Search Results (982)

Digital Content Marketing (DCM) has become a key tool for brands to influence Brand Attitude on social commerce platforms, but its impact on consumer perception remains poorly understood in the emerging market context of the hospitality industry. Based on the theory of Uses and Gratifications (U&G), this study aims to explore the direct and indirect impact of DCM on Brand Attitude, with Consumer Trust and Digital Engagement playing mediating roles among social commerce consumers in Iraq. A quantitative cross-sectional design was employed, with 386 respondents obtained using the social media distribution of a self-administered online questionnaire. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used in Smart PLS 4.0 to analyse the proposed model. All eight hypotheses were supported. Consumer Trust (β = 0.487) and Digital Engagement (β = 0.361) were both positively influenced by DCM, and both mediators had significant positive effects on Brand Attitude (β = 0.394 and β = 0.286, respectively). The total indirect effect of DCM on Brand Attitude (β = 0.351) was significantly larger than the direct effect (β = 0.187), supporting the strength of mediated effects. Serial mediation through Trust and then Engagement was also confirmed. These findings extend the application of U&G theory within an Iraqi social commerce context and offer evidence-based implications for hospitality brands designing content strategies on digital platforms. The findings are only applicable within the context of Iraq and the non-probability sample studied. Full article

Online furniture commerce has expanded, yet sustaining continuance purchase intention (CPI) remains challenging because furniture is a high-involvement product that consumers prefer to inspect physically. Drawing on the Stimulus–Organism–Response (SOR) framework, this study examines how social media marketing activity, product quality, and customer experience are associated with CPI through transactional appraisals (perceived value and customer satisfaction) and relational appraisals (customer trust and customer loyalty). An exploratory sequential mixed-methods design combined a three-round e-Delphi study with 19 experts and a survey of 1351 Thai consumers who had purchased furniture online. Data were analyzed using covariance-based structural equation modeling, bootstrapped indirect-effect analysis, and fuzzy-set qualitative comparative analysis (fsQCA). Customer loyalty and customer trust showed the strongest positive associations with CPI. Customer satisfaction showed a negative direct but significant positive indirect association through customer loyalty. Constraining the SAT → CPI path to zero significantly worsened model fit; however, the negative coefficient did not support the hypothesized positive association and should be interpreted cautiously as a model-conditional association. The fsQCA identified multiple sufficient configurations associated with high CPI, illustrating equifinality. The findings extend SOR to high-involvement commerce by integrating transactional and relational appraisals and complementary symmetric and configurational evidence. Full article

Short-form video (SFV) marketing generates structured consumer ratings and unstructured feedback that can be analyzed for e-commerce decision support. This study examines associations between five content- and creator-related perceptions, Creating Shared Value (CSV), and purchase intention among 409 respondents in Bulgaria. From a business intelligence (BI) perspective, this study combines descriptive profiling, clustering, sentiment analysis, formative partial least squares (PLS) path modeling, group-difference testing, and machine-learning prediction to produce descriptive profiles, explanatory associations, consumer segments, text-analytic findings, and predictive results. CSV is specified as a formative composite of eight noninterchangeable product/economic, community/social, and relational indicators associated with short-form video creators. Clarity (β = 0.260), willingness to use (β = 0.226), similarity (β = 0.205), likability (β = 0.177), and empathy (β = 0.130) are positively associated with CSV; CSV is positively associated with purchase intention (β = 0.567). The model explains 69.1% of CSV variance and 32.2% of purchase-intention variance. Six formative weights are significant, all absolute loadings exceed 0.72, and indicator VIFs remain below 5. The group-difference hypothesis is partially supported by 18 omnibus ANOVAs with Holm correction for multiple testing. Significant differences in both CSV and purchase-intention means are found across age groups and categories based on the number of influencers followed. Significant differences in purchase-intention means are also found across social-media use-frequency and daily SFV-viewing-time categories. Clustering, sentiment-analysis, and machine-learning results are interpreted as complementary BI outputs rather than evidence of causal effects or validated campaign effectiveness. Full article

Understanding how visual content shapes consumers’ persuasion processes in social media environments has become an important research issue. Drawing primarily on the Persuasion Knowledge Model (PKM) and parasocial relationship (PSR) theory, while incorporating insights from self-reference theory and the Elaboration Likelihood Model (ELM), this study investigates how three Instagram image types—brand selfies, consumer selfies, and packshots—influence purchase intention through persuasion knowledge and examines the moderating role of PSR. A between-subjects experiment was conducted with 460 participants recruited through Amazon Mechanical Turk. The results revealed significant differences in persuasion knowledge and purchase intention across image types. Brand selfies were associated with lower levels of persuasion knowledge and higher purchase intention, whereas consumer selfies were associated with higher persuasion knowledge and lower purchase intention, with packshots showing intermediate effects. Persuasion knowledge mediated the relationship between image type and purchase intention, and PSR attenuated the negative effect of persuasion knowledge on purchase intention. These findings suggest that visual image composition itself can function as a persuasive cue and highlight the important role of relational context in shaping consumers’ responses to persuasive intent in influencer marketing. Full article

