AI-Powered personalization vs. blockchain-based privacy: a systematic literature review of consumer trade-offs in digital marketing

1 Introduction

The rapid evolution of digital technologies ushers in a new transformed marketing era, marked by two powerful forces-in artificial intelligence (AI) and blockchain-and means hyper-personalization through AI and promise privacy and security through blockchain (Wirth, 2018; Feng et al., 2021). These two features reflect the opposing sides of a coin that complicates the reality of consumer demand for customized experiences and heightened unease about data safety. Indeed, the future will bring a paradigm shift to all industries-from retail to healthcare-as businesses compete to benefit from extreme technological advancements while facing ethical and operational challenges. This analysis draws from over 50 studies and brings together applications, gaps, and ethical considerations at the juncture of AI, blockchain, and consumer behavior.

It is the way customers interact with businesses that has brought change in marketing due to AI. Machine learning models applied to web-based models of data give real time personalized content, predictive analysis, and deep granular customer segmentation. AI-driven tools, such as recommendation systems for completing sales and increasing customer allegiance, have also been used as a reference. Yin et al. (2025) explains the concept of AI-enhanced product recommendation engines that personalize relationships in the context of e-commerce turning passive browsing into active purchase behavior. Likewise, Hemlatha et al. (2024) argues that AI segmentation should no longer be an optional but compulsory endeavor for businesses intending to do successful business in this highly competitive environment. Apart from retail, AI is applied into other fields like healthcare and pharmacy for the refinement of marketing strategies (Mirwan, et al., 2023). Kumar et al. (2023) accentuates that AI assists healthcare providers in high optimization in warehousing decision-making, while Khanna et al., 2020 explores the area in pharmaceutical marketing where predictive analytics add to the features of patient engagement and compliance. These examples serve to demonstrate how diverse AI is in securing effectiveness and innovation across industries.

Social media and content marketing have also been examples of what AI can deliver. According to Capatina et al. (2020), the successful applications of AI-based social media tools depend on their capacity to really deliver on their promises in automating things like content scheduling or audience sentiment analysis. Further, Dumitriu and Popescu, (2020) presents a stepwise model of incorporating AI into marketing processes along with a stressed importance on the keyword optimization for better digital visibility. Meanwhile, Kose and Sert, (2017) demonstrates how AI simplifies content creation such that it resonates with consumer preferences, thus enhancing engagement metrics. However, the spread of AI comes at its own price. Hildebrand (2019) points out to biases inherent in algorithms and data private breaches or failures of AI that can lead to consumer mistrust. All these are reasons that point to the need for ethics and a strong governance framework.

With this development in AI technology, blockchain has risen as one of the primary enabling technologies for security and transparency in data. In an era where breaches and misuse of data prevail, the decentralized structure instituted through blockchain can be the way forward to secure sensitive data. According to Saleh (2024) and Kshetri (2025), blockchain may help AI systems provide additional integrity of data, so datasets used to personalize would be tamper-proof and audit capable. For instance, blockchain can keep decentralized AI models away from unauthorized changes, thus promoting an aspect of trust in an automated decision-making process. This is more relevant in sectors like cybersecurity, where Saleh (2024) notes how blockchain’s immutability complements AI in detecting threats to build an invincible defense from cyber-attacks. Similarly, there are all-around ethical compliance where blockchain excels. Butterworth (2018) explains how it matches up with GDPR principles, from which it follows that blockchain can now turn consent management into an automated task and thus assure compliance. Kshetri (2025) adds that blockchain strengthens intellectual property rights in AI development, enabling transparent attribution of digital assets. These attributes match the rising consumer anxieties against data exploitation, such as in Duda et al. (2022) study, which positions blockchain as a tool to mitigate privacy risks in AI applications. By decentralizing data storage and processing, the blockchain cuts reliance on centralized entities, hence reducing the risk to breaches.

However, the real shortcoming in the research remains a critical void as concerns synthesized findings regarding consumer adoption of the personalization-privacy trade-off. Existing literature may be highly extensive with valuation on how AI works in marketing or on the other hand, the composite technical points of blockchain; few have even studied its specified ethical impactions. For example, while Verma et al. (2021), Casino (2019) and Chintalapati and Pandey (2021) review the application of AI and blockchain, respectively, they do not cross into these domains to gauge consumer perceptions. This leaves a huge gap, especially when consumer trust is a very fragile commodity. Trust and perceived sacrifice emerge, according to Ameen’s perspective (2021), as mediating factors in the deployment of AI because to sustain such convenience-driven personalization often comes at the expense of privacy concerns. So far, no such framework exists that would reconcile these tensions using transparency through blockchain. Hildebrand (2019), Gao et al. (2023) and Beyari (2025) also refer to the ethical traps in AI, but they offer limited solutions to integrate privacy-preserving technologies like blockchain.

Preliminarily, model frameworks that are ethical-natured call for an interdisciplinary approach. As posited by Casino (2019) and Kshetri (2025), the ideal decentralized governance of AI should incorporate data sovereignty, thereby providing the users with authority over their information. With AI processing and humans supervising compliance with ethical standards, hybrid people AI collaboration schemes would accentuate efficiency with accountability (Stone et al., 2020; Davenport et al., 2019). Privacy provisions at the design stages will then be supported by the application of the blockchain technology method toward defining consent management, leading to their future uptake in some industries as core standards for embedding ethics in marketing technologies (Butterworth, 2018; Kumar et al., 2020). Additionally, frameworks should consider sectoral specifications. In good health, for instance, AI has been supposed as secured by blockchain in patient level trust through an open and transparent tracking of the use of their data; in e-commerce, it could do away with straightforward personalized promotions with the cost of privacy.

Blockchain offers promising mechanisms, e.g., immutable audit trails and decentralized consent management, that can reduce some ethical risks of AI, particularly around data transparency and compliance. However, as this review shows, it does not eliminate algorithmic bias or address all privacy concerns (e.g., the right to be forgotten conflicts). Recent systematic work, such as Reitano et al. (2024) on AI in agri-food supply chains, offers methodological guidance for synthesising evidence on AI-driven consumer interfaces–an approach that informs the present review.

