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.

The rapid growth of Buy Now, Pay Later (BNPL) services has expanded consumer access to credit, but it has also raised concerns about user satisfaction, trust, and long-term sustainability. This study develops and validates the Experience–Interaction–Trust model to explain and predict user disengagement in BNPL platforms. A multi-stage research design was employed to explore and test the experiential dimensions that shape consumer interactions with BNPL applications. In the exploratory phase, a bilingual corpus of approximately 39,000 authentic Arabic and English app reviews was analyzed. Text preprocessing, sentiment analysis, and topic modeling uncovered eleven core constructs: Harmony, Effortlessness, Favorability, Neglect, Breakdown, Automation, Boundedness, Disparity, Friction, Churn Risk, and NWOM. In the confirmatory phase, PLS regression and XGBoost were used to test and predict the relationships among constructs. The EXIT model demonstrates theoretical robustness and predictive utility, offering practical guidance for BNPL providers seeking to minimize churn, enhance user trust, and support sustainable digital financial innovation.

This study examined the effectiveness of Trade Kings Zambia Limited’s advertising strategies in Mwalubemba Village, a rural setting in Zambia. It explored the relationship between advertising awareness, consumer behaviour, and purchase decisions while assessing the effectiveness of various advertising channels, including radio, television, social media, print media, and billboards. A concurrent mixed-methods research design was employed, utilizing surveys, focus group discussions (FGDs), and key informant interviews (KIIs) to gain insights into consumer engagement with advertising. Data were analysed using Stata Version 16 and Excel. Quantitative data analysis revealed that about 79% of respondents were aware of Trade Kings' advertising channels, with radio (42%) and television (21%). Despite billboards being visually dominant, awareness remained low at 5%. Radio advertising emerged as the most effective channel (27%), followed by television (23%) and social media (20%), reinforcing previous studies that highlight the dominance of traditional mass media in rural markets. However, only 49% of consumers cited advertising as their primary source of product awareness, with 23% relying on word-of-mouth recommendations. Product benefits (39%) and affordability (27%) were the strongest purchase motivators, while emotional appeal (8%) and celebrity endorsements (3%) had minimal impact. Qualitative insights from FGDs and KIIs revealed that consumers preferred locally relatable radio ads delivered in native languages and voiced by familiar announcers, fostering greater trust and recall. Social media advertising showed promise among younger demographics but faced limited penetration due to digital access challenges. While Trade Kings' advertising efforts have effectively created product awareness and influenced consumer decisions, gaps remain in digital outreach, in-store engagement, and messaging for lesser-known products. The study concludes that rural consumers in Zambia prioritize functionality and affordability in purchase decisions, requiring marketing strategies that emphasize product education, affordability, and localized engagement. Recommendations include expanding radio campaigns with consumer testimonials, integrating community-driven marketing efforts, and diversifying in-store promotions to increase consumer interaction. These insights provide valuable guidance for Trade Kings, policymakers, and marketers seeking to optimize advertising strategies in rural markets.

Does brand anthropomorphism matter? Impact on brand loyalty and consumer decision-making

In the context of Sustainable Development Goal 3 (SDG 3), which focuses on promoting good health and well-being, the alarming rise in global obesity rates presents a significant public health challenge. Despite heightened awareness of health issues, unhealthy food brands consistently garner greater consumer engagement on social media than healthy brands. To explore this disparity, this study seeks to investigate the role of marketer-generated content (MGC) on social media engagement levels, for devising strategies to enhance the impact of healthy food messaging. By analysing 21,967 social media posts categorized through an automated machine learning (AutoML) model on Google Cloud Vertex AI, this study compares content types and engagement across Instagram and Twitter. Using MANCOVA and gradient boosting models (GBMs), it examines the interplay of platform, content, and brand type on consumer interactions. The findings highlight significant differences in content strategies, identifying approaches that yield higher engagement essential for health-focused brands.

