Search Results (247)

Modelling and quantifying human socialness onto robots remain a challenge, due to the complex mechanisms and reasoning processes that incorporate human intelligence to enable social behaviours. In this paper, we propose a novel approach of modelling human sociability in the social context of human–robot interactions by deriving the sociability score, which integrates both legible and trustable motion. To generate socially accepted motions, with potential deployment in real-time and dynamic environments, a new procedure is developed to encode human trustability onto robot motion, with the introduction of the trustability score, which explores perceived benevolence and the importance of initial trust. By applying the trust region of predictability, trustably and socially predictable trajectories are thus generated that can be identified and interpreted by humans consistently as verified by experiments. The experimental results also demonstrated that the scores computed by the proposed method can effectively capture their respective defined characteristics. Furthermore, to generate and evaluate predictable trajectories independent of other trajectories, a modified predictability score computation has been derived. Finally, as a step towards creating social intelligence, we train a deep learning-based classifier to identify socially predictable trajectories, mimicking humans’ ability to recognise such motion. Full article

Optoelectronic devices are emerging as a cornerstone of advanced renewable energy technologies, offering innovative routes for energy harvesting, conversion, and management with high efficiency and versatility. This review summarizes recent advances in the semiconductor materials engineering field, device configurations, and light–matter interaction mechanisms that underpin advanced optoelectronic systems for solar energy harvesting, solar-driven chemical conversion, and smart grid integration, among others. Emphasis is placed on the breakthroughs achieved in the perovskite and hybrid photovoltaics, photoelectrochemical energy conversion, and nanostructured optoelectronic platforms that enable much-increased light absorption, reduced recombination losses, and scalable large-scale fabrications. Moreover, the challenges closely linked with long-term stability, environmental durability and benevolence, and worldwide deployment are critically addressed, together with the emerging opportunities in AI design, tandem device technological solutions, integrated energy systems, and machine learning approaches for optimizing device performance, thermal management, and energy storage capabilities. Finally, the present review concludes by outlining the future research directions that could accelerate the transition toward high-performance, cost-effective, and sustainable optoelectronic solutions responsive to global renewable energy requirements. Full article

Background/Objectives: Large language models (LLMs) and generative AI are rapidly being integrated into healthy ageing initiatives for tasks ranging from companionship and cognitive support to personalised health advice and reduction in social isolation among older adults. Current ethical discussions predominantly address bias, privacy, and accuracy, leaving unresolved three critical governance questions: How do LLM sentiments towards transformative technologies diverge from human values in ageing contexts? What epistemic status do LLM outputs hold when applied to geriatric care? When is trust in those outputs justified for older adults? And who bears responsibility when AI-informed decisions affect functional ability or well-being? Methods: The framework was developed through normative conceptual analysis, synthesizing philosophical principles of medical knowledge and trust, ethical theories of responsibility, empirical evidence on LLM sentiment divergence, digital ageism, and applications of AI in geriatric care (structured searches in PubMed, PhilPapers, and relevant databases, January 2020–March 2026). Results: The integrated framework produces (i) adaptation of SAIA for multidimensional evaluation of human–machine value convergence specific to healthy ageing values (functional ability, autonomy, dignity, equity); (ii) a four-tier classification of LLM outputs tailored to geriatric scenarios; (iii) conditions for warranted trust calibrated to age-related vulnerabilities such as cognitive decline and digital divide; and (iv) responsibility allocation via RACI models with testable hypotheses linking governance design to trust calibration and patient safety outcomes. Conclusions: Without explicit societal alignment and epistemic governance, generative AI risks reinforcing benevolent ageism, automation bias, and responsibility gaps in healthy ageing. The 2025–2027 period offers a decisive window to shape institutional norms that place functional capacity, human dignity, and value convergence at the centre of AI deployment in geriatric care. Full article

