Search Results (36)

The proliferation of generative artificial intelligence (AI) as an autonomous recommendation agent fundamentally challenges traditional paradigms of marketing communication. As AI systems increasingly mediate consumer–brand relationships, understanding how artificial agents construct persuasive discourse—distinct from human communicators—becomes critical for developing effective dual-channel marketing strategies. Grounded in Source Credibility Theory and the Computers Are Social Actors (CASA) paradigm, this study investigates the semantic and structural divergence between AI-generated product recommendations and human influencer marketing messages in social commerce contexts. Employing a mixed-methods computational approach integrating term frequency analysis, TF-IDF weighting, Latent Dirichlet Allocation (LDA) topic modeling, and BERT-based contextualized semantic embedding analysis (KR-SBERT), we examined 330 Instagram influencer posts and 541 AI-generated responses concerning inner beauty enzyme products—a hybrid category combining functional health claims with hedonic beauty appeals—in the Korean social commerce market. AI-generated responses were collected through a systematically designed query protocol with empirically grounded prompts derived from actual consumer search behaviors, and analytical robustness was verified through sensitivity analyses across multiple parameter thresholds. Our findings reveal a fundamental divergence in persuasive architecture: human influencers construct experiential narratives exhibiting message characteristics typically associated with peripheral-route cues (sensory descriptions, emotional testimonials, social context), while AI recommendations employ systematic, evidence-based discourse exhibiting message characteristics typically associated with central-route argumentation (functional mechanisms, ingredient specifications, objective criteria). Topic modeling identified four distinct thematic clusters for each source type: human discourse centers on embodied experience and relational consumption, whereas AI discourse organizes around informational utility and rational decision support. Jensen–Shannon Divergence analysis (JSD = 0.213 bits) confirmed moderate distributional divergence, while chi-square testing (χ² = 847.23, p < 0.001) and Cramér’s V (0.312, indicating a medium-to-large effect) demonstrated statistically significant and substantively meaningful differences. These findings extend CASA theory by demonstrating that AI recommendation agents develop a characteristic “AI communication signature” distinguishable from human persuasion patterns. We propose an integrated Dual-Agent Persuasion Proposition—synthesizing CASA, ELM, and Source Credibility perspectives—suggesting that AI and human recommenders serve complementary functions across different stages of the consumer decision journey—a proposition whose predictions regarding sequential persuasive effectiveness and consumer processing routes await experimental validation. These findings carry implications for AI content strategy optimization, platform design, and emerging regulatory frameworks for AI-generated content labeling. Full article

Efficient coordination within the supply chain of prefabricated construction remains a significant challenge due to the high level of interdependence among supply chain participants, the complexity of information flows, and the sensitivity of construction processes to communication delays. This study proposes an integrated methodological framework that combines fuzzy logic and social network analysis (SNA) to evaluate the structural stability and interaction dynamics of supply chain participants. First, a synthetic indicator—link stability—is introduced to quantify the robustness of relationships between supply chain actors. Link stability is defined as a function of five determinants: collaboration level, trust level, communication quality, adoption of digital tools, and effectiveness of dispute resolution. Fuzzy logic is applied to calculate this indicator for each pair of participants, reducing subjectivity in expert assessments. Second, the link stability matrix is used to compute a wide set of centrality measures, including degree, betweenness, closeness, eigenvector, PageRank, information, harmonic, and second-order centralities. These metrics reveal the structural influence of each actor within the network and allow for the identification of core, semi-peripheral, and peripheral roles. A heatmap demonstrates a highly centralized network structure dominated by managerial and design roles. The results contribute to improving supply chain resilience, enhancing communication pathways, and supporting decision-making in prefabricated construction projects. Full article