Purpose: This study aims to analyze the role of video-based promotional strategies in increasing the economic value of Kejapa Coffee, a local product of Tigawasa Village, Buleleng, Bali. Research methods: This study uses a descriptive qualitative approach. The data collecting techniques used include field observation, video content documentation, and interviews with business owners. Promotional media were analyzed through Instagram, WhatsApp Business, and the use of barcodes on product packaging. The video production process was carried out using a digital camera and video editing applications. Results and discussion: The results show that video-based promotions increase product visibility, strengthen quality perceptions, and encourage consumer interaction. The economic impact is evident in increased demand, market expansion, and new business opportunities based on local coffee. Implication: Video promotional strategies can be an effective instrument in strengthening the local economy, increasing the competitiveness of MSMEs, and developing village potential based on superior products and cultural narratives.

Artificial intelligence technologies have reshaped consumer experiences, while also introducing negative design effects. Although existing research has realized these effects, the understanding of their underlying mechanisms remains limited. Based on survey data, this study investigates the relationship between negative design features of AI-generated advertisements and consumers’ online retaliation behaviors. The results show that creative homogeneity, visual inauthenticity, and narrative emptiness in AI advertising design are significantly and positively associated with consumers’ online retaliatory behaviors through the mediating effects of perceived betrayal, perceived anger, and revenge desire, specifically including public exposure, online complaints, purchase avoidance, and negative word-of-mouth. In addition, technological literacy positively moderates the relationship between perceived betrayal and revenge desire, such that consumers with higher technological literacy are more likely to engage in rational retaliation. The study not only enriches theoretical understanding of AI advertising design and consumer interaction, but also offers practical implications for brand and product promotion.

Purpose: This study examines the effects of online customer reviews, E-promotion, and perceived ease of use on the purchase intention of cosmetic products on the Shopee Live platform. Research Methodology: This study uses a quantitative approach to test the relationship between variables and confirm hypotheses that have been formulated based on previous theories and findings. The study population was composed of all potential buyers of cosmetics during live shopping on Shopee, the exact number of which is still unknown. The sampling technique was carried out using a non-probability sampling technique, more precisely purposive sampling. The sample size was calculated using the Slovin formula so that the final number of respondents was 100. Results: The results of the study stated that online customer reviews, E-promotion, and perceived ease of use have a significant impact on purchase intention, both partially and simultaneously. Conclusions: This study concludes that businesses should manage customer reviews well, design effective promotions, and ensure ease of navigation and transactions to increase consumer buying intent. Limitations: Although the observations show a relationship between online customer reviews, E-promotion, and perceived ease of use on purchase intention, this study has limitations because it only focuses on correlational relationships and does not directly examine causality relationships or other factors that may influence customer purchase intention. Contributions: The present study contributes to cosmetic businesses by optimizing digital strategies through the live shopping feature to increase consumer interaction and purchase intention.

Influencer marketing in the digital world has turned into a trendy approach that enables businesses to connect with consumers through the voice of reputable figures on platforms such as Instagram, YouTube, and Tik Tok. Conventional advertising was often not relatable, sincere, or trustworthy, which this form of marketing offers. This study aims to explore the impact of influencer marketing on brand perception and consumer engagement. Specifically, it examines the impact of marketing strategies, the type of content, and the reputation of influencers on customer perceptions and brand trust. A primary data gathering method was used in a quantitative study design. One hundred randomly chosen respondents were given a standardized questionnaire. Regression and correlation tests were used in SPSS to evaluate the data, which was gathered via Google Forms, and look at the correlations between the variables. Findings indicate that influencer marketing techniques can account for more than three-quarters of the variance, and they significantly enhance consumer interactions. Furthermore, there is a high correlation between influencer credibility and brand authenticity and trust, and 56.4% of consumers' perceptions are influenced by the sort of material. These results underscore the importance of realistic engaging content when it comes to establishing positive brand relationships. Influencer marketing is one of the most effective tactics to engage with more people and increase brand awareness. By investing in authentic influencer collaborations and numerous content programs, brands are in a more favorable position to foster loyalty and build long-lasting customer relationships.

