMSEA = 0.066). All measures had factor loading values between 0.731 and 0.938 (p < 0.01). The average variance extracted (AVE) and the composite reliability (CR) for each construct were greater than the recommended metrics of Hair et al. (2017) of 0.500 and 0.700, and the metrics range from 0.708 to 0.874 for AVE and from 0.878 to 0.965 for CR. The model can, therefore, be considered an ideal measurement model with good convergent validity and internal consistency. Furthermore, the correlation indices between each variable were lower than the √AVE value of each factor, which is illustrated in Table 1. As a result, the data of the research scales can be concluded to have excellent discriminant validity (Fornell and Larcker, 1981).

Table 1. Assessment of correlations.

	[1]	[2]	[3]	[4]	[5]	[6]	Mean	SD
[1] Awareness	0.841						3.82	1.217
[2] Confidentiality	0.836**	0.881					3.96	1.200
[3] PUSDU	0.824**	0.832**	0.935				4.09	1.243
[4] Integrity	0.794**	0.812**	0.845**	0.900			3.78	1.228
[5] Trust	0.835**	0.872**	0.856**	0.806**	0.872		3.66	1.201
[6] BI	0.709**	0.727**	0.719**	0.777**	0.790**	0.916	3.55	1.196

Awareness: awareness about privacy protection, Confidentiality: perceived information confidentiality, PUSDU preventing unauthorized secondary data usage, Integrity: perceived integrity, Trust: trusting beliefs, BI behavioral intention to use. Coefficient in Italic: $\sqrt{AVE}$ , SD std. deviation.

**p < 0.01.

Table 2. Confirmatory factor analysis evaluation.

Measurement items	β
Awareness about privacy protection (AVE: 0.708, CR: 0.878)
Companies seeking information online should disclose the way the data are collected, processed, and used.	0.924
A good consumer online privacy policy should have a clear and conspicuous disclosure.	0.858
It is very important to me that I am aware and knowledgeable about how my personal information will be used.	0.731
Perceived information confidentiality (AVE: 0.776, CR: 0.932)
All communications with this site are restricted to the site and me.	0.884
I am convinced that this site respects the confidentiality of the transactions received from me.	0.919
This site uses some security controls for the confidentiality of transactions.	0.882
This site checks all communications between the site and me for protection from wiretapping or eavesdropping.	0.836
Risk of unauthorized secondary data usage (AVE: 0.874, CR: 0.965)
Online companies should not use personal information for any purpose unless it has been authorized by the individuals who provided the information.	0.938
When people give personal information to an online company for some reason, the online company should never use the information for any other reason.	0.938
Online companies should never sell the personal information in their computer databases to other companies.	0.940
Online companies should never share personal information with other companies unless it has been authorized by the individuals who provided the information.	0.924
Perceived integrity (AVE: 0.809, CR: 0.927)
Online companies should take more steps to make sure that the personal information in their files is accurate.	0.882
Online companies should have better procedures to correct errors in personal information.	0.898
Online companies should devote more time and effort to verifying the accuracy of the personal information in their databases	0.918
Trusting Beliefs (AVE: 0.760, CR: 0.940)
Online companies would be trustworthy in handling information	0.801
Online companies would tell the truth and fulfill promises related to (the information) provided by me.	0.867
I trust that online companies would keep my best interests in mind when dealing with (the information).	0.891
Online companies are, in general, predictable and consistent regarding the usage of (the information).	0.903
Online companies are always honest with customers when it comes to using (the information) that I would provide.	0.892
Behavioral Intention (AVE: 0.840, CR: 0.955)
I intend to continue using this site in the future.	0.897
I expect my use of this site to continue in the future.	0.927
I will frequently use this site in the future.	0.923
I will strongly recommend others to use this site.	0.919

Goodness-of-fit statistics for the baseline model:χ² = 602.645, df = 215, χ²/df = 2.803, p < 0.01, NFI = 0.955, IFI = 0.964, CFI = 0.963, RMSEA = 0.066.

Awareness: awareness about privacy protection, Confidentiality: perceived information confidentiality, PUSDU preventing unauthorized secondary data usage, Integrity: perceived integrity, Trust: trusting beliefs, BI behavioral intention to use. AVE average variance extracted, CR composite reliability.

Evaluation of the structural model and the indirect and total effects

Structural equation modeling, which was conducted using the test model, was used in order to test the goodness-of-fit and the hypotheses that were proposed by the research model. Also, the indirect effects in the model were also evaluated. This process we implemented using the AMOS 26.0 program. The results of the analysis showed that the goodness-of-fit statistics of the structural equation model exhibited well-defined indicators (χ² = 585.317, df = 203, χ²/df = 2.883, p < 0.01, NFI = 0.975, RFI = 0.968, IFI = 0.983, TLI = 0.971, CFI = 0.982, and RMSEA = 0.049). The total variance in the data for trusting beliefs was 74.0%, and it was 79.9% for behavioral intention to use online e-commerce platforms, which indicated that the structural equation model had a satisfactory level of prediction for both trusting beliefs and behavioral intention to use. Each hypothesis in the model reflects the presence of significant effects. The relationships between awareness about privacy protection and trusting beliefs (β = 0.048* and p < 0.05), perceived information confidentiality and trusting beliefs (β = 0.473** and p < 0.01), preventing unauthorized secondary data usage and trusting beliefs (β = 0.060** and p < 0.01), perceived information integrity and trusting beliefs (β = 0.714** and p < 0.01), and trusting beliefs and behavioral intentions to use online e-commerce platforms (β = 0.713** and p < 0.01) were in particular positively significant, so Hypothesis 1, Hypothesis 2, Hypothesis 3, Hypothesis 4, and Hypothesis 5 were therefore supported.

The findings of the indirect and total effects that were tested for the structural equation model were as follows. There were indirect effects for two of the four indirect paths in total. These included perceived information confidentiality-trusting beliefs- behavioral intention to use the online e-commerce platform (β = 0.377** and p < 0.01) and perceived information integrity-trusting beliefs-behavioral intention to use the online e-commerce platform (β = 0.509** and p < 0.01). The results of the total effect showed, in contrast, that there were significant total effects of the remaining three variables on behavioral intention to use online e-commerce platforms except for awareness about privacy protection (β_awareness = 0.034) and preventing unauthorized secondary data usage (β_PUSDU = 0.043). Trusting beliefs (β_trust = 0.713**) had the largest total effect on behavioral intention, which was followed by perceived information integrity (β_integrity = 0.509**) and perceived information confidentiality (β_{confidentiality} = 0.337**). Detailed findings about the hypotheses and the indirect effects are reported in Table 3.

Table 3. Evaluation of structural model and indirect and total effect.

Paths			β	t-values
Awareness	→	Trust	0.048	2.024*
Confidentiality	→	Trust	0.473	15.747**
PUSDU	→	Trust	0.060	2.588**
Integrity	→	Trust	0.714	19.675**
Trust	→	BI	0.713	17.442**
Indirect path		β	Lower	Upper
Awareness → Trust → BI		0.034	−0.040	0.104
Confidentiality → Trust → BI		0.337**	0.203	0.375
PUSDU → Trust → BI		0.043	−0.047	0.109
Integrity → Trust → BI		0.509**	0.341	0.494

Total effect on behavioral intention: β_awareness = 0.034, β_{confidentiality} = 0.337**, β_PUSDU = 0.043, β_integrity = 0.509**, β_trust = 0.713**.

Variance explained trust and behavioral intention: R² for trust = 0.740, R² for behavioral intention = 0.799.

Goodness-of-fit statistics for the baseline model:χ² = 585.317, df = 203, χ²/df = 2.883, p < 0.01, NFI = 0.975, RFI = 0.968, IFI = 0.983, TLI = 0.971, CFI = 0.982, RMSEA = 0.049.

*p < 0.05; **p < 0.01.

Comparison of the chi-square differences in regards to gender, age, and frequency of use

We divided the data into two groups according to the K-means cluster of the SPSS 26.0 program for gender, age, and frequency of use in order to examine the difference of gender, age, and frequency of use on each path of the study model. This means that gender was divided into male (n = 397) and female (n = 389), age was divided into low age group (n = 726) and high age group (n = 54), and frequency of use was divided into low frequency of use group (n = 437) and high frequency of use group (n = 343). After that, we imported the data of the three groups into the structural equation model in order to perform an invariance test to explore the differences. Nevertheless, the goodness-of-fit statistics of the three baseline models presented satisfactory results (Gender: χ² = 963.532, df = 406, χ²/df = 2.373, p < 0.01, NFI = 0.959, RFI = 0.949, IFI = 0.976, TLI = 0.970, CFI = 0.975, RMSEA = 0.040; Age: χ² = 964.673, df = 406, χ²/df = 2.376, p < 0.01, NFI = 0.955, RFI = 0.943, IFI = 0.971, TLI = 0.964, CFI = 0.970, RMSEA = 0.042; Frequency of use: χ² = 979.354, df = 406, χ²/df = 2.412, p < 0.01, NFI = 0.958, RFI = 0.948, IFI = 0.975, TLI = 0.969, CFI = 0.974, and RMSEA = 0.043). However, all the hypotheses would not be significantly different depending on gender and age. For instance, gender and age were not significantly different in the research model of this study, so Hypotheses 6, which contains Hypothesis 6a, Hypothesis 6b, Hypothesis 6c, Hypothesis 6d, and Hypothesis 6e, and Hypothesis 7, which contains Hypothesis 7a, Hypothesis 7b, Hypothesis 7c, Hypothesis 7d, and Hypothesis 7e, were not supported. Also, the results of testing the frequency of use revealed significant differences in the two relationships between perceived information confidentiality and trusting beliefs (Δχ²[1] = 15.716 and p < 0.01) and preventing unauthorized secondary data usage and trusting beliefs (Δχ²[1] = 14.200 and p < 0.01). Thus, Hypothesis 8b and Hypothesis 8c were supported. More detailed outcomes of the analysis regarding the invariance test are illustrated in Tables 4–6.

