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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.

Referral marketing can be identified as the commercial form used to encourage word-of-mouth (WOM) communication. It has become popular in recent times, supporting organisations to attract new customers towards making strides in how customers are pursued to purchase certain types of products/industries. However, some factors impact the success of referral marketing programmes. This study focuses on analysing the impact of such factors as brand, rewards and celebrity endorsement on user engagement in social media referral marketing programmes.

Further, this study discusses the moderating impact of social ties on this relationship mainly focusing on the cosmetic industry. The data were collected from respondents with exposure to referral marketing programmes through the social media platforms: Facebook, LinkedIn, WhatsApp and Instagram. The structural equation modelling (PLS-SEM) was used to analyse the data collected.

The results of the study revealed that brand, reward and celebrity endorsement significantly impact the participation willingness of customers in social media referral marketing programmemes. Moreover, it showed that the social tie moderates only the brand impact on the participation willingness of customers, whereas it does not moderate the impact of rewards and celebrity endorsement on the participation willingness of customers. In doing so, the study looks extensively into the elements that drive or shape customer behaviour within this specific market niche by investigating the effect of brand, incentives and endorsements by celebrities on customer engagement.

Accordingly, this study sheds light on the complex relationship among brands, rewards and celebrity endorsements, while also considering social ties within the context of social media referral marketing initiatives. It also delivers useful information for academics as well as practitioners. Limited studies have been conducted in this field, and therefore, this study adds some knowledge, also highlighting the moderation effect of social ties.