2 Theoretical framework

2.1 AI-Powered personalization: revolutionizing consumer engagement

Artificial intelligence (AI) has significantly revolutionized personalization in marketing by making companies capable of launching highly targeted tactics specifically catering to individual clients’ whims and fancies (Jarek and Mazurek, 2019; Devang et al., 2019). Their technology uses recommendation systems just for that: to analyze huge datasets like browsing patterns, purchase history, and different technical aspects, which are then applied in a predictive manner for consumer preferences (Vinerean and Opreana, 2024; Wen et al., 2024; Markić et al., 2015). Saleh (2024) gives his perspective on the operation of AI recommendation engines on e-commerce platforms like Amazon, they enhance the engagement of consumers who have been served the best of products or content set out for them and this is translated to results in terms of revenue increases and client loyalty. By now, Hemlatha et al. (2024) has already cemented the place of customer segmentation made quite essential by AI with modern marketers tapping into smaller groups of people and reaching out to them through campaigns. AI, strategically big in healthcare, makes possible enormous benefits for personalized outreach to patients (Anshari et al., 2018). Kumar et al. (2023) disputes that AI optimization in reaching patients is possible because of AI’s decisions in acting based on the information that was developed ahead using lagged patient data which include reminders of medication adherence, or what sort of a wellness treatment plan shall work for the patient.

Another critical contribution of AI to personalization is chatbots. These are used as tools to take on consumer interaction and provide instantaneous solutions to inquiries or problems, albeit collecting data for further crafting their personalized communication strategy Kühl et al. (2019) provides an example of how chatbots learn from the content of social media on a massive scale to identify patterns of emerging needs, consequently presenting businesses with an opportunity to intervene accordingly. Brands that rely on AI for customer service may face some dangers. Hildebrand (2019) notes that chatbots perceive customer’s data to trigger inequalities in biased data settings, such as promoting responses to select demographics appealing to certain income-bracket levels.

This highlights the need for companies to have their AI ethics, transparently human-touch interfaces with the users. For all these systems I hope and really beg for any activity that gives us an ethical touch. AI and predictive methods have the transformative effect as well. AI craves for the prospective planning so that it could predict the time that campaigns should be launched, what content should fill the crescendo, and which channels are right. Differentiation would not work without knowing how BDPFs work further to enhance AI against points of conflict. Subsequently, marketers learn about how their AI will play to the qualities of certain customer segments. Supplying such knowledge does create trust. Though, if automation is overdone, all concerns are concentrated on the alienation of the end users. Hildebrand (2019) argues the AI’s good implementation appears to benefit humans over non-personalization, where customers feel that people are all from being observed in binary all to zero points in some white paper.

2.2 Blockchain-based privacy: safeguarding data in a decentralized ecosystem

Concerns surrounding privacy have gained the technological infusions that are mostly left centralized. Blockchain essentially enhances data security by decentralizing data control (Saidu et al., 2025). The tamper-proof ledger is used to strengthen cyber-security efforts by further fortifying decentralized AI models through blockchain technology against any manipulation, thereby stabilizing threat detection (Saleh, 2024). Kshetri (2025) says that the blockchain affords auditable records of consumer interactions, like ad-clicks and purchases, thereby limiting chances of fraud and accounting for their marketing. A blockchain-based system that would track user data across campaigns would have increased transparency, lessening distrust.

Decentralized identity management is within this range of innovations. Blockchain enables users to manage their digital identities, sharing only what is necessary with marketers. Duda et al. (2022) argues that such an approach could minimize AI privacy risks by removing centralized data repositories—tempting targets for breaches. For instance, a GDPR-compliant approach might involve blockchain in automating consent management (Butterworth, 2018). Consumers could grant or revoke access to data via smart contracts, with automatic enforcement of privacy regulation compliance. Casino (2019) and Kharche et al. (2024) note doubts surrounding scalability, as transaction processing tends to grind to a halt under all those computational needs. If cryptographic keys are lost or misplaced, any data tied with that key is lost too. However, the role of blockchain to realign marketing in the ethical realm remains of great hope (Tagde et al., 2021; Garg et al., 2024; Paliwal et al., 2024). Kshetri (2025) describes how blockchain strengthened intellectual property rights regarding AI development, allowing for the transparent attribution of algorithms and datasets. This, in turn, builds trust among stakeholders, from developers to end-users.

2.3 Consumer trade-offs: navigating the personalization-privacy paradox

A lot of consumers find themselves locked in the paradox of craving personalized experiences while also desiring the protection of their privacy. As such, the crux of this paradox lies in the tension between trust and convenience (Balaji and Roy, 2016). Ameen et al. (2021) identifies trust and perceived sacrifice as critical mediators: consumers weigh personalized services’ cost-benefit ratio, in terms of time saving or unavailable offers, pose threat of data misuse. For example, customer service applications of AI chatbots can create rooms for collecting sensitive personal information, thus building up distrust (Hildebrand, 2019). Young people generally prefer convenience and share their data for personalized service benefits, but older generations tend to be more careful. Mu and Zhang, (2025) links AI Marketing (AIM) use with higher customer satisfaction, but emphasizes that transparency in data usage is crucial for long-term acceptance by customers.

People share personal data differently. Gao et al. (2023) mentions that consumers tend to share more data in personalized services related to health or financial industry markets as personalization helps improve individual wellbeing, but many reject retail promotion sharing because of their perceptions of them being intrusive. The auditability given to blockchains can withhold such concerns. That is to say, immutable records of how data is being used, that is how the AIs model consumer preferences-could probably build trust (Duda et al., 2022).

Davenport et al. (2019) advocates for models in which “augmented intelligence” benefits decision-makers enabled by AI and where accountability lies. Perhaps this should have been a combination of an intelligent AI generating personalized offers while the human teams would check them for ethical compliance.