The research investigates the effects of AI-driven chatbots paired with virtual influencers upon purchasing choices made by consumers who focus on sustainable behavior within the FMCG industry. The research utilizes qualitative methods to study secondary data which demonstrates AI-powered chatbots boost customer relationships and personalized experiences and sustainable awareness alongside virtual influencers who strengthen brand reliability and environmental awareness. The use of FMCG industry technologies leads brands toward better consumer interactions and increased conversion rates and strengthened brand loyalty. The implementation of these technologies demands strategic research because data security problems and ethical consequences together with changing AI functionality must receive serious attention. The research suggests that FMCG businesses should implement strategic AI marketing solutions although they need to tackle consumer trust issues together with transparency concerns. Future investigations should entail empirical testing through primary research activities and methods for ethical AI management in addition to studies about how cultural elements affect marketing AI adoption practices. The acquired knowledge will help improve AI-based tactics that promote sustainable and consumer-focused marketing operations in the FMCG industry.

With the growing number of brands entering the metaverse, the influence of consumer interactions on brand-related attitudes and behaviors remains understudied. To address this gap, we conducted an experiment involving participants who used the Meta Quest 2 headset to interact with objects or products in a branded metaverse. Our findings revealed that presence positively impacted brand attitudes and enjoyment, whereas cybersickness had a negative effect. Importantly, enjoyment mediated the effects of presence and cybersickness on attitudes toward both the brand and the immersive experience as well as behavioral intentions. These results offer valuable insights for both theoretical understanding and practical application of interactive brand engagement in the metaverse.

Optimizing flexibility rewards for aggregator–consumer interactions: a bilevel multi-follower approach

The rapid growth of digital technology has transformed social media into a strategic platform for consumer interaction and digital advertising. Organizations are increasingly required to utilize data-driven approaches to improve marketing effectiveness. This study aims to analyze the role of social media analytics in enhancing consumer engagement and optimizing digital advertising campaign performance in the Indonesian context. A qualitative research design using a systematic literature review was employed. Data were collected from reputable academic databases, including Scopus, Web of Science, Google Scholar, and ScienceDirect. The selected studies were analyzed using content analysis and thematic synthesis to identify key patterns and relationships among variables. The findings indicate that social media analytics significantly improves consumer engagement through content optimization, sentiment analysis, personalization, and influencer evaluation. In addition, analytics enhances advertising effectiveness by enabling precise targeting, real-time performance monitoring, and cross-channel attribution. These mechanisms contribute to higher click-through rates, conversion rates, and return on advertising spend. The study confirms that social media analytics supports evidence-based marketing decisions and strengthens competitive advantage in the digital marketplace. The results provide both theoretical and practical implications for organizations seeking to improve their digital marketing strategies through data-driven approaches.

This study compares consumer complaints about the return processes of Trendyol, Hepsiburada, and n11—the leading e-commerce platforms operating in Türkiye. A total of 345 user complaints obtained from the Şikayetvar platform between May 2025 and October 2025 were analysed using the qualitative content analysis method. The findings indicate that the primary issues triggering return processes across all three platforms are product-related; furthermore, uncertainties regarding shipping logistics and the operational flow of the return process negatively affect consumer trust. The study reveals that the loss of trust in e-commerce platforms is shaped not only by the presence of return or compensation mechanisms but also by process-based factors, such as how the platform manages the process and the extent to which it informs and guides users. In this regard, the research contributes to the service failure literature by addressing the return process within the context of platform–consumer interaction and provides practical implications for improving return processes for e-commerce platforms.

