| •RQ E1: How do CEO characteristics and top management team (TMT) composition influence the extent to which firms adopt energy-efficient AI applications? •RQ E2: To what extent do executive compensation structures and board oversight mechanisms influence firms’ decisions to prioritize energy-efficient AI deployment over performance-maximizing alternatives? •RQ E3: How do regulatory and normative institutional pressures influence firms’ deployment of AI for environmental purposes such as compliance monitoring, reporting accuracy, or biodiversity impact assessment? •RQ E4: How do stakeholder pressures (from customers, investors, or NGOs) shape the breadth and depth of AI applications targeting environmental performance (e.g., circular economy models, material footprint reduction)? •RQ E5: Can the integration of AI capabilities for environmental analytics (e.g., pollution monitoring, water usage optimization) be considered a strategic resource contributing to sustained competitive advantage? •RQ E6: How do firms navigate the paradox between maximizing operational efficiencies through AI and mitigating potential environmental harm (e.g., rebound effects, electronic waste from sensors)? •RQ E7: How can we cluster transparency initiatives about AI’s environmental footprint and which consequences do these different regulations have on countries’ AI competitiveness and tech firms’ engagement in improvements of their environmental performance? | •RQ S1: How will the AI transformation affect employability? •RQ S2: Which business and management roles are most resilient to high AI Occupational Exposure, and what strategic reskilling pathways can organizations and educational institutions implement to sustain employability? How can tech firms support this process? •RQ S3: How can firms foster and measure “creative business problem-solving with technology” as a core capability in an AI-augmented workforce? •RQ S4: How might the introduction of AI-specific taxation (e.g., robot tax) influence the distribution of economic value between technology providers, employees, and the state? •RQ S5: How should business and management education evolve to address both technical literacy and ethical foresight for professions under high AI exposure? •RQ S6: What are effective pedagogical approaches to integrate AI safety, ethics, and responsible innovation into business and management curricula at universities? •RQ S7: What normative boundaries should guide how individuals and organizations relate to AI models, especially when emotional attachment and moral responsibility are projected onto non-human agents? | •RQ G1: How do national differences in AI regulation influence countries’ pursuit of competitive advantage in emerging technologies? And how can regulatory frameworks, such as the EU AI Act, balance these dual objectives of protecting stakeholders and fostering AI-driven innovation? •RQ G2: What strategies do AI firms employ to navigate, bypass, or undermine existing regulatory constraints, and what are the implications for regulatory design? And how do AI firms operating under a “move fast and break things” ethos manage ethical and legal uncertainty in the absence of clear regulatory guidelines? •RQ G3: How can corporate governance mechanisms within firms complement or compensate for the limited capacity of national regulators in addressing the fast-evolving ESG risks of AI technologies? •RQ G4: How does corporate governance shape the extent to which AI is granted decision-making autonomy in strategic or operational roles, particularly at the board and top management team (TMT) level? •RQ G5: What are the implications of hybrid human-AI decision-making models for traditional corporate governance structures, and how should boards adapt to these evolving dynamics? •RQ G6: To what extent does corporate governance influence a firm’s commitment to ensuring diversity in generative AI systems, especially in light of potential model divergence and polarization risks? •RQ G7: How does corporate governance mediate the organizational willingness to exert meaningful control over AI systems, especially under conditions of uncertainty and “pessimism aversion”? •RQ G8: How can corporate governance frameworks incorporate insights about the biases of developers and tech entrepreneurs to anticipate and mitigate unintended consequences of AI innovations? •RQ G9: In what ways can boards and governance bodies be equipped to address the ethical blind spots that arise from technologist-driven innovation cultures, as exemplified by the “technically sweet” phenomenon? |