Search Results (155)

In the big data era, understanding and influencing consumer behavior in digital marketing increasingly relies on large-scale data and AI-driven analytics. This narrative, concept-driven review examines how big data technologies and machine learning reshape consumer behavior analysis across key decision-making areas. After outlining the theoretical foundations of consumer behavior in digital settings and the main data and AI capabilities available to marketers, this paper discusses five application domains: personalized marketing and recommender systems, dynamic pricing, customer relationship management, data-driven product development and fraud detection. For each domain, it highlights how algorithmic models affect targeting, prediction, consumer experience and perceived fairness. This review then turns to synthetic data as a privacy-oriented way to support model development, experimentation and scenario analysis, and to dark data as a largely underused source of behavioral insight in the form of logs, service interactions and other unstructured records. A discussion section integrates these strands, outlines implications for digital marketing practice and identifies research needs related to validation, governance and consumer trust. Finally, this paper sketches future directions, including deeper integration of AI in real-time decision systems, increased use of edge computing, stronger consumer participation in data use, clearer ethical frameworks and exploratory work on quantum methods. Full article

Advertising research highlights the crucial role of creative strategy in shaping consumer behavior. Yet, limited attention has been paid to how creative appeal and message strategy jointly influence persuasion in social media contexts. This study examines the interactive effects of informational versus transformational appeals and personal versus social-experience message strategies on consumer attitudes and purchase intentions. A 2 (creative appeal) × 2 (message strategy) experimental design was implemented using Facebook post advertisements for a fictitious beer brand. Data was collected from 231 participants randomly assigned to one of four ad conditions. Results show that informational appeals outperform transformational appeals in generating immediate purchase intentions. Attitudes toward the ad and attitude toward the brand mediated these effects, consistent with the Dual Mediation Hypothesis. Moreover, in accordance with the Construal Level Theory, message strategy moderates the relationship: informational appeals were most effective when paired with personal strategies but lost persuasive power under social-experience strategies. These findings advance the theoretical understanding of digital advertising persuasion by explicating how creative appeal and message strategy jointly shape both attitudinal and behavioral responses. Practically, the results suggest that advertisers seeking short-term conversions should combine informational appeals with personal strategies. Full article

Under the influence of the long-tail effect, market segmentation and personalized demand provide room for small brands to grow. Meanwhile, consumer behavior patterns have also shifted, with increased acceptance of low-priced, highly practical goods. This paper constructs a two-tier competitive supply chain model. The manufacturer invests in big data from e-commerce platforms and decides on the production of products by combining sales data and consumer preferences. The two retailers are a head brand retailer, which is larger, and a newcomer brand retailer, which is smaller, and both consider advertising to expand their markets. The paper distinguishes four types of advertising strategies (NA, R₁A, R₂A, BA). Secondly, the differential game model is used to discuss the optimal solutions of different advertising strategies under the relevant situations of demand perturbation and demand non-perturbation. Again, empirical analyses are used to verify the robustness of the model by fitting it with the simulation model. Finally, the paper further extends the model to the symmetric domain to explore the optimal retailer capacity in the market, and comes to the following conclusions (1) In the case of non-disturbed demand, the differences in retailer size and competitiveness can promote a more efficient allocation of resources, and the advertisements placed by small brands are the most effective in terms of market share and profitability, which can also improve the overall performance of the supply chain. (2) Demand perturbation makes the unilateral advertisers more susceptible to external disturbances, and the profit is uncertain while the advertisers’ investment increases. (3) In the expansion model, the maximum capacity of small-brand retailers is 3. When retailers exceed 3, it is difficult for other retail brands to enter the market. Full article

The Big Five Model has been widely applied across various areas for detecting problematic or even antisocial behaviors. This research explores its potential to identify behavior patterns and usage profiles in digital environments, such as social media use, digital gaming, and related activities. This study first conducted a literature review on mobile phone use, video game addiction, and social media overuse through the lens of the Big Five Model. Then, empirical data from 492 participants were analyzed to assess how each personality trait is associated with exposure to excessive internet use. The results shown that individuals with high openness and extraversion are more likely to engage intensively with social media and online entertainment, whereas those with higher levels of neuroticism, agreeableness, or conscientiousness display lower exposure. These findings align with previous research linking personality traits to neuroanatomical patterns that shape behavioral tendencies. This study suggests that specific personality traits, as defined by the Big Five Model, influence the use of digital media and advertising channels, potentially fostering addictive behaviors in users with higher openness and extraversion. Full article

