Artificial Intelligence and the Great Reset: Impacts and Perspectives for Italian SMEs Business Model Innovation
Abstract
:1. Introduction
2. Literature Review
2.1. Systems Thinking and the Great Reset
2.2. Artificial Intelligence and Sustainable Business Model Innovation
3. Research Framework and Hypothesis Development
3.1. Relationship between the Attitude towards Adopting AI Solutions and Sustainable Business Model Innovation
3.2. Relationship between Interdependence with Partners and the Attitude towards the Adoption of AI Solutions
3.3. Relationship between Entrepreneurial Orientation and Propensity to Adopt AI Solutions
3.4. Relationship between Entrepreneurial Orientation and Sustainable Business Model Innovation
3.5. Relationship between Partners with Interdependent Activities and Tasks and Sustainable Business Model Innovation
3.6. Relationship between Sustainability Orientation and Entrepreneurial Orientation
4. Materials and Methods
4.1. Sample Selection
4.2. Measures
4.3. Data Collection
5. Results and Discussions
5.1. Analysis of Reliability and Convergent Validity
5.2. Discriminant Validity Analysis
5.3. Evaluation of the Structural Model
5.4. Discussion of Results
6. Implication, Limitation, and Future Research
6.1. Theoretical Implications
6.2. Economic Implication
6.3. Managerial Implications
6.4. Limitation and Future Research
7. Conclusions
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- AI adoption is affected by collaboration and knowledge sharing among organizations; it is influenced by processes promoting access to external resources and those related to open innovation. Engaging with partners facilitates AI adoption by sharing resources and expertise, accelerating technological advancements. This supports the collaborative goals of the Great Reset (H2b, H5).
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- A negative attitude towards AI from managers adversely impacts the adoption of a sustainable business model. Consequently, companies should try to reduce its effects by reducing the fears related to these new technologies and those related to implementing disruptive innovations in company’s processes (H1b).
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- The adoption of AI influences entrepreneurial strategies towards sustainability and innovation, confirming the necessity for digital entrepreneurs to integrate AI into decision-making processes. Therefore, on one hand, a strong entrepreneurial orientation facilitates the adoption of AI, while on the other hand, it can help manage risks by avoiding uncalculated investments and preventing financial losses (H3a, H3b, H4).
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- Fostering an entrepreneurial mindset and promoting continuous learning can mitigate resistance to AI (H3b) and harness its potential for sustainability (H4, H6).
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- Although collaboration is essential for business model innovation from the perspectives of open innovation, excessive dependence on partners can lead organizations to lose control over their strategic and operational directions, thereby limiting their ability to respond to market changes and innovate autonomously (H2b).
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Characteristics | Types | Value | % |
---|---|---|---|
Industry | Manufacturing | 14 | 16.47 |
Service | 17 | 20 | |
Information technology | 21 | 24.70 | |
Healthcare | 11 | 12.94 | |
Energy | 14 | 16.47 | |
Other | 8 | 9.41 | |
Area | North | 35 | 41.17 |
Center | 26 | 30.58 | |
South | 24 | 28.23 | |
Requisites | At least 3% R&D | 26 | 30.58 |
At least 20% qualified staff | 34 | 40 | |
