Search Results (50)

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

This paper develops and evaluates a predictive analytics framework for influencer marketing return on investment (ROI), integrating hybrid deep learning architectures with trust-aware modelling to address the dual purpose of (a) developing a rigorous evaluation framework for influencer campaign performance and (b) examining the effectiveness of influencer marketing predictors. The concept of influencer marketing has quickly grown to be one of the most effective mediums within the contemporary digital advertising landscape. Due to the growing number of brands dedicating huge amounts of budgets to social media partnerships, the importance of data-driven approaches that can predict the outcomes of campaigns and, consequently, ensure the best possible return on investment (ROI) has become urgent. This paper introduces a machine learning system that can be used to forecast the sales of products promoted by influencer marketing campaigns based on campaign-level features, including type of platform, influencer type, type of campaign, time of the year, number of engagements, estimated reach, and campaign duration. A publicly available influencer marketing ROI dataset was trained and tested on an XGBoost regression model with a coefficient of determination ($R^2$) of 0.95 indicating high predictive power and generalization. The results show that engagement metrics and estimated reach are some of the most impactful factors in sales performance, and additional contextual factors like platform selection, type of campaign, and timing of the year also moderate results. In addition to predictive modelling, this paper explains how artificial intelligence (AI) can be strategically integrated throughout the influencer marketing lifecycle. With the inclusion of AI-based analytics, marketers will be able to leverage their intuitive decision-making processes with quantifiable and replicable measures and approaches that can lead to true consumer trust and lasting brand resonance. The framework proposed can provide practitioners and researchers with a scalable basis for implementing intelligent systems in the context of influencer marketing. Recent computer science research further demonstrates that AI-driven frameworks spanning generative content modelling, AI-powered CRM architectures for understanding consumer preferences on social media, and parasocial-trust models of influencer engagement provide strong methodological complements to the predictive approach developed here, while governance and project management considerations for deploying such systems are increasingly addressed in the literature. Concurrently, a growing body of influencer marketing research examines how platform affordances shape information-seeking and trust, how influencer attributes and social satisfaction mediate purchase intention, how influencer marketing drives sustainable consumption, and how social media measurably shapes health-related behaviours all of which motivate the predictive and trust-modelling objectives of this work. Full article

Background: Digital marketing increasingly functions as a complex adaptive system characterised by non-stationary environments, strategic interaction, and multi-agent competition. Programmatic advertising exemplifies this complexity, where decisions must be made in real time under uncertainty. Under such conditions, traditional static optimisation methods often fail to deliver robust performance. This review synthesises bio-inspired computational approaches, reinforcement learning (RL), multi-agent reinforcement learning (MARL), and agentic artificial intelligence (AI) to develop an integrated theoretical perspective on adaptive optimisation in digital marketing. Methods: Following PRISMA 2020 guidelines, we conducted a systematic search of peer-reviewed research across six databases: Scopus, IEEE Xplore, ACM Digital Library, SpringerLink, ScienceDirect, and arXiv, supplemented by manual reference checking. Each computational paradigm is explicitly grounded in foundational biological literature, including work on evolution, foraging, swarm intelligence, and immune cognition. Reinforcement learning supports adaptive decision-making through mechanisms closely aligned with operant conditioning and foraging behaviour. Multi-agent reinforcement learning extends these principles to interactive marketing ecosystems via decentralised coordination and swarm-based learning. Agentic AI further advances adaptive capability by introducing goal-directed reasoning, memory, and higher-level decision orchestration. Contributions: The review identifies persistent fragmentation across marketing sub-domains and a lack of formal mathematical grounding for widely used bio-inspired analogies. To address these gaps, the study proposes a multi-layer bio-inspired framework and outlines a structured research agenda to guide the development of autonomous digital marketing systems. Full article