Table 4. Assessment of comparative gender differences.

Paths			Male (n = 391)		Female (n = 389)		Baseline model	Nested model
Paths			β	t-values	β	t-values	Baseline model	Nested model
Awareness	→	Trust	0.093	2.835**	−0.005	−0.142	χ²(406) = 963.532	χ²(407) = 964.551
Confidentiality	→	Trust	0.481	11.498**	0.458	1.688**	χ²(406) = 963.532	χ²(407) = 963.536
PUSDU	→	Trust	−0.021	−0.650	0.154	4.419**	χ²(406) = 963.532	χ²(407) = 963.935
Integrity	→	Trust	0.726	14.002**	0.690	13.777**	χ²(406) = 963.532	χ²(407) = 967.048
Trust	→	BI	0.753	12.886**	0.668	11.768**	χ²(406) = 963.532	χ²(407) = 963.541
Chi-square difference test:					Goodness-of-fit statistics for the baseline model: χ²/df = 2.373, p < 0.01, NFI = 0.959, RFI = 0.949, IFI = 0.976, TLI = 0.970, CFI = 0.975, RMSEA = 0.040. **p < 0.01.
Δχ²(1) = 1.019, p > 0.05 (H6a: Not supported)
Δχ²(1) = 0.004, p > 0.05 (H6b: Not supported)
Δχ²(1) = 0.403, p > 0.05 (H6c: Not supported)
Δχ²(1) = 3.516, p > 0.05 (H6d: Not supported)
Δχ²(1) = 0.009, p > 0.05 (H6e: Not supported)

Table 5. Evaluation of age differences.

Paths			Low age group (n = 726)		High age group (n = 54)		Baseline model	Nested model
Paths			β	t-values	β	t-values	Baseline model	Nested model
Awareness	→	Trust	0.038	1.551	0.282	2.619**	χ²(406) = 964.673	χ²(407) = 965.533
Confidentiality	→	Trust	0.460	14.948**	0.627	5.141**	χ²(406) = 964.673	χ²(407) = 964.810
PUSDU	→	Trust	0.076	3.192**	−0.029	−0.286	χ²(406) = 964.673	χ²(407) = 964.735
Integrity	→	Trust	0.732	19.094**	0.280	2.583**	χ²(406) = 964.673	χ²(407) = 967.256
Trust	→	BI	0.715	16.741**	0.657	4.663**	χ²(406) = 964.673	χ²(407) = 964.791
Chi-square difference test:					Goodness-of-fit statistics for the baseline model: χ²/df = 2.376, p < 0.01, NFI = 0.955, RFI = 0.943, IFI = 0.971, TLI = 0.964, CFI = 0.970, RMSEA = 0.042. **p < 0.01.
Δχ²(1) = 0.860, p > 0.05 (H7a: Not supported)
Δχ²(1) = 0.137, p > 0.05 (H7b: Not supported)
Δχ²(1) = 0.062, p > 0.05 (H7c: Not supported)
Δχ²(1) = 2.583, p > 0.05 (H7d: Not supported)
Δχ²(1) = 0.118, p > 0.05 (H7e: Not supported)

Table 6. Assessment of the moderating effect of frequency of use by using invariance test.

Paths			Low-frequency group (n = 437)		High-frequency group (n = 343)		Baseline model	Nested model
Paths			β	t-values	β	t-values	Baseline model	Nested model
Awareness	→	Trust	0.134	4.171**	−0.032	−0.943	χ²(406) = 979.354	χ²(407) = 981.790
Confidentiality	→	Trust	0.314	8.881**	0.571	12.687**	χ²(406) = 979.354	χ²(407) = 995.070
PUSDU	→	Trust	0.330	9.353**	−0.177	−5.286**	χ²(406) = 979.354	χ²(407) = 993.554
Integrity	→	Trust	0.730	14.410**	0.658	13.952**	χ²(406) = 979.354	χ²(407) = 982.516
Trust	→	BI	0.699	12.200**	0.739	13.228**	χ²(406) = 979.354	χ²(407) = 98.354
Chi-square difference test:					Goodness-of-fit statistics for the baseline model: χ²/df = 2.412, p < 0.01, NFI = 0.958, RFI = 0.948, IFI = 0.975, TLI = 0.969, CFI = 0.974, RMSEA = 0.043. **p < 0.01.
Δχ²(1) = 2.436, p > 0.05 (H8a: Not supported)
Δχ²(1) = 15.716, p < 0.01 (H8b: Supported)
Δχ²(1) = 14.200, p < 0.01 (H8c: Supported)
Δχ²(1) = 3.162, p > 0.05 (H8d: Not supported)
Δχ²(1) = 1.000, p > 0.05 (H8e: Not supported)

Awareness: awareness about privacy protection, Confidentiality: perceived information confidentiality, PUSDU risk of unauthorized secondary data usage, Integrity: perceived integrity, Trust: trusting beliefs, BI behavioral intention to use.

Discussion and implications

E-commerce platforms can be considered mercantile stores that are converted into online shop fronts for the sole purpose of trading goods and services over the Internet. Furthermore, e-commerce platforms allow various businesses to reach customers worldwide as well as execute sales and purchases that are made on the Internet. However, the e-commerce platforms represent ~1% of the worldwide retail market segment share, and 85% of that worldwide market segment share still resides in physical stores (Dabhade, 2022). It is of little wonder that in e-commerce, consumer trust plays an important role between consumers and suppliers because universal interchangeability and universal trust are the main characteristics of online value transfers (Radic et al., 2022a). Thus, there is great potential for e-commerce platforms to provide the best customer experience based on consumer trust by looking at the long-term future. The aim of this study was to consequently evaluate the mediating effect of trusting beliefs between various security measures and the behavioral intention to use e-commerce platforms. The study encompassed the following variables that included awareness about privacy protection, perceived information confidentiality, preventing unauthorized secondary data usage, information integrity, trusting beliefs, age, gender, and frequency of use in regard to measuring online shoppers’ viewpoints. The conceptual model deciphered the influencing predominance that was drawn out by the dependent variables behavioral intention to use e-commerce platforms (79.9%) and trusting beliefs (74.0%), which is consistent with the synopsized results. The aforementioned values demonstrate the conclusiveness of various variables with online shoppers’ behavioral outcomes.

Our study brought forward the relationship between awareness about privacy protection, perceived information confidentiality, preventing unauthorized secondary data usage, and information integrity with trusting beliefs in order to use e-commerce platforms in regards to the antecedents of trusting beliefs towards using e-commerce platforms. Information integrity is subsequently displayed as the most substantial antecedent (β = 0.714 and t = 19.675), which is followed by perceived information confidentiality (β = 0.473 and t = 15.747), preventing unauthorized secondary data usage (β = 0.060 and t = 2.588), and awareness about privacy protection (β = 0.048 and t = 2.024). Our research subsequently proposed the relationship between trusting beliefs and behavioral intention to use e-commerce platforms with respect to behavioral intention to use e-commerce platforms. Trusting beliefs were displayed as a robust antecedent (β = 0.713 and t = 17.442) of the behavioral intention to use e-commerce platforms.

This study’s findings exhibited that awareness about privacy protection has a robust impact on online shoppers trust beliefs towards e-commerce platforms. This is due to the fact that many countries have enforced laws on personal data protection and privacy, which brings them in line with the global data protection standards. The consumers’ awareness about privacy protection boosted the consumer's trust beliefs towards e-commerce platforms, which is vital for the modern digital economy, international financial transactions, and various online services. Moreover, as governments legally recognize online consumers’ rights and enforce legal consequences to the violators, trust beliefs towards e-commerce platforms are growing, because consumers are protected by fundamental human rights (Cheon, 2019). Perceived information confidentiality is needed because there is too much information about a person in the public domain without it, which could be a source of discomfort. The comfort level grows and the willingness of consumers to share personal details grows with it as the consumer and e-commerce platforms establish trust based on information confidentiality. The heart of every e-commerce retail strategy is the consumer data. As a result, e-commerce platforms must secure consumer information confidentiality, since strong and healthy relationships are built on trust (Goundar et al., 2021). The results of our study show that online shoppers have strong trust beliefs towards e-commerce platforms that have well-established measures in regard to protecting the consumers’ personal information, which is in relation to preventing unauthorized secondary data usage. This finding corresponds with a KPMG study that was conducted using 18,000 online consumers in 50 countries, in which 41% of the respondents outlined the paramount importance of having control of their personal data secondary usage (KPMG International, 2017). This study’s findings exhibited that information integrity has a robust impact on the consumer's trust beliefs. Consumers need to be confident that e-commerce platforms will responsibly protect their personal data, or the consumers will not provide access to it. Trust is developed once this relationship is established, and so long as the e-commerce platform continues to deliver on this trust and even exceeds, its value proposition increases and the relationship continues to grow. This is due to the fact that information integrity is one of the key attributes of trust in e-commerce (Li and Chau, 2018; Cuesta-Valiño et al., 2022). The importance of consumer trust cannot be understated, and once it’s lost, which is like with any human relationship, it’s very difficult to regain. As a result, e-commerce platforms need to be thoughtful about how they obtain consumers' data and what they do with it because consumers' trust beliefs are essential toward behavioral intention to use e-commerce platforms. Moreover, consumer trust is also the pathway regarding how e-commerce platforms obtain meaningful data, which is necessary to drive consumer loyalty (Cuesta-Valiño et al., 2022; Nghiêm-Phú, 2022).