Identifying the spatio-temporal evolutionary patterns and driving mechanisms of rural tourism network attention is essential for predicting the development trends of the rural tourism industry and delivering refined industrial governance. Taking 356 prefecture-level cities in China from 2015 to 2024 as basic research units, this paper constructs a comprehensive evaluation system for rural tourism network attention based on multi-source data. Furthermore, its spatio-temporal evolution characteristics and internal influencing factors are systematically investigated by means of spatial autocorrelation analysis and geographically weighted regression. The results indicate that the overall level of rural tourism network attention in China shows an obvious fluctuating growth trend, which can be divided into three successive stages, namely steady growth (from 0.8530 in 2015 to 1.2028 in 2019), explosive growth (from 1.9563 in 2020 to 3.7471 in 2021) and high-level fluctuation (maintained in the high range of 2.4–3.4). In addition, with the continuous iteration of internet communication media, the guiding influence of traditional search platforms has gradually weakened, while emerging social media and short-video platforms have become the core carriers of online tourism traffic. Correspondingly, media innovation persistently reshapes the spatial distribution pattern of rural tourism network attention. In terms of spatial characteristics, rural tourism network attention has undergone a significant transformation from geographical gradient polarization to overall regional equilibrium. Specifically, from 2015 to 2024, the overall Moran’s I index remained positive, with values ranging from 0.0116 to 0.1358, indicating an overall trend of gradual decline. High-attention areas are predominantly concentrated in economically developed urban agglomerations, whereas remote and economically underdeveloped regions exhibit contiguous low-value aggregation characteristics, which reveals a remarkable trend of balanced development nationwide. In view of driving mechanisms, highway network density, tourism income, rural tourism resource and enrollment of university students are identified as the core driving factors dominating the spatio-temporal evolution of rural tourism network attention. Moreover, the intensity of the influence of each factor presents distinct spatial heterogeneity. This study further reveals that the spatial heterogeneity of rural tourism network attention calculated using multi-source fused data shows a remarkable convergent characteristic, which can reflect the actual distribution of the rural tourism market more objectively and accurately. Meanwhile, rural tourism network attention is typically characterized by scale-dependent with the spatial distribution at the macro-scale being more balanced than that at the meso- and micro-scales. Full article

The tourism industry is witnessing an increase in innovative AI-generated content (AIGC). AI-generated images are a key visual form of AIGC, and this study focuses on a specific subset of them: AI-generated images that realistically depict destination scenery, which we define as AI-generated photos (AIGP). Based on construal level theory, this study explored the visual cues of AIGP. Two empirical studies were conducted to examine the influence of scene presentation within AIGP on tourists’ travel intention. The findings revealed that panoramic series in AIGP, as opposed to close-up series, significantly enhanced travel intention. The results further suggested an indirect association through destination images and a moderating role of AIGP source. Specifically, the effects of scene presentation were found to be diminished when an AIGP was sourced from user-generated content. This study makes several notable contributions. First, it expands the application of AIGP within the tourism sector. Second, it enriches the literature on visual cues and destination images by delineating the process through which AIGP scene presentation influences travel intention. Third, the research clarifies the moderating role of the AIGP source, expanding the boundary of AIGP effects. These insights add depth to the dual fields of social media and destination marketing, highlighting the contextual variability in the efficacy of AIGC-driven marketing strategies. Full article

Although social media influencers (SMIs) play a pivotal role in destination marketing, the underlying factors through which they shape tourists’ interest and travel intentions remain underexplored. Based on the modified attention interest desire action (AIDA) model, this study addresses this gap by investigating how the characteristics of SMIs influence travel intentions through destination perceived trust and destination perceived attractiveness. A total of 416 valid questionnaires were analyzed using partial least squares structural equation modeling (PLS-SEM) and an artificial neural network (ANN). The results revealed that SMIs’ similarity, expertise, physical attractiveness, social attractiveness, sincerity, and visibility enhanced tourists’ destination perceived trust and destination perceived attractiveness, thereby influencing travel intentions. Additionally, the ANN analysis complements the PLS-SEM results by comparing predictive performance and identifying the relative importance of SMI characteristics. These findings provide several suggestions for destination management organizations to improve influencer marketing. Full article