Internet advertising, while enabling unprecedented commercial reach, has become a pervasive vehicle for deceptive practices that inflict measurable harm on consumers. This study empirically investigates the structural relationships between internet advertising falsity and consumer harm by integrating analyses of the mediating role of consumer cognitive processes and the moderating role of consumer vulnerability within a unified structural framework. Survey data were collected from 600 adult consumers with online purchase experience in the Republic of Korea—an advanced digital economy characterized by exceptionally high mobile-commerce penetration, mature e-commerce infrastructure, and evolving digital consumer protection regulation—and analyzed using structural equation modeling (SEM) with AMOS 24.0, supplemented by Hayes’ PROCESS macro Model 59 for conditional process analysis. All 13 hypotheses were supported, although path magnitudes varied substantially across falsity dimensions and mediator pathways—with direct effects ranging from β = 0.156 (false scarcity) to β = 0.224 (performance exaggeration), and indirect effects dominated by the risk assessment distortion pathway. Among the four sub-dimensions of advertising falsity—factual misrepresentation, performance exaggeration, price deception, and false scarcity—performance exaggeration exerted the strongest direct effect on consumer harm. The three cognitive mediators—perceived advertising credibility, risk assessment distortion, and purchase decision pressure—all demonstrated significant partial mediation, with risk assessment distortion emerging as the most powerful indirect pathway. All four consumer vulnerability dimensions—digital literacy level, demographic vulnerability, prior victimization experience, and impulsive buying tendency—significantly moderated the falsity–harm relationship, with low-digital-literacy consumers experiencing approximately 1.7 times the adverse effect of high-literacy counterparts. Moderated mediation analysis revealed that the conditional indirect effect for the high-vulnerability group was approximately 2.3 times that of the low-vulnerability group, confirming that the cognitive harm mechanism intensifies systematically for vulnerable consumers. These findings advance consumer vulnerability theory in the digital context and offer evidence-based implications for consumer protection policy, platform governance, and digital literacy education. Full article

Blockchain-based auctions often utilize smart contracts to automate auction rules, with much research focusing on enhancing privacy and fairness through cryptographic techniques. However, the authenticity of external data input into these systems is frequently overlooked. In particular, rational nodes may manipulate bidding data by submitting false types to maximize their utility, compromising market fairness and the reliability of auction outcomes. The aim of this study is to propose an alternative blockchain-based auction mechanism to incentivize nodes to report types honestly. We propose the Mixed-Node Advertising Auction (MNAA) mechanism for digital advertising auctions on blockchain systems. MNAA integrates quasi-linear and value maximization utility models to design allocation and pricing rules that eliminate nodes’ incentives to misreport their types, ensuring the authenticity of data submitted to the auction. To enhance efficiency, MNAA employs state channel technology and off-chain smart contracts, reducing main chain interactions. Theoretical analysis confirms that MNAA incentivizes truthful behavior and ensures security and correctness. Simulation results show that MNAA outperforms Generalized Second Price (GSP), Mixed Bidders with Private Classes (MPR), and Vickrey–Clarke–Grooves (VCG) auctions in terms of liquid social welfare (LSW), publisher revenue, and allocation efficiency, while also improving the transaction throughput and showing good performance in terms of transaction costs and latency. Full article

With the growing integration of AR/VR/Metaverse technologies across luxury brands, advertising has shifted to providing consumers with a personalised experience in which they can engage with brands via digital avatars. Given the considerable success of Metaverse advertising, it is apparent that organisations need to reinvent their advertising strategies to enhance consumer experience and brand engagement over digital platforms. However, this reinvention would require organisations to develop an advertising strategy that creates a coherent brand experience for consumers and provides them with an immersive brand experience on Metaverse platforms. As a result, this study undertakes a bibliometric approach to provide a comprehensive understanding of how integrating Metaverse platforms with advertising strategies can enhance brand engagement among consumers. More precisely, a keyword search strategy is formulated, and a multi-database search is performed across key databases, including Scopus, EBSCOhost, and ProQuest. In doing so, results from Scopus databases are visualised through network and overlay visualisation maps to understand how key themes/knowledge structures are associated with Metaverse advertising and brand engagement. Besides this, the study also showcases the key theoretical perspectives (i.e., psychological perspectives, value-based perspectives, technology/innovation perspectives, and social interaction perspectives) across these studies to understand how brands have well-infused Metaverse advertising to enhance brand engagement among consumers. Lastly, the study also provides a deeper understanding of the key challenges that are associated with the widespread implementation of Metaverse advertising. Full article

This study assesses the effectiveness of social media advertising campaigns in the supermarket sector by combining managerial insights with multi-criteria decision analysis (MCDA) to support informed, sustainable decision-making. Considering the ever-increasing complexity of digital communication and the growing need for sustainable marketing resources, supermarkets require effective methods to evaluate social media platforms beyond isolated metrics. The study employs the Visual PROMETHEE program, an MCDA that incorporates qualitative insights from 27 supermarket managers in Northern Greece, along with the PROMETHEE II multi-criteria decision analysis method. At the outset, managers evaluated the importance of thirty-four social media performance factors with a five-point scale. Seven core evaluation criteria are identified by aggregating importance ratings and qualitative analysis: return on investment, revenue contribution, lead generation, engagement, cost efficiency, feedback, electronic word of mouth (eWoM), and reach. The use of these criteria later led to the evaluation of seven major social media platforms. A transparent ranking of platforms is presented, based on the results. The ranking highlights significant performance differences across financial, engagement, and reputational dimensions. The findings demonstrate the importance of integrating managerial guidance with multi-criteria analysis to inform long-lasting and evidence-based marketing decisions in retail. Full article

