# Validating the Adoption of Heterogeneous Internet of Things with Blockchain
## 5 Discussion  
This study measures the effect of integrating blockchain technology into IoT technology. We firstly identified the critical factors related to the adoption of blockchain and IoT technologies. The significance of the study concentrates on developing a comprehensive theoretical conceptual model that can explain a large proportion of variance in the intention to adopt blockchain with IoT technology. The Technology Acceptance Model (TAM) was used and developed to assess the level of integration of blockchain and IoT technology. The results of the data analysis gathered throughout the survey show the strength of the proposed blockchain IoT technology model. The statistical analysis on the four dimensions of factors revealed a significant positive influence in terms of adopting blockchain IoT technology. The study confirmed the hypotheses with strong correlations in four dimensions of factors and sub-factors. This was done by including the opinions of specialists in both blockchain and IoT technologies. The dimensions of factors were evaluated using the survey. When comparing the different dimensions and factors of the model with respect to mean, data-related factors were found to be the most influential in relation to the other factors. In particular, data validity was the most critical \begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & \multicolumn{2}{l}{**Pathcoefficient**} & \multirow{2}{*}{\(p\)-Value} & \multirow{2}{*}{\(t\)-Value} & \multirow{2}{*}{\(R^{2}\)} & **Hypothesis** \\
**Path Relationship** & & & & & & **Validation** \\  & & & & & & **Validation** \\ \hline
**H1: Attitude–Behavior Intention** & 0.832 & 0 & 24.864 & 0.885 & Supported \\ H1A: Perceived Usefulness—Attitude & 0.728 & 0.005 & 10.648 & 0.581 & Supported \\ H1B: Perceived Ease of Use—Attitude & 0.626 & 0 & 14.149 & 0.711 & Supported \\ H1C: Personal Competency—Attitude & 0.47 & 0 & 10.687 & 0.588 & Supported \\ H1D: Transaction Intention—Attitude & 0.754 & 0.005 & 11.059 & 0.605 & Supported \\
**H2: Social—Behavior Intention** & 0.828 & 0.002 & 15.602 & 0.755 & Supported \\ H2A: Social Network—Social & 0.799 & 0 & 14.295 & 0.721 & Supported \\ H2B: Reputation—Social & 0.722 & 0.002 & 17.362 & 0.792 & Supported \\
**H3: Data—Trust** & 0.94 & 0 & 26.357 & 0.898 & Supported \\ H3A: Data Integrity—Data & 0.627 & 0 & 9.964 & 0.557 & Supported \\ H3B: Data Validity—Data & 0.762 & 0 & 12.237 & 0.655 & Supported \\ H3C: Data Governance—Data & 0.676 & 0 & 14.513 & 0.727 & Supported \\ H3D: Data Privacy—Data & 0.568 & 0 & 11.593 & 0.63 & Supported \\
**H4: Security—Trust** & 0.728 & 0 & 17.013 & 0.786 & Supported \\ H4A: Transaction Risk—Security & 0.745 & 0 & 14.347 & 0.723 & Supported \\ H4B: Technology Risk—Security & 0.724 & 0 & 15.232 & 0.746 & Supported \\
**H0A: Behavior Intention—Adopting** & & & & & \\
**Blockchain in IoT** & 0.904 & 0 & 34.336 & 0.936 & Supported \\
**H0B: Trust–Adopting Blockchain in IoT** & 0.923 & 0 & 24.331 & 0.882 & Supported \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the hypothesis testing results. factor, and more attention should be paid to it. Thus, professionals and users of blockchain and IoT systems have to be aware of data validation strategies and regulations. As was discussed in Section 3.2, data validity focuses on the correctness of data, since the methodology involved in blockchain IoT technology requires a level of distribution and partitioning. This maintains an open environment that produces massive data that requires reliable and efficient communication between connected devices. Adaptive buffers, error detection values, and thresholds are some example of techniques to guarantee data validity [68]. When it comes to the coefficient of determination \(R^{2}\), which measures the proportion of the variance in the dependent variable that is predictable from the independent variable(s), it is notable from Table 4 that the behavioral intention factor and its sub-factors had the highest value of 0.94.