Innovative Pathways: Leveraging AI Adoption and Team Dynamics for Multinational Corporation Success
Abstract
1. Introduction
- Does the orientation to adopt AI models influence innovation performance?
- Does team innovativeness positively mediate the relationship between the orientation to adopt AI models and innovation performance?
- Does technology adoption positively moderate the relationship between the orientation to adopt AI models and team innovativeness?
2. Theoretical Underpinnings and Hypothesis Development
2.1. Technology Acceptance Model and Conceptual Framework
2.2. Hypothesis Development
2.2.1. Orientation to Adopt AI Models
2.2.2. Team Innovativeness
2.2.3. Technology Orientation
3. Research Methods
3.1. Data Collection and Sampling
3.2. Ethical Approval and Research Authorization
3.3. Measurement Model
3.4. Data Analysis Methods
4. Research Findings
4.1. Respondents’ Profile
4.2. Common Method Bias
4.3. Measurement Model
4.4. Structural Model
4.5. Hypothesis Testing
5. Discussion
5.1. Theoretical Implications
5.2. Practical Implications
- Managerial Guidance for MNCs
- Policy Recommendations for Innovation Ecosystems
- Strategic Role of Governments in AI-Driven Innovation
5.3. Limitations and Future Research
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Construct | Items | Sources |
Orientation to Adopt AI | OAAI-O1: “AI allows multilevel actors’ participation, contribution, process and outcomes” | (Rothwell et al., 1974; Upadhyay et al., 2023) |
OAAI-O2: “Actors’ participation, contribution, process and outcomes are supported by AI” | ||
OAAI-O3: “AI provides various ways to collaborate, participate, use process to generate outcomes” | ||
OAAI-A1: “AI is affordable in a use context” | (Faraj & Azad, 2012; Upadhyay et al., 2023) | |
OAAI-A2: “I require AI in a use context” | ||
OAAI-A3: “In a use context, AI is affordable” | ||
OAAI-G1: “AI helps to create new digital artefacts, products and services” | (Turner & Fauconnier, 1999; Upadhyay et al., 2023) | |
OAAI-G2: “AI provide APIs and libraries to build new digital artefacts, products and services” | ||
OAAI-G3: “I can develop new digital artefacts, products and services using AI APIs and Libraries” | ||
Team Innovativeness | TI1: “Our team creates new ideas, processes, products, and systems that are critical to our team’s innovation success.” | (Bamgbade et al., 2022; Upadhyay et al., 2023) |
TI2: “Our team tends to be an early adopter of the innovative technologies.” | ||
TI3: “Our team actively seeks opportunities to apply innovative ideas and solutions.” | ||
TI4: “Our team proactively uses innovative technologies to address changing project or task needs.” | ||
Technology Orientation | TO1: “Our firm uses innovative technologies in providing solutions” | (Bamgbade et al., 2022) |
TO2: “Our firm uses state of the art of technology for products development” | ||
TO3: “Our firm is very proactive in providing innovative solutions to respond to clients’ needs” | ||
TO4: “Our firm has the will and the capacity to build and market innovative solutions” | ||
Innovation Performance | IP1: “Highly responsive attitude towards environmental changes” | (Jansen et al., 2006; Tian et al., 2021) |
IP2: “Actively innovates for products and services” | ||
IP3: “Develops manufacturing process to improve quality and lower costs” | ||
IP4: “‘Focuses on developing marketing process to improve products services” |
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Factors | Range | Number | Frequency (%) |
---|---|---|---|
Gender | Male | 267 | 65% |
Female | 141 | 34% | |
Age | 20–25 | 57 | 14% |
26–30 | 138 | 34% | |
31–35 | 147 | 36% | |
35–40 | 29 | 7% | |
Above 40 | 39 | 10% | |
Team Leaders | Supervisors | 31 | 8% |