Spam messages are unwanted, irrelevant, or potentially harmful messages sent in bulk to large numbers of recipients via email, SMS, or social media. These messages pose a threat of spam to individual users and commercial companies. They threaten digital communication platforms by enabling phishing, malware distribution, service disruption, and unsolicited advertisements. Several mechanisms have been used in the literature to detect spam over digital communication systems. This includes rule-based filtering, Bayesian filtering, heuristic analysis, and machine learning (ML) techniques. Traditional rule-based and heuristic analyses were insufficient to cope with evolving attack patterns. Meanwhile, ML models can present modern, dynamic, appropriate, and efficient solutions in this manner. This study aims to evaluate and compare several basic ML models for spam detection, considering popular benchmark datasets on several communication platforms as a comprehensive comparative study. The experimental results demonstrate that the tested models achieve good accuracy, precision, recall, and F1-score on each investigated benchmark dataset. However, the performance of all models has decreased drastically when the trained models are tested on an unseen dataset. Recommendations for future required enhancements to handle this reduction in the performance of ML techniques for unseen datasets are provided. Finally, extra experimental tests have shown the positive impact of applying some of these recommendations. Full article

This study examines sector-specific patterns of remarketing implementation in manufacturing and service industries and evaluates their effectiveness during digital transformation from a sustainability perspective. Using a mixed-method approach, the research combines descriptive analysis of enterprises’ digital maturity with Monte Carlo simulation modeling of remarketing campaign performance based on key parameters such as budget allocation, conversion efficiency, customer lifetime value, personalization intensity, and investment in AI-driven analytics. The results demonstrate that remarketing enhances traffic, user engagement, and return on investment; however, its sustainability depends on sectoral characteristics and behavioral responsiveness. AI-powered personalization is identified as a critical factor in reducing ad fatigue and improving conversion stability. While manufacturing firms tend to achieve higher but more volatile returns, service-sector companies demonstrate more stable outcomes due to greater digital adaptability and more intensive use of dynamic advertising tools. The findings highlight that sustainable remarketing strategies require sector-specific adaptation to balance economic efficiency, technological investment, and long-term consumer engagement, thereby supporting resilient and sustainable business development in the digital economy. Full article

Background/Objectives: Excessive social media usage in the current times and high rates of food advertising can impact the health status of individuals by increasing food cues related to perceived hunger and, thus, dietary behaviour. This study examined the association between social media usage patterns, food-related advertising, and dietary choices among Gen Z individuals. Methods: A cross-sectional study was carried out amongst 314 young adults between the ages of 18 and 25 in Surat city, Gujarat. Data was collected for social media usage, the most used platforms, preferred content, and eating patterns. Anthropometric measurements (height and weight) were also recorded. Perceived hunger responses to 12 social media-based food images were assessed using a ten-point Visual Analogue Scale (VAS). Statistical analyses were performed using SPSS (version 26.0), with the significance level set at p < 0.05. Results: YouTube and Instagram were the most used social media apps. There were no significant differences observed between the BMI of participants using social media for 2 h a day and those using it 3+ hours a day. However, a significant association between the BMI of those who viewed advertisements for ready-to-eat foods (p = 0.004) and the BMI of those who viewed advertisements for food delivery platforms (p = 0.001) was seen. A significant difference between usage of Pinterest (p = 0.02), Instagram (p = 0.047), and BMI was also found. Conclusions: Social media marketing and food content are shaping the dietary choices of young adults, and more studies need to be conducted in Pan India to understand the reasons. Such evidence will be crucial for guiding nutrition policies, digital marketing regulations, and youth-focused awareness programmes. Full article

Online paid advertising is a dynamic form of online marketing that requires precision and a quick response to market changes. Automated tools are used to greatly simplify the process of creating and managing Pay-Per-Click (PPC) campaigns. The purpose of this research is to evaluate the impact of implementing an automated PPC management tool (Dotidot) on campaign performance in a travel agency compared with standard Google Ads campaigns. A structured multi-criteria procedure is first applied to select the most suitable tool for the Czech and Slovak markets. The core contribution of the paper is the observational case study of Dotidot and its performance comparison. The subject of the research is a comparative analysis of three PPC automated tools: Conviu, Dotidot, and BlueWinston. The significance of this topic lies in highlighting the relatively new possibilities of digital marketing and choosing the right tool for using automation. This research can also stimulate other researchers in this field and expand knowledge. The analysis is carried out using the method of systematic comparison based on established criteria, the result of which is a recommendation of the preferred tool and a brief discussion of its implementation options. The analyzed tools are used mainly on the Czech and Slovak markets and are oriented towards e-commerce (electronic commerce). In addition to e-commerce, a case study of the use of digital promotion in sports organizations is also presented. The research results in a systematic comparison of Conviu, Dotidot, and BlueWinston tools according to predefined criteria. The research also includes a brief discussion on the possibilities of implementing the recommended tool in practice, as well as an assessment of its benefits and limitations. Full article