Finally, the present study confirmed the moderating effect of frequency of use on perceived information confidentiality and preventing unauthorized secondary data usage on trusting beliefs in regards to the subject of the moderating effects. Our study results are supported by Li and Chau (2018), who argue that with a higher frequency of use, the consumers’ overall online shopping competence grows. Also, if e-commerce platforms deliver on their promise of consumers’ information confidentiality and do not misuse secondary data usage, the consumers’ confidence in the e-commerce platform flourishes, which ultimately increases the consumers trusting beliefs.

Theoretical implications

Firstly, this research aimed to test a conceptual model between the consumers’ perceptions of security and how those perceptions may affect the consumers’ behavioral intention to use e-commerce platforms. The proposed model variables were more precisely drawn from social contract and economic theories because the mediating effect of trusting beliefs between various security measures and the intention to use e-commerce platforms was examined. The proposed model accordingly explains 79.9% of the variance in the behavioral intention to use e-commerce platforms, whereas trust beliefs had robust explanatory power for an online consumer’s behavioral intention to use e-commerce platforms, which explained 74.0% of the variance.

Second, this study is an early attempt to use age, gender, and frequency of e-commerce platform usage on the consumers' trusting beliefs and subsequent behavioral intention to use e-commerce platforms. The COVID-19 pandemic boosted the growth rate of e-commerce (Misra et al., 2022), and the growing adoption of e-commerce also increases the importance of studying the moderating role of age, gender, and frequency of e-commerce platform usage while examining the potential barriers to adopt e-commerce platforms. The results of the moderating effects demonstrate the need for additional research on the frequency of e-commerce platform usage. This study could accordingly offer a base model to various upcoming studies in order to distinguish barriers in regard to the consumer’s consumers’ perceptions of security and how those perceptions may affect the consumers’ behavioral intention to use e-commerce platforms.

Third, our findings show that the various security measures are the antecedents of the consumer’s trusting beliefs, which in return have a direct positive impact on behavioral intention to use e-commerce platforms. This study sheds light on the aforementioned relationship and how e-commerce relationships based on trust beliefs could be enhanced while identifying an approach that deserves additional academic attention.

Practical implications

This study has several distinctive practical implications. First, the result of this study offers practitioners insights in regards to increasing trust in e-commerce platforms. Our findings unveiled that e-commerce platforms enhanced security features based on the protection of personal data and transparent consumer protection rules and addressed that the responsibility of personal data secondary data usage promotes the consumer’s trusting beliefs in regards to using e-commerce platforms and consumer’s engagement in economic transactions. Thus, once the aforementioned enhanced security features are met and the consumer’s trusting beliefs in regards to using e-commerce platforms are elevated, e-commerce platforms can create positive consumer experiences based on the obtained consumer data, because they have the knowledge of what their consumers like, what they don’t like, and why they are engaged with an e-commerce platform. The security features of e-commerce platforms can subsequently strengthen the positive consumer experience because consumers can harvest all the benefits of the product portfolio (Cuesta-Valino et al., 2023). Furthermore, e-commerce platforms should constantly communicate with their consumers in regard to privacy protection, information confidentiality, preventing unauthorized secondary data usage, and information integrity by answering consumer questions and addressing any concerns in order to keep the consumers engaged (Rohden et al., 2023). This way the e-commerce platforms will understand what consumers are trying to convey by demonstrating the commitment to finding answers and then sharing every part of them in order to create a trustful experience-led consumer journey.

Second, the e-commerce platforms that clearly outline their state-of-the-art data security systems, protocols, and processes that ensure that consumer data is completely safe and secure are a step closer to creating a first good impression in regards to building trust on an e-commerce surface. Various difficulties during the implementation process of customer-company relationships, such as the use of e-commerce platforms, could accordingly be mitigated by implementing better marketing-oriented processes that are based on consumers trust beliefs that can enhance values for those that are related to marketing management (Alonso-Garcia et al., 2023). Moreover, e-commerce platforms should frequently update data and post security features on their social media platforms. Hence, this way, the e-commerce platforms would provide high-quality valuable content that builds trust among their consumers.

Third, companies frequently do not disclose to customers that they are using their data in alternative ways. They acquire and analyze secondary data in order to better target potential buyers. According to our findings, trust is a critical facilitator. The more people trust a brand, the more likely they are to provide their data. Usage transparency and the protection of the consumers’ secondary data build trust. The demand for Internet privacy is increasing, so progressive businesses now view privacy as a competitive advantage. Companies should guarantee that basic contract rules drive website interactions in order to establish trust. Privacy policies should also spell out the fundamental parameters of the agreement. An e-commerce company should respect the privacy of its customers and secure it with robust encryptions and stringent standards that govern how the data is handled. Companies should be transparent when sharing personal or profile information with customers and obtain their permission before sharing it with third parties. It is suggested that these websites prioritize their effectiveness and highlight their security features in order to enhance security features and boost consumers’ trust in e-commerce platforms. They may additionally consider developing a new section on their website in regards to raising awareness about and addressing data security and privacy issues. Furthermore, these sites may use some new technologies, such as blockchain in order to improve the security of the transactions (Kim et al., 2022). A significant change in the e-commerce environment is on the horizon because consumers are progressively shopping en route via their smartphones. Thus, as consumers become more comfortable with this technology and as they increase their frequency of using smartphones for shopping, their perceived information confidentiality and preventing unauthorized secondary data usage will boost their trusting beliefs towards e-commerce platforms. e-commerce platforms that utilize new technologies can accordingly secure consumer privacy and build a culture of trust because they have the ability to protect consumer personal information online.

Limitations and future research

This study is not without limitations, but these limitations offer avenues for future research. The first limitation is the conceptual model. Our study is grounded on specific constructs based on social contract theory and economic theory. Thus, future studies should use constructs that are extracted from other theories and models and compare the results from this type of approach with the results of our study. The second limitation is the survey itself. We employed a self-administered online survey in this study. The potential self-response bias has to be subsequently acknowledged, and the generalization of the findings from this study should be taken cautiously. The survey was composed and validated by following the procedural methods set by Jordan and Troth (2020) in order to mitigate the potential impact of a self-response bias, but the independent and dependent variables did not duplicate the composition of the hypotheses. Next, our sample represents the population trends of online buying behavior in India (Statista, 2023), and the study may indicate the issue of a sampling bias. The future studies may consider the random sampling technique as an effective method for data collection to reduce the sampling bias in order to avoid this. Also, future studies may explore other groups and broaden their findings by including diverse demographic or sociographic groups. The third limitation is the study design, which was cross-sectional. Dowker et al. (2019) argue in these types of studies that the causal relationship between the variables cannot be established, group effects are not discernible, and frequencies are not determined. The future studies could employ a longitudinal study design in order to overcome this limitation of the present study. Moreover, this study limited its findings to comprehending the behavioral intentions of consumers and the significance of trusting beliefs in this context. We recommend that future research endeavors extend the current findings by examining the willingness of customers to return or continue their intention to use e-commerce platforms. Understanding the impact of trusting ideas, as well as characteristics like website engagement, user interface design, customized experiences, and others, on customers’ persistence behavior is an intriguing topic.

Conclusion

Several studies have explored customers’ attitudes toward information technologies, especially customers’ trust in e-sellers (Nghiêm-Phú, 2022). These studies emphasized understanding the evolving consumer environment and behavior toward online platforms, which need to be considered when formulating marketing strategies and building trust with such platforms (Cuesta‐Valiño et al., 2024). The study draws its findings from social contract and economic theories, indicating the role of trusting beliefs on consumer behavior towards e-commerce platforms. The study indicates various dimensions of online platform security and data-sharing policy that are expected by consumers as a part of their social contract with such platforms, and once received, they are more likely to develop trust and an intention to use their services. Every new engagement on an e-commerce platform constitutes a new data point. Thus, consumer data is the most valuable asset in e-commerce, which leads to valid concerns that the consumers’ personal information could be misused. Moreover, there are certain fears that the consumers’ personal data could be passed around like trading cards by e-commerce platforms with vested interests in order to influence the consumers’ thinking and change their habits, because the consumers’ personal information, online habits, and social media habits are exposed on e-commerce platforms. This study provides a new perspective on the importance of security features based on awareness about privacy protection, perceived information confidentiality, preventing unauthorized secondary data usage, information integrity, and trusting beliefs to use e-commerce platforms. Trust in the brand leads to gratitude and connection to the brand, which have a beneficial impact on engagement (Rohden et al., 2023; Cuesta‐Valiño et al., 2024). Literature indicates that the role of social exchange about security measures is crucial to developing trust, which in turn influences consumer behavior. The results of this study offer additional points for future research in order to further explore the importance of security features and consumers' trusting beliefs towards behavioral intention to use e-commerce platforms with the digital data genie out of the bottle.

