Artificial intelligence research: A review on dominant themes, methods, frameworks and future research directions

Abstract

This article presents an analysis of artificial intelligence (AI) in information systems and innovation-related journals to determine the current issues and stock of knowledge in AI literature, research methodology, frameworks, level of analysis and conceptual approaches. By doing this, the article aims to identify research gaps that can guide future investigations. A total of 85 peer-reviewed articles from 2020 to 2023 were used in the analysis. The findings show that extant literature is skewed towards the prevalence of technological issues and highlights the relatively lower focus on other themes, such as contextual knowledge co-creation issues, conceptualisation, and application domains. While there have been increasing technological issues with artificial intelligence, the three identified areas of security concern are data security, model security and network security. Furthermore, the review found that contemporary AI, which continually drives the boundaries of computational capabilities to tackle increasingly intricate decision-making challenges, distinguishes itself from earlier iterations in two primary aspects that significantly affect organisational learning in dealing with AI's potential: autonomy and learnability. This study contributes to AI research by providing insights into current issues, research methodology, level of analysis and conceptual approaches, and AI framework to help identify research gaps for future investigations.

Keywords

• Artificial intelligence
• Classification
• Literature review
• Technological issues
• Research agenda