As artificial intelligence systems proliferate across critical societal domains, understanding the nature, patterns, and evolution of AI-related harms has become essential for effective governance. Despite growing incident repositories, systematic computational analysis of AI incident discourse remains limited, with prior research constrained by small samples, single-method approaches, and absence of temporal analysis spanning major capability advances. This study addresses these gaps through a comprehensive multi-method text analysis of 3494 AI incident records from the OECD AI Policy Observatory, spanning January 2014 through October 2024. Six complementary analytical approaches were applied: Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) topic modeling to discover thematic structures; K-Means and BERTopic clustering for pattern identification; VADER sentiment analysis for emotional framing assessment; and LIWC psycholinguistic profiling for cognitive and communicative dimension analysis. Cross-method comparison quantified categorization robustness across all four clustering and topic modeling approaches. Key findings reveal dramatic temporal shifts and systematic risk patterns. Incident reporting increased 4.6-fold following ChatGPT’s (5.2) November 2022 release (from 12.0 to 95.9 monthly incidents), accompanied by vocabulary transformation from embodied AI terminology (facial recognition, autonomous vehicles) toward generative AI discourse (ChatGPT, hallucination, jailbreak). Six robust thematic categories emerged consistently across methods: autonomous vehicles (84–89% cross-method alignment), facial recognition (66–68%), deepfakes, ChatGPT/generative AI, social media platforms, and algorithmic bias. Risk concentration is pronounced: 49.7% of incidents fall within two harm categories (system safety 29.1%, physical harms 20.6%); private sector actors account for 70.3%; and 48% occur in the United States. Sentiment analysis reveals physical safety incidents receive notably negative framing (autonomous vehicles: −0.077; child safety: −0.326), while policy and generative AI coverage trend positive (+0.586 to +0.633). These findings have direct governance implications. The thematic concentration supports sector-specific regulatory frameworks—mandatory audit trails for hiring algorithms, simulation testing for autonomous vehicles, transparency requirements for recommender systems, accuracy standards for facial recognition, and output labeling for generative AI. Cross-method validation demonstrates which incident categories are robust enough for standardized regulatory classification versus those requiring context-dependent treatment. The rapid emergence of generative AI incidents underscores the need for governance mechanisms responsive to capability advances within months rather than years. Full article

As artificial intelligence (AI) agents become deeply integrated into fitness systems, trustworthy human–AI agent interaction has become pivotal for user engagement in smart home fitness (SHF) e-commerce platforms. Grounded in the Computers Are Social Actors (CASA) framework, this study empirically investigates how, acting as AI fitness coaches, AI agents’ technical and social features shape users’ active engagement in the in-home social e-commerce context. A mixed-method approach was employed, combining computational text mining of 17,582 user reviews from fitness e-commerce platforms with a survey (N = 599) of Chinese consumers. The results show that (1) the technical–social features of AI agents serving as AI fitness coaches include visibility, gamification, interactivity, humanness, and sociability; (2) these five technical–social features of AI agents positively influence user compliance via both cognitive and emotional trust in AI agents; (3) these five technical–social features of AI agents serving as AI fitness coaches positively impact active engagement via both cognitive and emotional trust in AI agents. This study extends the CASA framework to the domain of AI coaching by demonstrating the parallel roles of cognitive and emotional trust in AI agents. For designers and managers in the fitness e-commerce industries, this study offers actionable insights for designing AI agents integrating functional and social features that foster trust and drive behavioral outcomes. Full article

Historic urban landscapes, despite their cultural significance, often face neglect, limiting their potential to increase the value of historical centers. Defined as a complex sociotechnical network that involves a variety of agencies incorporating material, immaterial, natural, and artificial elements, these landscapes present significant challenges for architects because of their layered and diverse components. Actor–network theory (ANT) is used as a methodological and ontological framework to address this complexity. However, a notable research gap exists on the basis of the lack of clear representation and practical application of ANT to address the complexity of these historic urban landscapes. To bridge this gap, this study uses Bey’s palace as a case study to develop a comprehensive framework based on a digital mapping approach rooted in ANT. This framework traces, visualizes, and analyzes historic urban landscapes as intricate systems of agencies, leveraging graph theoretical algorithms and computational analysis tasks from network analysis tools to increase their effectiveness. This investigation is based on two key concepts: the actor/actant and the actor network. The research employed Bruno Latour’s concepts of translation, agency, and the mapping controversies technique grounded in graph-theoretic algorithm tasks to decipher the complexities of Bey’s palace system. The results identify seven clusters as actor networks and highlight the roles of key actors/actants, such as Ahmed Bey, decorative elements, courtyard gardens, and Moorish architecture. This methodological approach provides architects and urban planners with practical tools to better understand, analyze and preserve historic urban landscapes, enriching their cultural and historical value. By transforming contested discourses into measurable networks indicators, this interdisciplinary framework directly supports SDG11 (Sustainable Cities and Communities), especially Target 11.4, in safeguarding cultural heritage by enabling the prioritization, monitoring and governance of cultural, social and infrastructural assets in historic urban landscapes. Full article

