Search Results (167)

Person–Environment Fit Theory explains organizational match in beliefs and values influences employee satisfaction and motivation in the workplace. Automated systems [e.g., artificial intelligence (AI)] and advanced technology have been integrated into business operations to compete in the digital era. However, how employee technology orientation and individual differences influence workplace preferences is underexplored. This study advances how organizations can strategically attract talent aligned with their technological infrastructure and work design. Parallel mediation path analysis was conducted on a surveyed U.S. convenience sample (SPSS PROCESS Model 4; N = 912). Technology adaptability was positively associated with preference for a job role highlighting working with automated systems relative to emphasizing supportive coworkers. Technology adaptability related to a greater need to belong and job satisfaction (as parallel mediators) and thereby less preference for a role working with automated systems (i.e., preference for a supportive coworkers job ad). The findings reveal that job ads promoting automated systems do not unilaterally attract tech-adaptive employees. Belonging needs and job satisfaction can function as psychological factors that redirect tech-savvy workers towards socially enriched roles. Proactively advertising social belonging and job satisfaction cues alongside advanced technology use could more comprehensively appeal to tech-adaptive job seekers. This can signal a better value congruence between an organization and these job seekers. Full article

This study aims to investigate the antecedents to ‘green personal care product’ (GPCP) awareness and purchase intention. Based on the theory of planned behavior (TPB) and sustainable consumption (SC), a conceptual model is developed and tested. Data collected from 455 Gen Z consumers from India were analyzed using structural equation modeling. The results confirmed a positive association of subjective norms, perceived behavioral control (PBC), and social media influence with attitude towards GPCPs. The findings indicate that attitude and health consciousness are positively associated with awareness, which in turn leads to purchase intention. Further, the findings support the positive role of environmental consciousness in driving consumers to engage in pro-environmental behavior. This study underscores the importance of health consciousness and environmental consciousness in driving individuals to engage in green purchase behavior. The findings from this study provide valuable insights into an individual’s attitude towards GPCPs and awareness of GPCPs, especially in the context of environmental sustainability. This study recommends actionable strategies to policymakers and marketers to advertise the benefits of consuming healthy products. The implications for theory and practice are discussed. Full article

Artificial intelligence (AI) has transformed programmatic advertising (PA) by enabling automated, data-driven personalization within digital marketing ecosystems. However, this transformation has also intensified concerns regarding privacy, consumer trust, and the long-term sustainability of these ecosystems. In this study, sustainability is conceptualized as the capacity of digital marketing systems to maintain long-term functionality without eroding consumer trust or amplifying privacy-related tensions that may undermine system stability over time. This study addresses the research question: How does AI-driven PA affect the sustainability of digital marketing ecosystems through the personalization–privacy paradox? A systematic literature review, conducted in line with PRISMA guidelines, synthesizes prior research across four interrelated clusters: AI-driven personalization, privacy concerns, consumer trust, and consumer attitudes and behavior. The findings indicate that consumer responses to AI-driven personalization are not linear and are shaped by factors such as transparency, perceived control, and the perceived legitimacy of data practices. The review further shows that the paradox of personalization and privacy operates as a persistent condition within AI-driven advertising ecosystems, where the benefits of personalization coexist with ongoing privacy-related tensions. This study contributes to the literature by synthesizing fragmented research on AI-driven PA and by highlighting the central role of trust and transparency in understanding how personalization and privacy tensions shape consumer responses in digital marketing ecosystems. Full article

Personalized advertising is now routine in digital commerce and new media, but relevance does not always translate into acceptance. When consumers read tailored messages as signs of tracking or inference rather than as useful assistance, they may feel exposed and avoid the advertising. Drawing on advertising avoidance theory, privacy calculus theory, and control agency theory, this study examines a privacy-risk account of personalized advertising avoidance. Survey data from 502 consumers and structural equation modeling show that privacy concern, privacy fatigue, and prior negative experiences are directly associated with advertising avoidance. Privacy concern, prior negative experiences, and perceived personalization are also positively associated with perceived risk; perceived industry self-regulation is negatively but non-significantly associated with perceived risk; and perceived risk is positively associated with advertising avoidance. These results position perceived risk as a close downstream appraisal through which consumers make sense of data-driven targeting. The study contributes to digital commerce and interactive marketing research by showing that consumers evaluate personalized advertising not only as a cue of relevance, but also as a possible source of privacy vulnerability. It also offers implications for transparent targeting, visible user control, platform governance, and less intrusive personalization. Full article

