# Validating the Adoption of Heterogeneous Internet of Things with Blockchain
## 3 Conceptual Model and Research Hypotheses
### Trust Factors  
#### 3.2.1 Data Related Factors  
Data in this context indicates the extent to which data are produced and exchanged in IoT systems. It also influences the decision makers in adopting blockchain into IoT environments. Different models and studies have suggested a number of data-related factors that could influence the decision makers trust and thus affect adoption. On the basis of the literature, a number of factors have been defined, as listed below. **Data integrity. ** This focuses on acquiring data assurance, completeness, consistency, and reliability over the entire data life-cycle. Most of the issues related to data integrity have appeared because of the cloud services attached to IoT; one of the drawbacks is the lack of control over the data available in cloud storage [47; 48; 49]. The essential design of blockchain is a series of growing blocks hashed with cryptography to provide data integrity by enabling a fully decentralized system. [PERSON] et al. [47] proposed a blockchain-based framework to enable decentralized data integrity verification for IoT data stored in the semi-trusted cloud. Moreover, [PERSON] et al. [48] presented an integrated model using ablockchain technology. The distributed virtual machine agent model is deployed in the cloud using mobile agent technology. It is able to ensure reliability for data storage, monitoring, and verification, which is also essential for building a blockchain integrity protection mechanism. In essence, blockchain technologies can enable decentralized and trustworthy features for the IoT, thus, blockchain technology has an impact regarding IoT adoption. **Hypothesis 3A (H3A). **_Data integrity has a positive effect on IoT blockchain technology adoption._  
**Data validity. ** This factor is concerned with the correctness of the data. The challenge has broadened because of the vast scale of IoT systems, distributed agents, and network partitioning. Thus, blockchain technology has integrated with other supported technologies: P2P networks, distributed ledgers, asymmetric encryption, and smart contracts. These technologies overcome the limitations of IoT data validity to ensure secure, reliable, open, fair, efficient, and intelligent communication [50]. [PERSON] et al. [51] introduced a testbed to evaluate the impact of blockchain on validating data through accelerating block mining rates and network conditions on the capacity of public blockchains. The integration of IoT and blockchain systems becomes increasingly important to secure IoT data; thus, the following is hypothesized:  
**Hypothesis 3B (H3B). **_Data validity has a positive influence on IoT blockchain technology adoption._  
**Data governance. ** This refers to the process of formal coherence of people, processes, and technology to enable organizations to leverage data as an enterprise benefit. This is part of most data-driven capabilities such as change management, data warehousing, database design, and other related data processes and repositories. It focuses on defining data strategy, policies, standards, procedures, and metrics. It is involved in tracking the delivery of data management projects and services. Regulations on data are always updated to control privacy or other rules for each domain. In health, [PERSON] et al. [52] claimed the lake on how effectively construct data governance for IoT enabled digital IS ecosystem. Thus, they improved the 4I framework, which is iteratively developed and updated using the Design Science Research (DSR) method. This method addresses the organization's processing need to maintain the robustness of the governance model and provide coverage across the entire data life-cycle in IoT-enabled digital IS ecosystems. In addition, [PERSON] et al. [53] overcame the issue by concentrating on how to present, secure, protect, and integrate data. They presented a distributed architecture for managing IoT data extraction and processing that also includes algorithms for the assessment of data quality and security levels of the considered sources.