Managers | 277 | 68% | |
CEO’s | 102 | 25% | |
Education Level | High school certificate | 4 | 1% |
Bachelor’s degree | 129 | 31% | |
Master’s degree | 160 | 39% | |
Ph.D. degree | 28 | 7% | |
Other professional education | 89 | 22% | |
Experience | 1 year or less | 15 | 4% |
1–5 years | 83 | 20% | |
6–10 years | 157 | 38% | |
11–15 years | 140 | 34% | |
More than 15 years | 15 | 4% | |
Company Size | Small (1–50 employees) | 150 | 36.6% |
Medium (51–500 employees) | 200 | 48.8% | |
Large (501 + employees) | 60 | 14.6% | |
Industry Affiliation | Technology | 120 | 29.3% |
Manufacturing | 90 | 22.0% | |
Healthcare | 70 | 17.1% | |
Finance | 60 | 14.6% | |
Retail | 40 | 9.8% | |
Other (Specify) | 30 | 7.3% | |
Team Size | 1–5 Members | 180 | 43.9% |
6–10 Members | 150 | 36.6% | |
11–20 Members | 60 | 14.6% | |
21+ Members | 20 | 4.9% | |
Total | 410 | 100% |
Constructs | Items | Loadings | CA | CR | AVE | VIF |
---|---|---|---|---|---|---|
Innovation Performance (IP) | IP1, IP2, IP3, IP4 | 0.889, 0.882, 0.848, 0.863 | 0.893 | 0.926 | 0.757 | |
Team Innovativeness (TI) | TI1, TI2, TI3, TI4 | 0.842, 0.843, 0.900, 0.932 | 0.902 | 0.932 | 0.774 | 1.238 |
Orientation to Adopt AI (OAAI) Models | OAAI1, OAAI2, OAAI3 | 0.710, 0.822, 0.813 | 0.920 | 0.934 | 0.612 | 1.208 |
OAAI14, OAAI5, OAAI6 | 0.831, 0.692, 0.776 | |||||
OAAI7, OAAI8, OAAI9 | 0.841, 0.812, 0.727 | |||||
Technology Orientation (TO) | TO1, TO2, TO3, TO4 | 0.908, 0.886, 0.918, 0.926 | 0.931 | 0.951 | 0.828 | 1.367 |
Fornell-Larcker Criterion | ||||
Constructs | OAAI | IP | TI | TO |
OAAI | 0.782 | |||
PI | 0.449 | 0.870 | ||
TI | 0.415 | 0.418 | 0.880 | |
TO | 0.428 | 0.418 | 0.434 | 0.910 |
Heterotrait-Monotrait (HTMT) Ratio | ||||
Constructs | OAAI | IP | TI | TO |
OAAI | ||||
PI | 0.494 | |||
TI | 0.443 | 0.462 | ||
TO | 0.457 | 0.458 | 0.471 | |
TO × OAAI | 0.267 | 0.145 | 0.085 | 0.405 |
Constructs | R2 | Q2 | F2 | SRMR | NFI |
---|---|---|---|---|---|
Innovation Performance | 0.278 | 0.224 | 0.125 | 0.059 | 0.846 |
Team Innovativeness | 0.268 | 0.244 | 0.097 |
Control Variables | Original Sample (O) | T statistics | p Values | Confidence Interval (2.5%; 97.5%) | |
---|---|---|---|---|---|
Company Size -> IP | 0.236 | 2.049 | 0.040 | 0.006, 0.458 | |
Industry Affiliation -> IP | −0.080 | 0.602 | 0.547 | −0.335, 0.180 | |
Team Size -> IP | −0.090 | 0.671 | 0.502 | −0.351, 0.174 | |
Hypothesis Relationships | Direct Effects | ||||
H1 | OAAI -> TI | 0.296 | 5.540 | 0.000 | 0.189, 0.396 |
H2 | OAAI -> IP | 0.344 | 5.703 | 0.000 | 0.224, 0.460 |
H3 | TI -> IP | 0.290 | 4.866 | 0.000 | 0.172, 0.404 |
Mediating Effect | |||||
H4 | OAAI -> TI -> IP | 0.086 | 3.235 | 0.001 | 0.041, 0.143 |
Moderating Effect | |||||
H5 | TO × OAAI -> TI | 0.105 | 2.063 | 0.039 | 0.006, 0.205 |
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Javed, H.; Goncalves, M.; Thirunavukkarasu, S. Innovative Pathways: Leveraging AI Adoption and Team Dynamics for Multinational Corporation Success. Businesses 2025, 5, 28. https://doi.org/10.3390/businesses5030028
Javed H, Goncalves M, Thirunavukkarasu S. Innovative Pathways: Leveraging AI Adoption and Team Dynamics for Multinational Corporation Success. Businesses. 2025; 5(3):28. https://doi.org/10.3390/businesses5030028
Chicago/Turabian StyleJaved, Hasnain, Marcus Goncalves, and Shobana Thirunavukkarasu. 2025. "Innovative Pathways: Leveraging AI Adoption and Team Dynamics for Multinational Corporation Success" Businesses 5, no. 3: 28. https://doi.org/10.3390/businesses5030028
APA StyleJaved, H., Goncalves, M., & Thirunavukkarasu, S. (2025). Innovative Pathways: Leveraging AI Adoption and Team Dynamics for Multinational Corporation Success. Businesses, 5(3), 28. https://doi.org/10.3390/businesses5030028