The increasing complexity of artificial intelligence systems and algorithms can negatively affect individual attitudes and prompt them to adopt coping behaviors. Based on the technology threat avoidance theory, this study explores the mechanisms through which the supervisibility of recommendation algorithm throughput influences the coping behaviors of targeted advertising audiences using scenario-based online experiments conducted between August 2022 and January 2023. In particular, we examine the mediating effect of perceived threat and the moderating effects of privacy concerns and self-efficacy. The results reveal that perceived threat arising from the characteristics of recommendation algorithm throughput determines the possible coping behaviors of targeted advertising audiences. Moreover, perceived threat mediates the impact of the supervisibility of algorithm throughput on audiences’ coping behaviors. Furthermore, users’ privacy concerns and self-efficacy positively moderate the influence of the supervisibility of algorithm throughput on perceived threat and the relationship between perceived threat and audiences’ coping behaviors, respectively. The results help to explain the black box of the relationship between the characteristics of recommendation algorithms and audience responses from an algorithm throughput perspective. The findings offer implications for improving the effectiveness of personalized recommendations and provide a new perspective for studying the transparency of algorithmic recommendations in consumer behavior. Full article

This paper proposes a model to predict users’ personal interests from their gaze. Current image processing technologies enable us to identify each user’s gaze and pupil diameter. However, they cannot identify user interests. The model uses deep learning technologies. It consists of mechanisms to handle the two kinds of gaze: one specific to individuals and the other common to many people. Its training method utilizes the human property that user interests in objects in vision affect their gaze. Since it is known that pupil diameters increase when users view what they are interested in, we can train the model to predict users’ personal interests using the pupil diameters as labels. Notably, the paper not only proposes a model structure to predict personal interests but also examines the process of the modification of parameters of the model to reveal requisites to predict personal interests from gaze. The method enables us to provide personalized information to each user, such as recommendations in VoD services, advertisements during e-shopping, plate annotations in restaurant menus, and so on. Full article

This study examined how distinct dimensions of grandiose narcissism shape responses to self-relevant video advertising framed as either recognition/validation or status challenge. Drawing on the Narcissistic Admiration and Rivalry Concept, we tested a dual-path process model in which two proximal mechanisms–perceived recognition and autonomy-related resistance (operationalized as perceived freedom threat and state reactance)–are associated with advertising-related outcomes. Community adults (N = 598) were randomly assigned to view one of two video advertisements and subsequently reported perceived recognition, resistance, and consumer responses. Recognition framing increased perceived recognition but did not directly influence consumer outcomes. Process analyses revealed distinct personality-linked patterns that were consistent with the proposed dual-path model. Narcissistic admiration was associated with more favorable attitudes toward the advertisement and brand, as well as stronger purchase intentions, indirectly through higher perceived recognition. In contrast, narcissistic rivalry showed contrasting indirect associations, with positive indirect associations through recognition alongside negative indirect associations through resistance. Moderated mediation by message framing was not supported. Overall, the findings are consistent with the view that self-relevant advertising can simultaneously activate affirmation-related and autonomy-protective processes that may partially offset one another at the aggregate level. Importantly, consumer responses appear to depend on whether the persuasive encounter is construed as authentic recognition or as an autonomy threat–an interpretive dynamic that is especially pronounced among individuals high in narcissistic rivalry. Full article

As social networks continue to expand, smart advertising increasingly depends on machine learning to deliver personalized and effective advertisements. Federated Learning (FL) is a distributed learning paradigm that supports privacy-preserving advertising by training models locally while avoiding direct sharing of raw user data. However, ensuring the correctness, reliability, and operational robustness of FL-driven smart advertising systems remains a significant challenge, particularly in distributed and user-facing environments. In this study, we investigate the use of model checking as a formal verification technique for validating key properties of an FL-based smart advertising workflow in social networks. We combine a structured finite-state modeling approach with Linear Temporal Logic (LTL) specifications and model-checking tools to assess correctness, availability, and baseline privacy requirements. Using controlled simulation-based configurations, we show that, for a setup with 100 users and 20 edge servers, the system delivers advertisements to all users and the global model successfully processes 200 out of 200 requests. We further analyze verification overhead through detection-time measurements, observing an increase in average detection time from 10.05 s to 11.98 s as the number of users rises from 20 to 100. These results indicate that the proposed framework can provide practical assurance for FL-enabled smart advertising workflows, support more reliable deployment in distributed intelligent systems, and improve trustworthiness in real advertising applications. Full article