At least one patent | 25 | 29.41 |
Construct | Latent | Items | Cronbach’s Alpha | Composite Reliability | Average Variance Extracted (AVE) | |
---|---|---|---|---|---|---|
General Attitude Towards AI—Negative | GSN | GSN3 | 0.9140 | 0.943 | 0.959 | 0.887 |
GSN5 | 0.9360 | |||||
GSN6 | 0.9634 | |||||
General Attitude Towards AI—Positive | GSP | GSP1 | 0.8393 | 0.963 | 0.963 | 0.725 |
GSP4 | 0.7825 | |||||
GSP5 | 0.9181 | |||||
GSP6 | 0.8358 | |||||
GSP7 | 0.8712 | |||||
GSP8 | 0.7943 | |||||
GSP9 | 0.8317 | |||||
GSP10 | 0.8785 | |||||
GSP11 | 0.8661 | |||||
GSP12 | 0.8891 | |||||
Initiated-Task Interdependence | IT | IT1 | 0.8991 | 0.887 | 0.92 | 0.742 |
IT2 | 0.8490 | |||||
IT3 | 0.8580 | |||||
IT4 | 0.8379 | |||||
Sustainability Orientation | SO | SO1 | 0.9454 | 0.96 | 0.969 | 0.863 |
SO2 | 0.9087 | |||||
SO3 | 0.9593 | |||||
SO4 | 0.9057 | |||||
SO5 | 0.9242 | |||||
Entrepreneurship Orientation | INN | INN1 | 0.9207 | 0.809 | 0.884 | 0.721 |
INN2 | 0.6846 | |||||
INN3 | 0.9188 | |||||
PRO | PRO2 | 0.9077 | 0.831 | 0.921 | 0.854 | |
PRO3 | 0.9403 | |||||
RT | RT1 | 0.9422 | 0.842 | 0.91 | 0.836 | |
RT2 | 0.7821 | |||||
RT3 | 0.9098 | |||||
Sustainable Business Model Innovation | VCA | VCA1 | 0.7833 | 0.738 | 0.882 | 0.789 |
VCA2 | 0.8102 | |||||
VCA3 | 0.6857 | |||||
VCR | VCR1 | 0.9082 | 0.872 | 0.922 | 0.797 | |
VCR2 | 0.9088 | |||||
VCR3 | 0.8602 | |||||
VP | VP2 | 0.9332 | 0.84 | 0.926 | 0.862 | |
VP3 | 0.9236 |
GSN | GSP | IT | SO | INN | PRO | RT | VCA | VCR | VP | |
---|---|---|---|---|---|---|---|---|---|---|
GSN | 0.942 | . | . | . | . | . | . | . | . | . |
GSP | −0.235 | 0.828 | . | . | . | . | . | . | . | . |
IT | 0.333 | 0.612 | 0.861 | . | . | . | . | . | . | . |
SO | −0.058 | 0.384 | 0.286 | 0.929 | . | . | . | . | . | . |
INN | −0.244 | 0.614 | 0.328 | 0.781 | 0.849 | . | . | . | . | . |
PRO | −0.228 | 0.432 | 0.066 | 0.642 | 0.816 | 0.924 | . | . | . | . |
RT | −0.295 | 0.254 | −0.012 | −0.013 | 0.026 | 0.171 | 0.915 | . | . | . |
VCA | 0.31 | 0.399 | 0.476 | 0.472 | 0.422 | 0.362 | −0.173 | 0.888 | . | . |
VCR | 0.225 | 0.383 | 0.387 | 0.784 | 0.699 | 0.655 | −0.082 | 0.76 | 0.893 | . |
VP | 0.459 | 0.204 | 0.483 | 0.553 | 0.903 | 0.382 | 0.236 | −0.272 | 0.766 | 0.928 |
HP | Relationship | Original Est. | Bootstrap Mean | Bootstrap SD | T Stat. | 2.5% CI | 97.5% CI |
---|---|---|---|---|---|---|---|
HP1a | GSP → SBMI | −0.293 | −0.28 | 0.163 | −1.797 | −0.602 | 0.042 |
HP1b | GSN → SBMI | −0.339 | −0.336 | 0.091 | 3.711 | −0.148 | −0.511 |
HP2a | IT → GSP | 0.456 | 0.459 | 0.063 | 7.258 | 0.336 | 0.585 |
HP2b | IT → GSN | 0.426 | 0.424 | 0.083 | 5.153 | 0.253 | 0.576 |
HP3a | EO → GSP | 0.517 | 0.507 | 0.078 | 6.601 | 0.338 | 0.645 |
HP3b | EO → GSN | −0.419 | −0.407 | 0.103 | −4.078 | −0.583 | −0.189 |
HP4 | EO → SBMI | 0.725 | 0.719 | 0.093 | 7.775 | 0.532 | 0.896 |
HP5 | IT → SBMI | 0.359 | 0.345 | 0.157 | 2.283 | 0.011 | 0.636 |
HP6 | SO → EO | 0.73 | 0.727 | 0.058 | 12.558 | 0.599 | 0.824 |
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Muto, V.; Luongo, S.; Percuoco, M.; Tani, M. Artificial Intelligence and the Great Reset: Impacts and Perspectives for Italian SMEs Business Model Innovation. Systems 2024, 12, 330. https://doi.org/10.3390/systems12090330
Muto V, Luongo S, Percuoco M, Tani M. Artificial Intelligence and the Great Reset: Impacts and Perspectives for Italian SMEs Business Model Innovation. Systems. 2024; 12(9):330. https://doi.org/10.3390/systems12090330
Chicago/Turabian StyleMuto, Valerio, Simone Luongo, Martina Percuoco, and Mario Tani. 2024. "Artificial Intelligence and the Great Reset: Impacts and Perspectives for Italian SMEs Business Model Innovation" Systems 12, no. 9: 330. https://doi.org/10.3390/systems12090330