Acknowledgements

This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) under the metaverse support program to nurture the best talents (IITP-2024-RS-2023-00254529) grant funded by the Korea government(MSIT).

Author contributions

The authors confirm their contribution to the paper as follows: Introduction: Nidhi Singh, Richa Misra; Materials: Nidhi Singh, Richa Misra, Wei Quan, Aleksandar Radic, Sang-Mook Lee, Heesup Han; Methods: Wei Quan, Aleksandar Radic; Data collection: Nidhi Singh, Richa Misra, Wei Quan, Aleksandar Radic, Sang-Mook Lee, and Heesup Han; Data analysis and interpretation Wei Quan, Aleksandar Radic, Heesup Han; Draft preparation: Wei Quan, Aleksandar Radic; Writing and review: Nidhi Singh, Richa Misra, Wei Quan, Aleksandar Radic, Sang-Mook Lee, and Heesup Han. All authors read, edited, and finalized the manuscript.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ethical approval

Considering the observational nature of the study and in the absence of any involvement of therapeutic medication, no formal approval of the Institutional Review Board of the local Ethics Committee was required. All procedures performed were in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki. Informed consent was obtained from each respondent participating in the study.

Informed consent

This article obtained informed consent from all the human participants.

Competing interests

The authors declare no competing interests.

https://retail.economictimes.indiatimes.com/news/e-commerce/e-tailing/percentage-of-online-shopping-scam-victims-down-to-74-report/92049310#:~:text=e%2Dcommerce%20fraud%3A%20Percentage%20of,Report%2C%20Retail%20News%2C%20ET%20Retail

Supplementary information

The online version contains supplementary material available at https://doi.org/10.1057/s41599-024-03395-6.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

Adhikari, K; Panda, RK. Users’ information privacy concerns and privacy protection behaviors in social networks. J Glob Mark; 2018; 31, 2 pp. 96-110. [DOI: https://dx.doi.org/10.1080/08911762.2017.1412552]

Adu P, Miles DA (2023) Dissertation research methods: a step-by-step guide to writing up your research in the social sciences. Taylor & Francis, New York

Aeron, P; Jain, S; Kumar, A. Revisiting trust toward E-retailers among Indian online consumers. J Internet Commer; 2019; 18, 1 pp. 45-72. [DOI: https://dx.doi.org/10.1080/15332861.2019.1567186]

Agrebi, M; Chandon, JL; Zaichkowsky, JL. Customer satisfaction and loyalty with corporate multi-brand websites in an era of social media and misinformation. Int J Internet Mark Advert; 2022; 16, 4 pp. 394-421.

Akgül, Y. Structural equation modeling approaches to E-service adoption; 2019; Hershey, IGI Global: [DOI: https://dx.doi.org/10.4018/978-1-5225-8015-7]

Aldas-Manzano, J; Ruiz-Mafe, C; Sanz-Blas, S; Lassala-Navarr´ e, C. Internet banking loyalty: evaluating the role of trust, satisfaction, perceived risk and frequency of use. Serv Ind J; 2011; 31, 7 pp. 1165-1190. [DOI: https://dx.doi.org/10.1080/02642060903433997]

Alonso-Garcia, J; Pablo-Marti, F; Nunez-Barriopedro, E. Omnichannel management in B2B. Complexity-based model. Empirical evidence from a panel of experts based on fuzzy cognitive maps. Ind Mark Manag; 2021; 95, pp. 99-113. [DOI: https://dx.doi.org/10.1016/j.indmarman.2021.03.009]

Alonso-Garcia, J; Pablo-Marti, F; Núñez-Barriopedro, E; Cuesta-Valiño, P. Digitalization in B2B marketing: omnichannel management from a PLS-SEM approach. J Bus Ind Mark; 2023; 38, 2 pp. 317-336. [DOI: https://dx.doi.org/10.1108/JBIM-09-2021-0421]

Amin, H; Hamid MRA,; Lada, S; Anis, Z. The adoption of mobile banking in Malaysia: the case of Bank Islam Malaysia Berhad (BIMB). Int J Bus Soc; 2008; 9, 2 43.

Anderson, JC; Gerbing, DW. Structural equation modeling in practice: a review and recommended two-step approach. Psychol Bull; 1988; 103, 3 411. [DOI: https://dx.doi.org/10.1037/0033-2909.103.3.411]

Balapour, A; Nikkhah, HR; Sabherwal, R. Mobile application security: role of perceived privacy as the predictor of security perceptions. Int J Inf Manag; 2020; 52, [DOI: https://dx.doi.org/10.1016/j.ijinfomgt.2019.102063]

Bandara, R; Fernando, M; Akter, S. Privacy concerns in E-commerce: A taxonomy and a future research agenda. Electron. Mark.; 2020; 30, 3 pp. 629-647. [DOI: https://dx.doi.org/10.1007/s12525-019-00375-6]

Barratt, MJ; Lenton, S. Representativeness of online purposive sampling with Australian cannabis cultivators. Int J Drug Policy; 2015; 26, 3 pp. 323-326. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25433986][DOI: https://dx.doi.org/10.1016/j.drugpo.2014.10.007]

Barretta, PG; Fuat Fırat, A. Innovation from virtual brand community members may only be virtually effective. Int J Internet Mark Advert; 2022; 16, 1/2 pp. 186-202.

Berget, G., & Kvikne, B. (2022). Making research more inclusive: Is Universal Design of Research the answer?. In Transforming our World through Universal Design for Human Development (pp. 77–84). I. Garofolo et al. (Eds.) IOS Press. publication. https://doi.org/10.3233/SHTI220823

Bowling, A. Research methods in health: investigating health and health services; 2023; Berkshire, McGraw-Hill Education:

Calder, G; Radic, A; Ryu, HB; Ariza-Montes, A; Han, H. COVID-19 and pro-environmental behavior at destinations amongst international travellers. Front Psychol; 2022; 13, 879300. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35496140][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9047711][DOI: https://dx.doi.org/10.3389/fpsyg.2022.879300]

Callanan, C; Jerman-Blažič, B; Blažič, AJ. User awareness and tolerance of privacy abuse on mobile Internet: an exploratory study. Telemat Inf; 2016; 33, 1 pp. 109-128. [DOI: https://dx.doi.org/10.1016/j.tele.2015.04.009]

Carlson, J; O’Cass, A. Creating commercially compelling website-service encounters: an examination of the effect of website-service interface performance components on flow experiences. Electron Mark; 2011; 21, 4 pp. 237-253. [DOI: https://dx.doi.org/10.1007/s12525-011-0073-z]

Castillo de Mesa, J; Gómez-Jacinto, L; López Peláez, A; Erro-Garcés, A. Social networking sites and youth transition: the use of Facebook and personal well-being of social work young graduates. Front Psychol; 2020; 11, 230. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32132959][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7040231][DOI: https://dx.doi.org/10.3389/fpsyg.2020.00230]

Castillo‐Abdul, B; Pérez‐Escoda, A; Núñez‐Barriopedro, E. Promoting social media engagement via branded content communication: a fashion brands study on instagram. Mob Media Commun; 2022; 10, 1 pp. 185-197.

Cavalinhos, S; Marques, SH; de Fátima Salgueiro, M. The use of mobile devices in‐store and the effect on shopping experience: a systematic literature review and research agenda. Int J Consum Stud; 2022; 45, 6 pp. 1198-1216. [DOI: https://dx.doi.org/10.1111/ijcs.12690]

Chawla, N; Kumar, B. E-commerce and consumer protection in India: the emerging trend. J. Bus. Ethics; 2022; 180, 2 pp. 581-604. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34257470][DOI: https://dx.doi.org/10.1007/s10551-021-04884-3]

Chen, L; Liu, HW. A review of privacy protection in e-commerce. J Adv Manag Sci; 2015; 3, 1 pp. 50-53.

