Examining the limitations of AI in business and the need for human insights using Interpretive Structural Modelling

Alawamleh, Mohammad; Shammas, Natalie; Alawamleh, Kamal; Bani Ismail, Loiy

doi:10.1016/j.joitmc.2024.100338

Journal of Open Innovation: Technology, Market, and Complexity

2024

DOI: 10.1016/j.joitmc.2024.100338

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Examining the limitations of AI in business and the need for human insights using Interpretive Structural Modelling

Mohammad Alawamleh,

Natalie Shammas,

Kamal Alawamleh

et al.

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2024

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Cited by 2 publications

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Tourism entrepreneurship research: a mixed embeddedness approach

Manalu,

Simatupang,

Novani

2024

Cogent Business & Management

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Tourism entrepreneurship research: a mixed embeddedness approach

Manalu,

Simatupang,

Novani

2024

Cogent Business & Management

View full text Add to dashboard Cite

Exploring Facilitators and Barriers to Managers’ Adoption of AI-Based Systems in Decision Making: A Systematic Review

Marocco,

Barbieri,

Talamo

2024

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Introduction—Decision making (DM) is a fundamental responsibility for managers, with significant implications for organizational performance and strategic direction. The increasing complexity of modern business environments, along with the recognition of human reasoning limitations related to cognitive and emotional biases, has led to a heightened interest in harnessing emerging technologies like Artificial Intelligence (AI) to enhance DM processes. However, a notable disparity exists between the potential of AI and its actual adoption within organizations, revealing skepticism and practical challenges associated with integrating AI into complex managerial DM scenarios. This systematic literature review aims to address this gap by examining the factors that influence managers’ adoption of AI in DM. Methods—This study adhered to the PRISMA guidelines. Articles from 2010 to 2024 were selected from the Scopus database using specific keywords. Eligible studies were included after rigorous screening and quality assessment using checklist tools. Results—From 202 articles screened, a data synthesis of 16 eligible studies revealed seven major interconnected factors acting as key facilitators or barriers to AI integration within organizations. These factors—Managers’ Perceptions of AI, Ethical Factors, Psychological and Individual Factors, Social and Psychosocial Factors, Organizational Factors, External Factors, and Technical and Design Characteristics of AI—were then organized into a complex analytical framework informed by existing theoretical constructs. Discussion—This contribution provides valuable insights into how managers perceive and interact with AI systems, as well as the conditions necessary for successful integration into organizational DM processes.

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Examining the limitations of AI in business and the need for human insights using Interpretive Structural Modelling

Cited by 2 publications

References 108 publications

Tourism entrepreneurship research: a mixed embeddedness approach

Tourism entrepreneurship research: a mixed embeddedness approach

Exploring Facilitators and Barriers to Managers’ Adoption of AI-Based Systems in Decision Making: A Systematic Review

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