In modern business conditions, where competitiveness is evident across all operational segments, it is necessary to adopt a proactive management approach, i.e., to actively manage business performance. To keep pace with evolving trends and technologies, a daily presence on social networks and an adequate level of product promotion are necessary. This paper proposes a Fuzzy MCDM (Multi-Criteria Decision-Making) model to define the future direction of a perfumery regarding the application of digital marketing, using a two-phase group decision-making process. A total of ten digital advertising variants were considered, combining five social networks: Facebook, Instagram, TikTok, YouTube, and Threads. The results obtained through the application of the original Fuzzy MCDM model indicate that companies should focus their efforts on promoting designer and oriental perfumes via Facebook, Instagram, and TikTok in order to enhance growth and potentially expand their business operations. Full article

Digital content platforms rely on value co-creation among users, creators, and the platform. Content value, including historical accumulation, influences platforms’ pricing strategies, quality decisions, and monetization potential. This study explores the impact of content value on pricing and quality strategies under centralized and decentralized content configurations. We capture the relationship between historical content quality and user engagement. The interplay of historical quality and content type is characterized as content value dynamics, which influence platforms in managing content supply, user engagement, and revenue generation under different modes. Results show that operational modes offer distinct advantages depending on subsidy levels, advertising revenue-sharing mechanisms, and the platform development stage. The centralized mode performs better under limited subsidies, particularly by offering content with higher continuity. The decentralized mode benefits from diverse creation and flexible incentives to achieve rapid market scaling when subsidies are sufficient. Notably, higher advertising revenue-sharing is not always optimal, especially in low-advertising environments. Additionally, the platform development stage affects optimal mode selection. The decentralized mode with low subsidies may achieve early profitability, whereas the centralized mode offers greater potential for sustainable long-term growth. This work contributes to platform-based supply chain theory by incorporating dynamic content value into operational decision-making. It provides operational insights for platforms regarding mode design and the optimization of value-based monetization strategies. Full article

The growth of electronic word-of-mouth (eWOM) on digital platforms has heightened the need to distinguish authentic user-generated content from covert promotional material. This study proposes an integrated framework combining Natural Language Processing (NLP), machine learning, and Latent Dirichlet Allocation (LDA) to classify sentiment and detect advertising features in online game reviews. Reviews from the Steam platform were analyzed using Support Vector Machine (SVM), Decision Tree, and Naïve Bayes classifiers, with class imbalance addressed through SMOTE and SMOTE–Tomek techniques. The SMOTE-augmented SVM achieved the highest performance, with 98.18% overall accuracy and 97.52% negative sentiment detection. LDA and Quality Function Deployment (QFD) further uncovered latent promotional themes, providing insights into how advertising elements manifest in positive reviews and how negative feedback reflects genuine user concerns. The framework assists platform managers in enhancing eWOM credibility and supports marketers in designing data-driven advertising strategies. By bridging sentiment analysis with covert marketing detection, this research contributes a novel methodological approach for assessing review trustworthiness, improving transparency, and fostering consumer trust in digital information environments. Full article

Decision-making theory has developed over many decades at the intersection of economics, mathematics, psychology, and engineering. Its classical foundations include Bernoulli’s expected utility theory, von Neumann and Morgenstern’s rational choice theory, and the criteria proposed by Savage, Wald, Hurwicz, and others. However, in real-world contexts, decisions are made under uncertainty, incompleteness, and unreliability of information, which classical approaches do not adequately address. To overcome these limitations, modern multi-criteria decision-making methods such as Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), VlseKriterijumska Optimizacija I Kompromisno Resenje (Compromise solution approach) (VIKOR), and ELimination Et Choix Traduisant la REalité (Elimination and Choice Expressing Reality) (ELECTRE), as well as their fuzzy and Z-number extensions, are widely applied to the modeling and evaluation of complex systems. These Z-number extensions are based on the concept of Z-numbers introduced by Lotfi Zadeh in 2011 to formalize higher-order uncertainty. This study introduces the Z-Regret principle, which extends Savage’s regret criterion through the use of Z-numbers. Supported by Rafik Aliev’s mathematical justifications concerning arithmetic operations on Z-numbers, the model evaluates regret not only as a loss relative to the best alternative but also by incorporating the degree of confidence and reliability of this evaluation. Calculations for the selection of digital advertising platforms in terms of performance assessment under various scenarios demonstrate that the Z-Regret principle enables more stable and well-founded decision-making under uncertainty. Full article