Search Results (61)

This study investigates how emotionally structured online interaction networks shape consumer perception of new energy vehicle (NEV) technology. Drawing on discussion forum data from two leading NEV brands, Brand_T and Brand_B, we focus on how users respond to brand technological narratives and how these responses translate into distinct patterns of peer-to-peer interaction. Using a behavioral network analysis framework, we integrate sentiment analysis, topic modeling, and Exponential Random Graph Modeling (ERGM) to uncover the psychological and structural mechanisms underlying consumer engagement. Three main findings emerge. First, users display brand-specific emotional-cognitive profiles: Brand_T communities show broader technological engagement but more heterogeneous emotional responses, whereas Brand_B communities exhibit more emotionally aligned discussions. Second, emotional homophily is a robust driver of interaction ties, particularly in Brand_B forums, where positive sentiment clusters into dense and supportive discussion subnetworks. Third, perceived technological benefits, rather than risk sensitivity, are consistently associated with higher interaction intensity, underscoring the motivational salience of anticipated gains over cautionary concerns in shaping engagement behavior. The study contributes to behavioral science and transportation behavior research by linking consumer sentiment, cognition, and social interaction dynamics in digital environments, offering an integrated theoretical account that bridges the Elaboration Likelihood Model, social identity processes, and behavioral network formation. This advances the understanding of technology perception from static individual evaluations to dynamic, group-structured outcomes. It highlights how emotionally patterned interaction networks can reinforce or recalibrate technology-related perceptions, offering practical implications for NEV manufacturers and policymakers seeking to design psychologically informed communication strategies that support sustainable technology adoption. Full article

As a representative case that embodies both the attributes of a Buddhist sacred site and those of a UNESCO World Heritage site, Mount Wutai provides a distinctive research setting for examining behavioral mechanisms in temple tourism. This study aims to construct an integrated model to systematically test the effects of enjoyment, memorability, attitude, subjective norm, and perceived behavioral control (PBC) on revisit intention (RI), while incorporating social media exposure as a moderating variable. Based on data collected through a two-wave on-site survey, this study analyzed 617 tourists in Mount Wutai and employed structural equation model to examine the relationships among the variables. The results indicate that all five psychological antecedents exert significant positive effects on revisit intention, among them, PBC demonstrating the most substantial impact. Further analysis reveals that social media exposure significantly moderates the relationships among enjoyment, memorability, attitude, subjective norm, and revisit intention, most notably in the “memorability–RI” relationship, whereas its moderating effect on the “PBC–RI” relationship is not significant. These findings not only enrich the theoretical framework by integrating emotional attribution, behavioral cognition, and digital media engagement but also provide practical implications for sacred tourism destinations, enabling them to enhance visitor loyalty through digital communication and experience optimization. Full article

Drawing upon the Elaboration Likelihood Model (ELM) and Trust Transfer Theory, this study investigates how AI digital avatars influence consumer trust and purchase intention. Using survey data collected from 378 valid respondents, the proposed model was tested with Partial Least Squares Structural Equation Modeling (PLS-SEM). The results reveal that brand awareness and perceived quality significantly affect purchase intention through product trust, while social endorsement cues, anthropomorphism, and interaction quality indirectly influence purchase intention through AI Avatar trust. Furthermore, both AI Avatar trust and product trust have significant direct effects on purchase intention, highlighting the critical role of trust in AI-driven persuasion processes. This study validates the dual-route persuasion mechanism of the ELM in AI marketing contexts and extends the application of trust theory to human–AI interactions, offering valuable insights for future research on AI brand endorsement and consumer psychology. Full article

In the era of Artificial Intelligence-Generated Content (AIGC) transforming advertising production, existing research lacks comprehensive exploration of how AIGC advertisements shape consumer responses. This study integrates attention allocation theory and the Elaboration Likelihood Model (ELM) to investigate dual cognitive processing mechanisms of relevant and divergent AI advertisements via eye-tracking experiments and questionnaires. Findings reveal that relevant AI advertisements enhance perceived usefulness (PU) through product area attention allocation, improving purchase intention; Divergent AI advertisements boost perceived entertainment (PE) via non-product creative cues, positively influencing ad attitudes; and product involvement (PI) moderates these paths as high PI strengthens PU’s role in central processing, while low PI amplifies PE’s effect in peripheral processing. By constructing a dual-path cognitive model, this research bridges gaps in understanding AI advertising’s implicit attention mechanisms and explicit perceptual outcomes. The findings provide theoretical guidance for advertisers to optimize AIGC strategies, balancing technological utility and creative appeal to achieve precise attention guidance and enhance smart marketing effectiveness. Full article

