Paper Type
Short
Paper Number
PACIS2026-1101
Description
This study develops and empirically examines a startup-specific model of Artificial Intelligence (AI) capability in emerging innovation ecosystems. Although AI capability is increasingly recognized as a driver of innovation and competitive performance, prior research has largely focused on established firms, with limited attention to startups. Drawing on Resource-Based Theory (RBT), this study adapts Mikalef and Gupta’s (2021) AI capability framework to reflect the structural fluidity, resource constraints, and experimentation-driven nature of early-stage ventures in Qatar. The proposed model conceptualizes AI capability as a multidimensional construct comprising tangible (AI data modeling, AI stack, funding), human (technical AI skills, leadership in AI), and intangible (agile execution, risk proclivity) factors. Using survey data from AI startups, the study will employ PLS-SEM to examine the relationships between these factors, AI capability, and venture performance. The findings contribute to AI and digital entrepreneurship research and provide practical implications for strengthening AI readiness in emerging ecosystems.
Recommended Citation
Vatanasakdakul, Savanid; Al-Mohanadi, Deema; and Aoun, Chadi, "AI Capability of Startups in Qatar" (2026). PACIS 2026 Proceedings. 1.
https://aisel.aisnet.org/pacis2026/di_entren/di_entren/1
AI Capability of Startups in Qatar
This study develops and empirically examines a startup-specific model of Artificial Intelligence (AI) capability in emerging innovation ecosystems. Although AI capability is increasingly recognized as a driver of innovation and competitive performance, prior research has largely focused on established firms, with limited attention to startups. Drawing on Resource-Based Theory (RBT), this study adapts Mikalef and Gupta’s (2021) AI capability framework to reflect the structural fluidity, resource constraints, and experimentation-driven nature of early-stage ventures in Qatar. The proposed model conceptualizes AI capability as a multidimensional construct comprising tangible (AI data modeling, AI stack, funding), human (technical AI skills, leadership in AI), and intangible (agile execution, risk proclivity) factors. Using survey data from AI startups, the study will employ PLS-SEM to examine the relationships between these factors, AI capability, and venture performance. The findings contribute to AI and digital entrepreneurship research and provide practical implications for strengthening AI readiness in emerging ecosystems.
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