Artificial Intelligence (AI)-driven hyper-personalisation has reconfigured digital marketing by embedding predictive analytics and automated decision-making into everyday consumer interactions. While these systems enhance marketing efficiency and engagement, they simultaneously intensify concerns regarding surveillance, behavioural manipulation, and erosion of consumer autonomy. Existing global scholarship recognises these risks; however, limited context-specific analysis interrogates how such dynamics unfold within emerging regulatory environments. This study addresses that gap by critically examining how AI-driven hyper-personalisation reshapes consumer privacy in South Africa and evaluating the adequacy of its regulatory framework in comparison with international standards such as the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). A desktop-based systematic literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to synthesise peer-reviewed studies published between 2019 and 2025. The findings reveal that AI-driven personalisation systems operate through opaque algorithmic profiling mechanisms that weaken informed consent and shift control away from consumers. Although South Africa’s Protection of Personal Information Act (POPIA) establishes foundational data protection principles, it lacks AI-specific provisions addressing explainability, automated decision-making, and effective consumer redress. In contrast, while the GDPR and CCPA provide more explicit safeguards, enforcement and technical interpretability challenges persist even within these jurisdictions. The study argues that the regulatory disparity between South Africa and more mature digital economies exposes local consumers to heightened risks within transnational digital marketing ecosystems. It concludes that strengthening AI-specific governance, enhancing institutional audit capacity, and embedding ethical design principles are necessary to reconcile technological innovation with consumer rights in both South African and global contexts.

Neuroadaptive retailing: How quantum-enhanced biometric systems drive consumer engagement through cognitive and emotional alignment.

ABSTRACT As consumers increasingly adopt privacy-enhancing technologies (PETs) to protect personal information, firms face growing challenges in preserving consumer data integrity and the reliability of downstream analytics. By intervening at the point of data collection, end-user PETs introduce systematic distortions that reshape the data environment on which business analytics depend, yet their implications remain insufficiently understood. To address this gap, this study develops two complementary conceptual frameworks. The Data Integrity Framework characterizes how different end-user PETs generate missing values and measurement errors across attributes, entities, and relationships, offering a structured lens for conceptualizing privacy-induced data distortions. Building on this foundation, the Analytics Adaptation Framework provides guidance on how firms can assess and adapt their data analytics in response to these data distortions. To demonstrate their applicability, an illustrative simulation case study in product recommendation shows how key characteristics of end-user PET adoption—adoption rate and pattern, protection mechanism and intensity—systematically shape analytics outcomes. Together, the study advances IS research on data management by linking consumer privacy protection to data integrity and analytics adaptation, highlighting how consumer-driven privacy technologies fundamentally alter firms’ data and analytical environments.

The digital technology landscape together with online platforms and data-driven business models has reached an advanced state of development which creates serious privacy and cybersecurity problems for personal data protection in India. The Government of India established the Digital Personal Data Protection Act DPDP Act 2023 to control personal data handling practices because of the increasing volume of digital transactions and incidents of data abuse. The legislation establishes new digital governance frameworks which protect user privacy rights while creating systems that hold organizations accountable for their personal data handling practices. The present study examines the impact of India’s Digital Personal Data Protection Act on corporate compliance requirements and business operations. The research focuses on analyzing how organizations are adapting to new legal obligations related to consent management, data processing, cybersecurity measures, grievance redressal systems, and data protection responsibilities. The study also evaluates the operational and financial challenges faced by companies in implementing compliance frameworks. The research team used a descriptive research design to collect data which they obtained from secondary sources that included government reports and legal documents and industry publications and research studies. The research findings demonstrate that the DPDP Act requires businesses to take greater responsibility for managing data and protecting consumer privacy rights. Organizations need to develop more effective security systems and create clear consent processes and establish methods for monitoring their adherence to laws. The research results show that large companies with sophisticated technology systems can meet regulatory requirements more easily than small and medium-sized businesses which struggle with their operational and financial operations. The study reveals that businesses face significant difficulties due to their need to spend money on compliance requirements and employee training and technology updates. The Act will achieve its goal of increasing consumer trust while establishing digital business credibility and promoting responsible data management practices. The study shows that organizations need to develop awareness about the DPDP Act and prepare their legal systems and invest in technology and control their regulatory activities for effective implementation. The research helps explain how digital privacy laws interact with corporate governance practices and business sustainability in India's digital economy.