This research explores the language features used by leading consumer brands with successful marketing in their promotional messages. Coca-Cola, McDonald’s, PepsiCo, Mondelez, and Unilever were selected because they appear in Effie’s Most Effective Marketers’ Index and are active on a range of media platforms. A group of 225 marketing texts, made up of social media posts, video advertisement transcripts, and website content, was examined using a corpus-based method based on Biber’s MDA framework. The goal was to find common lexicogrammatical patterns in top consumer brands on five different dimensions. Many advertisements included personal pronouns, commands, and words that suggest possibility or necessity. The findings also show that most social media posts provided information, yet had a moderate impact on persuasion. Abstract nouns, passive voice, and formal connectors were found to make the website and press release texts the most impersonal and explicit. The research discovered that Unilever’s language was more informational and abstract, but McDonald’s language was mixed-purpose and non-abstract. Overall, the results indicate that brands use vocabulary and grammar to fit each platform, but maintain their brand identity. Thus, successful consumer brands use different lexicogrammatical patterns in various media to achieve their objectives. Full article

Background: Robust evidence demonstrates that tobacco use acts as a causal and, therefore, modifiable risk factor for the development of type 2 diabetes mellitus (T2DM). However, its specific population-level impact in Mexico has not yet been quantified. Objective: This study aimed to estimate the population attributable fraction (PAF) of T2DM associated with tobacco use among Mexican adults, utilizing data from the 2021 National Health and Nutrition Survey (ENSANUT). Methods: A nested case–control analysis was conducted within the complex sampling design of the ENSANUT. Adults aged 20 years or older were included. Cases were defined as individuals with a self-reported medical diagnosed T2DM diagnosis; controls were individuals without T2DM. Exposure status was categorized as current person who smokes, former person who smokes, and never person who smokes. A logistic regression model was employed, adjusting for key covariates including age, sex, socioeconomic status, and comorbidities. The PAF was subsequently calculated using the Miettinen formula. Results: The adjusted PAF for T2DM attributable to smoking was 10.1% (95% CI: 4.07–14.97). This finding suggests that approximately one in eight T2DM cases could be prevented through the elimination of tobacco use. The association was more pronounced among men and individuals with a history of heavy tobacco use. Conclusion: The estimated PAF for T2DM due to tobacco use underscores the significant contribution of policies established within the WHO Framework Convention on Tobacco Control to the prevention of chronic diseases. The implementation and strengthening of such policies, including increased tobacco taxes, comprehensive smoking bans in public places, on-package warnings, and advertising prohibitions, would prove highly beneficial. These findings show a strong population-level association between tobacco use and T2DM, but causality cannot be established. Future longitudinal studies in Mexico are needed to confirm these results. Full article

This study investigates how Moroccan users experience and interpret digital content that seems tailored to their personal profiles. While many participants recognize the relevance of such content, their willingness to engage depends less on accuracy and more on whether they feel respected and in control. Based on 629 survey responses and analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM), the findings indicate that perceived control is the most influential factor in building trust, which in turn strongly predicts engagement. Conversely, when content feels intrusive or when users have concerns about how their data is managed, trust declines—even if the targeting appears accurate. These results imply that people do not simply react to what they receive but also to the manner in which it is delivered and explained. In a rapidly digitizing environment like Morocco, where awareness of data rights remains limited, trust and transparency emerge as essential foundations for meaningful digital interaction. The study provides practical insights for marketers and platforms aiming to design targeting strategies that are not only effective but also ethically responsible and aligned with users’ expectations. Full article

Artificial intelligence (AI) is increasingly used in marketing to deliver personalized messages and services. Although such tools create new opportunities, their acceptance by consumers depends on several factors that go beyond technology itself. This study examines how trust and ethical perceptions, familiarity and exposure to AI, digital consumer behavior, and identity concerns shape acceptance of AI-based personalized advertising. The analysis draws on data from 650 Greek consumers, collected through a mixed-mode survey (online and paper), and tested using logistic regression models with demographic characteristics included as controls. The results show trust and ethical perceptions of acceptance as factors, while familiarity with AI tools also supports positive attitudes once trust is established. In contrast, digital consumer behavior played a smaller role, and identity-related consumption was negatively associated with acceptance, reflecting concerns about autonomy and self-expression. Demographic factors, such as age and income, also influenced responses. Overall, the findings suggest that acceptance of AI in marketing is not only a technical matter but also a psychological and social process. This study highlights the importance for firms to build trust, act responsibly, and design personalization strategies that respect consumer identity and ethical expectations. Full article

