Antecedents and performance outcomes of employees’ data analytics skills: an adaptation structuration theory-based empirical investigation

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ABSTRACT How do organizations develop and manage employees’ data analytics skills to create business value and enhance organizational competitive advantage? In order to address this prominent and critical research question for IS research, we conceptualize and operationalize data analytics skills at the individual level and develop a nomological network model to examine its critical antecedents and outcomes from the lens of adaptation structuration theory. We test our core proposition and research model using survey data collected from 258 frontline employees of three data-intensive research institutes in China. We discover that data-driven culture, data analytics affordance, and individual absorptive capacity are positively associated with employees’ data analytics skills, which in turn, have positive influences on their task and innovative performance. We classify the employees into digital immigrants and digital natives based on age and examine the different influences of three salient antecedents on data analytics skills between the two groups. The research findings suggest that data-driven culture plays a more significant role in driving data analytics skills for digital immigrants, while data analytics affordance exhibits a stronger influence on data analytics skills for digital natives.