The interplay of data-driven insights and AI anxiety in shaping the impact of AI capabilities on circular economy capability

• AI Capabilities do not directly enhance Circular Economy Capability (CEC). • Data-driven insights mediate the relationship between AIC and CEC. • AI anxiety negatively moderates the insights' effect on CEC. In a world facing pressing environmental challenges like climate change and resource scarcity, Artificial Intelligence (AI) is widely regarded as a powerful tool to enhance and support sustainability goals via Circular Economy Capability (CEC). The organizational capacity to leverage this technology, Artificial Intelligence Capability (AIC), is conceptualized through the lens of the Resource-Based Theory (RBT) as the capacity to effectively implement and utilize AI to generate strategic value. However, the direct relationship between AIC and CEC is not straightforward. The purpose of this research is to investigate this nuanced relationship by examining how socio-technical factors such as Data-Driven Insights (DDI)—actionable inferences derived from analytics over data—and AI Anxiety—stemming from employees' fear of job loss—shape the relationship between AIC and CEC. Using a moderated mediation model and Partial Least Squares Structural Equation Modeling (PLS-SEM), we analyzed data from firms with moderate to high technology maturity. While the study’s results are primarily based on context-specific evidence, which invites further investigation into generalizability to other settings, our findings suggest that the direct effect of AIC on CEC is not significant. Instead, DDI significantly mediate the relationship, confirming that AIC must be bundled with actionable insights to create value. Crucially, AI anxiety negatively moderates the effect of DDI on CEC. This means that while organizations may generate valuable insights, employee resistance and fear hinder their effective translation into sustainability practices. This study highlights the critical socio-technical barriers to AI adoption and their impact on achieving sustainability goals.

Authors

Institutions

Publication Details

Journal
Journal of Industrial Information Integration
Published
2025-11-24
DOI
https://doi.org/10.1016/j.jii.2025.101019
Primary Topic
Environmental Sustainability in Business
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The interplay of data-driven insights and AI anxiety in shaping the impact of AI capabilities on circular economy capability

Camilo Restrepo‐Estrada, José Arias-Pérez, Robinson Garcés-Marín
Journal of Industrial Information Integration
Environmental Sustainability in Business
article

The interplay of data-driven insights and AI anxiety in shaping the impact of AI capabilities on circular economy capability

Camilo Restrepo‐Estrada, José Arias-Pérez, Robinson Garcés-Marín
article en

Abstract

• AI Capabilities do not directly enhance Circular Economy Capability (CEC). • Data-driven insights mediate the relationship between AIC and CEC. • AI anxiety negatively moderates the insights' effect on CEC. In a world facing pressing environmental challenges like climate change and resource scarcity, Artificial Intelligence (AI) is widely regarded as a powerful tool to enhance and support sustainability goals via Circular Economy Capability (CEC). The organizational capacity to leverage this technology, Artificial Intelligence Capability (AIC), is conceptualized through the lens of the Resource-Based Theory (RBT) as the capacity to effectively implement and utilize AI to generate strategic value. However, the direct relationship between AIC and CEC is not straightforward. The purpose of this research is to investigate this nuanced relationship by examining how socio-technical factors such as Data-Driven Insights (DDI)—actionable inferences derived from analytics over data—and AI Anxiety—stemming from employees' fear of job loss—shape the relationship between AIC and CEC. Using a moderated mediation model and Partial Least Squares Structural Equation Modeling (PLS-SEM), we analyzed data from firms with moderate to high technology maturity. While the study’s results are primarily based on context-specific evidence, which invites further investigation into generalizability to other settings, our findings suggest that the direct effect of AIC on CEC is not significant. Instead, DDI significantly mediate the relationship, confirming that AIC must be bundled with actionable insights to create value. Crucially, AI anxiety negatively moderates the effect of DDI on CEC. This means that while organizations may generate valuable insights, employee resistance and fear hinder their effective translation into sustainability practices. This study highlights the critical socio-technical barriers to AI adoption and their impact on achieving sustainability goals.

Journal of Industrial Information IntegrationVol. 49
Emory Healthcare (US), Emory University (US), Emory University Hospital (US), Universidad de Antioquia (CO), Universidad de Medellín (CO)
Openalex Percentile: Top 53%
Environmental Sustainability in Business
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.