Chen, YH; Wu, JJ; Chang, HT. Examining the mediating effect of positive moods on trust repair in e‐commerce. Internet Res; 2013; 23, 3 pp. 355-371. [DOI: https://dx.doi.org/10.1108/10662241311331772]

Cheon I (2019) E-commerce in free trade agreements and the Trans-Pacific Partnership. In: Chen L, Kimura F (eds) Developing the digital economy in ASEAN. Routledge, pp. 91–108

Chi, X; Han, H. Emerging rural tourism in China’s current tourism industry and tourism behaviors: the case of Anji County. J Travel Tour Mark; 2021; 38, 1 pp. 58-74. [DOI: https://dx.doi.org/10.1080/10548408.2020.1862026]

Choi, Y; Lee, J; Choe, Y. Online travel information filtering: Role of commercial cues in trust and distrust mechanisms. J. Travel Tour. Mark.; 2021; 38, 7 pp. 710-724. [DOI: https://dx.doi.org/10.1080/10548408.2021.1985037]

Chung, J; Demiris, G; Thompson, HJ. Ethical considerations regarding the use of smart home technologies for older adults: an integrative review. Annu Rev Nurs Res; 2016; 34, 1 pp. 155-181. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26673381][DOI: https://dx.doi.org/10.1891/0739-6686.34.155]

Collier, J. Applied structural equation modeling using AMOS: basic to advanced techniques; 2020; New York, Routledge: [DOI: https://dx.doi.org/10.4324/9781003018414]

Correa, T. Digital skills and social media use: how Internet skills are related to different types of Facebook use among ‘digital natives. Inf Commun Soc; 2016; 19, 8 pp. 1095-1107. [DOI: https://dx.doi.org/10.1080/1369118X.2015.1084023]

Cuesta-Valino, P; Gutiérrez-Rodríguez, P; Núnez-Barriopedro, E; García-Henche, B. Strategic orientation towards digitization to improve supermarket loyalty in an omnichannel context. J Bus Res; 2023; 156, [DOI: https://dx.doi.org/10.1016/j.jbusres.2022.113475]

Cuesta-Valiño, P; Gutiérrez-Rodríguez, P; Núnez-Barriopedro, E. The role of consumer happiness in brand loyalty: a model of the satisfaction and brand image in fashion. Corp Gov (Bingley); 2022; 22, 3 pp. 458-473. [DOI: https://dx.doi.org/10.1108/CG-03-2021-0099]

Cuesta‐Valiño, P; Gutiérrez‐Rodríguez, P; García‐Henche, B; Núñez‐Barriopedro, E. The impact of corporate social responsibility on consumer brand engagement and purchase intention at fashion retailers. Psychol Mark; 2024; 41, 3 pp. 649-664. [DOI: https://dx.doi.org/10.1002/mar.21940]

Dabhade A (2022) Amazon announces Doug Herrington as CEO of Worldwide Amazon Stores. The Economic Times. https://economictimes.indiatimes.com/tech/technology/amazon-announces-doug-herrington-as-ceo-of-worldwide-amazon-stores/articleshow/92369263.cms. Accessed 3 Aug 2022

Degeling, M, Utz, C, Lentzsch, C, Hosseini, H, Schaub, F, & Holz, T (2018). We value your privacy.. now take some cookies: Measuring the GDPR's impact on web privacy. arXiv preprint arXiv:1808.05096

Dogra, N; Adil, M. Should we or should we not? Examining travelers’ perceived privacy, perceived security and actual behavior in online travel purchases. J Vacat Mark; 2024; 30, 1 pp. 123-142. [DOI: https://dx.doi.org/10.1177/13567667221122103]

Dowker, A; De Smedt, B; Desoete, A. Individual differences in arithmetical development. Front Psychol; 2019; 10, 2672. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31849776][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6902078][DOI: https://dx.doi.org/10.3389/fpsyg.2019.02672]

Eastin, MS; Brinson, NH; Doorey, A; Wilcox, G. Living in a big data world: predicting mobile commerce activity through privacy concerns. Comput Hum Behav; 2016; 58, pp. 214-220. [DOI: https://dx.doi.org/10.1016/j.chb.2015.12.050]

Efe, HA. The relation between science student teachers’ educational use of web 2.0 technologies and their computer self-efficacy. J Balt Sci Educ; 2015; 14, 1 142. [DOI: https://dx.doi.org/10.33225/jbse/15.14.142]

Enaizan, O; Eneizan, B; Almaaitah, M; Al-Radaideh, AT; Saleh, AM. Effects of privacy and security on the acceptance and usage of EMR: the mediating role of trust on the basis of multiple perspectives. Inform. Med. Unlocked; 2020; 21, 100450. [DOI: https://dx.doi.org/10.1016/j.imu.2020.100450]

Ermakova T, Baumann A, Fabian B, Krasnova H (2014) Privacy policies and users’ trust: does readability matter? In: 20th Americas Conference on Information Systems (AMCIS’14). Association for Information Systems publication, (AIS), Curran Associates, Inc. (EDs.) pp. 1–12

Esmaeilzadeh, P. The impacts of the privacy policy on individual trust in health information exchanges (HIEs). Internet Res; 2020; 30, 3 pp. 811-843.MathSciNet ID: 4241738[DOI: https://dx.doi.org/10.1108/INTR-01-2019-0003]

Featherman, MS; Hajli, N. Self-service technologies and e-services risks in social commerce era. J Bus Ethics; 2016; 139, 2 pp. 251-269. [DOI: https://dx.doi.org/10.1007/s10551-015-2614-4]

Fernandes J, Machado CF, Amaral L (2020) Methodology used for determination of critical success factors in adopting the new general data protection regulation in higher education institutions. In: Machado C, Davim JP (eds) Research methodology in management and industrial engineering. Springer International Publishing, Cham, pp. 71–109

Fornell, C; Larcker, DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res; 1981; 18, 1 pp. 39-50. [DOI: https://dx.doi.org/10.1177/002224378101800104]

Fue, ZQ; Ye, Z; Yang, J; Li, YA; Song,. Which Privacy Policy Works Privacy Assurance or Personalization Declaration? An Investigation of Privacy Policies and Privacy Concerns. J. Bus. Ethics; 2022; 176, 4 pp. 781-798. [DOI: https://dx.doi.org/10.1007/s10551-020-04626-x]

Fuller, CM; Simmering, MJ; Atinc, G; Atinc, Y; Babin, BJ. Common methods variance detection in business research. J Bus Res; 2016; 69, 8 pp. 3192-3198. [DOI: https://dx.doi.org/10.1016/j.jbusres.2015.12.008]

Ghazali, E; Nguyen, B; Mutum, DS; Mohd-Any, AA. Constructing online switching barriers: examining the effects of switching costs and alternative attractiveness on e-store loyalty in online pure-play retailers. Electron Mark; 2016; 26, 2 pp. 157-171. [DOI: https://dx.doi.org/10.1007/s12525-016-0218-1]

Ghondaghsaz, N; Chokparova, Z; Engesser, S; Urbas, L. Managing the tension between trust and confidentiality in mobile supply chains. Sustainability; 2022; 14, 4 2347. [DOI: https://dx.doi.org/10.3390/su14042347]

Ghouse, SM; Duffett, RG; Chaudhary, M. How Twitter advertising influences the purchase intentions and purchase attitudes of Indian millennial consumers?. Int J Internet Mark Advert; 2022; 16, 1–2 pp. 142-164.

Gong, Z; Han, Z; Li, X; Yu, C; Reinhardt, JD. Factors influencing the adoption of online health consultation services: the role of subjective norm, trust, perceived benefit, and offline habit. Front Public Health; 2019; 7, 286. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31637229][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6787145][DOI: https://dx.doi.org/10.3389/fpubh.2019.00286]

Goundar S, Lal K, Chand A, Vyas P (2021) Consumer perception of electronic commerce–incorporating trust and risk with the technology acceptance model. In: Goundar S (ed) E-Services. IntechOpen, pp. 15–35

Gulati, R; Wohlgezogen, F; Zhelyazkov, P. The two facets of collaboration: cooperation and coordination in strategic alliances. Acad Manag Ann; 2012; 6, 1 pp. 531-583. [DOI: https://dx.doi.org/10.5465/19416520.2012.691646]

Gupta, K; Singh, N. Consumption behaviour and social responsibility: a consumer research approach; 2020; Singapore, Springer Nature: [DOI: https://dx.doi.org/10.1007/978-981-15-3005-0]

Hair, J; Hollingsworth, CL; Randolph, AB; Chong, AYL. An updated and expanded assessment of PLS-SEM in information systems research. Ind Manag Data Syst; 2017; 117, 3 pp. 442-458. [DOI: https://dx.doi.org/10.1108/IMDS-04-2016-0130]

Hair, JF, Jr; Sarstedt, M; Ringle, CM; Gudergan, SP. Advanced issues in partial least squares structural equation modeling; 2017; Thousand Oaks, Sage:

Hajli, N. The impact of positive valence and negative valence on social commerce purchase intention. Inf Technol People; 2019; 33, 2 pp. 774-791. [DOI: https://dx.doi.org/10.1108/ITP-02-2018-0099]

Han, F; Li, B. Exploring the effect of an enhanced e-commerce institutional mechanism on online shopping intention in the context of e-commerce poverty alleviation. Inf Technol People; 2020; 34, 1 pp. 93-122.MathSciNet ID: 3268916[DOI: https://dx.doi.org/10.1108/ITP-12-2018-0568]

Hang, L; Kim, DH. Design and implementation of an integrated IoT blockchain platform for sensing data integrity. Sensors; 2019; 19, 10 2228.2019Senso.19.2228H[PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31091799][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6567376][DOI: https://dx.doi.org/10.3390/s19102228]

Harborth, D; Pape, S. How privacy concerns, trust and risk beliefs, and privacy literacy influence users’ intentions to use privacy-enhancing technologies: the case of Tor. Data Base Adv Inf Syst; 2020; 51, 1 pp. 51-69. [DOI: https://dx.doi.org/10.1145/3380799.3380805]

Hattingh, HL; Knox, K; Fejzic, J; McConnell, D; Fowler, JL; Mey, A; Wheeler, AJ. Privacy and confidentiality: perspectives of mental health consumers and carers in pharmacy settings. Int J Pharm Pract; 2015; 23, 1 pp. 52-60. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24761999][DOI: https://dx.doi.org/10.1111/ijpp.12114]

Head, MM; Hassanein, K. Trust in e-commerce: evaluating the impact of third-party seals. J Electron Commer; 2002; 3, 3 pp. 307-325.