In her final novel, Daniel Deronda (1876), George Eliot (1819–1880) repeatedly stages moments in which gentile characters project expectations onto Jewish women, drawing on inherited cultural representations from literature, history, and the performing arts. These moments reveal how limited their real-world knowledge of Jews—particularly Jewish women—was, and how readily they relied on cultural templates rather than lived experience. George Eliot herself, however, had undertaken extensive study of Jewish history, religion, and culture in preparation for the novel, including research into the Talmud, Mishna, kabbalah, and halacha (Jewish law). Yet this knowledge is purposefully not afforded to her characters. This article examines George Eliot’s increasing understanding of Jewish society, and her shifting attitudes towards Judaism, and explores how allusions to Jewish women in history, literature, and performance shape the gentile characters’ othering of Mirah Lapidoth, a young Jewish woman fleeing enforced familial exploitation, whom Daniel rescues from drowning in the Thames. Two significant conceptual terms underpin my argument. Objectification refers here not only to eroticisation or aestheticisation, but to the broader process by which Mirah is perceived as a symbolic figure—as an image, a type, or role—rather than a fully realised person. Othering denotes the interpretative habit by which gentile characters position Mirah through pre-existing stereotypes or literary precedents, instead of understanding her as a subject with her own history and interiority. Rescue describes the narrative mechanisms by which Mirah is brought into focus, first through Daniel’s intervention, then through her placement within the Meyrick household, and finally through marriage, though always within structures that continue to idealise, discipline, or contain her. I argue that George Eliot’s deployment of familiar stereotypes does not reinforce them; instead, she exposes them as cultural constructions that must be deconstructed or exorcised before she reconstructs her own version of Jewish culture and identity, which she referred to as “the inner life of modern Judaism” in her notebooks. I also argue that Daniel’s rescue of Mirah, rather than an act of pure benevolence, becomes a further site of objectification, othering her as an idealised model of Jewish womanhood rather than acknowledging her as an autonomous individual. Full article

Gender-based violence (GBV) in higher education is increasingly recognized as a systemic problem across offline and online contexts, yet the pathways linking gender-related attitudes, victimization, and formal help-seeking remain insufficiently understood in Southern Europe. This study examined whether Sexual Harassment/Assault and Coercive Control mediate associations between ambivalent sexism, Acceptance of Dating Violence, Perceived Behavioral Control, and Formal Help-Seeking Intentions among women students in Greek higher education. An anonymous online survey was completed by 550 women students, and structural equation modeling tested direct, mediated, and multi-group associations by age, education level, and perceived financial situation. Coercive Control was the strongest predictor of Formal Help-Seeking Intentions, followed by Acceptance of Dating Violence and Perceived Behavioral Control, whereas Hostile and Benevolent Sexism had no significant direct effects. Mediation analyses showed that Coercive Control, rather than Sexual Harassment/Assault, provided the more consistent pathway to help-seeking intentions. Multi-group analyses indicated broadly stable patterns, with selected differences by age, education, and financial situation. The findings suggest that university GBV policies should move beyond incident-based responses, address patterned Coercive Control, and improve students’ perceived ability to access formal support services. Full article

Ageism—encompassing stereotypes, prejudice, and discrimination across age groups—affects how individuals perceive and experience their own ageing. This study, based on a large sample (N = 1047), compared three age cohorts and explored intra-group variability among older adults (65–75 vs. ≥76 years). Results indicated that attitudes towards ageing were influenced by life stage, knowledge about ageing, perceived ageism, and internalised stereotypes. Participants aged 65–75 years showed more favourable attitudes, greater knowledge, and better emotional wellbeing compared to the ≥76 group, which exhibited higher hostile ageism and lower psychological wellbeing. A forward stepwise logistic regression (explained 35.9% of the variance) identified five predictors of a positive self-perception of ageing: lower perceived age discrimination; generally positive attitudinal profile; endorsement of benevolent stereotypes; absence of hostile ageism; and belonging to the 65–75 group. The findings highlighted the psychosocial complexity of ageing and call for interventions promoting positive ageing and reducing ageism. Full article

Collaborative robots, or cobots, are designed to improve productivity and safety in industrial settings. However, effective Human–Robot Collaboration (HRC) relies heavily on the human operator’s trust in the robotic partner. This study posits that trust is significantly enhanced by the robot’s ability to adapt to unpredictable human behavior. To achieve this adaptability, we propose applying an Adversarial Reinforcement Learning (ARL) framework to the robot’s activity planning. We model the assembly process as a Markov Decision Process (MDP) on a Directed Acyclic Graph (DAG). The robot learns an assembly policy using an on-policy algorithm while a simulated human agent, trained with the same algorithm, acts as an adversary that introduces disturbances and delays. We applied the proposed approach to a simple industrial case study and evaluated it on complex assembly sequences generated synthetically. Although the ARL-trained robot did not outperform conventional assembly optimization algorithms in terms of task completion time, it guaranteed robustness against human variability. This ensured task completion within a bounded timeframe regardless of human actions. By demonstrating consistent performance and adaptability in the face of uncertainty, the robot exhibits the Ability and Benevolence components of the ABI model of trust. This fosters a more resilient and trustworthy collaborative environment. Full article

