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

Abstract

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 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 tackling both fields of research: three papers deliver both methodological and empirical findings for extending machine learning in marketing research by examining model architectures as well as applying machine learning to recent marketing problems. In addition, two research papers contribute to research by providing knowledge about technostress issues of data scientists as a heterogeneous and highly digitalized occupational group as well as examining different coping strategies for effectively overcoming stress due to the use of digital technologies. Beyond that, the findings deliver practical implications for marketing managers who aim to improve the performance of data scientists in a contemporary marketing environment.%%%%Aufgrund der jungsten Entwicklungen, die durch die Big Data Revolution verursacht wurden, hat die Bedeutung von Data Science fur Unternehmen massiv zugenommen. Im Marketingkontext sind verschiedene Arten von Kundendaten in enormen Mengen verfugbar und mussen so effizient wie moglich…