Description of the reflective measurement model
| Construct reliability, convergent validity, and descriptions of items | Items' outer loadings |
|---|---|
| RIE: Regenerative innovation ecosystem (α = 0.725, CR = 0.829, AVE = 0.548) | |
| RIE1: AI improves cross-disciplinary collaboration in our creative projects | 0.774 |
| RIE2: AI-driven tools have led to sustainability-focused redesigns in our business processes | 0.702 |
| RIE3: AI enables us to identify and access new market opportunities in creative industries | 0.775 |
| RIE4: AI supports the seamless integration of sustainable practices into our creative workflows | 0.707 |
| ARO: AI-driven resource optimization (α = 0.795, CR = 0.867, AVE = 0.621) | |
| ARO1: AI optimizes material and resource use in our creative projects | 0.806 |
| ARO2: AI-driven predictive maintenance reduces unplanned downtime and enhances creative operational efficiency | 0.797 |
| ARO3: Our supply chains for creative production have become more efficient through AI-powered optimization | 0.856 |
| ARO4: AI-based systems actively contribute to waste minimization in our creative processes | 0.683 |
| AES: AI-enhanced environmental sustainability (α = 0.691, CR = 0.766, AVE = 0.452) | |
| AES1: AI systems help reduce energy consumption in our creative production processes | 0.717 |
| AES2: AI has improved water management efficiency in our creative operations | 0.707 |
| AES3: Our AI systems are fundamental in minimizing environmental waste across our creative projects | 0.561 |
| AES4: Having real-time data from AI helps lower our creative projects' environmental footprint | 0.691 |
| SLA: Self-learning and adaptability of AI-enabled creative business models (α = 0.700, CR = 0.819, AVE = 0.537) | |
| SLA1: Our AI systems autonomously adapt to new challenges in creative projects | 0.527 |
| SLA2: AI tools are designed for self-learning and continuous improvement based on feedback from creative projects | 0.779 |
| SLA3: Our AI-driven creative business models evolve independently without manual intervention | 0.767 |
| SLA4: AI technologies in our organization self-adjust and repair in response to unforeseen creative workflow issues | 0.821 |
| SCC: Stakeholder collaboration and co-creation (α = 0.727, CR = 0.832, AVE = 0.560) | |
| SCC1: AI tools strengthen collaboration with stakeholders across creative industries | 0.772 |
| SCC2: We use AI platforms to co-create innovative products and services with external partners | 0.542 |
| SCC3: AI improves resource and data sharing with our creative collaborators | 0.833 |
| SCC4: AI enhances stakeholder engagement quality throughout all stages of our creative projects | 0.810 |
| Construct reliability, convergent validity, and descriptions of items | Items' outer loadings |
|---|---|
| RIE1: AI improves cross-disciplinary collaboration in our creative projects | 0.774 |
| RIE2: AI-driven tools have led to sustainability-focused redesigns in our business processes | 0.702 |
| RIE3: AI enables us to identify and access new market opportunities in creative industries | 0.775 |
| RIE4: AI supports the seamless integration of sustainable practices into our creative workflows | 0.707 |
| ARO1: AI optimizes material and resource use in our creative projects | 0.806 |
| ARO2: AI-driven predictive maintenance reduces unplanned downtime and enhances creative operational efficiency | 0.797 |
| ARO3: Our supply chains for creative production have become more efficient through AI-powered optimization | 0.856 |
| ARO4: AI-based systems actively contribute to waste minimization in our creative processes | 0.683 |
| AES1: AI systems help reduce energy consumption in our creative production processes | 0.717 |
| AES2: AI has improved water management efficiency in our creative operations | 0.707 |
| AES3: Our AI systems are fundamental in minimizing environmental waste across our creative projects | 0.561 |
| AES4: Having real-time data from AI helps lower our creative projects' environmental footprint | 0.691 |
| SLA1: Our AI systems autonomously adapt to new challenges in creative projects | 0.527 |
| SLA2: AI tools are designed for self-learning and continuous improvement based on feedback from creative projects | 0.779 |
| SLA3: Our AI-driven creative business models evolve independently without manual intervention | 0.767 |
| SLA4: AI technologies in our organization self-adjust and repair in response to unforeseen creative workflow issues | 0.821 |
| SCC1: AI tools strengthen collaboration with stakeholders across creative industries | 0.772 |
| SCC2: We use AI platforms to co-create innovative products and services with external partners | 0.542 |
| SCC3: AI improves resource and data sharing with our creative collaborators | 0.833 |
| SCC4: AI enhances stakeholder engagement quality throughout all stages of our creative projects | 0.810 |
Note(s): α = Cronbach's alpha, CR = composite reliability, AVE = average variance extracted. Outer loadings are taken from Figure 2
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