AI‐Enabled Talent Acquisition and Eco‐Innovation Capabilities as Strategic Drivers of Sustainable Business Performance
研究基于837名美国中小企业员工和企业家数据，发现AI人才招聘和可持续创业导向通过环境创新能力和人力资本敏捷性提升可持续绩效，环境不确定性仅调节生态创新路径。
ABSTRACT This research discusses the contributions of AI‐enabled talent acquisition and sustainable entrepreneurial orientation to the strategic drivers of sustainable business performance, with a focus on the mediating effect of environmental innovation capability and sustainable human capital agility, and the moderating effect of environmental uncertainty. The study is based on sustainability strategy, dynamic capabilities, and contingency theory, as well as on digital HRM and entrepreneurial behaviors as centralized processes that enable SMEs to develop environmentally responsible and resilient business outcomes. The survey involved 837 senior SME employees and entrepreneurs in the United States, using a cross‐sectional quantitative design. The hypothesized causal relationships were tested using structural equation modeling (PLS‐SEM), which helped the researchers assess predictive validity, whereas machine‐learning models, such as neural networks, random forests, and regularized linear regression, implemented in JASP, enhanced robustness. The results show that AI‐based talent acquisition and sustainable entrepreneurial orientation are very effective in devising strategies for sustainable business performance. Both sustainable human capital agility and environmental innovation capability are potent mediators, showing how digitalized talent systems and sustainability‐focused entrepreneurial strategies translate into eco‐innovation and business social responsibility outcomes. Environmental uncertainty only modulates the environmental innovation capability pathway, suggesting that eco‐innovation is especially effective in turbulent, unpredictable environments. Machine‐learning validation confirms strong predictive accuracy, and neural networks perform optimally. The proposed research contributes to the sustainability and business strategy literature by combining SEM and machine learning to produce complementary explanatory and predictive information. It provides practical advice to SMEs on developing nimble, innovative, and environmentally sustainable business models that can succeed amid technological shifts and environmental uncertainty.