Herrando, C; Jimenez-Martinez, J; Martin-De Hoyos, MJ. Tell me your age and I tell you what you trust: the moderating effect of generations. Internet Res; 2019; 29, 4 pp. 799-817. [DOI: https://dx.doi.org/10.1108/IntR-03-2017-0135]

Hoffman, DL; Novak, TP; Peralta, MA. Information privacy in the marketspace: Implications for the commercial uses of anonymity on the Web. Inf Soc; 1999; 15, 2 pp. 129-139. [DOI: https://dx.doi.org/10.1080/019722499128583]

Hou, L. Superior bargaining power: the good, the bad and the ugly. Asia Pac Law Rev; 2019; 27, 1 pp. 39-61. [DOI: https://dx.doi.org/10.1080/10192557.2019.1661589]

Hoy, MG; Milne, G. Gender differences in privacy-related measures for young adult Facebook users. J Interact Advert; 2010; 10, 2 pp. 28-45. [DOI: https://dx.doi.org/10.1080/15252019.2010.10722168]

Hung, SW; Cheng, MJ; Lee, CJ. A new mechanism for purchasing through personal interactions: fairness, trust and social influence in online group buying. Inf Technol People; 2021; 35, 5 pp. 1563-1589. [DOI: https://dx.doi.org/10.1108/ITP-05-2020-0329]

Ioannou, A; Tussyadiah, I. Privacy and surveillance attitudes during health crises: Acceptance of surveillance and privacy protection behaviours. Technol Soc; 2021; 67, [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34642512][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8497958][DOI: https://dx.doi.org/10.1016/j.techsoc.2021.101774]

Jarvenpaa, SL; Tractinsky, N; Saarinen, L. Consumer trust in an Internet store: a cross-cultural validation. J Comput-Mediat Commun; 1999; 5, 2 JCMC526.

Jordan, PJ; Troth, AC. Common method bias in applied settings: The dilemma of researching in organizations. Aust J Manag; 2020; 45, 1 pp. 3-14. [DOI: https://dx.doi.org/10.1177/0312896219871976]

Joseph-Vaidyan KV (2008) Factors that enhance customer trust in e-commerce Web sites: an examination of e-commerce success metrics using Internet audience rating. Dissertation, Capella University

Kang R, Dabbish L, Fruchter N, Kiesler S (2015) {“My} data just goes {Everywhere:”} user mental models of the internet and implications for privacy and security. In: Proceedings of the 11th Symposium on Usable Privacy and Security (SOUPS 2015). pp. 39–52

Khan AG (2016) Electronic Commerce: A Study on Benefits and Chalenges in an Emerging Economy Global J. Manage and Bus Res: B Econ and E-commerce 16(01):1–9

Kim, C; Tao, W; Shin, N; Kim, KS. An empirical study of customers’ perceptions of security and trust in e-payment systems. Electron Commer Res Appl; 2010; 9, 1 pp. 84-95. [DOI: https://dx.doi.org/10.1016/j.elerap.2009.04.014]

Kim, DJ; Ferrin, DL; Rao, HR. A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decis. Support Syst.; 2008; 44, 2 pp. 544-564. [DOI: https://dx.doi.org/10.1016/j.dss.2007.07.001]

Kim, DJ; Ferrin, DL; Rao, HR. Trust and satisfaction, two stepping stones for successful e-commerce relationships: a longitudinal exploration. Inf Syst Res; 2009; 20, 2 pp. 237-257. [DOI: https://dx.doi.org/10.1287/isre.1080.0188]

Kim, J; Radic, A; Chua, B-L; Koo, B; Han, H. Digital currency and payment innovation in the hospitality and tourism industry. Int J Hosp Manag; 2022; 107, 103315. [DOI: https://dx.doi.org/10.1016/j.ijhm.2022.103314]

Kim, Y; Wang, Q; Roh, T. Do information and service quality affect perceived privacy protection, satisfaction, and loyalty? Evidence from a Chinese O2O-based mobile shopping application. Telemat Inf; 2021; 56, [DOI: https://dx.doi.org/10.1016/j.tele.2020.101483]

KPMG International (2017) The truth about online consumers. 2017 Global Online consumer report. KPMG International. https://assets.kpmg/content/dam/kpmg/xx/pdf/2017/01/the-truth-about-online-consumers.pdf

Kruikemeier, S; Boerman, SC; Bol, N. Breaching the contract? Using social contract theory to explain individuals’ online behavior to safeguard privacy. Media Psychol; 2020; 23, 2 pp. 269-292. [DOI: https://dx.doi.org/10.1080/15213269.2019.1598434]

Lăzăroiu, G; Neguriţă, O; Grecu, I; Grecu, G; Mitran, PC. Consumers’ decision-making process on social commerce platforms: online trust, perceived risk, and purchase intentions. Front Psychol; 2020; 11, 890. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32499740][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7244253][DOI: https://dx.doi.org/10.3389/fpsyg.2020.00890]

Lee, KW; Han, H; Hwang, J. Developing a matrix for assessing serviceability of US online travel agency websites. Soc Behav Pers; 2017; 45, 1 pp. 115-126. [DOI: https://dx.doi.org/10.2224/sbp.5479]

Leonard, LN; Jones, K. Trust in C2C electronic commerce: Ten years later. J. Computer Inf. Syst.; 2021; 61, 3 pp. 240-246.

Levine, DP. Self-seeking and the Pursuit of Justice; 2019; Routledge, Biography by David P. Levine: [DOI: https://dx.doi.org/10.4324/9780429439568]

Li C, Chau PY (2018) Revealing the Black Box of privacy concern: understanding how self-disclosure affects privacy concern in the context of on-demand services through two competing models. In: The ecosystem of e-business: technologies, stakeholders, and connections: 17th workshop on e-business, WeB 2018, Santa Clara, CA, USA, December 12, 2018, Revised Selected Papers 17. Springer International Publishing, Germany, pp. 53-62. Editors: Jennifer J. Xu, Bin Zhu, Xiao Liu, Michael J. Shaw, Han Zhang, Ming Fan

Liaropoulos, A. A social contract for cyberspace. J Inf Warf; 2020; 19, 2 pp. 1-11.

Lin, JCC; Lu, H. Towards an understanding of the intention to use a web site. Int J Inf Manag; 2000; 20, 3 pp. 197-208. [DOI: https://dx.doi.org/10.1016/S0268-4012(00)00005-0]

Lissitsa, S; Kol, O. Four generational cohorts and hedonic m-shopping: association between personality traits and purchase intention. Electron Commer Res; 2021; 21, pp. 545-570. [DOI: https://dx.doi.org/10.1007/s10660-019-09381-4]

Liyanage, SIH. Producing green knowledge and innovation: a framework for greening universities; 2022; Switzerland, Springer International Publishing: [DOI: https://dx.doi.org/10.1007/978-3-030-97850-1]

Loch, KD; Carr, HH; Warkentin, ME. Threats to information systems: today's reality, yesterday’s understanding. Mis Quarterly; 1992; 16, 2 pp. 173-186. [DOI: https://dx.doi.org/10.2307/249574]

Malhotra, A; Kubowicz Malhotra, C. Evaluating customer information breaches as service failures: an event study approach. J Serv Res; 2011; 14, 1 pp. 44-59. [DOI: https://dx.doi.org/10.1177/1094670510383409]

Malhotra, NK; Kim, SS; Agarwal, J. Internet users’ information privacy concerns (IUIPC): the construct, the scale, and a causal model. Inf Syst Res; 2004; 15, 4 pp. 336-355. [DOI: https://dx.doi.org/10.1287/isre.1040.0032]

Malik, A; Hiekkanen, K; Nieminen, M. Privacy and trust in Facebook photo sharing: age and gender differences. Program; 2016; 50, 4 pp. 462-480. [DOI: https://dx.doi.org/10.1108/PROG-02-2016-0012]

Manosuthi, N; Lee, J-S; Han, H. Impact of distance on the arrivals, behaviours and attitudes of international tourists in Hong Kong: a longitudinal approach. Tour Manag; 2020; 78, 103963. [DOI: https://dx.doi.org/10.1016/j.tourman.2019.103963]

Markos E, Peña P, Labrecque LI, Swani K (2023) Are data breaches the new norm? Exploring data breach trends, consumer sentiment, and responses to security invasions. J Consum Aff 1–31

McCoubrey, H; White, ND. Textbook on jurisprudence; 1999; 3rd edn Blackstone Press Limited:

