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A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.

This conceptual work examines how, in times of post-COVID-19 paradigm shift, the employee performance management (PM) process can help multinational corporations (MNCs) strengthen their talent management and, at the same time, meet their future needs.

We take a conceptual approach and present our perspective on what we see as the most critical trends shaping PM and talent management. Contingency theory and Volatility, Uncertainty, Complexity, and Ambiguity (VUCA) theory provide a sound theoretical framework for understanding and responding to the complex and rapidly changing business context post-COVID-19.

Drawing on these theories, we create a framework providing a means of understanding why and how MNCs can maintain talent and, at the same time, develop new talent through the PM process.

Importantly, our study emphasizes the critical role that project management and talent management techniques play for both practitioners and scholars. In order to gain and sustain a competitive edge in the ever-changing VUCA (Volatility, Uncertainty, Complexity, and Ambiguity) landscape, these processes necessitate ongoing reassessment and adaptation. As Plato eloquently stated, “Our Need Will Be the Real Creator,” encapsulating our vision for the proactive and dynamic nature of effective project management and talent management practices.

The study establishes the benefits of an agile and flexible PM approach to help develop talent and pave the way for future research in this increasingly critical area