Examining Technostress at Different Types of Data Scientists' Workplaces

TLDR: This article classify different types of data scientists’ workplaces through performing latent 1 class analysis using several workplace attributes within a sample of n=486 German data scientists and reveals considerable distinctions regarding the intensity of technostress creators, strains due to ICT use, and job performance.

Abstract: Data scientists represent a heterogeneous occupational group that has reached high relevance due to the wide-spread availability of quantitative data generated in the rapid progress of digital transformation. These employees play a crucial role in gaining competitive advantages for companies out of such big data. In this context, employees who frequently analyse data often occupy different job titles and, therefore, are difficult to detect. At the same time, a psychological downside of digitalization, which is called technostress, has risen. However, these issues caused by the use of information and communication technologies are rarely examined in the context of specific occupational groups and workplace attributes. Considering these challenges, this article extends current technostress research by focusing on technostress within the specific job class of data scientists. We classify different types of data scientists’ workplaces through performing latent 1 Derra et al.: Examining Technostress at Different Types of Data Scientists’ Workplaces Published by AIS Electronic Library (AISeL), © Scandinavian Journal of Information Systems, 2022 34(1), 71-118 Derra et al.: Examining Technostress at Different Types of Data Scientists’ Workplaces 72 class analysis using several workplace attributes within a sample of n=486 German data scientists. Subsequently, we reveal considerable distinctions between these classes regarding the intensity of technostress creators, strains due to ICT use, and job performance. We discuss our empirical findings and deliver theoretical contributions as well as practical implications for both employees and employers and starting points for future research.