Exploring the Factors Influencing Big Data Technology Acceptance

Abstract

The success of new technology depends on user acceptance. Therefore, discovering the antecedents of technology use is pivotal to overcoming the lack of user acceptance in the field of technology adoption. Factors of critical technological capability, in particular, are overlooked and largely neglected in the literature. Accordingly, the body of literature on the field of technology adoption is inconclusive as to which technological capability factors influence technology acceptance. Big Data has received great attention in academic literature and industry papers. Most of the experiments and studies focused on publishing results of big data technologies development, machine learning algorithms, and data analytics. To the best of our knowledge, there is not yet any comprehensive empirical study in the academic literature on big data technology acceptance. This research makes an attempt to identify factors influencing big data technology acceptance from an industrialorganizational context. With the help of existing technology acceptance theories, literature studies, industry technical papers, and vendor publications on data management technologies ranging from conventional data warehousing to big data storage technologies (e.g., Hadoop Distributed File System), 32 factors have been identified for use in the qualitative study of this research. By using prominent qualitative research methods including focus groups and one-on-one interviews, this research has identified 12 factors as possible antecedents of ii perceived usefulness and intention to use big data technology. These 12 factors include scalability, data storage and processing capabilities, functionality, performance expectancy, security and privacy considerations, reliability, data analytics capability, flexibility, facilitating conditions, output quality, required skills and training, and costeffectiveness. The qualitative studies were conducted using industry experts with experience in big data technologies as well as traditional data management technologies. To further validate the factors identified by the qualitative study, a quantitative model is developed. The theoretical foundation of this model is drawn from the Technology Acceptance Model (TAM) developed by Fred Davis (1993). This model allows plugins of external factors to its latent constructs of perceived usefulness (PU) and perceived ease of use (PEOU). Primary data for the quantitative study were collected from big data (Hadoop User Groups) users in the United States who work in different industries including software and internet services, financial services, healthcare, consulting and professional services, telecommunications, manufacturing, retail, marketing, and logistics. The structural equation modeling (SEM) software, AMOS, was used for empirical verification and validation of our proposed model using 349 survey responses. The statistical results of this model provide a compelling explanation of the relationships among the antecedent variables and the dependent variables. The analysis o...

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