How can generative artificial intelligence improve digital supply chain performance in manufacturing firms? Analyzing the mediating role of innovation ambidexterity using hybrid analysis through CB-SEM and PLS-SEM

Abstract

Artificial intelligence capabilities (AIC) can influence supply chain management (SCM) in multiple ways. This study explores how generative artificial intelligence capabilities (GAIC) could affect digital supply chain performance (DSCP) through ambidexterity innovation (AMI), which includes both elements, exploratory and exploitative innovations in the manufacturing firms (MFs) in Jordan as a developing and emerging economy. This study adopted a quantitative methodology for the data collection process applying a cross-sectional approach through testing deductive-hypotheses techniques. 263 valid surveys were used for analysis using hybrid analysis measurements (i.e., PLS-SEM, and CB-SEM). Further, it was applied data reliability, convergent validity, and discriminant validity tests. Additionally, examined the mediating effect of exploratory innovation (EXPI), and exploitative innovation (EXTI) on DSCP. The study findings assured that the proposed direct and indirect causal associations illustrated in the study model were accepted due to that all associations between the dimensions s were statistically significant. The findings of the GAIC supported a positive relationship between GAIC and the DSCP, GAIC on EXPI and EXTI, and EXPI and EXTI on DSCP respectively. Furthermore, the mediating effect of EXPI and EXTI is statistically significant, which was confirmed. This study developed a conceptual model to merge GAIC, AMI, and DSCP. This study provides new outcomes that bridge the existing research gap in the literature by testing the mediation model with a focus on the MF benefits of GAIC to improve levels of EXPI, EXTI, and DSCP in Jordan as a developing and emerging economy. Furthermore, this study is considered unique, as it was the first study in Jordan, and through applying hybrid analysis measurements using both PLS-SEM and CB-SEM methods.