Abstract
Artificial Intelligence (AI) tools, applications, and capabilities have received tremendous attention from industry practitioners, scholars, and policymakers. Despite the substantial progress of the literature on AI, there is a considerable scarcity of research investigating the effects of AI capability, considering the importance of a data-driven culture and whether a data-driven culture truly mediates the relationship between AI capability and firm performance from a sustainable development perspective. Anchored by the resource-based theory (RBT), we developed a high-order model of AI capability and its resources (tangible, intangible, and human). We used a two-stage approach, with PLS-SEM in the first and fsQCA in the second. The findings from the first step suggest that AI capability directly impacts firm performance and that data-driven culture mediates the relationship between AI capability and firm performance. The results from the second step indicated that different configurations of AI resources could be considered for firms to achieve high performance but that AI infrastructure is a crucial resource. Our study advances the literature on AI capability and sustainable development goals. Similarly, it contributes to moving the RBT theory forward by suggesting that AI capability is a paramount variable that substantially influences firm performance. Simultaneously, it is harmoniously connected with SDG 9 (industry, innovation, and infrastructure) and SDG 12 (responsible consumption and production).
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Data Availability
The data supporting this study's findings are available from the corresponding author upon request.
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Contributions
Samuel Fosso Wamba: Idea generation and formulation, Lead the research goals and aims,
Development of the research model, Led the survey design and pilot testing,
Co-writing of the first draft, advanced draft, and final paper.
Maciel M. Queiroz: Lead the writing of the first draft, Co-writing of the advanced draft and final paper, Validation of the selected theories, Validation of the final survey, PLS-SEM data analysis.
Ilias O. Pappas: Development of the fsQCA model, fsQCA data analysis, Co-writing of the advanced draft and final paper.
Yulia W. Sullivan: Co-writing of the advanced draft and final paper,
Validation of the selected theories.
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Appendix 1
Appendix 1
1.1 Demographic profile of the respondents
N = 205 | Percentage | |
|---|---|---|
Age | ||
18 - 30 | 34 | 16.58 |
31 - 43 | 107 | 52.20 |
44 - 56 | 38 | 18.54 |
57 - 69 | 18 | 8.78 |
70 - 82 | 8 | 3.90 |
Gender | ||
Male | 138 | 67.30 |
Female | 67 | 32.70 |
Education | ||
High school | 12 | 5.85 |
Bachelors' degree | 96 | 46.83 |
Masters’ degree | 51 | 24.88 |
Postgraduate | 29 | 14.15 |
Ph.D. | 17 | 8.29 |
Industry | ||
Banking/finance | 27 | 13.17 |
Computers/software | 50 | 24.39 |
Consulting | 10 | 4.88 |
Insurance | 6 | 2.93 |
Manufacturing | 49 | 23.90 |
Medicine/health | 13 | 6.34 |
Publishing/communications | 5 | 2.44 |
Hotel/restaurants | 6 | 2.93 |
Transportation | 5 | 2.44 |
Construction | 8 | 3.90 |
Education | 7 | 3.41 |
Retail and wholesale trade | 6 | 2.93 |
Others | 13 | 6.34 |
Position | ||
Vice-President | 39 | 19.02 |
Director of Operations | 36 | 17.56 |
Business Analyst | 21 | 10.24 |
Project Manager | 17 | 8.29 |
Operations Manager | 14 | 6.83 |
Director of Supply Management | 8 | 3.90 |
Distribution Manager | 8 | 3.90 |
Director of Logistics & Distribution | 7 | 3.40 |
Supply Chain Manager | 6 | 2.93 |
Director of Global Procurement | 6 | 2.93 |
Logistics Analyst | 4 | 1.95 |
Demand Planning Manager | 4 | 1.95 |
Production Planner | 3 | 1.46 |
Global Sourcing Manager | 2 | 0.98 |
Process Improvement Manager | 2 | 0.98 |
Purchasing Manager | 2 | 0.98 |
Sourcing Specialist | 2 | 0.98 |
Transportation Specialist | 2 | 0.98 |
Import/Export Specialist | 1 | 0.49 |
Quality Systems Auditor | 1 | 0.49 |
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Fosso Wamba, S., Queiroz, M.M., Pappas, I.O. et al. Artificial Intelligence Capability and Firm Performance: A Sustainable Development Perspective by the Mediating Role of Data-Driven Culture. Inf Syst Front (2024). https://doi.org/10.1007/s10796-023-10460-z
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DOI: https://doi.org/10.1007/s10796-023-10460-z