Exploring big data traits and data quality dimensions for big data analytics application using partial least squares structural equation modelling

Abstract The popularity of big data analytics (BDA) has boosted the interest organisations into exploiting their large scale data. This technology can become a strategic stimulation for to achieve competitive advantage and sustainable growth. Previous BDA research, however, focused more on introducing traits, known as Vs while ignoring quality when examining application BDA. Therefore, this study aims explore effect traits dimensions application. formulated 10 hypotheses that comprised relationships accuracy, believability, completeness, timeliness, ease operation, constructs. conducted survey using questionnaire collection instrument. Then, partial least squares structural equation modelling technique was used analyse hypothesised between findings revealed significantly affect all constructs operation construct significant contributes literature by bringing new insights field may serve guideline future researchers practitioners studying