Meinert, DB; Peterson, DK; Criswell, JR; Crossland, MD. Privacy policy statements and consumer willingness to provide personal information. J Electron Commer Organ; 2006; 4, 1 pp. 1-17. [DOI: https://dx.doi.org/10.4018/jeco.2006010101]

Meng, F; Guo, X; Peng, Z; Ye, Q; Lai, KH. Trust and elderly users’ continuance intention regarding mobile health services: the contingent role of health and technology anxieties. Inf Technol People; 2021; 35, 1 pp. 259-280. [DOI: https://dx.doi.org/10.1108/ITP-11-2019-0602]

Merhi, M; Hone, K; Tarhini, A. A cross-cultural study of the intention to use mobile banking between Lebanese and British consumers: extending UTAUT2 with security, privacy and trust. Technol Soc; 2019; 59, [DOI: https://dx.doi.org/10.1016/j.techsoc.2019.101151]

Merhi, M; Hone, K; Tarhini, A; Ameen, N. An empirical examination of the moderating role of age and gender in consumer mobile banking use: a cross-national, quantitative study. J Enterp Inf Manag; 2021; 34, 4 pp. 1144-1168. [DOI: https://dx.doi.org/10.1108/JEIM-03-2020-0092]

Misra, R; Mahajan, R; Singh, N. Understanding factors affecting receptivity towards adopting electronic marketplace: a merchant perspective. e-Serv J; 2020; 12, 1 pp. 1-40. [DOI: https://dx.doi.org/10.2979/eservicej.12.1.01]

Misra R, Mahajan R, Singh N, Khorana S, & Rana NP (2022) Factors impacting behavioural intentions to adopt the electronic marketplace: findings from small businesses in India. Electronic Markets, 32(3), 1639–1660

Molinillo, S; Aguilar-Illescas, R; Anaya-Sánchez, R; Liébana-Cabanillas, F. Social commerce website design, perceived value and loyalty behavior intentions: the moderating roles of gender, age and frequency of use. J Retail Consum Serv; 2021; 63, [DOI: https://dx.doi.org/10.1016/j.jretconser.2020.102404]

Morimoto, M. Consumers’ information control and privacy concerns in personalised social media advertising. Int J Internet Mark Advert; 2022; 17, 3–4 pp. 325-352.

Muscanell, NL; Guadagno, RE. Make new friends or keep the old: Gender and personality differences in social networking use. Computers Hum. Behav.; 2012; 28, 1 pp. 107-112. [DOI: https://dx.doi.org/10.1016/j.chb.2011.08.016]

Nghiêm-Phú, B. Customers’ perspectives on marketing mix elements in social media-based purchases. Int J Internet Mark Advert; 2022; 16, 1–2 pp. 81-97.

Nghiêm-Phú, B; Bagul, A. An extended model of destination image formation: The inclusion of sensory images. Eur. J. Tour. Res.; 2020; 24, pp. 2411-2411. [DOI: https://dx.doi.org/10.54055/ejtr.v24i.413]

Nissenbaum H, Daniel H (2009) TrackMeNot: resisting surveillance in web search. Oxford University Pres

Núnez-Barriopedro, E; Cuesta-Valiño, P; Mansori-Amar, S. The role of perceived usefulness and annoyance on programmatic advertising: the moderating effect of Internet user privacy and cookies. Corp Commun; 2023; 28, 2 pp. 311-324. [DOI: https://dx.doi.org/10.1108/CCIJ-03-2022-0033]

Ochsner M (2021) Representativeness of surveys and its analysis. FORS guide no.15, Version 10. pp. 1–20. Lausanne (FORS). https://doi.org/10.24449/FG-2021-00015

Öğütçü, G; Testik, ÖM; Chouseinoglou, O. Analysis of personal information security behavior and awareness. Comput Secur; 2016; 56, pp. 83-93. [DOI: https://dx.doi.org/10.1016/j.cose.2015.10.002]

Okazaki, S; Eisend, M; Plangger, K; de Ruyter, K; Grewal, D. Understanding the strategic consequences of customer privacy concerns: a meta-analytic review. J Retail; 2020; 96, 4 pp. 458-473. [DOI: https://dx.doi.org/10.1016/j.jretai.2020.05.007]

Özbölük, T; Akdoğan, K. The role of online source credibility and influencer identification on consumers' purchase decisions. Int. J. Internet Mark. Advertising; 2022; 16, 1-2 pp. 165-185. [DOI: https://dx.doi.org/10.1504/IJIMA.2022.120974]

Pagan, NM; Pagan, KM; Giraldi, JDME; Merlo, EM. Antecedents of trust and purchase intention in social commerce in Brazil. Int J Internet Mark Advert; 2022; 17, 3–4 pp. 271-298.

Palvia, P. The role of trust in e-commerce relational exchange: a unified model. Inf Manag; 2009; 46, 4 pp. 213-220. [DOI: https://dx.doi.org/10.1016/j.im.2009.02.003]

Pantano, E; Pizzi, G; Bilotta, E; Pantano, P. Shopping with(out) distancing: modelling the personal space to limit the spread of contagious disease among consumers in retail stores. J. Mark. Manag.; 2021; 37, 17-18 pp. 1764-1782. [DOI: https://dx.doi.org/10.1080/0267257X.2021.2003422]

Pillet JC, Carillo KD, Vitari C, & Pigni F (2023) Improving scale adaptation practices in information systems research: Development and validation of a cognitive validity assessment method. Inf Syst J 33(4):842–889

Poon, WC. Users’ adoption of e‐banking services: the Malaysian perspective. J Bus Ind Mark; 2008; 23, 1 pp. 59-69. [DOI: https://dx.doi.org/10.1108/08858620810841498]

Radic, A; Quan, W; Ariza-Montes, A; Lee, J-S; Han, H. You can’t hold the tide with a broom: cryptocurrency payments and tourism in South Korea and China. Tour Manag Perspect; 2022; 43, 100997.

Radic, A; Quan, W; Koo, B; Chua, BL; Kim, JJ; Han, H. Central bank digital currency as a payment method for tourists: application of the theory of planned behavior to digital Yuan/Won/Dollar choice. J Travel Tour Mark; 2022; 39, 2 pp. 152-172. [DOI: https://dx.doi.org/10.1080/10548408.2022.2061677]

Radic A, Koo B, Kim JJ, & Han H (2022b) No jab, no international travel? Linking TRA, mass media, motivation, and experience. J Vacat Mark 29(3):365–385

Raman, P. Understanding female consumers’ intention to shop online: the role of trust, convenience and customer service. Asia Pac J Mark Logist; 2019; 31, 4 pp. 1138-1160. [DOI: https://dx.doi.org/10.1108/APJML-10-2018-0396]

Raza A, Asif M, & Akram M (2022) Give your hunger a new option: Understanding consumers' continuous intention to use online food delivery apps using trust transfer theory. Int J Consum Stud 47(2), 474–495

Rinta-Kahila T, Soliman W (2017) Understanding crowdturfing: the different ethical logics behind the clandestine industry of deception. In ECIS 2017: Proceedings of the 25th European conference on information systems, Guimarães, Portugal, June 5–10, 2017

Rodríguez, PG; Villarreal, R; Valiño, PC; Blozis, S. A PLS-SEM approach to understanding E-SQ, e-satisfaction and e-loyalty for fashion e-retailers in Spain. J Retail Consum Serv; 2020; 57, [DOI: https://dx.doi.org/10.1016/j.jretconser.2020.102201]

Rohden, SF; Tassinari, G; Netto, CF. Listen as much as you want: the antecedents of the engagement of podcast consumers. Int J Internet Mark Advert; 2023; 18, 1 pp. 82-97.

Rosillo-Díaz, E; Blanco-Encomienda, FJ; Crespo-Almendros, E. A cross-cultural analysis of perceived product quality, perceived risk and purchase intention in e-commerce platforms. J. Enterp. Inf. Manag.; 2020; 33, 1 pp. 139-160. [DOI: https://dx.doi.org/10.1108/JEIM-06-2019-0150]

Santo, PE; Marques, AMA. Determinants of the online purchase intention: hedonic motivations, prices, information and trust. Balt J Manag; 2021; 17, 1 pp. 56-71.