Green FinTech involves facilitating sustainable payments, banking, and investment; nevertheless, it is subject to consumer trust and perceptions of ‘green’ value. The literature on this topic is fragmented, with information systems literature typically considering trust as a broad acceptance construct, while sustainable literature considers it as a risk of ‘greenwashing’ without integrating credibility into adoption models. This systematic review aggregates 15 empirical studies and addresses five research questions. RQ1 examines the theoretical models applied to examine trust in green/sustainable FinTech adoption. RQ2 examines the conceptualization and measurement of trust across different contexts, distinguishing institutional/provider trust, platform/tech trust, and sustainability claim credibility trust. RQ3 examines the function of trust within behavioral models (predictor, mediator, moderator). RQ4 examines methodological characteristics and quality indicators (research design, sampling frame, reliability, and bias). RQ5 examines the direct relationship between trust and adoption intention using meta-analysis. The systematic review follows a set of PRISMA guidelines, where we searched Scopus and Web of Science (2015–2026) and applied an RQ-based coding scheme to peer-reviewed articles. Measures of trust varied significantly (unidimensional, integrity–competence–benevolence, and technology-specific scales), limiting cross-study comparability. Using random effects, we found a significant positive relationship between trust and intention (pooled standardized direct path coefficient β = 0.27, 95% CI [0.14, 0.41]) with considerable heterogeneity (I² = 88%) and a wide prediction interval including near-zero effects. Literature essentially endorses trust as a significant yet context-dependent construct, emphasizing the necessity for measurement standardization, a more distinct differentiation between sustainability trust and general platform trust, regular reporting of reliability and bias assessments, and focused evaluations of boundary conditions (e.g., environmental skepticism, regulatory framework, and FinTech type). Full article

This study advances the Confucian Visual Transformation Model (CVTM) to analyse how Confucian values are visually reformulated in contemporary Malaysian Chinese art. Integrating artist interviews (n = 5), symbolic visual coding, and audience surveys (n = 227), the research addresses the lack of empirical frameworks for transcultural aesthetics. While an initial exploratory factor analysis (EFA) confirmed four dimensions—Ren (benevolence), He (harmony), WenZhi (technique-ideology), and MeiShan (aesthetic-moral)—it also revealed structural overlaps. Consequently, the study proposes CVTM 2.0, which replaces additive metrics with a tension-driven fusion mechanism. Key innovations include a Symbolic Tension Index (STI) for dynamic weighting and a fuzzy integration layer to handle overlap between WenZhi and MeiShan. Results indicate that Confucian dimensions are not static but are activated through compositional and material tensions. Theoretically, this reframes Confucian aesthetics as a context-responsive system; practically, it offers a replicable blueprint for analysing postcolonial identity negotiation in Southeast Asian art. Full article

The unbridled pursuit of statistical rationality has precipitated a crisis of value rationality in e-commerce ecosystems, leading to algorithmic involution—a dilemma characterized by destructive hyper-competition. To reconcile this theoretical paradox and explore effective governance pathways, this paper constructs a tripartite evolutionary game model involving e-commerce platforms, government regulators, and consumers. Simulation results indicate that high-intensity government deterrence constitutes the necessary stability foundation of hard constraints, while consumer activism acts as the decisive accelerator of the soft environment contingent on high synergistic gains and low information screening costs. Furthermore, a platform’s pivot toward “algorithm for good” is not driven by altruism, but by the rational calibration between short-term extractive gains and long-term benevolent returns. Sensitivity analysis confirms that reducing the ratio of these two factors is the effective lever to speed up system convergence. Finally, effective governance requires restructuring this payoff matrix by establishing dynamic penalty mechanisms and transparent low-cost feedback channels to render ethical algorithmic behavior a dominant strategy in terms of economic rationality. This research aims to guide the e-commerce ecosystem from a zero-sum game of involution toward a sustainable equilibrium of multi-party value co-creation. Full article

The ambivalent sexism theory supports differences in the manifestations of sexism among individuals with diverse genders and sexual orientations. However, it still remained unclear whether individuals who share common strong cultural values endorse different levels of sexism according to their gender and sexual orientation. The current study aimed to examine differences in sexist attitudes based on gender and sexual orientation among Korean adults. We first tested measurement invariance in a Korean Multi-dimensional Sexism Inventory (K-MSI) between heterosexuals (n = 374) and sexual minorities (n = 445), and second, we compared the latent means across groups using the Multiple Indicators Multiple Causes (MIMIC) model. The results confirmed the first-order six-factor structure of the K-MSI with adequate internal consistency, and supported partial scalar invariance across heterosexual and sexual minority men and women. The MIMIC model approach revealed significant age, gender, and sexual orientation differences in most of hostile sexism (HS) and benevolent sexism (BS) components. Overall, heterosexuals reported higher levels of sexism than non-heterosexuals within each gender. Gender differences in BS have become nuanced when sexual orientation was considered. The current study also provides an overview of Korean culture that may uniquely influences individuals’ sexist attitudes, which would interest international researchers. Full article

