Machine Learning and Technostress as Important Aspects for Improving the Performance of Data Scientists in Contemporary Marketing Contexts

Nicholas Daniel Derra
2021
Based on recent developments caused by the big data revolution, data science has massively increased its importance for businesses. Within the marketing context, various types of customer data have become available in enormous amounts and need to be processed as efficiently as possible for creating valuable knowledge. Therefore, data scientists' performance has become crucial for marketing departments to achieve competitive advantages in the modern highly digitalized economy. Within the raising
more » ... field of data science, machine learning has become an outstanding trend since these approaches are able to automatically solve numerous classification and prediction problems with enormous performance. Thus, machine learning is seen as a key technology which will radically transform business practice in the future. Even though machine learning has already been applied to various marketing tasks, research is still at an early stage requiring further investigations of how marketing can successfully benefit from machine learning applications. Besides these data-driven opportunities provided by digitalization, technostress has evolved into an enormous downside of digitalized workplaces, leading to a significant decrease in employees' performance. However, existing research lacks to provide evidence about different coping strategies and their potential to support employees in overcoming technostress. Furthermore, research currently fails to consider technostress regarding both highly digitalized occupational groups like data scientists and respective workplace environments for providing a deeper understanding of how employees suffer from stress caused by the use of digital technologies. Due to these recent challenges for data scientists, this cumulative thesis provides useful insights and new opportunities by focusing on machine learning and technostress issues as two aspects which promise major potentials for enhancing data scientists' performance in today's marketing contexts. Five research papers are included for effectively [...]
doi:10.15495/epub_ubt_00005398 fatcat:nvaoa7zd6fcu7ovn2dsyqp4nd4