With the rapid advancement of social media and AI technologies, influencer marketing has evolved significantly. Virtual influencers have emerged as alternatives to traditional human influencers. Grounded in the Elaboration Likelihood Model (ELM), this study examines how virtual influencers’ source credibility dimensions (expertise, attractiveness, and trustworthiness) affect consumer attitudes and purchase intentions. Using the case of virtual influencer Imma, this study collected 344 valid online survey responses. The empirical results show that, along the central route, perceived product value has a significant and positive effect on purchase intention. Along the peripheral route, the trustworthiness, attractiveness, and expertise of the virtual influencer all exert significant positive effects on purchase intention. However, product involvement moderates these effects differently: for high-involvement consumers, the effects of trustworthiness and attractiveness on purchase intention are significantly strengthened, while the moderating effects on expertise and perceived value remain non-significant. This study contributes to the emerging literature on virtual influencer marketing by demonstrating how source credibility dimensions and perceived value interact with product involvement to shape consumer responses. Additionally, virtual influencers offer sustainability benefits by minimizing carbon emissions from travel and physical production inherent in traditional influencer campaigns. The findings offer practical implications for marketers: virtual influencers can effectively enhance brand exposure, but their persuasive impact varies by product involvement requiring tailored content strategies for high- versus low-involvement products. Furthermore, future research could extend this work by examining the effects of different product categories and cultural contexts on the effectiveness of virtual influencer marketing. Full article

Mathematical creativity remains a complex and often contested construct, with its definition and measurement still subject to debate. While the four classic indicators—elaboration, flexibility, fluency, and originality have provided a foundation for research, they alone cannot resolve the field’s conceptual “fuzziness.” This paper examines mathematical creativity through three intersecting lenses: intelligence, intellect, and education. Intelligence is viewed as cognitive capacity, providing the mental resources for abstraction, problem transformation, and reasoning. Education offers the conceptual tools, heuristics, and domain knowledge necessary for productive problem solving. Intellect—closely associated with the personality trait of openness—supports curiosity, tolerance for ambiguity, and exploration. We argue that the interaction among these three factors influences the likelihood of producing mathematically creative processes and products. Drawing on contemporary research, we propose a model that integrates cognitive ability, educational attainment, and personality characteristics to better predict creative potential. This model highlights how educational environments can either foster or inhibit creativity and suggests that creativity is not a fixed trait but a dynamic outcome shaped by opportunity, knowledge, and affect. We conclude by discussing implications for assessment, curriculum design, and future research, encouraging a more nuanced approach to cultivating mathematical creativity across diverse educational and cultural contexts. Full article

With rapid digital transformation, online information and reviews have become more consequential, which may lead to a public opinion crisis. How to predict the persuasion effect is an important research problem in the design of a crisis communication strategy. The method for solving this problem is to propose a predictive framework for digital persuasion, grounded in the elaboration likelihood model. Within this framework, a database is constructed, and a machine learning algorithm integrating Bayesian networks and decision trees, BNTree (Bayesian Network and Tree), is proposed. The results demonstrate that BNTree can predict persuasion effects more accurately. In addition, the prediction of BNTree also reflects the major cognitive route of netizens and the critical influence factors for persuasion effects. These findings imply that integrating psychological theory into algorithm design can enhance predictive performance and interpretability, providing practical support for crisis communication in the digital era. Full article

