Analyzing Enablers of Big Data Computation of New Business Students: A TISM approach

Cultivating students’ ability to use emerging technologies for innovation is important for the reform of new business education. Technological change needs the thinking revolution essentially so the big data system is of great significance for the construction of talent training programs, implementation of competency requirements, and curriculum teaching design. This study was carried out to identify the main enablers of the big data computation (BTC) in improving the innovation capability of students majoring in business and developing a model based on the contextual relationship among the identified enablers. Initially, a systematic literature review was conducted to identify the enablers of BTC. Then, total interpretive structural modeling (TISM) was used to evolve mutual relationships among the identified 17 enablers for developing a hierarchical model. Five key driving enablers, namely data consciousness, system thinking, statistical thinking, model thinking, and analytical thinking were identified as main enablers. Image thinking played an important role in the process of data analysis and application. Suggestions were made to cultivate the student’s BTC from the following three aspects: adding system science courses in the big data specialty, increasing training opportunities for students in using tools and methods of big data management, and improving students' data literacy.