Despite the well-documented benefits of precision on consumer adoption rates, the impact of the degree of precision in personalized recommendations on consumer engagement is less understood. Utilizing a series of experiments across four studies, we investigate the extent to which precision influences consumer engagement with the recommended information. Contrary to conventional wisdom and existing findings, our results indicate that high precision in recommendations often decreases click-through intention. This effect is driven by an increase in consumer cynicism and a decrease in their sense of autonomy. However, these negative responses are mitigated when highly precise recommendations align with consumers’ consumption goals and the appropriate timing of exposure. As highly precise recommendations grows and concerns over consumer privacy intensify, understanding consumer responses to such precision is increasingly vital. This study provides a detailed analysis of the interplay between consumer perceptions and the precision of personalized recommendations, delivering crucial insights for marketers aiming to balance precision with consumer-perceived privacy in the dynamic realm of personalized marketing.

We study firms’ incentives to adopt a tracking technology to collect personal data that enable personalized pricing in an online market where some consumers have innate desires for privacy. In a model where two differentiated goods are sold under two different market structures (monopoly and duopoly), we find that the presence of these privacy-sensitive consumers alters the firms’ incentive to adopt personalized pricing. In particular, no firm uses personalized pricing in equilibrium if the proportion of privacy-sensitive consumers in the market is high. Competition, however, leads to wider use of personalized pricing. Privacy regulation that gives consumers control over whether a firm can track their online activities has the intended impact of protecting consumer privacy only if the proportion of privacy-sensitive consumers is low. Otherwise, the regulation makes the use of tracking technology more widespread. A key force that drives these results is the inability of a monopolist to commit to personalized prices that will give privacy-sensitive consumers a nonnegative net surplus. This deters these consumers from purchasing from the firm. If the proportion of privacy-sensitive consumers is high, the risk of losing these consumers induces the monopolist to adopt uniform pricing. Under duopoly, competition between firms alleviates the impact of the commitment problem because the rivals undercut each other’s prices. Privacy regulation also mitigates this impact because a firm can credibly commit to offering a uniform price to those consumers who reject tracking. Consequently, both competition and privacy regulation lead to increased use of tracking technology. This paper was accepted by Raphael Thomadsen, marketing. Funding: Z. Chen acknowledges financial support from the Social Sciences and Humanities Research Council of Canada [Grant 435-2019-0866]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2024.07170 .

Consumers dislike sharing data with fintechs, but greater access to data can improve loan market outcomes through better screening. We study how the California Consumer Privacy Act (CCPA), which grants users control over and mitigates concerns about sharing their data, affects fintech lending. After the CCPA’s introduction, fintechs’ loan rates decline relative to those of other lenders. In addition, rate dispersion across fintech loans increases, fintechs deny more applications, and they make greater use of nontraditional credit scoring models, whereas their default rates decline by more than those of other lenders. These results are consistent with an improved screening process enabled by additional data. Mortgage originations by fintechs also increase, suggesting that well-designed privacy regulation may enhance financial inclusion. This paper was accepted by Bo Becker, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2025.02874 .

Consumer privacy protection demands are complex and multifaceted. Traditional incentive mechanisms struggle to balance participation enthusiasm with privacy risk control, leaving consumers exposed to unfair contribution evaluations and privacy leakage risks. To address this, this paper develops a collaborative joint learning incentive mechanism integrating graph neural networks (GNN) and multi-agent reinforcement learning. The approach first constructs node relationship graphs using GNN, then measures data distribution similarity through graph convolutional networks, and finally establishes a multi-agent reinforcement learning framework where nodes act as intelligent agents. By leveraging joint reinforcement learning and a dual-objective reward function, the mechanism optimizes strategies. Experimental results demonstrate that GNN-Shapley achieves over 97% accuracy, while the privacy compensation mechanism elevates average accuracy to 98.27%. This methodology effectively alleviates participation bottlenecks and safeguards consumer rights.