The Wi-SUN FAN (Wireless Smart Ubiquitous Network Field Area Network) standard facilitates large-scale connectivity among smart devices in utility networks and smart cities. Specifically designed for Low-Power and Lossy Networks (LLNs), Wi-SUN FAN supports the formation of multiple Personal Area Networks (PANs) and mesh topologies with multi-hop transmissions. However, the node association process, divided into five junction states, often results in prolonged connection times, particularly in multi-hop networks, thereby limiting network scalability and reliability. This study analyzes the factors affecting these delays, with a particular focus on Join State 1 (JS1), which relies on PAN Advertisement (PA) packets that use asynchronous communication and the trickle timer algorithm, frequently causing significant delays. To overcome this challenge in JS1, we propose the Parallel Rendezvous (PR) strategy, which forms synchronized clusters of unassociated nodes and leverages the standard’s PAN Advertisement Solicit (PAS) packets to rapidly disseminate network information. The proposed algorithm, PR Wi-SUN FAN, is evaluated through simulations in various network topologies, demonstrating notable improvements in linear, fully connected, and mesh scenarios. The most significant gains are observed in the linear topology, with reductions of up to 71.22% in association time and 59.56% in energy consumption during JS1. Full article

As digital media companies pursue sustainable revenue, AI-based strategies like personalized advertising and dynamic paywalls have become prevalent. These monetization models involve different forms of consumer data collection, raising distinct privacy concerns. This study investigates how digital news users perceive privacy trade-offs between these two AI-driven models. Based on Communication Privacy Management Theory and Privacy Calculus Theory, we conducted a survey of 336 Korean news consumers. Findings indicate that perceived control and risk significantly affect users’ willingness to disclose data. Moreover, users with different privacy orientations prefer different monetization models. Those favoring dynamic paywalls tend to be more privacy-sensitive and show a higher willingness to pay for personalized, ad-free content. While personalization benefits are broadly acknowledged, the effectiveness of privacy control mechanisms remains limited. These insights highlight the importance of ethical, user-centered AI monetization strategies in journalism and contribute to theoretical discussions around algorithmic personalization and digital news consumption. Full article

Online advertising is vital for reaching target audiences and promoting products. In 2020, US online advertising revenue increased by 12.2% to $139.8 billion. The industry is projected to reach $487.32 billion by 2030. Artificial intelligence has improved click-through rates (CTR), enabling personalized advertising content by analyzing user behavior and providing real-time predictions. This review examines the latest CTR prediction solutions, particularly those based on deep learning, over the past three years. This timeframe was chosen because CTR prediction has rapidly advanced in recent years, particularly with transformer architectures, multimodal fusion techniques, and industrial applications. By focusing on the last three years, the review highlights the most relevant developments not covered in earlier surveys. This review classifies CTR prediction methods into two main categories: CTR prediction techniques employing text and CTR prediction approaches utilizing multivariate data. The methods that use multivariate data to predict CTR are further categorized into four classes: graph-based methods, feature-interaction-based techniques, customer-behavior approaches, and cross-domain methods. The review also outlines current challenges and future research opportunities. The review highlights that graph-based and multimodal methods currently dominate state-of-the-art CTR prediction, while feature-interaction and cross-domain approaches provide complementary strengths. These key takeaways frame open challenges and emerging research directions. Full article

The hybridization of Near-Field Communication (NFC) with Visible Light Communication (VLC) presents a promising framework for robust, secure, and efficient wireless transmission. By combining proximity-based authentication of NFC with high-speed and interference-resistant data transfer of VLC, this approach mitigates the inherent limitations of each technology, such as the restricted range of NFC and authentication challenges of VLC. The resulting hybrid system leverages NFC for secure handshaking and VLC for high-throughput communication, enabling scalable, real-time applications across diverse domains. This study examines integration strategies, technical enablers, and potential use cases, including smart street poles for secure citizen engagement, patient authentication and record access systems in healthcare, personalized retail advertising, and automated attendance tracking in education. Additionally, this paper addresses key challenges in hybridization and explores future research directions, such as the integration of Artificial Intelligence and 6G networks. Full article

