SOCRATES. Developing and Evaluating a Fine-Tuned ChatGPT Model for Accessible Mental Health Intervention

Cyberpsychology Behavior and Social Networking, Journal Year: 2025, Volume and Issue: unknown

Published: April 17, 2025

Language: Английский

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Exploring the determinants of AIGC usage intention based on the extended AIDUA model: a multi-group structural equation modeling analysis

Frontiers in Psychology, Journal Year: 2025, Volume and Issue: 16

Published: May 21, 2025

With the rapid development and widespread adoption of generative artificial intelligence (GenAI) technologies, their unique characteristics-such as conversational capabilities, creative intelligence, continuous evolution-have posed challenges for traditional technology acceptance models (TAMs) in adequately explaining user intentions. To better understand key factors influencing users' GenAI, this study extends AIDUA model by incorporating system compatibility, transparency, human-computer interaction perception. These variables are introduced to systematically explore determinants intention adopt GenAI. Furthermore, examines varying mechanisms influence across different groups application scenarios, providing theoretical insights practical guidance optimizing promoting GenAI technologies. During data collection phase, employed a survey method measure behavioral intentions other within proposed framework. The design included demographic information about respondents well detailed related use In processing analysis Structural Equation Modeling (SEM) approach was utilized examine path relationships among variables. Additionally, compare differences variable subgroups, multi-group structural equation modeling(MGSEM) conducted. (1) Effects on Key Expectations: Social significantly enhances performance expectancy (β = 0.109, p < 0.05) but negatively impacts effort -0.135, 0.01). Hedonic motivation notably mitigates -0.460, 0.001), yet shows no significant effect 0.396, 0.76). newly extended variables-technological transparency 0.428, compatibility 0.394, perception 0.326, 0.001)-demonstrate positive influences while generally mitigating expectancy. (2) Emotional Mechanisms: Performance negative emotions -0.446, 0.01), increases 0.493, 0.001). Negative exert usage -0.256, (3) MGSEM revealed heterogeneity paths segments. Specifically, systematic variations were observed characteristics (gender, age, educational level), occupational backgrounds, patterns (task types AI tool preferences). findings underscore heterogeneous nature diverse populations contexts. This reveals several model. Our results indicate that technological emerges strongest predictor expectancy, alongside perception, enhancing perceived performance. Regarding hedonic demonstrate most prominent effects, implying should emphasize experience enjoyability transparency. Notably, lack contradicting our initial hypothesis. groups, crucial implications differentiated systems tailored needs.

Language: Английский

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Does Digitalization Benefit Employees? A Systematic Meta-Analysis of the Digital Technology–Employee Nexus in the Workplace

Systems, Journal Year: 2025, Volume and Issue: 13(6), P. 409 - 409

Published: May 24, 2025

The adoption of digital technologies (DTs) in the workplace has emerged as a core driver organizational effectiveness, and many studies have explored intrinsic connection between two. However, due to wide range subdivisions employee performance, existing present inconsistent research conclusions on implementation effects DTs lack systematic review their impact psychology behavior for large sample data. To address this issue, employing random-effects model psychometric meta-analysis approach based subgroup meta-regression analyses, study examines 106 empirical studies, comprising 119 effect sizes. findings reveal that exhibit “double-edged sword” effect. On bright side, significantly enhance task innovation engagement, job satisfaction, efficacy. dark aggravate service sabotage, withdrawal behavior, burnout, work anxiety suppressive well-being, while influence turnover intention is non-significant. Furthermore, identifies moderating industry characteristics, technology usage types, demographic factors relationships behavioral psychological outcomes. help clarify logical relationship provide explanations differentiated previous studies. This provides information scientific management decisions regarding workplace.

Language: Английский

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Why do graduate students use generative AI in thesis writing? the influence of self-efficacy, time pressure, and trust

Current Psychology, Journal Year: 2025, Volume and Issue: unknown

Published: June 3, 2025

Language: Английский

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