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

Ayman wael Al-khatib, Moh'd Anwer AL-Shboul and Mais Khattab

Technology in Society, 2024, vol. 78, issue C

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.

Keywords: Generative artificial intelligence; Innovation ambidexterity; Digital supply chain; Manufacturing firms; Performance; Hybrid analysis; Jordan (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0160791X24002240
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:teinso:v:78:y:2024:i:c:s0160791x24002240

DOI: 10.1016/j.techsoc.2024.102676

Access Statistics for this article

Technology in Society is currently edited by Charla Griffy-Brown

More articles in Technology in Society from Elsevier
Bibliographic data for series maintained by Catherine Liu ().