Online shopping is expanding rapidly in emerging European markets, yet many consumers still combine digital channels with offline verification. This study asks whether the resulting heterogeneity among young consumers is genuinely complex or reducible to a simpler structure. Drawing on technology-acceptance and perceived-risk perspectives, it analyses survey data from 457 Romanian university students, of whom 269 were routed to eight attitudinal items on online smartphone purchasing. The questionnaire was distributed to students at two Romanian universities through institutional email and faculty social media accounts, yielding a non-probability convenience sample; no probability-based selection procedure was applied. After examining the dimensionality and reliability of the items through exploratory factor analysis, K-means clustering was used to segment respondents, and the solution was checked against hierarchical clustering. Two segments emerged: cautious, lower-online-orientation consumers, who report lower perceived convenience, speed, price advantage, and overall use of online purchasing, and online-oriented adopters, who view digital channels as efficient and convenient. Rather than many fine-grained segments, the data point to a dominant behavioural axis, namely overall orientation towards online purchasing. The findings provide evidence consistent with a parsimonious two-segment account of consumer heterogeneity in a rapidly developing digital market, and they suggest differentiated omnichannel strategies: confidence-building and risk-reduction measures for cautious consumers and experience optimisation for digital adopters. Full article

This study develops a computational framework for quantifying how technological disruptions are associated with shifts in media–public discourse alignment. Using the public release of ChatGPT (30 November 2022) as a temporal breakpoint, we examine whether the broad public availability of generative AI was associated with structural changes in the relationship between media agendas and online public discourse in South Korea. The framework combines PPMI-weighted semantic network construction with QAP correlation, MRQAP regression and Cohen’s q effect-size analysis, applied to 181,081 Korean-language texts encompassing media agendas (national newspapers, economic dailies, regional newspapers, and broadcast news) and public agendas (online communities) over six years (2019–2025). Results reveal that national newspapers lost their dominant agenda-setting position, with public alignment declining sharply (r: 0.678 → 0.430, Cohen’s q = 0.369, large effect), while economic papers rose from lowest to second-highest alignment (r: 0.352 → 0.567) by addressing market and industry dimensions that direct AI experience could not supply. Broadcasting emerged as the dominant structural anchor of public discourse (unique coefficient β: 0.263 → 0.569; its removal alone lowers model fit from R² = 0.517 to 0.347), while national newspapers’ unique contribution reversed in sign. Collective media explanatory power itself remained essentially stable (R² = 0.537 → 0.517), indicating a structural reconfiguration of which media align with public discourse rather than a wholesale weakening of media–public alignment—consistent with agenda-melding, in which publics integrate media coverage with firsthand technological experience. A residual analysis further shows that the variance media agendas leave unexplained is not noise but a structured, public-specific layer of discourse organized around the hands-on use of generative-AI tools (e.g., ChatGPT, image generation)—direct evidence of a bounded public autonomy in which public discourse is distinct from, and not reducible to, media agendas. These findings demonstrate the framework’s utility for detecting how publicly accessible AI adoption—exemplified by ChatGPT—is associated with shifts in media–public structural dynamics within discourse ecosystems and carry implications for computational social science, technology communication, and applied network analysis. Full article

This study applies Job Demands-Resources (JD-R) theory to music student psychology, with particular attention to the differentiated impacts of artificial intelligence across music student subgroups. Background: Music education requires intensive cognitive, motor, emotional, and social training. Methods: Employing a narrative literature review and conceptual framework approach, this paper draws on JD-R theory, technostress research, and cross-cultural scholarship to identify demands and resources in music education and examine their relationships with burnout and engagement. Results: Seven demands: technical, physical, temporal, performance, social–emotional, technostress, cultural identity. Resources: teacher support, peer collaboration, instruments, AI tools, curricula, and intrinsic motivation. Critically, AI-related demands and resources differ substantially across subgroups: composers face direct demand shocks (job displacement) and resource opportunities (creative augmentation) that vary by career stage, while performers experience more indirect impacts through recording market disruption and emerging self-media opportunities. Singers face a unique paradox whereby AI voice cloning creates short-term recording opportunities while potentially enabling long-term vocal replacement. Cultural identity demands arise from heritage-Western tensions in East Asia. Conclusions: This JD-R framework provides a nuanced lens for understanding music students’ well-being that accounts for subgroup-specific vulnerabilities and opportunities. 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