This study examines whether sexual appeals, specifically nudity (body coverage) and model gender, shape consumer responses to advertising. Guided by the Elaboration Likelihood Model, we test whether these factors operate as peripheral cues when argument strength is minimal (fragrance advertising). In a 3 (model gender: male, female, both) × 2 (clothing: swimwear vs. outdoor wear) between-subjects experiment (n = 195), participants viewed one of six real advertisements from the same luxury fragrance brand featuring the same professional models. Nudity level did not affect attitudes toward the ad, brand attitude, or purchase intention. By contrast, ads depicting both a male and a female model produced small but reliable omnibus improvements in brand attitude and purchase intention relative to single-model ads; attitudes toward the ad were unaffected. Mediation tests indicated that these differences were not explained by attitude toward the ad or brand attitude, consistent with peripheral-cue processes rather than the classic ad → brand attitude sequence. Practically, the results challenge the assumption that “more nudity” is persuasive and suggest that, in low-argument contexts, gender-balanced model composition can be a modest, context-dependent cue. Advertisers should prioritize brand/category fit, pretest in the intended media environment (and locale), and expect incremental rather than large effects. Full article

As augmented reality (AR) advertising becomes increasingly prevalent across digital platforms, understanding how its unique features influence consumer responses is critical for both theory and practice. Based on the elaboration likelihood model (ELM), this study develops and validates a dual-dimension content–dual-route processing model to investigate how different features of AR advertising influence consumer engagement. Specifically, it examines how product-related attributes (attractiveness, informativeness) and technology-related attributes (interactivity, augmentation) shape attitudes toward the ad and purchase intentions through cognitive (information credibility) and affective (enjoyment) pathways. Using data from an online survey (N = 299), the study applies partial least squares structural equation modeling (PLS-SEM) to test the proposed model. The results show that informativeness and augmentation significantly enhance information credibility, while attractiveness primarily influences emotional responses. Interactivity and augmentation positively influence cognitive and affective responses. Mediation analysis confirms the simultaneous activation of central and peripheral processing routes, with flow experience emerging as a significant moderator in selected pathways. By introducing a structured framework for AR advertising content, this study extends the applicability of the ELM in immersive media contexts. It underscores the combined impact of rational evaluation and emotional engagement in shaping consumer behavior and offers practical insights for designing effective AR advertising strategies. Full article

Online reviews increasingly shape experiential travel decisions. This study investigates how structural and linguistic features of user-generated content influence peer endorsement in wine tourism. While prior research has explored review valence and credibility, limited attention has been paid to how micro-level textual and identity cues affect social approval metrics such as likes. Grounded in the Elaboration Likelihood Model, the analysis draws on 7942 TripAdvisor reviews using automated web scraping, readability metrics, and multivariate regression. Results indicate that location disclosure significantly increases likes, while higher textual complexity reduces endorsement. Title length and reviewer contributions function as peripheral cues, with an interaction between complexity and title length compounding cognitive effort. Findings refine dual-process persuasion theory and offer practical insights for content optimization in post-pandemic tourism engagement. Full article

This study focuses on a profound paradox in eco-hotel evaluations: why do consumer ratings for location, a static asset, exhibit dynamic fluctuations? To solve this puzzle, we construct a two-stage signal-processing theoretical framework that integrates Signaling Theory and the Elaboration Likelihood Model (ELM). This framework posits that the dynamic trajectory of a hotel’s green attributes (encompassing eco-facilities, sustainable practices, and ecological experiences) constitutes a high-fidelity market signal about its underlying quality. We utilized natural language processing techniques (Word2Vec and LSA) to conduct a longitudinal analysis of over 60,000 real consumer reviews from Booking.com between 2020 and 2023. This study confirms that continuous improvements in green attributes result in significant positive spillovers to location scores, while any degradation triggers strong negative spillovers. More critically, consumer environmental involvement (CEI) acts as an amplifier in this process, with high-involvement consumers reacting more intensely to both types of signals. The research further uncovers complex non-linear threshold characteristics in the spillover effect, subverting traditional linear management thinking. These findings not only provide a dynamic and psychologically deep theoretical explanation for sustainable consumption behavior but also offer forward-thinking practical implications for hoteliers on how to strategically manage dynamic signals to maximize brand value. Full article

This study investigates how social media exposure shapes young adults’ attitudes and intentions toward sustainable products in Kuwait—a high-income, oil-dependent context undergoing a digital and cultural shift. Integrating the Theory of Planned Behavior (TPB) and the Elaboration Likelihood Model (ELM), the research explores the mediating roles of environmental responsibility, social influence, and consumer attitudes in the formation of sustainable purchase intentions. Data were collected via an online survey of 702 respondents aged 18–30 and analyzed using structural equation modeling (SEM). The findings reveal that social media usage significantly influences sustainable purchase intention, social influence, and consumer attitudes. Environmental responsibility emerges as a key driver of both attitudinal and normative pathways. Notably, the study finds no direct link between consumer attitude and intention, highlighting a persistent attitude–behavior gap. The results offer theoretical insights into digital persuasion and normative influence while providing practical guidance for marketers and policymakers promoting sustainability in digitally connected, non-Western societies. Full article

