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Fostering Trust and Loyalty in E-Commerce through Ethical Data

Practices

Rhea Natania Menaisa Sebayang

, Tiara Rizky Cahya

, Nurhidayah Yahya

, Rafidah Zainal

Universitas Lampung, Indonesia

Accounting Research Institute, Universiti Teknologi MARA, Shah Alam, Malaysia

Universiti Teknologi MARA, Shah Alam, Malaysia

DOI:

https://doi.org/10.51583/IJLTEMAS.2026.150600219

Received: 01 July 2026; Accepted: 06 July 2026; Published: 25 July 2026

ABSTRACT

This conceptual paper examines how ethical data practices foster consumer trust and loyalty in e-commerce,

with particular emphasis on data privacy, data security, and consumer perceived risk. As digital platforms

increasingly depend on personal and behavioural data for personalisation, payment processing, targeted

marketing, and customer engagement, consumer concerns regarding transparency, informed consent,

unauthorised data sharing, cybersecurity, and manipulative data use have become central to online exchange

relationships. Drawing on Social Exchange Theory, the paper proposes that consumers are more willing to

maintain relationships with e-commerce platforms when the exchange of personal data for convenience and

personalised services is perceived as fair, beneficial, secure, and trustworthy. The paper distinguishes ethical

data governance from regulatory compliance by arguing that lawful data processing represents only a minimum

threshold, whereas ethical governance also requires fairness, proportionality, accountability, respect for

autonomy, and consideration of consumer wellbeing. An integrated conceptual framework is developed in which

ethical data practices strengthen consumer loyalty through consumer trust, while perceived privacy and security

risk weakens the positive relationship between ethical data practices and trust. The discussion also considers

emerging governance challenges associated with generative artificial intelligence, algorithmic personalisation,

blockchain, and decentralised identity systems. The paper contributes to e-commerce and business ethics

literature by explaining how, and under what conditions, responsible data governance supports sustainable digital

relationships. Practical implications are presented for managers, marketers, IT teams, policymakers, and

consumers, together with measurable constructs and empirical testing directions for future research.

Keywords: Ethical Data Practices, Data Privacy, Data Security, Consumer Perceived Risk, Consumer Trust,

Consumer Loyalty, E-Commerce

INTRODUCTION

The rapid expansion of e-commerce has transformed the way businesses interact with consumers by increasing

convenience, personalisation, and global market access. At the beginning of 2025, approximately 5.56 billion

people used the internet and 5.78 billion used mobile phones worldwide (DataReportal, 2025). This extensive

digital participation has made online platforms a routine part of purchasing behaviour and has increased firms'

reliance on browsing histories, payment information, location data, product preferences, and engagement

patterns. Although these data support service improvement and personalised experiences, their collection and

use also expose consumers to privacy loss, profiling, data breaches, unauthorised sharing, and other forms of

misuse.

In e-commerce, privacy, security, and perceived risk are closely connected. Data privacy concerns whether

consumers understand and control how their information is collected, processed, stored, and shared, while data

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security concerns the safeguards used to protect that information from unauthorised access, loss, or disclosure.

Perceived risk arises when consumers believe that an online transaction may result in identity theft, payment

fraud, unwanted profiling, data misuse, or loss of control. Because consumers typically disclose personal and

financial information before receiving a product or service, trust in online commerce depends substantially on

how platforms communicate and govern their data practices.

Recent evidence demonstrates that responsible data management has become a significant determinant of

consumer confidence. PwC (2024) reported that data protection was the leading factor influencing consumer

trust, while Cisco (2024) found that many consumers would avoid organisations they did not trust with their

personal information. These findings indicate that privacy and security are no longer merely technical or

compliance concerns; they shape perceived risk, willingness to disclose information, transaction completion,

and the continuation of customer relationships.

Regulatory and privacy cases further demonstrate the consequences of weak governance. Actions involving

Amazon's Alexa and Ring services raised concerns regarding prolonged data retention, employee access,

inadequate safeguards, and the handling of deletion requests (Federal Trade Commission, 2023, 2024). Oracle's

2024 privacy settlement similarly reflected allegations concerning the creation and sale of consumer profiles

without adequate authorisation, although the company denied wrongdoing (Reuters, 2024). Such cases illustrate

how insufficient transparency and control may produce legal, reputational, and financial consequences. IBM

(2025) also reported that data breaches continue to impose substantial organisational costs, reinforcing the

strategic importance of prevention and accountable data governance.