The growing complexity of distribution-level virtual power plants (VPPs) demands a rethinking of how flexible demand is modeled, aggregated, and dispatched under uncertainty. Traditional optimization frameworks often rely on deterministic or homogeneous assumptions about end-user behavior, thereby overestimating controllability and underestimating risk. In this paper, we propose a behavior-aware, two-stage stochastic dispatch framework for VPPs that explicitly models heterogeneous user participation via integrated behavioral economics and social interaction structures. At the behavioral layer, user responses to demand response (DR) incentives are captured using a Prospect Theory-based utility function, parameterized by loss aversion, nonlinear gain perception, and subjective probability weighting. In parallel, social influence dynamics are modeled using a peer interaction network that modulates individual participation probabilities through local contagion effects. These two mechanisms are combined to produce a high-dimensional, time-varying participation map across user classes, including residential, commercial, and industrial actors. This probabilistic behavioral landscape is embedded within a scenario-based two-stage stochastic optimization model. The first stage determines pre-committed dispatch quantities across flexible loads, electric vehicles, and distributed storage systems, while the second stage executes real-time recourse based on realized participation trajectories. The dispatch model includes physical constraints (e.g., energy balance, network limits), behavioral fatigue, and the intertemporal coupling of flexible resources. A scenario reduction technique and the Conditional Value-at-Risk (CVaR) metric are used to ensure computational tractability and robustness against extreme behavior deviations. Full article

Online aggression and abusive language on social media pose a growing threat to democratic discourse, as they contribute to polarization, delegitimization of political actors, and the erosion of civil debate. While much of the current research relies on computational methods to detect hate speech, fewer studies investigate how online aggression functions discursively in specific socio-political contexts. This study addresses this gap by analyzing patterns of verbal aggression on Facebook and Twitter during two key events in Poland in 2020: the presidential election and the Women’s Strike. Adopting a mixed-method approach (combining sentiment analysis, content analysis, and discourse analysis) and comparing two socio-political events that generated extensive online debate, this study investigates the patterns and communicative functions of hostile and aggressive language on Facebook and Twitter. The study reveals that neutral posts dominated both datasets, but negative and aggressive posts were significantly more frequent during the Women’s Strike, where verbal aggression was used not only to reinforce group identity but also to express moral outrage, trauma, and demands for change. In contrast, aggression during the election campaign was less frequent but more calculated. It functioned as a strategic tool to delegitimize political opponents and reinforce partisan divides. Users employed vitriolic language and profanity as rhetorical tools to undermine authority, reinforce group identity, and mobilize supporters. The study also reveals asymmetric patterns of aggression, with public figures and institutions, particularly the ruling party, Church, and police, being primary targets. The findings have significant implications for understanding the dynamics of online debates and aggression patterns in social media. Full article

The European Green Deal (EGD) aims at driving the green transition in the EU and positions cities as pivotal actors in achieving climate neutrality and environment protection. Despite ambitious policy commitments, significant implementation gaps persist at the local level impeding urban green transitions. This study assesses barriers to the EGD urban implementation by integrating several methods (scoping literature review, expert consultations, and computational network analysis) to identify structural conditions hampering change. Barriers are clustered into five domains and reviewed by experts to distill eight structural conditions perpetuating the status quo of urban development, hindering transformative change. The findings illustrate how the emerged structural conditions, ranked by their in-degree centrality, regard insufficient policy implementation; upgrade of consolidated built environments’ layout; short-term mindset; lack of knowledge and data sharing among stakeholders; silos in policymaking and development processes; competition among stakeholders over space use; limited social acceptance; and limited financial resources. Conversely, high-out-degree barriers—such as limited technical expertise in urban departments and GDP-oriented paradigms—emerge as system triggers where targeted interventions could catalyze change. This research provides actionable insights for policymakers by identifying leverage points which could promote urban green transitions and enhance the EGD local implementation for accelerating urban green transitions. Full article

