How Does Experience with Delay Shape Managers’ Making-Do Decision: Random Forest Approach

Abstract

Making-do, a decision to start a construction task despite knowing that its preconditions are not fully ready, is a complex dilemma for construction managers. Managers' previous making-do decisions and the resulting consequence, delay, can have a significant impact on future making-do decisions. To understand how managers' experience with delay impacts their making-do decision and how it is handled differently in different countries, two surveys were administered, one in China and one in the United States (US), and 260 usable responses were collected. This study used: (1) the Mann-Whitney U test to examine whether delaying task starting time, when lacking precondition readiness, pays off with shorter delays; (2) a random forest approach to find important causes of delay that contribute to a making-do decision; and (3) an entropy-based decision tree to determine how much uncertainty in making-do decisions can be reduced by knowing managers' experience with delays in past projects. Results showed that in the United States, managers who preferred the making-do approach experienced up to 60% less task duration delay; whereas Chinese managers who preferred making-do experienced up to 100% more task duration delay due to lack of readiness in labor, equipment, material, management, and information flow. The contributions to the body of knowledge are the development of a random forest approach to quantitatively examine the relative importance of the causes of delay to the making-do decision and to reveal the fundamental differences in culture and management traditions that cause the difference between the two countries. The methods presented in this study will enable others to use a similar random forest approach repetitively for classification, prediction, and variable selection problems in civil engineering. The findings of this study will help project managers better understand underlying factors that trigger making-do decisions in China and the United States, and have more efficient collaboration and communication when they work on projects located in a foreign country.

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“…GIS, RFID, barcode, BIM, planning software) can be considered with machine learning applications. In this context, several powerful machine learning methods such as random forest (Zhang et al. , 2020) and support vector machine (Sanni-Anibire et al.…”

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“…[70] and based on combining multiple decision trees to produce a robust prediction model. It has been used effectively for classification and regression problems in several areas of construction management [71][72][73].…”

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“…Lack of skilled and unskilled manpower due to MCO (Madurai et al, 2020;Zhang et al,2020) .988 Retain the null hypothesis B6…”