Ethical data practices include transparent privacy communication, meaningful consent, data minimisation,

secure systems, responsible personalisation, and accessible consumer control. These practices can reduce

uncertainty and signal that a platform is committed to protecting consumer interests. Importantly, ethical

governance extends beyond formal legal compliance. A practice may satisfy a narrow legal requirement while

remaining ethically questionable when consent is confusing, data collection is excessive, algorithms manipulate

consumer choices, or information is used beyond reasonable expectations. Distinguishing these concepts is

therefore necessary to explain why some technically compliant practices may still fail to generate trust.

Existing e-commerce research has established the importance of usability, product quality, payment security,

delivery reliability, privacy, and trust. However, less attention has been given to an integrated explanation of

how ethical data practices influence loyalty through trust and how perceived privacy and security risk constrains

this process. In addition, emerging technologies such as generative artificial intelligence, algorithmic

recommendation systems, blockchain, and decentralised identity create new governance opportunities and risks

that require ethical evaluation. This paper addresses these gaps by developing a Social Exchange Theory-based

framework in which consumer trust mediates the relationship between ethical data practices and consumer

loyalty, while perceived privacy and security risk moderates the relationship between ethical data practices and

trust.

LITERATURE REVIEW

Evolution of E-Commerce and the Centrality of Consumer Data

E-commerce has evolved from a basic online transaction channel into a data-intensive ecosystem shaped by

mobile technology, automation, artificial intelligence, and platform-based business models (Laudon & Traver,

2023). Firms now compete not only through price and product availability, but also through personalised

recommendations, targeted advertising, seamless payment, rapid fulfilment, and predictive customer

engagement. Consumer data therefore function as a strategic resource that supports behavioural analysis, demand

forecasting, service design, and relationship management (Chen et al., 2012).

The commercial value of data must nevertheless be balanced against consumer rights and expectations.

Consumers often possess limited knowledge of how their information is collected, combined, shared, or

monetised (Martin & Murphy, 2017). Kishnani and Das (2024), for example, identified weaknesses in

transparency, authentication, breach notification, and other security practices across e-commerce websites.

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These concerns demonstrate that the continued expansion of digital commerce requires governance arrangements

that protect consumers while allowing firms to use data for legitimate and beneficial purposes.

Ethical Data Practices in the Digital Marketplace

Ethical data practices refer to the responsible collection, storage, processing, sharing, and use of consumer

information in ways that are transparent, secure, fair, and respectful of autonomy (Floridi & Taddeo, 2016). In

e-commerce, these practices include meaningful consent, purpose limitation, data minimisation, appropriate

retention periods, secure payment and information systems, responsible personalisation, understandable privacy

notices, and mechanisms that allow consumers to access, correct, delete, or restrict the use of their information.

Ethical data management is particularly important because consumers must disclose information before they can

fully evaluate how a platform will behave. Excessive tracking, hidden data sharing, manipulative interface

design, unclear consent, or discriminatory algorithmic outcomes may provide short-term commercial benefits

but can weaken trust and brand reputation over time (Martin & Murphy, 2017; Morić et al., 2024). Transparency

is therefore not limited to publishing a privacy policy. It also requires firms to explain data practices in accessible

language and provide consumers with choices that are understandable and practically usable. Research on

algorithmic systems similarly indicates that explainability and perceived fairness can strengthen user trust and

acceptance (Shin, 2021).

Ethical Data Governance Beyond Regulatory Compliance

Regulatory compliance and ethical data governance are related but conceptually distinct. Compliance refers to

adherence to applicable legal requirements governing the collection, processing, retention, sharing, and

protection of personal information. Regulations such as the General Data Protection Regulation establish

important principles including lawfulness, transparency, purpose limitation, data minimisation, security, and

accountability (European Parliament & Council of the European Union, 2016). These requirements provide an

essential baseline for organisational conduct.

Ethical data governance extends beyond this minimum baseline by asking whether data practices are fair,

proportionate, understandable, and respectful of consumer dignity and autonomy. A practice may be legally

permissible but ethically problematic when consumers are pressured into accepting unnecessary tracking, when

consent notices exploit information asymmetry, or when personalisation is designed to manipulate vulnerable

users. Ethical governance therefore evaluates not only whether an organisation is allowed to use data, but also

whether it should use the data in a particular manner and whether the potential benefits justify the possible harms.