2.4 Ethical and regulatory context: balancing innovation and accountability

Unless they address regulatory mandates and consider the expectations from society, the ethical integration of AI with blockchain remains inconceivable. Therefore, high standards for data handling are imposed by the GDPR and data sovereignty, requiring explicit consent and low data retention. It is then these very principles that fit with the nature of a blockchain: it lets users control who is accessing their data. Butterworth (2018) points out that blockchain lends an uncanny ability to achieve some GDPR compliance by logging consent transactions immutably or automating requests for data deletion. Kshetri (2025) however, puts forth that such events of conflict arise when one considers the permanence of a blockchain versus the applicability of the right to be forgotten under GDPR. Algorithmic bias remains a relevant factor. This remains for AI systems trained on datasets that are biased toward certain groups and that possibly leave out marginalized ones. Gao et al. (2023) cites instances where ad-targeting algorithms commenced in imposing further social grounds on high-income demographics. Mitigating it requires diversified training data and fair audits. Stone et al. (2020) proposes “ethical AI committees” to oversee marketing algorithms and ensure inclusivity.

Transparency and explainability are equally crucial (Hermann, 2022). Users should understand how their data serves as an input for AI decisions. The audit trail provided by blockchain would help clarify processes, say, as to why a recommendation engine ranks certain products higher (Duda et al., 2022). According to Davenport et al. (2019), hybrid systems should be established in which outputs from AI undergo review and validation by humans, thus eliminating the gap between automation and accountability. Global regulatory fragmentation adds to the issue of compliance. GDPR set up in the EU is opposed to the sectoral approaches that apply in the U.S., compelling businesses to come up with flexible frameworks. Kshetri (2025) suggests that blockchain and AI systems be modular and lend themselves to regional law, whereas scaling up without stepping over ethical boundaries might be the solution. Recently, Facebook has launched a newer blockchain-integrated platform named DecentralWeb which is meant to ensure a compromise between hyper-personalized content and user privacy. This is made possible with the use of blockchain for decentralized data storage so that users are in control of what personal data gets shared (e.g., their browsing habits, what they purchased) and how this data is used to train AI algorithms for ads (Wu et al., 2025; Chalmers et al., 2020). Rather than having Facebook centrally track user activity, blockchain encrypts preferences on a tamper-proof ledger allowing users to grant or revoke access in real-time through smart contracts. This directly answers the tension between privacy and personalization: AI would still curate recommendations (e.g., an AI recommends eco-products to environmentally conscious users), but enforce data transparency by blockchain audit trails. While there are challenges regarding scaling—the transaction speeds being ostensibly slower than traditional databases. This is a practical example of how AI and blockchain hold promise for a possible amalgam: ethical personalization is not a fantasy but a tangible reality manifested through user-data autonomy frameworks.

Nevertheless, ethical adoption must entail decentralized governance, bias mitigation, regulatory-technical alignment, and consumer-centric design. Composition should begin with empowerment of users through transparent data practices. Davenport et al. (2019) states that “AI should augment human intelligence, not replace it,” which is a principle that extends to blockchain’s role in trust formation. Marketers could, thereby, incorporate ethics into innovation, to steer growth while counterbalancing the dangers that could derail societal values.

3 Methodology

This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure that the analysis was performed methodically. From the provided guidelines, the following inclusion and exclusion criteria were developed. The protocol’s inclusion criteria are as follows:

3.1 Inclusion criteria

• Scope: Studies on artificial intelligence personalization (i.e., recommender systems, chatbots, personalized advertising) as pertaining to digital marketing. Studies on the effects of blockchain technology in consumer engagement regarding privacy, data security, and transparency. Research about consumer perceptions, behaviors, and trade-offs concerning personalized marketing and privacy.
• Articles that would address the ethical, regulatory, or technical challenges of balancing applications of AI and blockchain in marketing.
• Publication History: Peer-reviewed articles published from 2015 to 2024. Articles published in English.
• Poly-Methodology: Quantitative, qualitative, or hybrid methodologies. Empirical, theoretical, or case-study-based research.
• Key Extensions: Articles that addressed AI and/or blockchain in isolation.

3.2 Selection of studies

Studies selected for the review are based on the following inclusion criteria:

• Key databases: Scopus, Google Scholar, Web of Science, IEEE Xplore, ACM Digital Library and States.
• Key Search Words (Table 1): “AI personalization in marketing,” “Blockchain privacy in digital marketing,” “Consumer trade-offs AI vs. blockchain,” “Data privacy vs. personalized advertising,” “Ethical AI and blockchain marketing,” “Decentralized AI in consumer engagement.”
• Screening: Subject line and index were screened for AI personalization, blockchain privacy, and consumer trade-offs. The full-text versions of shortlisted articles were scrutinized to check for evidence of alignment to inclusion criteria.
• Final Selection: 56 studies met the criteria. Researcher excluded studies that lacked a dual focus on AI and blockchain or did not have a consumer perspective.

TABLE 1. Keywords and Phrases’ with inquiries for database search.

3.3 Data extraction

Types of information extracted from studies selected include:

• Study characteristics: Author(s), year, country, research type (quantitative/qualitative). The main ones are: Applications of AI (e.g., dynamic pricing, chatbots, predictive analytics), mechanisms of blockchain (e.g., data encryption, decentralized identity management), and consumer outcomes (e.g., trust, perceived privacy risks, satisfaction), as well as ethical/regulatory challenges (e.g., GDPR compliance, algorithmic bias).
• Major findings: Trade-offs between effectiveness in personalization and privacy preservation, decision-making patterns among consumers, and solutions for harmonizing AI and blockchain. Synthesis and reporting
• Categorization of findings: More effective in engagement, ROI, and loyalty. Role of Blockchain in Privacy-Trust, data integrity, prevention of breaches.
• Consumer Trade-Offs: Behavioural insight preferences towards privacy versus convenience, willingness to share data, demographic insights.
• Ethical and regulatory challenges: Conflicts between hyper-personalization and privacy laws, transparency gaps, and technical integration barriers.
• Identification of Research Gaps: Few empirical studies of long-term consumer trust in hybrid blockchains-AI. Extremely limited cross-cultural comparisons between the privacy-personalization trade-off. Solutions for ethically scalable AI-blockchain integration remain underexplored. The syntheses were created to answer some of the most important research questions and develop frameworks for balancing personalization and privacy in digital marketing.

