The acceptance and continued usage of open source data science and advanced analytics technologies

Due to the ubiquity of open source technologies in data science, organizations started to lean towards open source ecosystems. With the increasing number of citizen data scientists in organizations, individuals in business units with non-technical backgrounds also need to adopt and utilize these technologies. This signifies a substantial change for individuals who are used to working with proprietary technologies for which relying on a major vendor is inherently more reassuring. To address this problem, we develop and test a model that examines the trust-based determinants of the continued use of open source data science and advanced analytics technologies (OSDST) in organizations. We conducted a field survey methodology for data collection purposes. We used Partial Least Squares Structured Equation Modeling (PLS-SEM) to test the model's validity and the hypothesized relationships. Our results indicate that trust in OSDST strongly influences usefulness perceptions and user satisfaction. We have also found that situational normality and trust in the data science community are two direct determinants of trust in OSDST. Further, compared to the top management championship, we found that the championship of co-workers plays a more central role in shaping trusting beliefs. To the best of our knowledge, this is the first study investigating the individual use of OSDST in organizations, where most individuals actively adopt and utilize open source technologies for the first time.