This distinction strengthens the conceptual contribution of the present study. Legal compliance may reduce

exposure to sanctions, but it does not automatically produce relational trust. Consumers may remain sceptical

when formal compliance is not accompanied by credible transparency, meaningful control, responsible

innovation, and evidence that the firm prioritises their interests. Ethical data practices are therefore positioned

as relational signals of fairness and trustworthiness rather than as a simple reflection of regulatory conformity.

Data Privacy, Security, and Consumer Perceived Risk

Data privacy concerns consumers' control over personal information, whereas data security concerns the

technical and organisational safeguards that prevent unauthorised access, loss, misuse, or disclosure (Smith et

al., 2011). Although conceptually distinct, both influence perceived risk in online transactions. Perceived risk

includes the possibility of identity theft, payment fraud, unauthorised profiling, unwanted disclosure, or other

negative consequences associated with data sharing (Featherman & Pavlou, 2003).

Perceived privacy and security risk can discourage consumers from completing purchases, sharing accurate

information, or maintaining relationships with a platform. Pilakaew et al. (2024) found that privacy and security

risk influenced repurchase intention in online shopping, while Cisco (2024) demonstrated that consumer

engagement is strongly affected by confidence in organisational data practices. Clear communication, visible

security features, data minimisation, accessible controls, and prompt incident response can reduce uncertainty.

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Nevertheless, consumers may continue to perceive high risk when a platform has experienced breaches, relies

on opaque tracking, or operates in an environment with weak institutional protections.

Consumer Trust and Loyalty in E-Commerce

Consumer trust refers to the belief that an e-commerce platform is competent, reliable, honest, and willing to

protect consumer interests (Gefen et al., 2003). Trust is especially important in virtual exchanges because

consumers cannot fully observe the seller, inspect data-processing activities, or predict how information will be

used after disclosure. Privacy protection and perceived security therefore operate alongside service quality and

website performance as important antecedents of online trust. A meta-analysis by Kim and Peterson (2017)

confirmed significant relationships among online trust, privacy perceptions, loyalty, and repeat purchase

intention.

Consumer loyalty reflects continued purchasing, ongoing platform use, positive word-of-mouth, and resistance

to switching (Oliver, 1999). In highly competitive digital markets, trusted platforms are more likely to retain

consumers because trust reduces uncertainty and supports a favourable evaluation of future exchanges. Ashiq et

al. (2023) found that e-trust contributes to e-satisfaction and e-loyalty, while Li et al. (2025) demonstrated that

privacy concerns influence trust and purchase intention in live-streaming commerce. Ethical data practices may

therefore strengthen loyalty indirectly by creating the conditions under which consumers perceive a platform as

reliable and worthy of continued engagement.

Emerging Technologies and Ethical Data Governance

Generative artificial intelligence and algorithmic recommendation systems create opportunities for more

responsive customer service, personalised product discovery, automated content creation, and fraud detection.

However, these technologies may depend on extensive datasets and opaque processing, creating concerns

regarding data provenance, unauthorised use, privacy leakage, harmful bias, manipulation, and the inability of

consumers to understand automated decisions. The NIST Generative AI Profile identifies data privacy,

information integrity, harmful bias, security, and value-chain transparency among the risks that organisations

should govern across the AI lifecycle (National Institute of Standards and Technology, 2024).

Emerging AI governance frameworks reinforce the need for transparency, risk assessment, documentation,

human oversight, and accountability. The European Union's Artificial Intelligence Act adopts a risk-based

framework and includes obligations concerning transparency and the governance of certain AI systems and

general-purpose AI models (European Parliament & Council of the European Union, 2024). For e-commerce

firms, responsible implementation should include disclosure when automated systems influence

recommendations, pricing, product visibility, or customer communication; testing for discriminatory or

manipulative outcomes; and processes through which consumers can challenge significant automated decisions.

Blockchain and decentralised identity systems may also support traceability, credential verification, and greater

consumer control over data disclosure. Decentralised identifiers are designed to enable verifiable digital

identities without requiring exclusive dependence on a central identity provider (World Wide Web Consortium,

2022). These systems may reduce unnecessary disclosure by allowing consumers to prove selected attributes

rather than sharing complete identity records. Nevertheless, distributed records can create challenges relating to

correction, deletion, interoperability, security, accessibility, and consumer understanding. Their ethical value

therefore depends on governance design rather than on the technology alone.