Service robots (SRs) are increasingly deployed in commercial settings, yet the factors influencing their acceptance, particularly emotional design elements, remain understudied. This research investigates SR acceptance factors by integrating the technology acceptance model, the Computers Are Social Actors (CASA) framework, Kansei engineering (KE), and social presence theory (SPT) to examine how design elements influence user responses to SRs. Using structural equation modeling of survey data from 318 shoppers and hotel guests in China, we tested relationships between CASA attributes, emotional perceptions, social presence, and usage intention. The results revealed that communication style produced the strongest effects across all emotional dimensions, with cuteness and coolness directly influencing usage intention, while warmth and novelty operate through social presence mediation. Multi-group analysis identified significant gender differences in response patterns: male users prioritized communication-driven perceptions while female users responded more strongly to coolness attributes. These findings extend our understanding of acceptance factors in service robot adoption, highlighting the critical roles of emotional design, communication style, and gender differences, while suggesting differentiated design approaches for diverse user segments. Full article

Background/Objectives: Health and social care systems around the globe are currently undergoing a transformation towards personalized, preventive, predictive, participative precision medicine (5PM), considering the individual health status, conditions, genetic and genomic dispositions, etc., in personal, social, occupational, environmental, and behavioral contexts. This transformation is strongly supported by technologies such as micro- and nanotechnologies, advanced computing, artificial intelligence, edge computing, etc. Methods: To enable communication and cooperation between actors from different domains using different methodologies, languages, and ontologies based on different education, experiences, etc., we have to understand the transformed health ecosystem and all its components in terms of structure, function and relationships in the necessary detail, ranging from elementary particles up to the universe. In this way, we advance design and management of the complex and highly dynamic ecosystem from data to knowledge level. The challenge is the consistent, correct, and formalized representation of the transformed health ecosystem from the perspectives of all domains involved, representing and managing them based on related ontologies. The resulting business viewpoint of the real-world ecosystem must be interrelated using the ISO/IEC 21838 Top Level Ontologies standard. Thereafter, the outcome can be transformed into implementable solutions using the ISO/IEC 10746 Open Distributed Processing Reference Model. Results: The model and framework for this system-oriented, architecture-centric, ontology-based, policy-driven approach have been developed by the first author and meanwhile standardized as ISO 23903 Interoperability and Integration Reference Architecture. The formal representation of any ecosystem and its development process including examples of practical deployment of the approach, are presented in detail. This includes correct systems and standards integration and interoperability solutions. A special issue newly addressed in the paper is the correct and consistent formal representation Conclusions: of all components in the development process, enabling interoperability between and integration of any existing representational artifacts such as models, work products, as well as used terminologies and ontologies. The provided solution is meanwhile mandatory at ISOTC215, CEN/TC251 and many other standards developing organization in health informatics for all projects covering more than just one domain. Full article

Interaction is typically at the core of the value co-creation process through operant resource exchange in online collaborative innovation communities (OCICs). While some studies emphasize the facilitating effect of interaction on value co-creation, others have drawn opposite conclusions, such as more peer interaction leads to less idea generation. Thus, the purpose of this paper is to utilize the service ecosystem framework to clarify the overall relationship between interaction and value co-creation and to explore the moderating factors that may have contributed to the divergence and inconsistency of previous studies. We conducted a meta-analysis of 65 effect sizes obtained from 63 articles with a cumulative sample size of 25,185 between 2004 and 2023, using a random effects model. The results indicate that interaction has a significantly positive impact on user value co-creation within OCICs (r = 0.453, 95%CI [0.405, 0.499]), and the heterogeneity among studies was significant (Q = 1409.29, p < 0.001). The strength of this correlation was moderated by the types of interaction (human–computer or human–human interactions), the types of OCICs (business-sponsored or socially constructed online communities), and the number of involved OCICs (one or multiple online communities), but not by the cultural background. These findings support the service ecosystem perspective rather than resource scarcity theory by resolving the mixed findings regarding the relationship between interaction and user value co-creation. Furthermore, this study systematically examined the contingent factors separately across three levels, micro (types of actor interactions), meso (types and number of OCICs), and macro (cultural background), combining the whole and the part insights, and empirically integrating service ecosystems as the foundational paradigm and unit of analysis for value co-creation research for the first time. This research contributes to theoretical frameworks in service ecosystems and offers actionable insights for management practices in business and marketing. Full article

