Modeling AI Readiness and Adoption Intentions in Construction Management: A Technology Acceptance Model Approach

10 pages•Published: June 2, 2026

Navid Nickdoost, Jonghoon Kim, Soomin Park and Kwonsik Song

Abstract

Artificial Intelligence has emerged as a transformative force in construction management, enabling new capabilities in prediction, automation, and data-driven decision-making. Despite these advances, the industry continues to face significant barriers to widespread AI adoption, largely stemming from limited awareness, inconsistent training, and uncertainty regarding AI’s perceived value. This study empirically examines the behavioral and perceptual factors that influence construction professionals’ intention to adopt AI technologies. A structured survey of professionals was administered, incorporating constructs from the Technology Acceptance Model, perceived usefulness, ease of use, attitude, and behavioral intention, augmented by an awareness dimension. A total of 54 complete responses were included in the final analysis. Statistical analyses, including correlation and multiple regression modeling, were conducted to identify key predictors of adoption readiness. The results indicate that perceived usefulness and awareness are the strongest predictors of behavioral intention (p < 0.01), explaining 56% of its variance (R² = 0.564). Attitude exerted a positive but non-significant effect. Correlation results further confirm strong associations between usefulness, attitude, and intention, suggesting that adoption is primarily driven by perceived performance benefits. The findings highlight the need for targeted educational interventions and experiential learning opportunities that strengthen awareness and demonstrate AI’s tangible value in CM practice.

Keyphrases: artificial intelligence, behavioral intention, technology acceptance model (tam), technology adoption

In: Wesley Collins, Anthony Perrenoud and John Posillico (editors). Proceedings of Associated Schools of Construction 62nd Annual International Conference, vol 7, pages 284-293.

Links:	https://easychair.org/publications/paper/zBNF
	https://doi.org/10.29007/6f6p

BibTeX entry

@inproceedings{ASC2026:Modeling_AI_Readiness_Adoption,
  author    = {Navid Nickdoost and Jonghoon Kim and Soomin Park and Kwonsik Song},
  title     = {Modeling AI Readiness and Adoption Intentions in Construction Management: A Technology Acceptance Model Approach},
  booktitle = {Proceedings of Associated Schools of Construction 62nd Annual International Conference},
  editor    = {Wesley Collins and Anthony Perrenoud and John Posillico},
  series    = {EPiC Series in Built Environment},
  volume    = {7},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2632-881X},
  url       = {/publications/paper/zBNF},
  doi       = {10.29007/6f6p},
  pages     = {284-293},
  year      = {2026}}

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