Synthesis and Research Gap

The literature indicates that ethical data practices, privacy protection, security, perceived risk, trust, and loyalty

are closely related, but these relationships are often examined separately or as direct effects. Existing studies

provide less explanation of the relational mechanism through which ethical data governance generates loyalty

and the conditions that may weaken this process. The present study addresses this gap by positioning consumer

trust as a mediator between ethical data practices and loyalty and perceived privacy and security risk as a

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moderator of the ethical practices-trust relationship. It also differentiates ethical governance from legal

compliance and incorporates emerging technological developments into the conceptual discussion.

Theoretical Underpinning

This conceptual paper is grounded in Social Exchange Theory (SET), which explains that relationships are

maintained when parties perceive an exchange as beneficial, fair, and sufficiently predictable (Blau, 1964;

Homans, 1958). E-commerce involves a reciprocal exchange in which consumers provide personal data,

payment information, time, attention, and behavioural information, while firms provide product access,

convenience, personalisation, efficient transactions, and customer support. Because the exchange occurs under

conditions of information asymmetry, consumers must assess whether the expected benefits outweigh possible

privacy, security, and financial costs.

SET is appropriate for explaining ethical data governance because the quality of the exchange depends not only

on economic value but also on how each party treats the other. Ethical data practices signal that the firm

acknowledges its responsibility toward consumers and does not intend to exploit their informational

vulnerability. Informed consent, data minimisation, transparent communication, secure systems, fair algorithms,

and meaningful consumer control reduce perceived costs and strengthen the perceived fairness of the

relationship. In contrast, hidden tracking, excessive collection, weak security, and manipulative personalisation

increase perceived costs and may prompt consumers to restrict disclosure or terminate the relationship (Martin

& Murphy, 2017; Morić et al., 2024).

Trust functions as the principal relational mechanism in this exchange. Consumers cannot continuously monitor

how their information is processed, so continued participation depends on the belief that the platform is

competent, reliable, and committed to protecting their interests. Ethical data practices provide observable signals

that consumers use to infer integrity and benevolence. Once trust develops, consumers become more willing to

transact, repurchase, share necessary information, recommend the platform, and tolerate ordinary uncertainty

associated with online commerce (Gefen et al., 2003; Kim & Peterson, 2017).

Loyalty represents the continuation of a favourable exchange relationship. Repeated experiences of transparent

communication, secure transactions, and responsible data handling reinforce the expectation that future

interactions will also be beneficial. Trust, therefore, explains how ethical practices translate into continued

engagement rather than merely serving as another parallel predictor of loyalty. This mediation logic is consistent

with SET because trust stabilises the exchange and reduces the need for consumers to reassess every transaction

independently.

Perceived privacy and security risk serves as a boundary condition. Ethical statements and controls may have a

strong trust-building effect when consumers believe that risks are manageable. However, the same practices may

be less persuasive when consumers remain concerned about previous breaches, opaque technologies,

unauthorised sharing, identity theft, or weak enforcement. In SET terms, high perceived risk increases the

expected cost of participation and weakens the credibility of the benefits offered by the platform. This reasoning

supports the proposed moderating role of perceived privacy and security risk.

Conceptual Framework and Proposition Development

The proposed framework integrates ethical data practices, consumer trust, consumer loyalty, and perceived

privacy and security risk. Ethical data practices constitute the antecedent construct and include transparency,

informed consent, data minimisation, secure systems, responsible personalisation, accountability, and consumer

control. Consumer trust represents confidence in the platform's integrity, competence, reliability, and

benevolence, while consumer loyalty includes repurchase intention, continued use, positive word-of-mouth, and

reduced switching behaviour.

The framework proposes both a mechanism and a boundary condition. Consumer trust mediates the relationship

between ethical data practices and loyalty by explaining how responsible organisational conduct becomes a

durable customer relationship. Perceived privacy and security risk moderates the relationship between ethical

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data practices and trust by determining how strongly consumers interpret ethical practices as credible evidence

of protection.

Proposition 1: Ethical Data Practices and Consumer Trust

Ethical data practices are expected to positively influence consumer trust. Transparent communication reduces

uncertainty about how information is collected and used, meaningful consent demonstrates respect for autonomy,

and data minimisation limits unnecessary exposure. Secure systems, responsible personalisation, and accessible

consumer controls further signal competence and accountability. These practices provide evidence that the

platform is willing and able to protect consumer interests, thereby strengthening perceptions of integrity,

reliability, and benevolence (Gefen et al., 2003; Shin, 2021).

P1: Ethical data practices positively influence consumer trust in e-commerce.

