Exploring the Factors Influencing Big Data Technology Acceptance - Rahman, Mohammad Nayemur (ProQuest Dissertations & Theses, 2020)
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 industrial-organizational 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 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 cost-effectiveness. 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 of the structural model reveals that the hypothesis tests significant for eight out of 12 path relationships. This study successfully tests and validates four new variables relating to technological capabilities in adopting new technology: scalability, data storage and processing capability, flexibility, and reliability. The study finds the other four out of the eight variables significant which have also been validated by prior studies: performance expectancy, facilitating conditions, output quality, and required skills and training. Four external variables are found to be non-significant by the proposed model: functionality, security and privacy considerations, data analytics capability, and cost-effectiveness. Our proposed structural model also supports all core constructs of the TAM: perceived usefulness, perceived ease of use, behavioral intention, and actual use. The model is strongly supported in three important points of measurement which accounts for 80% of the variance in usefulness perceptions, 67% of the variance in usage intentions, and 85% in actual Hadoop usage. These findings make significant contributions to advance theory and provide insights to the foundation for future research to improve our understanding of user acceptance behavior. Industry big data professionals are the subjects of both qualitative and quantitative studies of this research; therefore, we assert that the industry provides important input for enhancing the existing TAM model and building information systems (IS) theory. From the practitioners’ point of view, this research provides companies with guidance on which technological features and capabilities to look for when buying a complex form of technology. Limitations of this study are discussed, and several promising new research directions are provided.