Sarkar, S; Chauhan, S; Khare, A. A meta-analysis of antecedents and consequences of trust in mobile commerce. Int J Inf Manag; 2020; 50, pp. 286-301. [DOI: https://dx.doi.org/10.1016/j.ijinfomgt.2019.08.008]

Sfenrianto, S; Wijaya, T; Wang, G. Assessing the buyer trust and satisfaction factors in the E-marketplace. J Theor Appl Electron Commer Res; 2018; 13, 2 pp. 43-57. [DOI: https://dx.doi.org/10.4067/S0718-18762018000200105]

Shabani, A (2020). Supply Chain Networks: Quantitative Models for Measuring Performance. [PhD-Thesis Research and graduation internal, Vrije Universiteit Amsterdam]. VU Research Portal

Shao, Z; Zhang, L; Li, X; Guo, Y. Antecedents of trust and continuance intention in mobile payment platforms: the moderating effect of gender. Electron Commer Res Appl; 2019; 33, [DOI: https://dx.doi.org/10.1016/j.elerap.2018.100823]

Sharma, G; Lijuan, W. Ethical perspectives on e-commerce: an empirical investigation. Internet Res; 2014; 24, 4 pp. 414-435. [DOI: https://dx.doi.org/10.1108/IntR-07-2013-0162]

Sharma, S. Nursing research and statistics—e-book; 2022; India, Elsevier Health Sciences:

Sheehan, KB; Hoy, MG. Flaming, complaining, abstaining: how online users respond to privacy concerns. J Advert; 1999; 28, 3 pp. 37-51. [DOI: https://dx.doi.org/10.1080/00913367.1999.10673588]

Sheth, J. Impact of Covid-19 on consumer behavior: Will the old habits return or die?. J. Bus. Res.; 2020; 117, pp. 280-283. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32536735][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7269931][DOI: https://dx.doi.org/10.1016/j.jbusres.2020.05.059]

Shin, W; Kang, H. Adolescents' privacy concerns and information disclosure online: The role of parents and the Internet. Computers Hum. Behav.; 2016; 54, pp. 114-123. [DOI: https://dx.doi.org/10.1016/j.chb.2015.07.062]

Siciliani, P; Riefa, C; Gamper, H. Consumer theories of harm: an economic approach to consumer law enforcement and policy making; 2019; London, Bloomsbury Publishing: [DOI: https://dx.doi.org/10.5040/9781509916887]

Singh, N; Sinha, N; Liébana-Cabanillas, FJ. Determining factors in the adoption and recommendation of mobile wallet services in India: analysis of the effect of innovativeness, stress to use and social influence. Int J Inf Manag; 2020; 50, pp. 191-205. [DOI: https://dx.doi.org/10.1016/j.ijinfomgt.2019.05.022]

Sinha, N; Singh, N. Understanding technology readiness and user’s perceived satisfaction with mobile wallets services in India. NMIMS Manag Rev; 2019; 37, 3 pp. 10-33.

Smith HJ, Milberg SJ, & Burke SJ (1996) Information privacy: Measuring individuals' concerns about organizational practices. MIS quarterly 20(2):167–196

Stahl BC (2006) Trust as fetish: a critical theory perspective on research on trust in e-commerce. In: Information Communications and Society symposium. 1st Edition, Book: Information Systems, Routledge publisher

Statista (2022) Internet usage in India—statistics & facts. https://www.statista.com/topics/2157/internet-usage-in-india/#topicOverview Accessed 25 Oct 2023

Statista (2023) Preferred channels for online shopping in India as of June 2022, by age. https://www.statista.com/statistics/1324686/india-online-purchasing-channels-by-age/ Accessed 25 Oct 2023

Suh, B., & Han, I. (2003). The impact of customer trust and perception of security control on the acceptance of electronic commerce. International Journal of electronic commerce, 7(3), 135–161

Tawalbeh, LA; Muheidat, F; Tawalbeh, M; Quwaider, M. IoT privacy and security: challenges and solutions. Appl Sci; 2020; 10, 12 4102.[COI: 1:CAS:528:DC%2BB3cXhs1ensbnJ] [DOI: https://dx.doi.org/10.3390/app10124102]

Tondeur, J; Sinnaeve, I; Van Houtte, M; Van Braak, J. ICT as cultural capital: the relationship between socioeconomic status and the computer-use profile of young people. N Media Soc; 2011; 13, 1 pp. 151-168. [DOI: https://dx.doi.org/10.1177/1461444810369245]

Untaru, E-N; Han, H. Protective measures against COVID-19 and the business strategies of the retail enterprises: differences in gender, age, education, and income among shoppers. J Retail Consum Serv; 2021; 60, [DOI: https://dx.doi.org/10.1016/j.jretconser.2021.102446]

Vieira KM, Brasil CV, Bressan AA, Klein LL, & Potrich ACG (2023) Proposal and validation of a financial protection perception scale. Int J Consum Stud 47(2):723–735

Vimalkumar, M; Sharma, SK; Singh, JB; Dwivedi, YK. ‘Okay google, what about my privacy?’: user’s privacy perceptions and acceptance of voice based digital assistants. Comput Hum Behav; 2021; 120, [DOI: https://dx.doi.org/10.1016/j.chb.2021.106763]

Vos, J. Doing research in psychological therapies: a step-by-step guide; 2023; UK, SAGE Publications:

Wei, K; Li, Y; Zha, Y; Ma, J. Trust, risk and transaction intention in consumer-to-consumer e-marketplaces: an empirical comparison between buyers’ and sellers’ perspectives. Ind Manag Data Syst; 2018; 119, 2 pp. 331-350. [DOI: https://dx.doi.org/10.1108/IMDS-10-2017-0489]

Wu, D; Luo, X; Shang, M; He, Y; Wang, G; Wu, X. A data-characteristic-aware latent factor model for web services QoS prediction. IEEE Trans Knowl Data Eng; 2020; 34, 6 pp. 2525-2538.

Xu H, Teo HH, Tan B (2005) Predicting the adoption of location-based services: the role of trust and perceived privacy risk. In: Association for Information Systems - 26th International Conference on Information Systems, ICIS 2005: Forever New Frontiers, 897–910, Las Vegas, NV

Yap, SF; Xu, Y; Tan, L. Coping with crisis: The paradox of technology and consumer vulnerability. Int. J. Consum. Stud.; 2021; 45, 6 pp. 1239-1257. [DOI: https://dx.doi.org/10.1111/ijcs.12724]

Yoon, HS; Occena, LG. Influencing factors of trust in consumer-to-consumer electronic commerce with gender and age. Int J Inf Manag; 2015; 35, 3 pp. 352-363. [DOI: https://dx.doi.org/10.1016/j.ijinfomgt.2015.02.003]

Yu, J; Moon, H; Chua, B; Han, H. Hotel data privacy: strategies to reduce customers’ emotional violations, privacy concerns, and switching intention. J Travel Tour Mark; 2022; 39, 2 pp. 213-225. [DOI: https://dx.doi.org/10.1080/10548408.2022.2061673]

Zhao, Y; Wang, L; Tang, H; Zhang, Y. Electronic word-of-mouth and consumer purchase intentions in social e-commerce. Electron Electron Commer Res Appl; 2020; 41, [DOI: https://dx.doi.org/10.1016/j.elerap.2020.100980]

Zhonglin, DTW. Statistical approaches for testing common method bias: problems and suggestions. Psychol Sci; 2020; 1, 215.

Zhu, R; Srivastava, A; Sutanto, J. Privacy-deprived e-commerce: the efficacy of consumer privacy policies on China’s e-commerce websites from a legal perspective. Inf Technol People; 2020; 33, 6 pp. 1601-1626. [DOI: https://dx.doi.org/10.1108/ITP-03-2019-0117]

Zwilling, M; Klien, G; Lesjak, D; Wiechetek, Ł; Cetin, F; Basim, HN. Cyber security awareness, knowledge and behavior: a comparative study. J Comput Inf Syst; 2022; 62, 1 pp. 82-97.

Word count: 18582

Show less

© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Translate

This study evaluates the consumers’ views on various security measures in e-commerce platforms and determines their impact on the trusting beliefs of consumers, which may further lead to an intention to use e-commerce platforms. The study also investigates the moderating effects of gender, age, and the frequency of e-commerce platform use on the proposed model. A total of 780 respondents were used for the study. We used structural equation modeling (SEM) to analyze the proposed trust-based model. The findings indicate a strong positive impact of information integrity and information confidentiality on the consumers’ trusting beliefs. In addition, the study indicates the strong mediating effect of trusting beliefs with the association between information integrity, information confidentiality, and behavioral intention to use e-commerce platforms. A moderating effect of frequency of use on the relationship between perceived information confidentiality and preventing unauthorized secondary data usage on trusting beliefs was found to be significant. This research underwent an in-depth examination of different security aspects that e-commerce firms should consider in order to develop the trusting beliefs of consumers in e-commerce environments.

Details

Title

An analysis of consumer’s trusting beliefs towards the use of e-commerce platforms

Author

Singh, Nidhi¹

; Misra, Richa¹

; Quan, Wei²

; Radic, Aleksandar³

; Lee, Sang-Mook⁴

; Han, Heesup²

¹ Jaipuria Institute of Management, Noida, India (GRID:grid.512249.9) (ISNI:0000 0004 1764 8954)
² Sejong University, College of Hospitality and Tourism Management, Seoul, Korea (GRID:grid.263333.4) (ISNI:0000 0001 0727 6358)
³ Independent Researcher, Dubrovnik (Croatia), Croatia (GRID:grid.263333.4)
⁴ Jeju National University, Department of Tourism Management, Jeju-do, Korea (GRID:grid.411277.6) (ISNI:0000 0001 0725 5207)

Pages

899

Publication year

2024

Publication date

Dec 2024

Publisher

Palgrave Macmillan

e-ISSN

2662-9992

Source type

Scholarly Journal

Language of publication

English

DOI

https://doi.org/10.1057/s41599-024-03395-6

ProQuest document ID

3078860904

An analysis of consumer’s trusting beliefs towards the use of e-commerce platforms

Jump to:

Full text

Abstract

Details

Suggested sources