Purpose This study aims to examine how the sequence of cross-device online behavioral advertising (OBA) affects ad avoidance, drawing on communication privacy management (CPM) theory. It explores perceived privacy invasiveness as a mediator and cross-platform delivery as a moderator. Design/methodology/approach Two experiments were conducted. Experiment 1 assessed the impact of cross-device sequence on perceived privacy invasiveness and ad avoidance. Experiment 2 investigated how cross-platform transfer moderates the relationship between sequence and perceived privacy invasiveness. Findings When OBA shifts from smartphone to computer, the change from a device with higher self-connection to one with lower self-connection disrupts privacy boundaries, increasing privacy invasiveness and ad avoidance. Conversely, OBA flowing from computer to smartphone maintains stable privacy boundaries, reducing perceived privacy invasiveness and ad avoidance. Moreover, when OBA occurs across different (vs the same) platforms, the turbulence between the collective privacy boundaries of the two platforms strengthens (vs weakens) the relationship between cross-device sequence and perceived privacy invasiveness. Research limitations/implications This study contributes to a deeper understanding of the factors influencing OBA avoidance, offering valuable insights into consumer privacy boundary management and advancing theoretical frameworks. Practical implications Advertisers should optimize sequences across devices to reduce avoidance and minimize cross-platform transfer to avoid heightened privacy concerns from information transfer between platforms. Originality/value This study offers novel insights into how device sequence affects OBA avoidance, integrating CPM theory. It explores the underexamined role of cross-platform transfer in shaping privacy perceptions, contributing new theoretical perspectives to digital advertising.

Customers often seek products and engage in internet shopping in the contemporary world. Disclosing personal details is a critical and even indispensable aspect of these transactions. This disclosure is essential for business. Nonetheless, this convenience engenders significant privacy concerns, as consumers are gradually becoming conscious of the potential risks to their privacy associated with their online activities. Therefore, it is essential to comprehend consumer privacy concerns and their dynamics comprehensively. Nevertheless, existing literature on this subject is still varied and ambiguous. This bibliometric analysis seeks to define privacy concerns in e-commerce and integrate and systematise existing evidence; this article analyses the intellectual framework, publication frequency, scope and subcategories of the existing research on privacy concerns. Thus, we review research on privacy concerns in e-commerce and present a summary. Our investigation elucidates findings regarding the study of disclosure and the factors that influence it. To provide guidelines for forthcoming research. This bibliometric analysis will provide a reference for scholars across several disciplines to evaluate the evolution of scientific papers on a specific issue over time, focusing mainly on consumer behaviour.

The study examines the issue of media targeting, its methods and practices, with particular attention to locality, media availability and the challenges associated with it. Purpose – To map the current role of local media within the interpretative framework of media targeting as a marketing media activity. To gain a deeper understanding of the knowledge and beliefs of media agency professionals regarding media targeting and local media supply. Design/methodology/approach – The research examines media targeting practices and beliefs regarding local media availability among leading media agency professionals through a secondary analysis of existing literature sources and market data and 11 in-depth expert interviews. The 11 semi-structured in-depth interviews were analyzed using the thematic text analysis method developed by Braun, & Clarke, (2006). Findings – The results show that digital media is increasingly important for targeting in both the national and local markets. Traditional local media offerings are diverse and numerous, but larger media agencies are not exploiting their potential, mainly due to their fragmentation, less information and different communication standards compared to larger and national media. The research highlights the tension between data-driven personalized advertising and consumer privacy, and the fact that excessive targeting can even provoke resistance among consumers. Originality – Media targeting and local media availability have been little studied in the literature in recent years, and the circle of leading media agency professionals has only rarely been the focus of research on the given topic.

