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Building information modelling (BIM) and augmented reality (AR) are unique technologies in the digitalized construction industry. In spite of the numerous benefits of BIM-AR, its adoption has been at a relatively slow pace. The purpose of this study is to investigate how the factors within technology–organization–environment (TOE) framework influence the adoption of BIM-AR in the context of construction companies in a developing country.

By using a mainly deductive quantitative design, survey data were collected from senior management of built environment companies in Ghana using questionnaires. The study adopted a mixture of both purposive and snowball sampling approaches. Partial least squares structural equation modelling was used to analyse how the factors within the TOE framework explain BIM-AR adoption in Ghana.

Findings from the study show that the top three factors within the TOE framework that facilitate the adoption of BIM-AR include ICT infrastructure within construction firms; the size of the construction firm, which may influence the financial capacity to accommodate BIM-AR; and competitive pressure. The inhibitors of BIM-AR at the company level included external support and trading partners’ readiness.

Implicit is that the significant factors will be useful to policymakers and companies in developing programs that appeal to non-adopters to aid in mitigating their challenges and further enhance BIM-AR adoption.

The value of this paper has been the use of the theoretical framework TOE to explain the adoption factors of BIM-AR in the Ghanaian construction industry. The originality of the paper is further anchored in consideration of BIM-AR, which is quite nascent in emerging countries.

Challenges associated with ensuring the accuracy and reliability of cost estimation of highway construction bid items are of significant interest to state highway transportation agencies. Even with the existing research undertaken on the subject, the problem of inaccurate estimation of highway bid items still exists. This paper aims to assess the accuracy of the cost estimation methods employed in the selected studies to provide insights into how well they perform empirically. Additionally, this research seeks to identify, synthesize and assess the impact of the factors affecting highway unit prices because they affect the total cost of highway construction costs.

This paper systematically searched, selected and reviewed 105 papers from Scopus, Google Scholar, American Society of Civil Engineers (ASCE), Transportation Research Board (TRB) and Science Direct (SD) on conceptual cost estimation of highway bid items. This study used content and nonparametric statistical analyses to determine research trends, identify, categorize the factors influencing highway unit prices and assess the combined performance of conceptual cost prediction models.

Findings from the trend analysis showed that between 1983 and 2019 North America, Asia, Europe and the Middle East contributed the most to improving highway cost estimation research. Aggregating the quantitative results and weighting the findings using each study's sample size revealed that the average error between the actual and the estimated project costs of Monte-Carlo simulation models (5.49%) performed better compared to the Bayesian model (5.95%), support vector machines (6.03%), case-based reasoning (11.69%), artificial neural networks (12.62%) and regression models (13.96%). This paper identified 41 factors and was grouped into three categories, namely: (1) factors relating to project characteristics; (2) organizational factors and (3) estimate factors based on the common classification used in the selected papers. The mean ranking analysis showed that most of the selected papers used project-specific factors more when estimating highway construction bid items than the other factors.

This paper contributes to the body of knowledge by analyzing and comparing the performance of highway cost estimation models, identifying and categorizing a comprehensive list of cost drivers to stimulate future studies in improving highway construction cost estimates.