This study investigates how innovative social media marketing activities influence consumer inspiration, food pleasure, and behavioral intentions in the context of hedonic food consumption and digital marketing innovation. Data collected from 425 consumers who had tried Dubai chocolate products in Türkiye were analyzed using the partial least squares structural equation modeling (PLS-SEM) method with SmartPLS 4 software. The results indicate that personalization, trendiness, and advertisement dimensions significantly enhance consumer inspiration, whereas entertainment and interaction dimensions show no significant effects. Consumer inspiration positively influences repurchase intention, recommendation intention, willingness to pay more, and food pleasure. Furthermore, food pleasure exerts a significant positive effect on recommendations and willingness to pay more but not on repurchase intention. Mediation analysis revealed that food pleasure partially mediates the relationships between consumer inspiration, recommendation intention, and willingness to pay more, whereas no mediating effect was found for repurchase intention. These findings contribute to innovation and knowledge literature by demonstrating how digital marketing activities foster emotional engagement, enhance consumer experiences, and promote sustainable behavioral intentions in the hedonic food sector. Full article

Background: Migration and place of birth are increasingly recognised as social determinants of health among sexual minority populations. Among men who have sex with men (MSM), being born outside the country of residence may shape access to healthcare, community resources, and social capital networks. In Japan, however, little is known about how being born outside Japan is associated with social capital, health behaviours, and mental health among MSM. Methods: Data were drawn from a large cross-sectional online survey conducted in 2025 of 8150 MSM living in Japan, recruited via community-based in-person outreach and targeted geo-social networking application advertisements. Multivariable logistic regression analyses examined associations between country of birth and social, behavioural, and health-related outcomes. Results: Foreign-born MSM were younger and more concentrated in the Greater Tokyo metropolitan region. Being born outside Japan was associated with higher odds of HIV testing across all timeframes and higher levels of both gay and heterosexual social capital. Foreign-born MSM were also more likely to have disclosed their sexuality to friends and family. However, they were less likely to be aware of LGBT or HIV prevention organisations, despite higher participation once engaged. No differences were observed in suicidal ideation or unprotected anal intercourse with casual partners, although foreign-born MSM were more likely to report unmet need for mental health care. Conclusions: Foreign-born MSM in Japan demonstrate strong engagement in HIV prevention and higher social capital, alongside persistent barriers to community awareness and mental health service access. These findings highlight the importance of addressing structural and informational barriers and supporting community-based organisations to improve equitable health and wellbeing outcomes among MSM in Japan. Full article

Background/Objectives: Sarcopenia is a prevalent age-related condition strongly influenced by protein intake. This study assessed the quality, evidence base, and practical utility of YouTube videos on nutrition and sarcopenia. Methods: A structured YouTube search (We searched YouTube in April 2024 using the term ‘sarcopenia diet) identified 41 eligible videos. Three trained raters independently assessed each video using the Global Quality Scale (GQS), DISCERN, JAMA benchmarks, subjective impression ratings, technical quality indicators, and additional binary variables. Interrater reliability was examined using intraclass correlation coefficients (ICCs) and Fleiss’ Kappa. Reviewer comments were analyzed qualitatively using Mayring’s structured content analysis. Results: Overall video quality varied substantially. ICCs indicated moderate to high agreement for DISCERN (0.698), JAMA (0.702), and subjective impression ratings (0.779), but minimal agreement for sound and video quality. Fleiss’ Kappa showed moderate agreement for scientific soundness (κ = 0.522) and advertisement content (κ = 0.385), while agreement was low for health-related risks and dietary recommendations. Qualitative analysis identified frequent concerns regarding insufficient scientific evidence, vague or impractical protein guidance, limited relevance for older adults, and personal bias; positive features were less common. Conclusions: YouTube nutrition content on sarcopenia shows substantial variability and frequent deficits in evidence-based quality and practical relevance. While some videos provide useful introductory information, many are of limited value for lay audiences. Strengthening digital health literacy and expanding expert-driven, evidence-based online resources are essential to support informed decision-making and preventive strategies. 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

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