Artificial intelligence in the defense sector: an RBV and isomorphism perspectives to the case of the Lebanese Armed Forces

Abstract

Purpose

This paper aims to dynamically analyze the opportunities and challenges of AI in the defense sector in Lebanon or any security agency or any organization with sensitive data through a resource-based view perspective, the adoption of artificial intelligence (AI)/narrow AI applications in the Lebanese Armed Forces (LAF) and to diagnose the current strategic orientation toward innovation and technology within the LAF while avoiding isomorphism.

Design/methodology/approach

The methodology is based on a qualitative interpretive case-study approach collected from several departments of the LAF. In fact, there is a developing convention to use qualitative research approaches among which case studies to study information technology phenomena (Trauth and Jessup, 2000; Benbasat et al., 1987; Klein and Meyers, 1999). Data were collected through centered semi-structured in-depth interviews (two to three hours each) with an interview guide coded abductively between the researchers and the interviewees conducted in numerous departments of the LAF with their top officials and generals (O1, O2, O3…); the anonymity of the interviewees was kept due to the sensitivity of the data collected, which took place between September 2018 and March 2019. Data consolidation and processing were conducted using NVivo.

Findings

This paper shows that the LAF is undeniably facing many challenges among which isomorphism caused by the lack of resources; it also shows that narrow AI applications provide new avenues for the LAF to avoid such institutional isomorphism.

Originality/value

The role of narrow AI in limiting isomorphism in the defense sector.

Citation

Chedrawi, C. and Atallah, Y. (2022), "Artificial intelligence in the defense sector: an RBV and isomorphism perspectives to the case of the Lebanese Armed Forces", Journal of Asia Business Studies, Vol. 16 No. 2, pp. 279-293. https://doi.org/10.1108/JABS-09-2020-0377