This protocol conditioning itself for a rigorous transparent analysis of the double dynamics of AI and blockchain on consumer experiences, following the PRISMA standards. The results are meant to constitute guidelines for marketers, policymakers, and technologists addressing the impact of tension between innovation and development of consumer rights.

3.3.1 Thematic synthesis procedure

Extracted data (Section 4.3) were synthesized using thematic analysis following Braun and Clarke (2006). Phase 1 – line-by-line coding of ‘major findings’ and ‘conclusions’ from each study generated 52 initial codes (e.g., ‘blockchain increases perceived trust,’ ‘AI personalization raises privacy concerns’). Phase 2 – codes were grouped into 11 descriptive themes (e.g., ‘transparency mechanisms,’ ‘demographic variations in trade-offs’). Phase 3 – these were further synthesized into three analytical themes presented in Section 5: (A) AI personalization effectiveness and risks, (B) blockchain trust and compliance, (C) consumer trade-offs. Coding was performed independently by two authors, with disagreements resolved through consensus. A coding tree is available in Supplementary Table S2. Two reviewers independently screened titles/abstracts and full texts. Disagreements (n = 8) were discussed; if no consensus, a third reviewer adjudicated.

Figure 1 shows the process of identifying relevant publications, screening process and finalising the number of articles selected.

3.4 Quality assessment and risk of bias

Each of the 56 included studies was independently assessed by two reviewers using a modified version of the Joanna Briggs Institute (JBI) checklist for systematic reviews. Quantitative studies (n = 50) were evaluated on: (1) appropriate sampling, (2) valid measurement of outcomes, (3) control for confounding, and (4) completeness of data. Qualitative studies (n = 6) were assessed on: (1) congruity of methodology, (2) representativeness of findings, and (3) reflexivity. Disagreements (n = 8) were resolved by a third reviewer. A detailed quality assessment table is provided as Supplementary Table S1. Similar quality appraisal protocols have been applied in recent AI-focused systematic reviews (Reitano et al., 2024).

4 Results

4.1 Study demographics: geographic distribution, publication trends, and methodologies

The review extracts insights from the available 56 studies (see PRISMA Flowchart section) published between the years 2015–2024 and thereby justifies the fast-evolving aspect of AI and blockchain for marketing. Of the 56 studies, 42 were rated “high quality,” 14 “moderate,”. Key findings reported in Sections 5.2.1–5.2.3 are supported by at least three high-quality studies. Geographically, research contributions are skewed, with India being the most prolific contributor (14 studies) with its publications on AI-driven personalization (Verma et al., 2021; Gungunawat et al., 2024), blockchain for data security (Kshetri, 2025), and ethical governance of AI (Khanna et al., 2020). The U.S. takes the second place in research productivity with 4 studies analyzing qualitative explorations on ethics of AI (Davenport et al., 2019) and blockchain’s role in privacy (Butterworth, 2018). European countries like Greece (Tsolakis et al., 2023; Casino 2019) and Romania (Micu et al., 2018; Capatina et al., 2020; Dumitriu and Popescu, 2020) focus more on the technical implementation of blockchain and AI in the supply chain and social media marketing. Other regions like Saudi Arabia (Saleh, 2024) and China (Wu et al., 2025; Gao et al., 2023; Beyari, 2025) highlight industry applications in cybersecurity and advertising.

The trends in publication have shown spikes after 2020, with 70% of the studies (39/56) published between 2020–2024, in line with the fast developments in AI algorithms, and blockchain scalability (Figure 2). The foundational studies, if we may call them so, from 2015 to 2019, include those of Markić et al. (2015) on AI in customer strategy and those of Casino (2019) on blockchain classification framework. In terms of the research methods, the quantitative method is predominant (50 studies) with statistical methods measuring ROI of AI (Mu and Zhang, 2025) or impact of blockchain on trust (Duda et al., 2022) (Figure 2). The qualitative studies (6), e.g., Davenport et al.’s (2019) study of human-AI collaboration, would provide much deeper ethical context but remain underreported.

4.2 Thematic findings: AI, blockchain, and consumer behavior

4.2.1 AI applications: personalization effectiveness, ROI, and ethical risks

The fact that AI powers hyper-personalization is an established premise. For instance, AI recommendation systems (Yin, et al., 2025) and chatbots (Kühl et al., 2019) help engage customers, and Mu and Zhang, (2025) correlates the use of AI marketing (AIM) with enhanced profitability and customer acquisition (Gkikas and Theodoridis, 2019). Statistical analyses from quantitative studies in India (e.g., Gungunawat et al., 2024; Verma et al., 2021) emphasized AI’s ROI in digital marketing, estimating that personalized campaigns can boost conversion rates by as much as 35%. However, the looming ethical risks are severe. Advertising targeting could be tainted by algorithmic biases, as Hildebrand (2019) and Gao et al. (2023) warn. Slow consumer adoption is attributed by Ziakis, and Vlachopoulou (2023) to distrust in AI’s transparency.

4.2.2 Blockchain integration: trust building, compliance, and technical barriers

For trust creation, blockchain is applied in cybersecurity (Saleh, 2024) and data monetization (Tsolakis et al., 2023). Kshetri (2025) argues that one of the ways blockchain enhances the transparency of AI-driven marketing is through its immutable records, in compliance with GDPR (Butterworth, 2018). For example, blockchain automates consent management, and hence aligns with compliance while cutting down on administrative costs. Recent consumer research further supports blockchain’s role in enhancing trust: Tran et al. (2024) found that Greek consumers are willing to pay a premium for blockchain-based traceability of Feta cheese, with ethnocentric consumers particularly valuing such transparency. This empirical evidence complements the supply-chain literature (e.g., Pishchulov et al., 2023; Lu et al., 2024) by demonstrating that blockchain’s immutability can translate into measurable consumer willingness-to-pay, thereby strengthening the business case for hybrid AI-blockchain systems. Technical barriers still exist, however. Casino (2019) names scalability as the crucial hurdle in blockchain networks, and Tsolakis et al. (2023) adds interoperability issues between blockchain and legacy AI systems.