ABSTRACT Non-fungible tokens (NFTs) represent an emerging innovation in tourism, enabling destinations to create digital assets embedded within socio-technical ecosystems. Drawing on assemblage theory and qualitative data from netnography and semi-structured interviews, this study explores how motivations and constraints of tourism NFT consumption emerge through consumer interactions. Findings identify self-extension motivations including memorization, symbolic meanings, and socialization, and self-expansion motivations including ownership, economic and transferability benefits, hedonism, and learning. Additionally self-restriction and self-reduction constraints are identified. These findings advance understanding of tourism NFTs from an interaction-centric perspective and provide practical strategies for destinations marketing using blockchain technologies.

As artificial intelligence (AI) increasingly engages in managing consumer interactions on e-commerce platforms, an important question arises: how do public AI-generated replies to negative reviews compare with human replies in fostering consumer trust? This study investigates consumer trust in reply source (AI review bots vs. human agents) across three reply strategies, namely, default, thinking, and feeling, in a public review context. AI review bots are defined as automated systems that publicly respond to consumer reviews. Using a controlled laboratory experiment, we find that when using the default strategy, human replies elicit greater trust than AI replies, mediated by higher perceived authenticity and persuasiveness. Conversely, when using the thinking strategy, AI replies outperform human replies in building consumer trust, as they are perceived as more authentic and persuasive. In the feeling strategy, there is a convergence between AI and human replies. These findings demonstrate that the effectiveness of AI versus human replies depends on the strategy adopted. Theoretically, this study extends research on human–AI interaction by introducing a three-strategy framework to systematically compare AI and human communicators in public review contexts. Practically, the results guide e-commerce sellers and platforms on when and how to deploy AI review bots to effectively manage consumer trust in response to negative reviews.

ABSTRACT Geopolitical disruptions have become recurrent and materially reshape global supply chains, particularly for innovative new products with limited life cycles. These disruptions not only constrain production capacity and supplier availability but also alter market potential and demand diffusion through consumer interactions. However, existing studies rarely examine how recurrent geopolitical disruptions, occurring at different stages of a product's life cycle, jointly affect new product diffusion and supply chain configuration. This study develops an AI-enabled, Physical Internet–based multi-period supply chain configuration model that integrates production–sales planning, dynamic demand evolution and flexible supplier selection. The proposed framework captures how geopolitical disruptions dynamically influence market potential, instant demand, processing costs, lead times and logistics options across the product life cycle. Our results show that early-stage disruptions substantially reduce profitability and trigger downstream supplier switching due to constrained capacity and impeded inventory buildup, while mid-period disruptions dampen demand volatility and inventory costs. Moreover, adaptive configuration through multiple planning horizons – enabled by AI-driven decision-making under the Physical Internet paradigm – can partially mitigate the adverse impacts of short-term disruptions. The findings offer actionable insights for designing resilient, data-driven global supply chains in an increasingly uncertain geopolitical environment.

