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Purpose: This study examines the key role of diversity management in supporting intellectual capital in organisations. Intellectual capital, which includes the knowledge, skills and innovative potential of employees, is recognised as a valuable resource that drives organisational success. By embracing diversity and managing it effectively, organisations can unleash the full potential of their intellectual capital and achieve a lot of benefits.

Methodology: The study is based on primary data. The research method used to achieve the objective and answer the research questions is a critical analysis of the literature on the subject, as well as an analysis of the qualitative research conducted by the author on the topic of building intellectual capital of enterprises in Poland conducted in 2019. The study used a dataset of 1,067 enterprises operating in Poland (with at least 10 employees).

Findings: It should be noted that this study underscores the crucial role of diversity management in enhancing intellectual capital within organisations. By embracing diversity and fostering an inclusive environment, organisations can tap into collective intelligence, creativity, and problem-solving capabilities of a diverse workforce. The benefits extend beyond organisational performance, encompassing innovation, employee engagement, and customer satisfaction.

Significance: The study highlights that it is imperative for organisations to implement effective diversity management strategies and continuously evaluate their progress to unlock the full potential of their intellectual capital and drive sustainable success in a rapidly evolving global landscape.

The current era of the fourth industrial revolution has attracted significant research on the use of digital technologies in improving construction project delivery. However, less emphasis has been placed on how these digital tools will influence the management of the construction workforce. To this end, using a review of existing works, this chapter explores the fourth industrial revolution and its associated technologies that can positively impact the management of the construction workforce when implemented. Also, the possible challenges that might truncate the successful deployment of digital technologies for effective workforce management were explored. The chapter submitted that implementing workforce management-specific digital platforms and other digital technologies designed for project delivery can aid effective workforce management within construction organisations. Technologies such as cloud computing, the Internet of Things, big data analytics, robotics and automation, and artificial intelligence, among others, offer significant benefits to the effective workforce management of construction organisations. However, several challenges, such as resistance to change due to fear of job loss, cost of investment in digital tools, organisational structure and culture, must be carefully considered as they might affect the successful use of digital tools and by extension, impact the success of workforce management in the organisations.

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.