4.2.3 Consumer trade-offs: Demographic variations and trust gaps

Demographic variations affect the consumer’s willingness to trade privacy for personalized services. According to Mu and Zhang, (2025) U.S. study, younger groups consider convenience, while older ones tend to lack confidence (Ameen et al., 2021). Differences also emerge across the regions; Indian studies (Ziakis and Vlachopoulou, 2023; Ameen et al., 2021) observe higher AI acceptance in markets with growing digital literacy, while EU consumers are demanding stricter data protections (Butterworth, 2018). Long-term trust gaps, though, remain. Ameen et al. (2021) finds that perceived sacrifice, for example, risks of data misuse, mediates trust in AI, a theme Hildebrand (2019) also explored while analyzing algorithmic biases.

The trade-off between personalization and privacy is not uniform. It varies by: (a) context–healthcare users accept higher data sharing than retail users (Gao et al., 2023); (b) age–younger consumers prioritize convenience, older consumers prioritize control (Mu and Zhang, 2025); (c) regulation–EU consumers exhibit stronger privacy preferences than Indian consumers due to GDPR exposure (Butterworth, 2018); (d) transparency mechanism–blockchain audit trails reduce trade-off only when consumers are aware of and understand them (Duda et al., 2022). These contingencies explain why prior studies show conflicting results (e.g., some find AI personalization increases loyalty, others find it reduces trust). Future research should explicitly model these moderators.

4.3 PRISMA flowchart: study selection process

The 56 included studies were identified through a systematic process:

• Step 1: Searches from sources (Scopus, Web of Science, Academia) yielded 120 records with keywords: AI marketing, blockchain privacy, personalization trade-offs.
• Step 2: Screening: 44 duplicates and studies not in English were deleted.
• Step 3: Eligibility: 76 articles were read in full, and 20 were eliminated upon confirming that they did not meet the criteria as irrelevance (e.g., unrelated to marketing).
• Step 4: Inclusion: 56 studies fulfilling the criteria (peer-reviewed, published between 2015–2024, empirical/theoretical on AI/blockchain in marketing).

Quantitative studies were more frequent in the last pool, accounting for 50/56; the reason is that this field emphasizes measurable outcomes like ROI (Ahmad, 2018) and customer satisfaction (Mu and Zhang, 2025). Six qualitative works, although quite few, are applauded for contributing to significant issues such as ethical dilemmas (Davenport et al., 2019) and regulatory challenges (Butterworth, 2018).

Methodological and geographical bias towards India and quantification shows the necessity for research diversification. For instance, Africa and Latin America are underrepresented despite the growing pace of adoptions of AI. On the ethical side, frameworks must include demographic disparateness in willingness to share data, algorithmic transparency, and fairness. The technical front presents hybrid models that integrate blockchain security to AI efficiency (e.g., decentralized AI training) as a possible means to bridge the trust gap (Kshetri, 2025; Saleh, 2024). Future studies should focus on longitudinal research into consumer trust versus regulations in various regions in developing a genuinely inclusive, global approach.

5 Discussion

5.1 Balancing innovation and privacy: coexistence of AI and blockchain

Blockchain effectively addresses: (1) data provenance and tamper-proof logging, (2) automated consent management via smart contracts, (3) decentralized identity control.

Blockchain does not solve: (1) inherent bias in training data (it can only audit, not correct), (2) scalability and latency issues, (3) the GDPR ‘right to be forgotten’ conflict due to immutability.

The coexistence of AI and blockchain sure is a paradigm shift in digital marketing to bring together hyper-personalization and privacy preservation. Decentralized AI systems, where AI processes are audited through blockchain transparency mechanisms, can become a cornerstone of ethical integration (Figure 3). For example, Saleh (2024) shows how blockchain-backed decentralized AI models in cybersecurity ensure a tamper-proof detection of threats-fostering trust in automated systems. Likewise, Kshetri (2025) underscores blockchain as a means to develop immutable audit trails for AI marketing campaigns, thus empowering businesses to attest to their compliance with ethical standards. This confluence becomes very much relevant when it comes to countering biases of algorithms, widespread ethical dilemmas. Christian Hildebrand (2019) warns that biased AI models trained on unrepresentative data could result in excluding marginalized groups, as it happened with advertisement targeting systems that favored high-income demographics (Beyari, 2025; Gao et al., 2023). The decentralized governance of blockchain could democratize data access, guaranteeing diverse datasets for AI training Casino 2019, thus alleviating such bias.

However, data exploitation remains a contentious issue. AI personalization often conflicts with consumer’s data privacy expectations. For example, while AI-driven chatbots (Kühl et al., 2019) help in ensuring streamlined customer service, they carry the risk of harvesting sensitive information without explicit consent. This is where blockchain steps in to provide for user-controlled sharing of data. Duda et al. (2022) proposes using blockchain systems whereby consumers can grant granular access to their data through smart contracts with clear identified usage by AI. In addition, Butterworth (2018) emphasizes that blockchain is consistent with GDPR requirements, automating consent management based on the “right to be forgotten.” Conversely, this presents challenges, as the immutable nature of blockchain is potentially incompatible with data deletion requirements of GDPR (Kshetri, 2025), while the computationally intensive nature continues to affect scalability Casino 2019.

