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The increasing urgency to address climate change in construction has made green construction (GC) and sustainability critical topics for academia and industry professionals. However, the volume of literature in this field has made it impractical to rely solely on traditional systematic evidence mapping methodologies.

This study employs machine learning (ML) techniques to analyze the extensive evidence-base on GC. Using both supervised and unsupervised ML, 5,462 relevant papers were filtered from 10,739 studies published from 2010 to 2022, retrieved from the Scopus and Web of Science databases.

Key themes in GC encompass green building materials, construction techniques, assessment methodologies and management practices. GC assessment and techniques were prominent, while management requires more research. The results from prevalence of topics and heatmaps revealed important patterns and interconnections, emphasizing the prominent role of materials as major contributors to the construction sector. Consistency of the results with VOSviewer analysis further validated the findings, demonstrating the robustness of the review approach.

Unlike other reviews focusing only on specific aspects of GC, use of ML techniques to review a large pool of literature provided a holistic understanding of the research landscape. It sets a precedent by demonstrating the effectiveness of ML techniques in addressing the challenge of analyzing a large body of literature. By showcasing the connections between various facets of GC and identifying research gaps, this research aids in guiding future initiatives in the field.

A growing number of organizational scandals, including Apple slowing old devices to increase the sales of new ones, and research on unethical pro-organization behaviors (UPBs) have heightened the need to explore the phenomenon. Extending the current understanding, the study's major purpose is to investigate individual-level factors that may shape their willingness to engage in UPBs. It also inquires whether moral disengagement processes influence this association.

After testing the reliability and validity of the latent constructs and ensuring common method bias did not contaminate the data, the study used the PLS-SEM approach to analyze the primary data collected from 408 full-time Pakistani employees.

Results add to the current understanding by revealing that individual-level dark factor Machiavellianism (MACH) significantly influences employees' willingness to engage in UPBs. Accordingly, affective commitment is another individual-level factor that encourages employees to be a part of UPBs. Lastly, results unveil that employees with a higher moral disengagement are more prone to engage in UPBs.

The study measured employees' willingness or intentions to engage in UPBs, not their actual involvement.

Results clarify to the top management that individuals high on MACH, affective commitment and moral disengagement are more prone to be involved in UPBs.

This study is among the preliminary ones that assess the direct associations between MACH, affective commitment, and UPBs, especially in the Pakistani context. Moreover, exploring the moderating role of moral disengagement between the above associations is also an under-researched phenomenon.