Proposition 2: Consumer Trust and Consumer Loyalty

Consumer trust is expected to positively influence loyalty because it reduces uncertainty and makes continued

engagement less psychologically and financially costly. Consumers who believe that a platform is competent,

honest, and protective of their information are more willing to repurchase, continue using the platform,

recommend it to others, and resist switching. Prior research consistently links online trust with satisfaction,

repeat purchase intention, and e-loyalty (Ashiq et al., 2023; Kim & Peterson, 2017).

P2: Consumer trust positively influences consumer loyalty in e-commerce.

Proposition 3: Mediating Role of Consumer Trust

Ethical data practices may not directly produce loyalty unless consumers recognise those practices as credible

evidence of responsible organisational behaviour. Through transparent, secure, and fair data handling, firms

reduce the perceived costs of data disclosure and increase confidence in the exchange. This confidence is

expressed as trust, which then encourages continued engagement and advocacy. Trust therefore constitutes the

relational pathway through which ethical data practices are converted into loyalty.

P3: Consumer trust mediates the relationship between ethical data practices and consumer loyalty in e-

commerce.

Proposition 4: Moderating Role of Perceived Privacy and Security Risk

Perceived privacy and security risk is expected to weaken the positive relationship between ethical data practices

and consumer trust. When risk is low, consumers are more likely to interpret privacy notices, security controls,

and responsible data-use commitments as convincing signals of integrity and competence. When risk is high,

consumers may remain concerned about hidden tracking, unauthorised sharing, cyberattacks, payment fraud, or

data breaches even when firms communicate ethical practices. Under such conditions, the expected cost of

participation remains high and the trust-building value of ethical practices is reduced (Featherman & Pavlou,

2003; Pilakaew et al., 2024).

P4: Perceived privacy and security risk weakens the positive relationship between ethical data practices

and consumer trust in e-commerce.

DISCUSSION

The framework advances the discussion of responsible digital commerce by treating ethical data practices as

relational signals rather than as purely technical controls or legal obligations. From a Social Exchange Theory

perspective, consumers evaluate whether disclosing personal information produces a fair balance between

benefits and potential costs. Ethical practices improve this balance by reducing information asymmetry, limiting

unnecessary exposure, and demonstrating that the firm recognises its responsibilities toward consumers. This

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interpretation explains why technical compliance alone may be insufficient when consumers perceive that data

use remains intrusive, manipulative, or inconsistent with their expectations.

The distinctive contribution of the framework is its explanation of how and under what conditions ethical data

practices influence loyalty. Trust provides the mechanism: responsible data practices strengthen perceptions of

integrity, competence, and benevolence, which support repeat engagement and positive word-of-mouth.

Perceived privacy and security risk provides the boundary condition: ethical practices have less influence on

trust when consumers continue to anticipate substantial harm. This integrated approach extends studies that

examine direct relationships among privacy, trust, and purchase intention by combining mediation and

moderation within a single conceptual model.

The inclusion of emerging technologies also broadens the relevance of the framework. Generative AI and

algorithmic personalisation can improve convenience but may simultaneously increase opacity, profiling,

privacy leakage, and manipulation risk. Blockchain and decentralised identity may improve traceability or

selective disclosure but can create challenges relating to permanence, interoperability, and accessibility. The

effect of these technologies on trust therefore depends on how they are governed. Technology adoption should

be assessed according to transparency, proportionality, consumer control, accountability, and the capacity to

prevent or remedy harm.

Theoretical Implications

This paper extends Social Exchange Theory into the context of digital data governance by conceptualising

personal-data disclosure as part of an ongoing reciprocal relationship. It demonstrates that the perceived fairness

of the exchange depends on the governance of data as well as the functional benefits of e-commerce. Ethical

practices reduce the perceived costs of participation and communicate that the firm accepts relational

responsibilities that extend beyond formal contractual and legal obligations.

The framework also contributes by integrating ethical data practices, trust, loyalty, and perceived risk into a

mechanism-and-boundary model. Trust explains the process through which ethical conduct becomes loyalty,

while perceived privacy and security risk explains why the same organisational practices may generate different

levels of trust across consumers and contexts. The explicit distinction between ethical governance and regulatory

compliance further clarifies that ethical responsibility is not exhausted by legal conformity.

Practical Implications

E-commerce managers should embed ethical data governance into corporate strategy, product design, marketing,

and customer relationship management. Privacy notices should be concise and understandable, consent should

be freely given and specific, and data collection should be limited to legitimate purposes. Organisations should

provide practical controls that allow consumers to access, correct, delete, or restrict the use of their information.