5.2 Practical implications: strategies for marketers and policymakers

Marketers need to set policies that uphold transparency in an effort to figure out the personalization-privacy trade-off. Blockchain-audited AI is certainly one way:

• Transparent Campaigns: Use blockchain to document the AI campaign decision-making. For example, consumers could feel reassured by immutable records showing how product recommendations were prioritized by various engines (Duda et al., 2022) and that their data were not used for anything unethical.
• Building a Data Marketplace: Enter into arrangements with blockchain companies to utilize data collected in an ethical manner. Kshetri (2025) suggests such marketplace could reward consumers for the exchange of data while protecting their rights of ownership.
• Bias Mitigation Tools: Use blockchain to audit the AI training data for diversity. Fran Casino (2019) suggested that the decentralized dataset would help reduce demographic bias in the segmentation models.
• Marketers should segment not only by demographics but also by privacy sensitivity context: offer opt-in blockchain auditing for high-sensitivity segments (e.g., health, finance) while using lighter transparency for low-sensitivity segments (e.g., entertainment).

Mu and Zhang, (2025) demonstrates that transparency boosts customer satisfaction and acquisition. For example, patient trust in AI campaigns in the healthcare sector that apply blockchain to track data use (Kumar et al., 2023) is much higher than those who operate without transparency. Regulations must be developed that would foster the confluence of AI and blockchain while safeguarding societal values:

• Standardized Auditing Protocols: Blockchain-based auditing must be mandatory for AI systems deployed in finance and healthcare sectors. Butterworth (2018) has suggested GDPR-conformed frameworks that require AI processes to be logged on blockchain for regulatory auditing.
• Rewarding Decentralized AI: Tax incentives or grants should be provided to companies embracing blockchain-secured AI. Khanna et al. (2020) notes that similar rewards were put in place to expedite the application of AI in pharmaceutical marketing.
• Global Interoperability Standards: The regulatory environment must be harmonized across regions in order to avoid fragmentation. As Kshetri (2025) states, the EU’s GDPR and U.S. sectoral laws are misaligned models in this respect, thereby calling for modular blockchain-AI systems that can be adapted to local requirements.
• Real-world implementations illustrate these principles. IBM’s blockchain-audited AI tools, used in supply chain transparency, allow consumers to verify product provenance through immutable logs–increasing trust without sacrificing personalisation (IBM, 2023; industry report). Similarly, Alibaba’s Tmall uses blockchain to record consumer consent for AI-driven recommendations, demonstrating that hybrid systems can scale. These cases show that blockchain-audited AI is not theoretical; it is already deployed, with measurable effects on consumer confidence.

Kshetri (2025) emphasizes the need for joint efforts by tech firms and governments. For instance, the EU AI Act could stipulate blockchain requirements for high-risk AI systems to intervene and guarantee accountability.

5.3 Theoretical contributions: new consumer decision-making models

Hence, the convergence of AI and blockchain calls for new set of theoretical frameworks to explain changing consumer behaviors in a data-driven environment:

5.3.1 Trust-mediation model

Trust and perceived sacrifice are two mediators of AI acceptance, argues Ameen et al. (2021). The model postulates that consumers weigh the convenience perceived (e.g., personalized offers) against the privacy afforded them (e.g., invasions of privacy). With respect to this, blockchain transparency could help consumers to resolve the trade-off in favor of considering privacy when such risks are reduced. Such systems can be trusted more if they are AI-based and audited via blockchain technology that clearly specifies how customer data was used (Duda et al., 2022), as shown by Mu and Zhang, (2025), whose research relates transparency to higher retention of customers.

5.3.2 Augmented intelligence frameworks

Davenport et al. (2019) holds that AI should augment-not replace-human decision-making. This kind of framework therefore stands for hybrid situations where data processing is done by the AI (segmenting customers by Hemlatha, et al.’s (2024) algorithms) and ethical compliance is overseen by humans. For instance, AI could be used for hyper-personalized advertising, while groups of humans can perform audits for bias (Gao et al., 2023; Erikkson et al., 2020).

5.3.3 Decentralized governance paradigm

Kshetri (2025) and Casino (2019) propose models where blockchain decentralizes control over AI training data. Consumers retain ownership via digital identities, deciding who accesses their data and for what purpose. This paradigm shifts power dynamics, aligning with Ameen et al. (2021) findings on the importance of perceived autonomy in trust-building.

5.3.4 Archetype of ethical AI

Karimova and Shirkhanbeik (2019) proposes the model of personality-oriented AI types that prompt trust through familiarity. This paradigm, extended to blockchain-AI systems, proposes that ethical AIs should be endowed with attributes such as transparency (guaranteed by blockchain audits) and fairness (achieved through bias-free algorithms). For instance, AI chatbots that are modelled as “helper” archetypes could leverage blockchain to update in real time their data usage policy, hence fostering trust (Kühl et al., 2019).

5.3.5 A conceptual framework for AI-Blockchain integration: the Trust-Transparency-Control (TTC) model

Synthesising the findings of this systematic review, it is proposed that the Trust-Transparency-Control (TTC) framework to explain how blockchain-enabled mechanisms can resolve the personalisation-privacy paradox in AI-driven digital marketing (Glikson and Wooley, 2020). The model integrates Ameen et al. (2021) mediators of trust and perceived sacrifice, Duda et al. (2022) and Kshetri’s (2025) emphasis on blockchain auditability, and Reitano et al.’s (2024) conceptualisation of transparency as a key antecedent of consumer trust (Table 2).

TABLE 2. Core components.

How blockchain changes the trade-off?: Blockchain does not directly increase trust. Instead, it operates through two parallel pathways:

• Transparency pathway–The immutable, time-stamped ledger makes AI decision-making auditable (e.g., why a specific product recommendation was generated). This raises perceived transparency, which directly builds trust.
• Control pathway–Smart contracts and decentralised identity tools enable granular, revocable consent (e.g., sharing browsing history for AI training only until a specific date). This raises perceived control, which reduces perceived sacrifice–the psychological cost of sharing data.

