Search Results (10)

Artificial intelligence has become increasingly embedded in digital marketing, reshaping how firms collect consumer data, personalize content, automate persuasion, and evaluate campaign performance. While these developments offer clear strategic benefits, they also raise important ethical questions concerning privacy, transparency, fairness, manipulation, and accountability within platform-mediated marketing environments. This article presents a structured thematic review of recent research on the ethics of AI-driven digital marketing. The review focuses on English-language peer-reviewed journal articles and high-quality conference proceedings published between January 2023 and September 2025. Searches were conducted across Scopus, ScienceDirect, SpringerLink, ACM Digital Library, IEEE Xplore, MDPI, PubMed, and selected academic repositories. After deduplication, screening, and full-text assessment, 91 studies were included in the final synthesis. Methodological quality was appraised using the Mixed Methods Appraisal Tool and Joanna Briggs Institute critical appraisal criteria, while the findings were examined through thematic synthesis. The review identifies five recurring ethical domains in the literature: data privacy and GDPR compliance, algorithmic transparency and explainable AI, algorithmic fairness in targeting and automated decision-making, dark patterns and deceptive interface design, and influencer or virtual influencer disclosure. The evidence suggests that privacy, consent, deceptive design, and transparency are the most extensively discussed areas, whereas the practical effectiveness of fairness interventions, explainability tools, and disclosure mechanisms remains more mixed and context-dependent. Across these themes, ethical risks appear not only as the result of individual corporate decisions but also as outcomes shaped by platform infrastructures, ranking systems, data access arrangements, and performance-oriented advertising metrics. The article contributes by organizing recent scholarship into a coherent thematic framework and by situating AI-driven digital marketing ethics within a broader platform-governance perspective. It argues that responsible implementation requires clearer distribution of accountability among businesses, platforms, regulators, and researchers. The review is limited by its focus on the 2023–2025 period, its English-language scope, and the methodological heterogeneity of the included studies, which prevents statistical meta-analysis. Full article

AI-targeted advertising has become central to consumer-facing marketing, yet it does not directly persuade consumers to purchase experiential products. This study proposes a cognitive scaffolding framework, arguing that AI advertising supports consumers’ autonomous value construction rather than directly generating purchase motivation. Using SEM with 412 Korean consumers in cultural experience tourism, the results show that AI advertising has no direct association with purchase intention. Instead, its association operates sequentially through perceived value and trust. Moreover, the value–purchase intention relationship is stronger among consumers with higher AI utilization. Complementary fsQCA shows that no single condition—including advertising effectiveness—is individually necessary for high purchase intention. A configurational analysis identifies a dominant sufficient path in which perceived value combines with AI utilization and does not require advertising effectiveness, alongside two secondary paths in which advertising effectiveness operates only in conjunction with other conditions. This pattern is consistent with a reconceptualization of AI advertising as one of several substitutable cognitive scaffolding inputs, interpretable as supporting rather than driving consumers’ autonomous value construction—a claim advanced as specific to experiential consumption contexts rather than as a general account of AI-mediated trust formation. Full article

This study explores how artificial intelligence (AI)-driven labour market analytics can support the alignment of university curricula with emerging skill demands and inform the development of targeted micro-credentials. A mixed-methods approach was applied, combining AI-assisted analysis of approximately 30,000 online job advertisements in Latvia with ESCO-based skill mapping, curriculum analysis, and expert interviews. The study develops and validates a three-layer analytical framework integrating labour market demand, professional standards, and programme learning outcomes. Three occupations—Personnel Specialist, Finance Manager, and Organisation Manager—were analysed at Riga Technical University as proof-of-concept cases. The findings demonstrate that strict one-to-one ESCO matching overestimates curriculum gaps because labour market and educational actors often describe competencies at different levels of abstraction. Composite matching significantly improves alignment estimates by identifying functionally equivalent competencies embedded across curricula. Nevertheless, the analysis reveals a persistent under-representation of digital competencies across all programmes, confirmed by industry experts. Interviews further identify a “pedagogical transfer gap”, where formally acquired competencies are insufficiently applied in practice, and highlight employer support for high-quality micro-credentials focused on technical upskilling. The study contributes an AI-assisted curriculum-monitoring framework that combines large-scale skill extraction, semantic alignment, and stakeholder validation, offering universities a practical tool for evidence-based curriculum renewal and lifelong learning development. Full article

