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This study investigates the relationship between outside directors, managerial compensation, and firm performance in the Korean insurance industry.

The authors employ a simultaneous equation framework by using three-stage least squares (3SLS) to address the endogeneity problems that could result from the joint determination of outside directors, firm performance, and executive compensation in Korean insurance companies.

The authors find that the ratio of outside directors on the board is negatively associated with insurance firm's value and financial profitability. In addition, this study's evidence shows that greater representation on the board by outside directors leads to a higher level of executive pay. In particular, the authors provide evidence that variable compensation scheme and outside directors who have backgrounds in the legal profession and former high-ranking government officials drive this study's main results.

This study adds to the literature by first demonstrating the interaction effects between outside directors, firm performance, and executive compensation in the Korean insurance industry. Unlike previous studies that typically focus on US companies, the authors study the Korean insurance sector that is an emerging power in the global insurance market, ranking seventh in terms of total premium volume, and show that the Korean insurance firm's outside directors system does not work in the manner that it is intended to function.

Cost overrun in infrastructure projects is a constant concern, with a need for a proper solution. The current estimation practice needs improvement to reduce cost overruns. This study aimed to find possible statistical modelling techniques that could be used to develop cost models to produce more reliable cost estimates.

A bibliographic literature review was conducted using a two-stage selection method to compile the relevant publications from Scopus. Then, Visualisation of Similarities (VOS)-Viewer was used to develop the visualisation maps for co-occurrence keyword analysis and yearly trends in research topics.

The study found seven primary techniques used as cost models in construction projects: regression analysis (RA), artificial neural network (ANN), case-based reasoning (CBR), fuzzy logic, Monte-Carlo simulation (MCS), support vector machine (SVM) and reference class forecasting (RCF). RA, ANN and CBR were the most researched techniques. Furthermore, it was observed that the model's performance could be improved by combining two or more techniques into one model.

The research was limited to the findings from the bibliometric literature review.

The findings provided an assessment of statistical techniques that the industry can adopt to improve the traditional estimation practice of infrastructure projects.

This study mapped the research carried out on cost-modelling techniques and analysed the trends. It also reviewed the performance of the models developed for infrastructure projects. The findings could be used to further research to develop more reliable cost models using statistical modelling techniques with better performance.