This paper examines traditional data envelopment analysis (DEA), cross efficiency (CE), and game efficiency (GE) models for software productivity analysis and ranking. Additionally, for CE models, secondary objectives of aggressive and benevolent formulations are considered. An entropy criterion is used to identify the best-performing model. Experiments are conducted using the ISBSG dataset. The results show that aggressive CE model formulations have the lowest entropy values and produce unique project rankings. The GE model is computationally intensive and does not provide sufficient benefits to justify the extra effort. A rule-based framework is introduced to identify discrepancies in project rankings across models. This framework helps managers pinpoint truly efficient projects. Full article

AI Recommender Systems (RecSys) function as personalised forecasting engines, predicting user preferences to reduce information overload. However, the efficacy of these systems is often bottlenecked by the “Last Mile” of forecasting: the end-user’s willingness to adopt and rely on the prediction. While the existing literature often assumes that algorithmic accuracy (e.g., low RMSE) automatically drives utilisation, empirical evidence suggests that users frequently reject accurate forecasts due to a lack of trust or cognitive friction. This study challenges the utilitarian view that users adopt systems simply because they are useful, instead proposing that sustainable adoption requires a state of Cognitive Absorption—a psychological flow state enabled by the Cognitive Economy of trust. Grounded in the Motivation–Opportunity–Ability (MOA) framework, we developed the Trust–Absorption–Intention (TAI) model. We analysed data from 366 users of a major predictive platform using Partial Least Squares Structural Equation Modelling (PLS-SEM). The Disjoint Two-Stage Approach was employed to model the reflective–formative Higher-Order Constructs. The results demonstrate that Cognitive Trust (specifically the relational dimensions of Benevolence and Integrity) operates via a dual pathway. It drives adoption directly, serving as a mechanism of Cognitive Economy where users suspend vigilance to rely on the AI as a heuristic, while simultaneously freeing mental resources to enter a state of Cognitive Absorption. Affective Trust further drives this immersion by fostering curiosity. Crucially, Cognitive Absorption partially mediates the relationship between Cognitive Trust and adoption intention, whereas it fully mediates the impact of Affective Trust. This indicates that while Cognitive Trust can drive reliance directly as a rational shortcut, Affective Trust translates to adoption only when it successfully triggers a flow state. This study bridges the gap between algorithmic forecasting and behavioural adoption. It introduces the Cognitive Economy perspective: Trust reduces the cognitive cost of verifying predictions, allowing users to outsource decision-making to the AI and enter a state of effortless immersion. For designers of AI forecasting agents, the findings suggest that maximising accuracy may be less effective than minimising cognitive friction for sustaining long-term adoption. To solve the cold start problem, platforms should be designed for flow by building emotional rapport and explainability, thereby converting sporadic users into continuous data contributors. Full article

Public trust in artificial intelligence (AI) is often assumed to promote acceptance by reducing perceived risks. Using a nationally representative survey of 10,294 Chinese adults, this study challenges that assumption and introduces the concept of vigilant trust. We argue that trust in AI does not necessarily diminish risk awareness but can coexist with, and even intensify, attention to potential harms. By examining four dimensions of trust—trusting stance, competence, benevolence, and integrity—we find that all of them consistently enhance perceived benefits, which emerge as the strongest predictor of AI acceptance. However, trust shows differentiated relationships with perceived risks: benevolence reduces risk perception, whereas trusting stance is associated with higher perceptions of both benefits and risks. Perceived risks do not uniformly deter acceptance and, in some contexts, are positively associated with willingness to adopt AI. By moving beyond the conventional view of trust as a risk-reduction mechanism, this study conceptualizes vigilant trust as a mode of engagement in which openness to AI is accompanied by sustained awareness of uncertainty. The findings offer a more nuanced understanding of public acceptance of AI and its implications for governance and communication. Full article

Background/Objectives: Current studies have shown that caregiving anxiety is associated with an individual’s dysfunctional thoughts. The aim of this study was to assess the mediating effect of caregivers’ forgiveness (benevolence, lack of avoidance and lack of revenge) on the relationship between dysfunctional thoughts and anxiety in the informal caregivers of dependent older adults. Methods: Participants were 222 family caregivers. We conducted path analysis to test the hypothesized model. Results: We found a model that showed a good fit (χ² = 3.410; χ²/gL = 5; p = 0.63; GFI = 0.994; CFI = 0.999; RMSEA = 0.001). It showed a direct and negative association between dysfunctional thoughts and lack of revenge, and this variable was related positively with both benevolence and lack of avoidance. In turn, benevolence was associated with lower levels of anxiety. The associations between dysfunctional thoughts and anxiety were mediated by caregiver forgiveness. Conclusions: Our research suggests the importance of health workers seeking to understand how individuals judge their avoidance, revenge and lack of benevolence, which affect individuals’ anxiety, for change. This study demonstrates the relevance of forgiving strategies in developing and testing informal caregiving assessments. It is necessary to detect and reduce avoidance and revenge related to caregivers. It is also necessary to detect and improve benevolence. Full article