Visual landscape assessment informs highway corridor planning decisions, yet conventional surveys scale poorly with corridor length. Multimodal large language models (MLLMs) offer a scalable alternative, but their alignment with road-user preferences remains poorly understood. This study aimed to quantify the extent to which MLLM-derived visual preference rankings align with those of road users. The comparison used 80 images across 16 landscape character groups along 418 km of Malaysia’s North–South Expressway. Five MLLMs (ChatGPT, Claude, Gemini, Kimi, and Qwen) were queried under three prompt formulations using complete pairwise comparison with AB/BA reversal, yielding 94,800 judgements. Bradley–Terry rankings were then compared against rating-scale responses from 400 road users. The five models converged strongly (Kendall’s W = 0.926), whereas baseline AI–human agreement was moderate (image-level ρ = 0.622; group-level ρ = 0.697). Divergences are concentrated in two opposing categories. Paddy landscapes, ranked first by humans, fell to thirteenth in the AI ranking, whereas advertisement-dominated scenes were overvalued. Excluding the paddy group raised correlations to 0.772 and 0.911. A theory-directed prompt achieved comparable gains (ρ = 0.775 and 0.929) and restored paddy to third rank. A hybrid AI-screening, human-targeted protocol is proposed for corridor-scale visual planning. Full article

The rapid proliferation of artificial intelligence (AI) in digital advertising has fundamentally transformed how brands communicate with consumers, shifting from generic mass messaging toward highly personalised, emotionally targeted experiences. Despite growing interest in AI-driven marketing, limited empirical research has examined how consumers’ socio-emotional processing mechanisms, particularly perceived empathy and social cognition, mediate the relationship between AI-powered ad personalisation and downstream consumer decision-making outcomes. This study addresses this gap by investigating the emotional and cognitive responses triggered by AI-personalised advertising among Romanian consumers. Using a quantitative survey design, data were collected from a sample of 234 adult respondents (18–65 years) in Romania, broadly aligned with key Romanian demographic distributions across age, gender, and residential area. Structural equation modelling using the Partial Least Squares (PLS-SEM) approach was employed to test the proposed conceptual model, which integrates constructs of AI-powered ad personalisation, trust in AI, perceived AI empathy, emotional arousal, cognitive elaboration, social cognition, consumer engagement, and purchase intention. The results reveal that perceived empathy toward AI-generated advertising positively influences emotional arousal and cognitive elaboration, which in turn significantly predict consumer engagement and purchase intention. Trust in AI emerged as a critical sequential mediator, while social cognition moderated the personalisation-to-trust pathway. The study yields a validated marketing model that captures the socio-emotional dynamics underlying consumer responses to AI advertising. These findings contribute to the theoretical understanding of human–AI interaction through a social cognition and emotions lens, while offering practical implications for the design of emotionally intelligent, AI-driven advertising strategies. Limitations and future research directions are discussed. Full article

The production and leasing of electric construction machinery play a critical role in the low-carbon transition. However, from a multi-cycle dynamic perspective, there is a lack of targeted research on how to enhance electric goodwill and AI-enabled maintenance service levels while maximizing enterprise profits. To fill this gap, this study incorporates AI-enabled O&M effort, R&D technology, AI-enabled maintenance effort, and advertising effort into a long-term dynamic framework to examine optimal decisions for the manufacturer and the lessor. We assume that the information in the leasing supply chain is symmetric, that the marginal profits of the manufacturer and the lessor are fixed parameters, and that the AI-enabled maintenance service effort level and the electric goodwill are taken as state variables. We develop differential game models across four decision cases: centralized (Case C), decentralized (Case D), unilateral cost-sharing contract (Case U), and bilateral cost-sharing contract (Case B). Results demonstrate monotonic state variable trajectories. Both Case U and Case B can achieve supply chain coordination, with the profit-sharing mechanism in Case B proving superior. In addition, the optimal cost-sharing proportion depends on the relative sizes of the manufacturer’s and the lessor’s marginal profits in both Case U and Case B. The AI-enabled maintenance service plays a significant role in enhancing equipment reliability and supply chain resilience. In addition, the impacts of key parameters on optimal decision variables, state variables, profits, and coordination of the leasing supply chain are comprehensively discussed. Full article

Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), enables immersive experiences across various fields, including entertainment, healthcare, and education. However, its data-intensive and interactive nature introduces significant cybersecurity and privacy challenges. This paper presents a detailed adversary model to identify threat actors and attack vectors in XR environments. We analyze key risks, including identity theft and behavioral data leakage, which can lead to profiling, manipulation, or invasive targeted advertising. To mitigate these risks, we explore technical solutions such as Advanced Encryption Standard (AES), Rivest–Shamir–Adleman (RSA), and Elliptic Curve Cryptography (ECC) for secure data transmission, multi-factor and biometric authentication, data anonymization techniques, and AI-driven anomaly detection for real-time threat monitoring. A comparative benchmark evaluates these solutions’ practicality, strengths, and limitations in XR applications. The findings emphasize the need for a holistic approach, combining robust technical measures with privacy-centric policies, to secure XR ecosystems and ensure user trust. Full article

An emerging topic that has the potential to enhance user experience, reduce crime, and target advertising is human emotion recognition, utilizing DeepFace and Artificial Intelligence (AI). The same feeling may be expressed differently by many individuals. Accurately identifying emotions can be challenging, in light of this. It helps to understand an emotion’s significance by looking at the context in which it is presented. Depending on the application, one must decide which AI technology to employ for detecting human emotions. Because of things like lighting and occlusion, using it in real-world situations can be difficult. Not every human emotion can be accurately detected by technology. Human–machine interaction technology is becoming more popular, and machines must comprehend human movements and expressions. When a machine recognizes human emotions, it gains a greater understanding of human behavior and increases the effectiveness of work. Text, audio, linguistic, and facial movements may all convey emotions. Facial expressions are important in determining a person’s emotions. There has been little research undertaken on the topic of real-time emotion identification, utilizing face photos and emotions. Using an Artificial Intelligence-based DeepFace approach, the proposed method recognizes real-time feelings from facial images and live emotions of persons. The proposed module extracts the facial features from an active shape DeepFace model by identifying 26 facial points to recognize human emotions. This approach recognizes the emotions of frustration, dissatisfaction, happiness, neutrality, and wonder. The proposed technology is unique, in that it implements emotion identification in real-time, with an average accuracy of 94% acquired from actual human emotions. Full article

Online advertising is a marketing approach that uses numerous online channels to target potential customers for businesses, brands, and organizations. One of the most serious threats in today’s marketing industry is the widespread attack known as click fraud. Traffic statistics for online advertisements are artificially inflated in click fraud. Typical pay-per-click advertisements charge a fee for each click, assuming that a potential customer was drawn to the ad. Click fraud attackers create the illusion that a significant number of possible customers have clicked on an advertiser’s link by an automated script, a computer program, or a human. Nevertheless, advertisers are unlikely to profit from these clicks. Fraudulent clicks may be involved to boost the revenues of an ad hosting site or to spoil an advertiser’s budget. Several notable attempts to detect and prevent this form of fraud have been undertaken. This study examined all methods developed and published in the previous 10 years that primarily used artificial intelligence (AI), including machine learning (ML) and deep learning (DL), for the detection and prevention of click fraud. Features that served as input to train models for classifying ad clicks as benign or fraudulent, as well as those that were deemed obvious and with critical evidence of click fraud, were identified, and investigated. Corresponding insights and recommendations regarding click fraud detection using AI approaches were provided. Full article

The user, usage, and usability (3U’s) are three principal constituents for cyber security. The effective analysis of the 3U data using artificial intelligence (AI) techniques allows to deduce valuable observations, which allow domain experts to design practical strategies to alleviate cyberattacks and ensure decision support. Many internet applications, such as internet advertising and recommendation systems, rely on click-through rate (CTR) prediction to anticipate the possibility that a user would click on an ad or product, which is key for understanding human online behaviour. However, online systems are prone to click on fraud attacks. We propose a Human-Centric Cyber Security (HCCS) model that additionally includes AI techniques targeted at the key elements of user, usage, and usability. As a case study, we analyse a CTR prediction task, using deep learning methods (factorization machines) to predict online fraud through clickbait. The results of experiments on a real-world benchmark Avazu dataset show that the proposed approach outpaces (AUC is 0.8062) other CTR forecasting approaches, demonstrating the viability of the proposed framework. Full article