With the rapid growth of social media use in Nepal and their engagement with customers, understanding how these platforms support customer loyalty becomes essential for retail businesses. This study investigates the role of social media marketing in shaping brand loyalty among retail customers in Nepal through an integrated research design that combines systematic bibliometric and survey data analysis. The bibliometric findings suggest a clear progression in the literature, moving from a foundational emphasis on relationship marketing and loyalty theory toward a more integrated framework that incorporates social media engagement and, ultimately, measurable business outcomes. The quantitative results based on survey data conducted with 100 Nepalese consumers active on Facebook, Instagram, and TikTok show that effective social media strategies and the ability to address implementation difficulties significantly enhance brand loyalty. In contrast, the direct influence of customer trust is limited. The findings highlight the importance of tailored and interactive content, supported by appropriate digital practices, particularly in regions with developing infrastructure. This study offers practical recommendations to improve digital engagement, encourage retailers to collaborate with local influencers, respond to customer feedback, maintain transparency in messaging, and enhance digital capabilities to implement effective social media strategies. It also underscores the value of producing culturally relevant content that aligns with local interests and behaviours. Full article

Consumer resistance remains a major barrier to the effectiveness of AI-enabled social media marketing despite advances in content personalisation, influencer marketing, and intelligent recommendation systems. This study investigates how content personalisation, influencer trust, and platform interactivity influence consumer resistance, user engagement, and purchase intention by proposing a behaviourally informed Deep Reinforcement Learning (DRL) framework that integrates empirical behavioural modelling with adaptive optimisation. Survey data were collected from 619 higher education students in Saudi Arabia and analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM), Multi-Group Analysis (MGA), and a Deep Q-Network (DQN)-based optimisation framework. The results show that content personalisation, influencer trust, and platform interactivity significantly increase user engagement while reducing consumer resistance. User engagement positively influences purchase intention, whereas consumer resistance negatively affects purchasing behaviour. Multi-Group Analysis revealed that Generation Z responded more strongly to personalisation and platform interactivity, whereas Generation Y showed greater responsiveness to influencer trust. The proposed behaviourally informed DQN framework incorporated latent behavioural constructs and statistically validated structural relationships into the reinforcement learning environment to generate adaptive marketing policies. Compared with conventional static and rule-based strategies, the proposed framework achieved approximately 36% higher optimisation performance across repeated behavioural simulations. The study contributes by positioning consumer resistance as the central behavioural construct, introducing an integrated behavioural–computational framework that embeds empirical behavioural relationships into the DRL state representation, reward mechanism, and policy-learning process, and providing practical guidance for developing transparent, trust-sensitive, and adaptive social media marketing strategies that enhance user engagement, reduce consumer resistance, and improve purchase intention in digital commerce environments. Full article

This article examines how media discourse in South Korea constructed Filipino caregivers as a legitimate yet conditional solution to the country’s care crisis. Moving beyond approaches that treat media coverage as a neutral reflection of policy debate, it analyzes news discourse as part of the broader politics of immigrant integration, showing how narratives of care, foreignness, affordability, and utility helped define the terms under which migrant caregivers could be welcomed. Drawing on a qualitative discourse analysis of English- and Korean-language newspaper coverage, the article traces how Filipino caregivers were alternately represented as desirable care providers, disciplined service workers, classed household resources, and ultimately precarious migrants whose social legitimacy remained revocable. The findings show that media and policy languages worked together to normalize a form of conditional belonging in which migrant caregivers were incorporated as useful labor but not recognized as fully deserving social subjects. Across the coverage, affordability emerged as a key discursive filter through which rights, value, and inclusion were negotiated, while competing narratives pushed the care regime toward more selective, informalized, and stratified forms. The article argues that secondary citizenship in this case was not only a legal or labor-market condition but also a discursive accomplishment produced through the intertwined narration of care needs, migrant labor, and unequal integration. Full article

This paper examines how technological innovation in next-generation probiotics shapes consumer trust through the lens of perceived safety. Rapid advances—spanning conventional cultures (Tier 1), postbiotics (Tier 2), and engineered microbial strains (Tier 3)—are transforming functional food architectures, yet consumer trust remains a critical determinant of their successful development, application, and adoption. Drawing on interdisciplinary evidence from food microbiology, consumer perception research, and regulatory analysis, this study examines and evaluates how these distinct technological innovation tiers alter public risk dynamics. Findings indicate that processing methodologies, media framing, and the spread of misinformation significantly influence public perceptions of microbial legitimacy, while the “Animation Gap” and “Contamination Anxiety” introduce qualitatively new cognitive friction points. Furthermore, regulatory inconsistencies across jurisdictions and variability in health claim substantiation further complicate market uptake. Streamlined case-based evidence highlights physical stability, sensory performance, and explicit value metrics that determine whether technological innovations are trusted or rejected by consumers. The paper argues that bridging the gap between scientific innovation and public acceptance requires proactive communication strategies, ethical marketing practices, and participatory engagement strategies grounded in empirical integrity. In addition, digital ecosystems, including social media and algorithm-driven content exposure, play an increasingly influential role in amplifying technology neophobia, underscoring the need for robust, targeted, evidence-based public communication in the evolving landscape of probiotic and functional food innovation. Full article