Neuromarketing studies the brain function as a response to marketing stimuli. A large amount of neuromarketing research uses data from electroencephalography (EEG) recordings as a response of individuals’ brains to marketing stimuli, aiming to identify the factors that influence consumer behaviour that they cannot articulate or are reluctant to reveal. Evidence suggests that individuals’ processing styles affect their reaction to marketing stimuli. In this study, we propose and evaluate a predictive model that classifies consumers as verbalizers or visualizers based on EEG signals recorded during exposure to verbal, visual, and mixed advertisements. Participants (N = 22) were categorized into verbalizers and visualizers using the Style of Processing (SOP) scale and underwent EEG recording while viewing ads. The EEG signals were preprocessed and the five EEG frequency bands were extracted. We employed three classification models for every set of ads: SVM, Decision Tree, and kNN. While all three classifiers performed around the same, with accuracy between 86 and 93%, during cross-validation SVM proved to be the more effective model, with kNN and Decision Tree showing sensitivity to data imbalances. Additionally, we conducted independent t-tests to look for statistically significant differences between the two classes. The t-tests implicated the Theta frequency band. Therefore, these findings highlight the potential of leveraging EEG-based technology to effectively predict a consumer’s processing style for advertisements and offers practical applications in fields such as interactive content designs and user-experience personalization. Full article

In an era when AI systems curate increasingly fine-grained aspects of everyday media use, understanding algorithmic fatigue and resistance is essential for safeguarding user agency. Within the horizon of a more algorithmic and hyper-personalized advertising environment, knowing how people resist algorithmic advertising is of immediate importance. This research formulates and examines a structural resistance model for algorithmic advertising, combining psychological and cognitive predictors such as perceived ad fatigue (PAF), digital well-being (DWB), advertising literacy (ADL), and perceived relevance (PR). Based on a cross-sectional survey of 637 participants, the research employs Partial Least Squares Structural Equation Modeling (PLS-SEM) and mediation and multi-group analysis to uncover overall processes and group-specific resistance profiles. Findings show that DWB, ADL, and PR are strong positive predictors of resistance to persuasion, while PAF has no direct effect. PAF has significant indirect influences through both PR and ADL, with full mediation providing support for the cognitive filter function of resistance. DWB demonstrates partial mediation, indicating that it has influence both directly and through enhanced literacy and relevance attribution. Multi-group analysis also indicates that there are notable differences in terms of age, gender, education, social media consumption, ad skipping, and occurrence of digital burnout. Interestingly, younger users and those who have higher digital fatigue are more sensitive to cognitive mediators, whereas gender and education level play a moderating role in the effect of well-being and literacy on resistance pathways. The research provides theory-informed, scalable theory to enhance the knowledge of online resistance. Practical implications are outlined for policymakers, marketers, educators, and developers of digital platforms based on the extent to which psychological resilience and media literacy underpin user agency. In charting resistance contours, this article seeks to maintain the voice of the user in a world growing increasingly algorithmic. Full article

This paper explores the concept of ephemeral cultural landscapes through the lens of public election advertising during the 2025 Australian Federal election in the regional city of Albury, New South Wales. Framing election signage as a transient cultural landscape, the study assesses the distribution of election signage (corflutes) disseminated by political candidates against demographic and socio-economic criteria of the electorate. The paper examines how corflutes and symbolic signage reflect personal agency, spatial contestation, and community engagement within urban and suburban environments. A detailed windscreen survey was conducted across Albury over three days immediately prior to and on election day, recording 193 instances of campaign signage and mapping their spatial distribution in relation to polling booth catchments, population density, generational cohorts, and socio-economic status. The data reveal stark differences between traditional party (Greens, Labor, Liberal) strategies and that of the independent candidate whose campaign was marked by grassroots support and creative symbolism, notably the use of orange corflutes shaped like emus. The independent’s campaign relied on personal property displays, signaling civic engagement and a bottom-up assertion of political identity. While signage for major parties largely disappeared within days of the election, many of the independent’s symbolic emus persisted, blurring the temporal boundaries of the ephemeral landscape and extending its visual presence well beyond the formal campaign period. The study argues that these ephemeral landscapes, though transitory, are powerful cultural expressions of political identity, visibility, and territoriality shaping public and private spaces both materially and symbolically. Ultimately, the election signage in Albury serves as a case study for understanding how ephemeral landscapes can materially and symbolically shape public space during moments of civic expression. Full article