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The construction industry has arrived at a crossroads of rapid technological progress. While it is foreseen that the advent of new construction technologies will disrupt the construction industry’s future, such disruptions often create the ideal environment for innovation. As poor payment practices continue to plague the construction industry, the advent of smart contracts has created an opportunity to rectify the inherent flaws in the mitigation of payment problems in traditional construction contracts. Given the intrinsic resistance of construction firms to such revolutionary changes, this study aims to understand the various factors influencing the adoption of smart contracts in the Singapore construction industry.

A mixed method was adopted involving quantifying respondents’ perceptions of the factors influencing smart contract adoption, and validation from a group of interviewees on the matter. Out of 461 registered quantity surveyor members contacted via the Singapore institute of surveyors and valuers website, 55 respondents took part in the survey. This is followed by semi-structured interviews to validate the survey results.

The findings indicate that construction firms have neither a significant knowledge of nor willingness to adopt smart contracts. A total of 29 institutional factors were also identified that significantly influence the adoption of smart contracts. The quantitative findings were further reinforced by qualitative interviews with five industry experts.

With recognition of and the successful formulation of the significant institutional drivers and barriers, the key findings of this study will be integral in driving the commercial adoption of smart contracts within the construction industry.

Demand forecasts are a key component of planning efforts and are crucial for managing core operations. This study aims to evaluate the use of several machine learning (ML) algorithms to forecast demand for residential construction in Jordan.

The identification and selection of variables and ML algorithms that are related to the demand for residential construction are indicated using a literature review. Feature selection was done by using a stepwise backward elimination. The developed algorithm’s accuracy has been demonstrated by comparing the ML predictions with real residual values and compared based on the coefficient of determination.

Nine economic indicators were selected to develop the demand models. Elastic-Net showed the highest accuracy of (0.838) versus artificial neural networkwith an accuracy of (0.727), followed by Eureqa with an accuracy of (0.715) and the Extra Trees with an accuracy of (0.703). According to the results of the best-performing model forecast, Jordan’s 2023 first-quarter demand for residential construction is anticipated to rise by 11.5% from the same quarter of the year 2022.

The results of this study extend to the existing body of knowledge through the identification of the most influential variables in the Jordanian residential construction industry. In addition, the models developed will enable users in the fields of construction engineering to make reliable demand forecasts while also assisting in effective financial decision-making.