Elnabwy, M T; Khalaf, D; Mlybari, E A and Elbeltagi, E (2025) An integrated machine learning approach for evaluating critical success factors influencing project portfolio management adoption in the construction industry. Engineering, Construction and Architectural Management, 32(11), pp. 7449-7468. ISSN 0969-9988
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | construction industry; critical success factors; machine learning (ML) algorithms; project portfolio management |
| Index terms: | portfolio management, construction industry, machine learning, critical success factor |
| Subjects: | industry analysis, artificial intelligence, control systems, economic analysis |
| Topics: | Project Management, Digital Applications, Business Strategy, Research Practice |
| Descriptive scope: | 3 PCT |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here