Analyzing the factors affecting big data and analytic systems usage via extended technology acceptance model

Big Data and Analytics (BVA) Systems are recognized as new information technologies. BVA systems are defined as a set of technologies used to capture, store, transfer, analyze and visualize large amounts of structured and unstructured data. Companies that use big data in information technologies and make large data-based decisions are 5 percent more efficient and 6 percent more profitable than their competitors. But company performance can be increased by the actual use of applied information technologies. For this reason, it is very important to understand how the employees who are expected to use the system decide to use the system. The Technology Acceptance Model (TKM) is widely used to predict user acceptance of different information technologies and to explain the reasons why employees use new systems. A research model has been proposed in this study based on an extensive review of literature pertaining to the Extended Technology Acceptance Model. This study explored the factors affecting BDA system use by integrating the key constructs of Technology Acceptance Model (TAM), Technology Acceptance Model 2 (TAM2) and Theory of Planned Behavior (TPB). Research model integrates constructs from TAM (perceived usefulness, perceived ease of use) TAM2 (job relevance, output quality, result demonstrability) and TPB (perceived behavioral control, subjective norms) in explaining user acceptance of BDA system. Using AMOS 25, data collected from 300 BDA system users in Turkey, was used to test the proposed research model. Results indicated that 56 percent of user intention to use BDA system is explained by perceived usefulness, perceived behavioral control and subjective norms. Among them, perceived usefulness have the strongest effect. This study expands the existing body of knowledge on the adoption of big data analytic systems and benefits big data analytics providers and system designer while helping in the formulation of their information system models.