Factors leading to organizational adoption of data science and advanced analytics technologies

Abstract

The aim of this research was to propose and empirically test critical factors for Data Science and Advanced Analytics Technologies (DSAAT) adoption based on the prior organizational technology adoption literature. First, drawing on Technology Organization-Environment (TOE) framework, we developed a survey instrument adapting validated measurement scales from prior literature. Then, we collected data from 140 respondents and empirically tested using PLS-SEM methodology the facilitating influence of technological, organizational, and environmental factors on the adoption intention, and inhibiting influence of lack of data driven decision making and internal expertise on the extent of actual adoption. Our results suggest that environmental factors i.e. mimetic pressure and the availability of external expertise play a significant role in influencing development of adoption intention, while there is no support for the influence of technological factors. The results of respondents from business functions suggest that the extent of data driven decision making has a positive effect on the extent of actual adoption, while for IT function respondents the lack of internal expertise moderates the effect of adoption intention on the extent of actual adoption. Considering the emergence of big data and hence Data Science and Advanced Analytics Technologies allowing extracting value from it, we feel that developing an adoption model and empirically testing it would be a stepping stone for further research on understanding the successful adoption and usage of these technologies in the future.