These actions demonstrate respect for autonomy and may strengthen trust more effectively than legalistic

disclosures alone.

IT and cybersecurity teams should support ethical commitments through encryption, access controls, secure

authentication, system monitoring, incident-response plans, vendor assessment, and regular security audits.

Firms using AI-driven personalisation should disclose when automated systems materially influence

recommendations, pricing, product visibility, or customer communication. Algorithmic impact assessments, bias

testing, data-provenance reviews, human oversight, and mechanisms for consumer challenge should be

established before and after deployment. Where blockchain or decentralised identity systems are adopted, firms

should ensure that consumers understand how credentials are stored, verified, corrected, revoked, and shared.

Boards and senior management should establish cross-functional data-governance oversight involving legal,

compliance, marketing, information technology, cybersecurity, ethics, and consumer-protection expertise. Key

performance indicators should extend beyond the absence of regulatory penalties to include complaint patterns,

consent withdrawal, privacy-control usage, breach response, algorithmic incidents, and consumer trust.

Policymakers should encourage interoperable standards and accountable AI governance while recognising

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differences in national regulatory capacity. Consumers should also be provided with accessible information and

tools that improve their ability to make informed privacy and security decisions.

Limitations and Future Research

As a conceptual study, this paper does not empirically validate the proposed relationships, and its framework

should therefore be interpreted as a theoretically grounded basis for future testing rather than as evidence of

causal effects. The discussion also draws on literature and cases from different institutional settings. Consumer

expectations and the credibility of organisational data practices may vary according to culture, regulatory

enforcement, digital literacy, platform type, and previous experience with privacy incidents.

Future research may operationalise ethical data practices through measurable dimensions such as transparency,

informed consent, data minimisation, security protection, fairness, accountability, responsible personalisation,

and consumer control. Consumer trust may be assessed through perceived integrity, competence, reliability, and

benevolence, while loyalty may be measured using repurchase intention, continued platform use, positive word-

of-mouth, and resistance to switching. Perceived privacy and security risk may include concerns regarding

unauthorised sharing, identity theft, payment fraud, data breaches, profiling, and loss of control. Researchers

should adapt validated scales from privacy, trust, perceived-risk, and e-loyalty literature and assess content

validity through expert review and pilot testing.

The mediation and moderation relationships may be examined using covariance-based Structural Equation

Modelling or PLS-SEM, depending on the research objective, model characteristics, sample size, and

distributional assumptions. Multi-group analysis could compare countries, age groups, levels of digital literacy,

prior breach experience, and different platform categories. Experiments could evaluate how changes in consent

design, privacy explanations, AI disclosure, or security assurances affect trust, while longitudinal studies could

determine whether trust persists after repeated transactions or privacy incidents. Qualitative case studies may

also examine how firms translate ethical principles into governance structures and operational decisions.

CONCLUSION

Ethical data practices are essential for fostering consumer trust and loyalty in e-commerce. Drawing on Social

Exchange Theory, this paper argues that consumers are more willing to maintain digital relationships when data

exchanges are perceived as fair, transparent, secure, and respectful of autonomy. The proposed framework

demonstrates that ethical data practices generate loyalty through the development of consumer trust, while

perceived privacy and security risk determines whether those practices are interpreted as credible and sufficiently

protective.

The study further establishes that ethical governance is broader than regulatory compliance. Legal conformity

provides a necessary baseline, but sustainable trust requires proportionality, accountability, meaningful

consumer control, and responsible technological innovation. As e-commerce firms increasingly adopt generative

AI, algorithmic personalisation, blockchain, and decentralised identity systems, the governance of data will

remain central to consumer confidence and long-term competitive sustainability. Empirical testing is now

required to validate the proposed mediation and moderation relationships across different consumer, platform,

and regulatory contexts.

Ethical Considerations

This conceptual paper does not involve human participants, animal subjects, or primary data collection.

Therefore, formal ethical approval was not required.

Conflict of Interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

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Data Availability

No primary dataset was generated for this conceptual paper. The discussion is based on publicly available

literature, reports, and cases cited in the reference list.

ACKNOWLEDGEMENT

The authors would like to express their sincere appreciation to the Accounting Research Institute (ARI),

Universiti Teknologi MARA, Malaysia, as a Higher Institution Centre of Excellence (HICoE), under grant

UiTM.800-3/1 DDJ.82 (012/2025) and the Ministry of Higher Education Malaysia for the funding and support

provided, which contributed to the completion of this paper.

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