Although blockchain technology is already over 12 years old, it is still most often described in terms of a promising innovation, and not as an element thoroughly integrated into the technological status quo. While blockchain-based digital currencies and other decentralized fintech solutions utilizing blockchain technology have gained significant popularity, blockchain is still only beginning to be adopted in other sectors.

Technological adoption can be analyzed as a process in which a technology proceeds from being a promising but optional innovation to becoming an invaluable, highly flexible tool that must be included by organizations in order to allow them to stay competitive. In this article, I will list some of the most common blockchain technology adoption Frameworks and models, which can provide an overview of the growing blockchain acceptance.

Current State of Blockchain Adoption

According to the World Economic Forum, blockchain is one of the most important new technologies that will greatly transform the global economy within this decade. Blockchain enables secure and efficient storing of all types of data in a distributed ledger of chronological blocks. This type of decentralized architecture enables organizations to increase their transparency and solve trust issues by investing in accountability, traceability and compliance.

Blockchain is an emerging technology that is likely to become pivotal in the current transformation of global supply chains, as it allows building a digital system distributing the control over the supply chains from a single, centralized company to a network of partners. The fact that blockchain technology is predominately considered a supply chain innovation greatly influences blockchain adoption models, as they have to take into consideration factors such as transparency, traceability, compliance and trust.

Problems Faced by Blockchain Technology Adoption

Although blockchain technology and decentralized solutions based on the blockchain such as smart contracts are universally considered to be highly promising, blockchain adoption nevertheless faces many issues which make its smooth implementation problematic. Even though blockchain is primarily a technological phenomenon, its successful adoption depends on many non-technological factors, such as regulatory framework, consumer behavior and public trust. The situation becomes even more complicated because blockchain is not a technology limited to a single industry or sector, but a technological phenomenon spinning across numerous different industries and sectors.

Sociological models of technology adoption that take into consideration psychological, demographic and behavioral factors have been known for decades. However, unique characteristics of blockchain technology related to issues like data security, privacy and transparency result in the fact that the most important aspect influencing blockchain adoption is trust, and theoretical frameworks of blockchain technology acceptance must consider the trust factor as a top priority in blockchain adoption.

Structural Equation Modeling

The most basic framework for blockchain adoption is Structural Equation Modeling (SEM). This analytical approach is based on theoretical models such as Technology Acceptance Model (TAM), Technology Organisation Environment (TOE) model, as well as the Unified Theory of Acceptance and Use of Technology (UTAUT) models. These frameworks aim to take into consideration many behavioral traits such as industry pressure, competitor influence, partner readiness, potential usefulness, and relative ease of use.

Models such as Structural Equation Modeling aim to help organizations understand how various factors influence individual behavior in relation to innovative technologies like blockchain. Unfortunately, despite high complexity, frameworks like SAM lack empirical evidence, and are highly theoretical and conceptual. As such, models like Structural Equation Modeling and other frameworks based on Technology Acceptance Model or the Unified Theory of Acceptance and Use of Technology are on their own not enough to provide an extensive and functional framework for multi-sector blockchain technology adoption.

Bayesian Network Analysis

At first sight, Bayesian network analysis is quite similar to Structural Equation Modeling. It focuses on analyzing the cause-and-effect relationship between various factors and their behavioral results, which enables building relationship-oriented models. Unlike Structural Equation Modeling, Bayesian network analysis is generally considered to be more useful in modeling causal connections and conditional potentialities.

Compared to Structural Equation Modeling, Bayesian network analysis can be more useful by using predefined models in combination with new data, which allows for effectively predicting non-linear causal relationships. However, Bayesian network analysis still has some limitations, which make it insufficient as a tool for analyzing blockchain acceptance models. Most importantly, conditional probabilities and causal relationships analyzed in Bayesian network analysis are exclusively dependent on historical data. This makes using Bayesian analysis to predict the adoption of a unique, unprecedented technology like blockchain highly problematic.

Furthermore, despite the fact that Bayesian network analysis can be used to create a structurally coherent theoretical model of blockchain technology adoption, the results will likely prove impractical in providing any explanations of casual relationships and behavioral adoption factors. The lack of distinction between variables means that Bayesian network analysis on its own is not enough to create useful blockchain technology adoption frameworks.

Integrated Two-Stage Analysis

A two-stage model combining the advantages of SEM and Bayesian network analysis has been suggested as a theoretical approach that can enhance Structural Equation Modeling with empirically validated Bayesian data in order to create a much more accurate predictive framework. A methodology utilizing both Structural Equation Modeling and Bayesian network analysis can help overcome limitations that both of these methods have on their own.

The integrated methodology, which extracts psychological constructs from the literature on technology adoption and builds a predictive model based on the extension of the TAM framework, is an effective predictive strategy. Two-stage approach enables analyzing blockchain adoption data collected from online user activities, which allows for building an empirically verified neural network-based predictive model for estimating the probability of blockchain adoption.

Combining Structural Equation Modeling and Bayesian methodology is currently the most promising theoretical framework for modeling blockchain adoption. It provides a structurally coherent model that can be enriched with empirical data to give practical insight into the future of blockchain acceptance.

Aniket Warty

Aniket Warty

Adventure Capitalist. I need no sanction for my life, permission for my freedom, or excuse for my wealth: I am the sanction, the warrant, and the reason. The creation of wealth is merely an extension of my innate freedom to produce.
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