In digital societies, users’ privacy decisions not only affect personal information security and application sustainability, but also profoundly influence the formulation and enforcement of relevant laws. However, existing research mainly adopts a dichotomous perspective of rationality and bounded rationality, lacking an integrated framework to explain the complexity of users’ privacy decisions. Therefore, this study integrates privacy calculus theory and Psychological Distance Theory within the Elaboration Likelihood Model (ELM) framework to explore their mediating and moderating roles in the relationship between perceived privacy relevance and disclosure intention. To achieve the research objectives, we employed the vignette method to design an online survey with a 2 (perceived relevance: low vs. high) × 2 (temporal distance: near vs. distant) between-subjects design, ultimately collecting 293 valid responses. The conclusions are as follows: (1) perceived relevance positively affects users’ privacy disclosure intention, and perceived benefits and perceived risks play a partial mediating role between them; (2) contrary to individuals’ common preference for near temporal distance (immediate rewards), distant temporal distance (delayed rewards) have a more pronounced enhancing effect on the positive relationship between perceived relevance and perceived benefits; (3) the results excluded the influence of control variables such as gender, age, and education level on privacy disclosure intention. This study not only proposes an integrated analytical framework, revealing that users’ privacy decisions are jointly influenced by both rational and irrational factors, but also provides practical insights for application developers and regulatory agencies. Finally, we also discuss the limitations of this study and directions for future research. Full article

The increase in battery demand, particularly from the mobility sector, has resulted in a significant increase in the required production capacities. Europe is facing a large-scale expansion of production capacities. Currently, the battery cell demand in the region accounts for approximately 25% of global demand, while only 10% of global production capacities are located there. This has motivated the announcement of a large number of production projects of over 2 TWh by 2030, which would mean overcapacity compared to projected European cell demand. In recent years, however, many of the announced Gigafactories have been delayed or cancelled. This paper aims to develop a risk assessment model for forecasting realistic future capacities for battery cell production in Europe. The proposed model combines an evaluation of industry announcements at the project level with a Monte Carlo simulation to translate the announced production projects into a European production capacity forecast. Therefore, the likelihood of implementation for individual projects is analysed within 11 topics (company, country and maturity related) and scenarios for future European production capacities are elaborated. Model validation indicates that from 54% to 75% of the announced capacities in Europe are likely to be realised (approx. 1.2 GWh–1.7 GWh by 2030). The majority of battery production projects announced in Europe are still in the planning phase (66%) with Germany, France, Scandinavia and Eastern Europe emerging as key regions. The modelling of production capacities predicts that dependency on cell imports to Europe will be reduced compared to today. Full article

Urban agriculture (UA) is being increasingly embraced to improve access to healthy food and enhance urban aesthetics. This study explores the framing of urban agricultural knowledge disseminated via social media through a coding framework and content analysis of eleven clips from the Sustainable Agriculture Foundation (Thailand) on Facebook and YouTube. It also examines factors influencing satisfaction using the Elaboration Likelihood Model (ELM) and proposes practical communication strategies. A survey of 200 experienced viewers was conducted and analyzed using SmartPLS 4.0. The findings from both analyses were used to develop communication strategies. Key results showed that leading farmers framed their messages by explaining actions, reasoning, outcomes, and associated health benefits. Clear, easy-to-understand messages drive higher engagement on social media by being accessible, memorable, emotionally appealing, and shareable. Source credibility and information quality significantly influence satisfaction with urban agriculture. Including self-efficacy as a moderating factor, along with source credibility and interactivity, also significantly impacts knowledge satisfaction. An integrated approach to promoting source credibility, information quality, interactivity, accessibility, and self-efficacy in urban agriculture should focus on community-led initiatives, feedback loops, and collaboration with local governments and schools, particularly in sustainability efforts. Full article