Purpose This special issue aims to bridge the gap between theoretical understanding and empirical advancement in neurophysiological research within retail and service contexts. While the retail and service sectors dominate global economies, the adoption of neurophysiological tools to study consumer behavior has been lagging. This editorial presents nine empirical studies demonstrating how neurophysiological measures capture dynamic, moment-by-moment consumer experiences across retail and service encounters. These contributions capture process-oriented dynamics and advance consumer neuroscience theory while offering actionable managerial and policy implications. The issue prioritizes empirical perspectives, methodological rigor and ethical practices to establish a foundation for future neuromarketing research. Design/methodology/approach This editorial encompasses nine empirical studies that utilize multimodal neurophysiological measures. Papers were selected through a rigorous peer review process, requiring authors to move beyond mere tool application to demonstrate how their findings advance existing theory. Studies employ diverse methodologies, including electroencephalography, event-related potentials, eye-tracking, electrodermal activity and facial electromyography. All papers combine neurophysiological measures with self-report scales and behavioral data. The editorial organizes contributions around three thematic clusters: technology-mediated interactions, sensory marketing and cognitive-moral constraints, reflecting contemporary retail and service research priorities and advancing what is termed “Neuromarketing 2.0.” Findings Neurophysiological measures reveal consumer processes undetectable with traditional methods. Technology-mediated research shows that AI credibility influences satisfaction through emotional engagement, while chatbots enhance arousal during purchase decisions. Empathy transfers customer emotions to employees, impacting service recovery outcomes. Sensory marketing studies show that multisensory imagery reduces cognitive load and enhances brand recall; stylized packaging designs engage consumers more powerfully than realistic depictions. Information overload degrades attention strategies, yet brand familiarity buffers against overload. Moral decision-making shows heightened cognitive processing during ethically conflicted choices. Collectively, these findings demonstrate that multimodal neurophysiological integration captures real-time mechanisms shaping consumer experiences in retail and service. Originality/value This special issue integrates multiple themes: technology-mediated consumer interactions, sensory marketing and cognitive and moral constraints in consumer decision-making. It illustrates how retail and service research is moving toward a more integrated measurement and modeling architecture toward a new phase named “Neuromarketing 2.0.” The editorial outlines future research directions and synthesizes the emerging consensus on methodological harmonization and theoretical rigor. By bridging lab-based precision with field-based applicability, establishing ethical guardrails and proposing longitudinal, cross-cultural validation pathways, this issue presents a comprehensive research agenda that advances transparency, inclusivity and rigorous innovation in retail and service.

The rapid development of digital technology has transformed marketing practices, particularly through the emergence of social commerce platforms such as TikTok Shop, which integrate content creation, interaction, and transaction within a single ecosystem. This study aims to examine the effectiveness of digital marketing strategies implemented through TikTok Shop in increasing the sales volume of Micro, Small, and Medium Enterprises (UMKM). Employing a quantitative research approach with an explanatory design, this study collected data from UMKM actors actively utilizing TikTok Shop as a digital marketing channel. Data were obtained through structured questionnaires and analyzed using descriptive and inferential statistical techniques to assess the relationship between TikTok Shop- based digital marketing strategies and UMKM sales performance. The results indicate that digital marketing strategies through TikTok Shop have a positive and statistically significant effect on UMKM sales volume. Key strategic elements, including content creativity, live streaming utilization, and active consumer interaction, were found to enhance product visibility, strengthen consumer trust, and stimulate purchasing decisions. The discussion highlights that TikTok Shop functions not only as a promotional medium but also as an integrated social commerce system that supports efficient marketing communication and transaction processes. This study concludes that TikTok Shop-based digital marketing represents a strategic and sustainable approach for improving UMKM sales performance and competitiveness in the context of ongoing digital transformation.

Purpose This study aims to investigate how avatar attire and conversational style jointly influence consumers' perceived artificial intelligence (AI) trust and purchase intentions, considering the moderating role of relationship orientation. Design/methodology/approach Three experimental studies (N = 1,070) were conducted. Study 1 tested the effects of avatar attire (professional vs. casual) and conversational style (competence- vs. warmth-oriented) on perceived AI trust and purchase intentions. Study 2 examined whether consumers' relationship orientation (communal vs. exchange) moderates the impact of attire. Study 3 employed a 2 (relationship orientation) × 2 (avatar attire) × 2 (conversational style) design to test the full moderated mediation model. Findings Avatars in professional attire elicit higher perceived trust and purchase intentions than casual attire. The effect of avatar attire and conversational-style interaction on perceived AI trust and purchase intentions depends on consumers' relationship orientation, rather than being driven solely by congruence. Professional attire is more persuasive in exchange-oriented contexts, whereas casual attire is more effective in communal contexts. Originality/value This research integrates the computers are social actors framework and the stereotype content model to clarify how visual appearance and communication style jointly shape consumer responses to AI avatars. The findings provide actionable guidance for designing virtual brand agents, highlighting the importance of aligning avatar appearance with communication style and relational context to enhance trust and purchase intentions in AI-mediated interactions.