arXiv subjects

Matt Price

Publications and source records attributed to Matt Price.

4 recordsLinked to original sources

arXiv2026

Work Design and Multidimensional AI Threat as Predictors of Workplace AI Adoption and Depth of Use

Artificial intelligence tools are increasingly embedded in everyday work, yet employees' uptake varies widely even within the same organization. Drawing on sociotechnical and work design perspectives, this research examines whether motivational job characteristics and multidimensional AI threat perceptions jointly predict workplace AI adoption and depth of use. Using cross-sectional survey data from 2,257 employees, we tested group differences across role level, years of experience, and region, along with multivariable predictors of AI adoption and use depth, specifically frequency and duration. Across models, job design, especially skill variety and autonomy, showed the most consistent positive associations with AI adoption, whereas threat dimensions exhibited differentiated patterns for depth of use. Perceived changes in work were positively associated with frequency and duration, while status threat showed a negative but not consistently significant relationship with deeper use. Findings are correlational given the cross-sectional and self-report design. Practical implications emphasize aligning AI enablement efforts with work design and monitoring potential workload expansion alongside adoption initiatives.

cs.CY↗

arXiv2026

Safety First: Psychological Safety as the Key to AI Transformation

Organizations continue to invest in artificial intelligence, yet many struggle to ensure that employees adopt and engage with these tools. Drawing on research highlighting the interpersonal and learning demands of technology use, this study examines whether psychological safety is associated with AI adoption and usage in the workplace. Using survey data from 2,257 employees in a global consulting firm, we test whether psychological safety is associated with adoption, usage frequency, and usage duration; and whether these relationships vary by organizational level, professional experience, or geographic region. Logistic and linear regression analyses show that psychological safety reliably predicts whether employees adopt AI tools but does not predict how often or how long they use AI once adoption has occurred. Moreover, the relationship between psychological safety and AI adoption is consistent across experience levels, role levels, and regions, and no moderation effects emerge. These findings suggest that psychological safety functions as a key antecedent of initial AI engagement but not of subsequent usage intensity. The study underscores the need to distinguish between adoption and sustained use and highlights opportunities for targeted organizational interventions in early-stage AI implementation.

cs.CY↗

arXiv2025

Revisiting UTAUT for the Age of AI: Understanding Employees AI Adoption and Usage Patterns Through an Extended UTAUT Framework

This study investigates whether demographic factors shape adoption and attitudes among employees toward artificial intelligence (AI) technologies at work. Building on an extended Unified Theory of Acceptance and Use of Technology (UTAUT), which reintroduces affective dimensions such as attitude, self-efficacy, and anxiety, we surveyed 2,257 professionals across global regions and organizational levels within a multinational consulting firm. Non-parametric tests examined whether three demographic factors (i.e., years of experience, hierarchical level in the organization, and geographic region) were associated with AI adoption, usage intensity, and eight UTAUT constructs. Organizational level significantly predicted AI adoption, with senior employees showing higher usage rates, while experience and region were unrelated to adoption. Among AI users (n = 1,256), frequency and duration of use showed minimal demographic variation. However, omnibus tests revealed small but consistent group differences across several UTAUT constructs, particularly anxiety, performance expectancy, and behavioral intention, suggesting that emotional and cognitive responses to AI vary modestly across contexts. These findings highlight that demographic factors explain limited variance in AI acceptance but remain relevant for understanding contextual nuances in technology-related attitudes. The results underscore the need to integrate affective and organizational factors into models of technology acceptance to support equitable, confident, and sustainable engagement with AI in modern workplaces.

cs.CY↗

arXiv2024

Using conditional GANs for convergence map reconstruction with uncertainties

Understanding the large-scale structure of the Universe and unravelling the mysteries of dark matter are fundamental challenges in contemporary cosmology. Reconstruction of the cosmological matter distribution from lensing observables, referred to as 'mass-mapping' is an important aspect of this quest. Mass-mapping is an ill-posed problem, meaning there is inherent uncertainty in any convergence map reconstruction. The demand for fast and efficient reconstruction techniques is rising as we prepare for upcoming surveys. We present a novel approach which utilises deep learning, in particular a conditional Generative Adversarial Network (cGAN), to approximate samples from a Bayesian posterior distribution, meaning they can be interpreted in a statistically robust manner. By combining data-driven priors with recent regularisation techniques, we introduce an approach that facilitates the swift generation of high-fidelity, mass maps. Furthermore, to validate the effectiveness of our approach, we train the model on mock COSMOS-style data, generated using Colombia Lensing's kappaTNG mock weak lensing suite. These preliminary results showcase compelling convergence map reconstructions and ongoing refinement efforts are underway to enhance the robustness of our method further.

astro-ph.CO↗