The digital banking sector is undergoing a structural recalibration — one that shifts the fundamental unit of customer strategy from the segment to the individual. For decades, financial institutions have grouped customers into demographic or behavioral clusters, deploying broadly tailored products with the assumption that proximity to a persona equates to relevance. That assumption is rapidly losing its empirical grounding. This paper examines the transition from traditional customer segmentation to AI-driven hyper-personalization in digital banking, with a specific focus on its measurable impact on Customer Lifetime Value (CLV). Through a secondary data analysis methodology synthesizing industry report from McKinsey Global Institute, Deloitte Insights, and Gartner, alongside peer-reviewed academic literature published between 2021 and 2026, this study constructs a four-stage operational framework — spanning data acquisition, real-time behavioral processing, predictive nudging, and adaptive feedback loops — designed for practical deployment within incumbent retail banks and digital-native challenger institutions. Central to the paper's argument is the concept of the Personalization Paradox: the counterintuitive finding that banks investing more heavily in personalization technologies frequently encounter initial declines in customer trust and engagement, unless data governance and transparency mechanisms are co-deployed. The paper situates this paradox within the broader 2026 banking landscape, characterized by intensifying competition from embedded finance providers, shifting consumer privacy expectations following post-GDPR regulatory evolutions, and the commoditization of core banking products. The proposed framework addresses not merely the technical architecture of AI personalization, but the organizational and ethical conditions under which it generates sustained CLV uplift. Findings indicate that institutions achieving full-cycle hyper-personalization deployment demonstrate CLV improvements of 15–40% over three-year horizons, alongside churn rate reductions of up to 25%, when personalization strategies are anchored in explainable AI systems and consent-driven data architectures. The paper concludes with actionable recommendations directed at Chief Digital Officers, product strategists, and data science leaders operating at the intersection of customer experience and institutional profitability.

The development of information technology has driven a major transformation in people's transaction patterns from direct interaction to a digital ecosystem that includes e-commerce, financial services, healthcare, education, and government administration. In the midst of the growth of the digital economy, consumers' personal data is developing into a strategic asset that is collected, analyzed, and utilized through big data and artificial intelligence for business purposes. The phenomenon of supervision-based economics raises new risks in the form of data misuse, weak security systems, and inequality in bargaining positions between consumers and business actors. Low digital literacy and complexity of service approvals also increase these vulnerabilities. Although various regulations have been present, including in Indonesia, the effectiveness of protection is still hampered by weak law enforcement, sub- optimal supervision, and the rapid development of digital business models. Therefore, strengthening personal data protection through regulatory harmonization, increasing platform accountability, and public education is an important step to maintain a balance between the growth of the digital economy and the protection of consumer privacy rights.

This study examines the legal gaps in regulating data proportionality in ASEAN digital banking, particularly in Indonesia, the Philippines, and Malaysia. The main legal issue lies in the absence of clear standards governing the limitation, justification, and classification of personal data, which leads to excessive and potentially invasive data processing practices in digital banking systems. This research aims to examine the concept of data proportionality in digital banking and to assess the adequacy of legal frameworks governing data proportionality in Indonesia, the Philippines, and Malaysia in order to identify existing regulatory gaps. This study employs a normative legal research method with a comparative approach. The findings reveal that although all three countries have established data protection frameworks, none comprehensively integrate data proportionality into digital banking regulations, resulting in fragmented and ineffective legal protection. Indonesia lacks detailed standards and risk-based mechanisms, while the Philippines and Malaysia show regulatory gaps in governing conventional digital banking services. These weaknesses contribute to increased risks of privacy violations and legal uncertainty. Therefore, this study suggests the need for regulatory reform, including clearer data classification, proportionality standards, and mandatory risk assessments, to ensure a balance between digital banking innovation and the protection of consumer privacy rights.

Strengthening the protection of individual privacy information is necessary for upholding and protecting individual consumer privacy rights and supports the healthy advancement of the e-commerce sector. It is also something consumers desire. Building on previous research regarding the data security of the Internet of Things (IoT), this paper provides insight into ways to protect consumer privacy when participating in e-commerce transactions, compares IoT data security with that of traditional technology, and conducts experimental analyses of consumer privacy protection strategies from four different perspectives. In addition to measuring how IoT security improves consumer privacy protection, the study demonstrates that using IoT technology to perform e-commerce transactions produced the highest quality level of 94%, with a transmission speed of 0.36 seconds, a confidentiality level of 91.99% for consumer location data, and transaction security of 93%. All of these results were greater than those obtained using traditional technologies.