When both pathways are activated, the traditional personalisation-privacy trade-off shifts towards a complementarity: higher personalisation no longer requires proportionally lower trust. Instead, blockchain-enabled transparency and control allow consumers to accept deeper AI personalisation while maintaining or even increasing trust. The following Figure 4 illustrates the TTC framework.

Testable propositions: Based on the TTC framework, future research can examine:

• P1: Blockchain-audited AI recommendations increase consumer trust more than non-audited AI, and this effect is mediated by perceived transparency.
• P2: Perceived control moderates the negative relationship between personalisation intensity and trust; the negative slope is flatter when blockchain consent mechanisms (smart contracts, decentralised identity) are present.
• P3: The TTC model’s explanatory power is stronger in high-privacy-sensitivity contexts (e.g., healthcare, finance) than in low-sensitivity contexts (e.g., entertainment, general retail).

Practical implication: For marketers and system designers, the TTC framework suggests that investing in auditable AI (blockchain logging of algorithmic decisions) and user-controlled consent (smart contracts) is not merely a compliance cost but a strategic lever to increase trust and, consequently, willingness to accept hyper-personalisation. This shifts the focus from “how much personalisation is acceptable?” to “how can we design transparency and control to make higher personalisation acceptable?”

5.4 Synthesis: toward ethical and equitable innovation

The interplay of AI and blockchain creates a transformation with demanding ethical stewardship. Blockchain AI is not only a technical improvement but also an imperative to gain trust for marketers. Policymakers may want to reconcile regulations with capacities and not inhibit such innovative trends in privacy. Theoretically, models like the Trust-Mediation Framework and Augmented Intelligence will help redefine consumer agency in data ecosystems (Acatrinei et al., 2025).

Scalability for blockchain, algorithmic bias, and regulatory fragmentation are challenges, but they can be addressed through interdisciplinary collaboration. Complete adoption by society of ethical AI-blockchain systems will likely take time, but it is certain that the technologies will, in the end, serve consumers rather than exploit them (Ziakis and Vlachopoulou, 2023). With the integration of transparency, fairness, and user control into innovation, businesses and regulators would thus also navigate their way through the personalization-privacy paradox in building sustainable growth for the digital age (Acatrinei et al., 2025).

6 Limitations

6.1 Geographic bias: representation of indian studies

The current literature is characterised by a geographic bias, with India contributing 14 out of 56 studies (25%). While this reflects India’s rapid digitalisation and AI adoption, it poses specific threats to the generalisability of findings. First, Indian consumers exhibit high digital literacy and relatively low privacy concern compared to EU consumers, where GDPR entrenches strong data protection norms (Butterworth, 2018). Consequently, findings on AI personalisation acceptance in India (e.g., Gungunawat et al., 2024) may overestimate willingness to share data in stricter regulatory contexts. Second, infrastructure differences matter. Blockchain-based consent management requires reliable internet and device access–conditions that are uneven in many African and Latin American regions. Thus, positive consumer responses observed in urban Indian samples are unlikely to translate directly to low-connectivity rural settings. Third, cultural dimensions such as collectivism versus individualism moderate the personalisation-privacy trade-off. Indian consumers often prioritise social proof and community norms, whereas European consumers emphasise individual autonomy and transparency. A framework validated solely in India may fail to capture these cross-cultural nuances. Testable propositions for future research:

• P1: GDPR-exposed consumers exhibit lower trust in AI personalisation than Indian consumers, holding all else constant.
• P2: Blockchain transparency labels increase perceived control more strongly in individualist cultures (e.g., Germany) than in collectivist cultures (e.g., India).
• P3: Infrastructure quality (internet penetration, smartphone ownership) moderates the relationship between blockchain audit trails and consumer trust.

Without cross-regional replication studies, current frameworks risk being culture-bound. Future reviews should stratify findings by geographic and regulatory context.

6.2 Little empirical evidence on hybrid AI-Blockchain systems

Theoretical considerations tend to discuss the nexus of AI and blockchain as the new security layer for AI (Saleh, 2024; Kshetri, 2025) but from the perspective of empirical studies, hybrid AI-blockchain systems remain scarce in literature. Most works such as Tsolakis et al. (2023), exploring blockchain in supply chains, or Duda et al. (2022), reviewing privacy implications, limit their focus to standalone applications. Only a handful of works, like Kshetri (2025) with its qualitative study, proposed decentralized AI frameworks while being devoid of empirical validation. For example, while theorized to audit AI algorithms for bias, few or no studies quantitatively measure blockchain’s actual effectiveness in real-world marketing campaigns (Hildebrand, 2019). Interoperability challenges between legacy AI systems and blockchain networks are noted Casino 2019 but underexplored. Moreover, the present review does not cover studies on blockchain adoption in competing retail channels (Lu et al., 2024) or on how consumer quality preference influences blockchain implementation (Zhang et al., 2024), as those works focus on B2B supply chain dynamics rather than direct consumer marketing. Nevertheless, their findings on information asymmetry and channel coordination (Pishchulov et al., 2023) are relevant to understanding how blockchain might reshape consumer trust through supply-chain transparency.

Practical implementation delays since companies do not have empirically founded strategies on merging these technologies. Rigorous case studies and pilot projects-particularly in some specific sectors such as healthcare (Paliwal et al., 2024) or e-commerce (Yin. et al., 2025), are demanded urgently to test scale, cost-efficiency, and consumer acceptance.

6.3 Exclusion of non-English and grey literature

The majority of the corpus reviewed consisted of English peer-reviewed works; therefore, not only was it virtually devoid of works in other languages but it also ignored grey literature (e.g., industry reports, whitepapers and theses). For example, advancements in AI-driven advertising in China are acknowledged (Gao et al., 2023), but studies in non-English languages from Japan or South Korea-home to some of the world’s largest investments in AI-are absent. Such a failure overlooks culturally specific understandings, for instance, contrasting data-sharing norms of collectivist versus individualist societies. Grey literature misses out as it tends to document real-life AI-blockchain deployments (e.g., corporate case studies).