Homophily, which means similarity breeds association, is one of the most fundamental principles in social organization. However, in some cases, homophily is not significant, because actors’ perceptions of others differ from the real situation. In this article, we use the term “subjective homophily” to describe the homophily where the perceived similarity of objects is considered. In addition, we also consider social influence, which is closely related to homophily and represents the diffusion of some attributes through associations. In short, the dynamic temporal logic ${\mathrm{LoSH}}_{\mathrm{G},\mathrm{M}}^{\mathrm{SC}}$ we propose in this article is based on computation tree logic (CTL), which is used to describe the evolution of networks by subjective homophily, and dynamic logic (DL), which provides the dynamic update operator for representing active social influence. Furthermore, we prove that the model checking problem and the validity checking problem for ${\mathrm{LoSH}}_{\mathrm{G},\mathrm{M}}^{\mathrm{SC}}$ are both PSPACE-complete. Finally, we use an example, named false consensus, to illustrate how logic captures the subjective homophily evolution of networks and the impact of active social influence on evolution and structure. Full article

This exploratory study examines the socio-technical dynamics of Artificial Intelligence Companions (AICs), focusing on user interactions with AI platforms like Replika 9.35.1. Through qualitative analysis, including user interviews and digital ethnography, we explored the nuanced roles played by these AIs in social interactions. Findings revealed that users often form emotional attachments to their AICs, viewing them as empathetic and supportive, thus enhancing emotional well-being. This study highlights how AI companions provide a safe space for self-expression and identity exploration, often without fear of judgment, offering a backstage setting in Goffmanian terms. This research contributes to the discourse on AI’s societal integration, emphasizing how, in interactions with AICs, users often craft and experiment with their identities by acting in ways they would avoid in face-to-face or human-human online interactions due to fear of judgment. This reflects front-stage behavior, in which users manage audience perceptions. Conversely, the backstage, typically hidden, is somewhat disclosed to AICs, revealing deeper aspects of the self. Full article

Trajectory prediction is essential for the safe driving of autonomous vehicles. With the advancement of advanced sensors and deep learning technologies, attempts have been made to reflect complex interactions. In this study, we propose a deep learning-based Multimodal Trajectory Prediction method that reflects traffic light conditions in complex urban intersection situations. Based on existing state-of-the-art research, the multi-path of multi-agents was predicted using a generative model, and the actor’s trajectory information, state, social interaction, and traffic light state, and scene context were reflected. Performance evaluation was conducted using metrics commonly used to evaluate the performance of stochastic trajectory prediction models. This study is meaningful in that trajectory prediction was performed by reflecting realistic elements of traffic lights in a complex urban environment. Future research will need to be conducted on efficient ways to reduce time and computational performance while reflecting different real-world environments. Full article

The study of opinion formation and dynamics is one of the core topics in sociophysics. In this paper, the results of computer simulation of opinion dynamics based on social impact theory are presented. The simulations are based on Latané theory in its computerised version proposed by Nowak, Szamrej and Latané. The active parameters of the model describe the volatility of the actors (social temperature T) and the effective range of interaction (governed by an exponent $\alpha$ in a scaling function of distance between actors). Initially, every actor i has his/her own opinion. Our results indicate that ultimately at least 90% of the initial opinions available are removed from the society. For a low social temperature and a long range of interaction, only one opinion survives. Also, a rough sketch of the system phase diagram is presented. It indicates a set of ($\alpha ,T$) leading either to (1) the dominance of the unanimity of the opinions or (2) mixtures of unanimity and polarisation, or (3) taking random opinions by actors, or (4) a mixture of the final fates of the systems. The drastic reduction of finally observed opinions vs. their initial variety may be generic for many sociophysical models of opinions formation but masked by assuming an initially small pool of available opinions (in the worst case, in models with only binary opinions). Full article