The prompt growth of smartphone use in the United States has strengthened concerns regarding electronic waste (e-waste), data privacy, and digital sustainability. Mobile phones are replaced frequently, often before the end of their functional life, thereby increasing waste generation and resource consumption. In this context, mobile phone repair has emerged as a strategically important practice for extending device lifespan, reducing e-waste, protecting user data, and promoting circular-economy principles. This study examines the strategic role of mobile phone repair in reducing e-waste, enhancing data privacy, and promoting digital sustainability in the United States. A mixed-methods approach was employed, combining a survey of 300 mobile phone users across the United States with semi-structured interviews with repair professionals and industry experts. Regression and correlation analyses were used to examine the relationships among repair frequency, e-waste reduction, data privacy awareness, and support for digital sustainability. The findings reveal that mobile phone repair significantly reduces e- waste (B = 0.38, p < 0.001), enhances data privacy awareness and secure repair behavior (B = 0.24, p < 0.001), and promotes digital sustainability by supporting circular economy practices (B = 0.29, p < 0.001). These results highlight the national importance of strengthening the U.S. repair ecosystem as a pathway toward more sustainable and secure digital consumption. The study further suggests that right-to-repair policies, standardized data-erasure practices, and consumer awareness initiatives could enhance the environmental and social value of repair services in the United States. Overall, the study demonstrates that mobile phone repair is not merely a technical service but a strategic sustainability mechanism with significant implications for environmental protection, consumer privacy, and national digital resilience.

Artificial intelligence-driven hyper-personalization has revolutionized marketing efficiency but simultaneously erodes consumer sovereignty through structural constraints on information exploration pathways. This study proposes the Information Foraging Autonomy Score (IFAS) - a novel metric operationalizing consumer decision autonomy within algorithmic recommendation ecosystems. Analysis draws from Cisco Consumer Privacy Survey 2024 (n=3,400 global consumers), Korea Consumer Commerce Association survey 2025-2026 (n=1,000 Korean consumers), EU Digital Services Act consumer impact study (n=2,300), and comparative firm cases including ZARA (SVD+RNN inventory optimization), Sephora Virtual Artist (AR personalization), and Coupang Rocket Membership (domestic subscription benchmark). A Difference-in-Differences model (n=1,247 matched pairs, 2024Q4-2026Q1) demonstrates subscription economies increase IFAS by +23.5 points (p<0.01) versus performance marketing environments. Key findings reveal hyper-personalization reduces choice autonomy by 30% through daily ad exposures exceeding 5,000 impressions (CTR decline -25%, brand avoidance +76%, privacy distrust 91%), while subscription models recover +40% autonomy at modest efficiency cost (ROAS 5.5x→4.9x). Performance marketing exhibits structural exploration suppression (IFAS=35), whereas subscription architectures occupy optimal Autonomy-Efficiency Matrix quadrant (IFAS=70, ROAS=4.9x). Korean food e-commerce applications demonstrate viability: regional specialty producers can implement "default box + 3 alternatives" structures maintaining diversity while leveraging subscription lock-in benefits. Theoretical contributions include IFAS framework extending information foraging theory to algorithmic marketing, practical Autonomy-Efficiency Matrix for strategy positioning, and Korea-specific PIPA/AI Basic Act 2026 compliance roadmap linking consumer rights (explanation/opt-out) to marketing KPIs. Findings challenge the personalization=utility paradigm, demonstrating autonomy preservation as long-term LTV prerequisite amid escalating privacy fatigue and regulatory pressures.

ABSTRACT Data security risk has evolved from a peripheral technical concern into a fundamental determinant of corporate strategy and financial stability. This review synthesizes over 130 high‐quality studies spanning economics, finance, and management, documenting how data breaches—including consumer privacy violations and intellectual property theft—affect firm value, investor behavior, and systemic risk. We trace the evolution of this literature from early event‐study methodologies to sophisticated risk measurement frameworks employing natural language processing (NLP) and large language models (LLMs), while examining governance mechanisms, insurance markets, and regulatory design. Our analysis identifies critical gaps in long‐term impact assessment, cross‐country institutional comparisons, and AI‐era vulnerabilities, and outlines an integrative research agenda for understanding and mitigating digital risk in the modern economy.

Blockchain technology adoption decisions and investment cost sharing in the context of consumer privacy protection in online shopping

Optimal decision-making and complex analysis in green supply chains considering blockchain and consumer privacy concern