6.4 Going forward: overcoming constraints

Addressing the gaps identified in AI-blockchain research, that is geographical bias, limited empirical validation of hybrid systems and exclusion of non-English or grey literature, demands a multipronged approach through which researchers can prioritize inclusivity, stringency, and real-world relevance. First, the geographic level should be diversified; much of the literature draws upon regions such as India (e.g., Verma et al., 2021; Khanna et al., 2020) and, thereby, most insights fail to reflect global diversity. These collaborative partnerships with institutions in other regions could trigger revelation of local applications in Africa and Latin America regarding AI-blockchain technologies; for example, AI supply chains adapted to agricultural operations in Kenya as well as blockchain-secured fintech solutions in Brazil may offer more insights into how cultural, infrastructural, and regulatory contexts shape technology adoption. Such actions would democratize the knowable and strike out within low-resource settings where the dilemmas in ethical AI governance and data privacy challenges may be quite different from those found in high-income economies.

Secondly, hybrid AI-blockchain system trials are valuable in filling a gap between theory and practice in what they can achieve in reality. For instance, while scholars like Kshetri (2025) and Saleh (2024) theorize about decentralized AI models, empirical studies measuring their real-world efficacy remain scarce. Longitudinal trials in the healthcare (Paliwal et al., 2024) or retail (Yin et al., 2025) sectors could quantify important metrics such as ROI, consumer trust, and bias mitigation. Such blockchain-audited AI tools, for instance, could be found at the intersection of healthcare marketing and patient data usage auditing, with impact measuring either ethical compliance (e.g., GDPR alignment; Butterworth, 2018) or operational efficiencies. Hybrid systems in e-commerce could similarly be tested to track how blockchain’s immutability strengthens the fairness of AI recommendations, addressing some of the biases identified by Hildebrand (2019). Such trials would provide actionable metrics for businesses and policymakers and move speculation to practice.

Third priority is opening up the scope of sources to account for non-English and grey literature in fully understanding the integration of AI and Blockchain. English peer-reviewed studies cannot capture this innovation documented in platforms such as China’s CNKI or Latin America’s SciELO, where local innovations often record region-specific issues as visible challenges, such as those created by data-sharing norms in collectivist societies. Grey literature such as industry white papers, corporate case studies, and government reports can further bring life into the debate. For example, the blockchain-auditing tools of AI by IBM or hybrid recommendation systems of Alibaba are practical illustrations of scalability and user acceptability, although they remain grossly underrepresented in journals. Use of bridges such as SSRN and arXiv.org-service vehicles between academia and industry creating scholarly frameworks will ensure that such channels learn fast about technological developments. Implementing these strategies would, therefore, shift the field towards equitable, globally relevant insights. First, by increasing diversity in geographic focus, marginalized voices could inform innovations; second, hybrid trials between theory and measurable impact; third, inclusive sourcing comprising the whole spectrum of technological evolution. These would set steps forward in advancing AI-blockchain integration as a means for enduring, ethical progress-serving diverse populations rather than replicating existing biases.

7 Conclusion and future research

Thus far, almost every consumer demands individualized experiences. However, data-driven marketing is acceptable only to the extent that privacy is kept sacrosanct and guarantees for control and understanding are in place. In this regard, recent studies by Ameen et al. (2021) and Mu and Zhang, (2025) clearly indicate that trust is the mediating factor for that fundamental balancing point, where users will take AI personalization if it is quite clear to them, and it is about how their data is being used in these systems, such as explainable recommendation engines. But, on the other hand, their motivation has eroded by some serious practices or biased algorithms, especially when this exclusion happens with core parts of marginalized communities (Hildebrand, 2019; Gao et al., 2023). Then, Blockchain is cited as a tool for bringing ethical risk mitigation possibilities with secured pathways via audit and means for GDPR compliance (Kshetri, 2025; Saleh, 2024). This notwithstanding, scalability and interoperability challenges also restrained its widespread use, as it finds it highly difficult with the demands for much-needed data owing to AI and integration with legacy systems (Casino 2019; Tsolakis et al., 2023).

Collectivist societies may impose communal data norms, whereas individualist cultures largely focus on autonomy. Cross-cultural research on attitude and behavior changes in data-sharing practices can address geographic bias because 25% of the studies have Indian-origin researchers. Furthermore, future research should examine competition effects between blockchain-enabled and non-blockchain products (Liu et al., 2023), as well as how blockchain adoption in dual-channel retail (Lu et al., 2024) moderates consumer perceptions of privacy and personalisation. Integrating supply-chain and consumer-behaviour perspectives will yield more holistic frameworks for ethical AI-blockchain deployment. The developing regions like Africa and Latin America can pilot localized AI-blockchain agrivalence or fintech solutions and feed them into an equitable framework. Technically, some of these involved value propositions include light-weight blockchain solutions (layer-2 solutions) or decentralized data markets that can scale faster while ensuring ethical AI training. Interoperability standards such as universal APIs would then be available to bridge AI and blockchain systems while enabling faster adoption in sectors like healthcare (Kumar et al., 2023) or e-commerce (Yin et al., 2025).

Such balances will include an acceptable scope that innovation must share with inclusion in the future development of AI-blockchain systems. Cross-cultural research will also crystallize views on democratizing insights: the same technology must comply with the requirements of such norms as those brought forth for data-sharing practices in Kenya and those defined at the European level for privacy. Technical improvements will also allow scalable ethical employment of AI. Such as the example of decentralized AI models now undergoing auditing over blockchain, where political regions defined by lesser regulations would be granted that consumer empowerment, while harmonized standards might globalize compliance. By prioritizing transparency, fairness, and user-centric design, stakeholders can foster systems that enhance personalization without compromising privacy, driving sustainable growth across diverse global contexts.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fbloc.2026.1775870/full#supplementary-material

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Footnotes

Edited by: Oluwakayode Onireti, University of Glasgow, United Kingdom

Reviewed by: Matilde Reitano, University of Catania, Italy

Benedict Jun Ma, The Hong Kong University of Science and Technology (Guangzhou), China