Exploring real estate blockchain adoption: An empirical study based on an integrated task-technology fit and technology acceptance model

Although many organizations have adopted blockchain technology (BCT) for efficiency and automation and users' adoption of BCT is becoming crucial for the companies, few studies have focused on the factors affecting the adoption of BCT in real estate sector. This study aims to assess the factors affecting the adoption of BCT in real estate by the lens of an extended Technology Acceptance Model (TAM) and Task-technology fit (TTF). This current study uses quantitative survey approach. Data were collected from 311 real sector buyers and sellers in China. Partial least square structural equation modeling (PLS-SEM) was used for data analysis. The study's findings indicate that attitude, perceived usefulness (PU) and data privacy and security (DPS) exerts highest influence in the proposed theoretical model. In addition, the findings also confirmed the significant impact of perceived ease of use (PEOU) on attitude, and TTF impact on PU and PEOU in adoption of BCT. Furthermore, the moderating impact of perceived compatibility (PC) on the relationship between TTF and PEOU was also confirmed. The study has useful practical implications for the real estate buyers and sellers that adoption of BCT improves efficiency, reduce transaction cost incur due to intermediaries, and enhance security system. Furthermore, the study suggests that automation achieved through BCT will facilitate customized agreements and improve process of title transfer.