Jan Schalowski is a PhD Candidate in the field of Marketing at Kühne Logistics University. Jan's work is embedded in the German Research Foundation (DFG) Research Unit about the marketing of hedonic media products in the context of digital social media. His research focus lies on the analysis of social networks for targeted marketing communication.
Previously, he worked as a research assistant at the Innovation Incubator at the Leuphana University of Lüneburg for almost three years. There he contributed significantly to the establishment of a regional start-up panel with finally 1,000 participants.
At the University of Hamburg, Jan Schalowski studied Business Administration and obtained a diploma degree (equivalent to M.Sc.) in 2012. In the scope of his diploma thesis in Marketing, he carried out a panel analysis to investigate the consequences of digital piracy on music sales.
|2012 - 2015|
Research Assistant at the Lüneburg Innovation Incubator, a research-driven EU project for regional development at the Leuphana University of Lüneburg, Germany
|2010 - 2011|
Internship at IBM in the field of managing a full-scope IT outsourcing account for a customer in the shipping and logistics sector, Hamburg, Germany
Student trainee at guenstiger.de, Hamburg, Germany
PhD Candidate in Marketing at Kühne Logistics University
|2005 - 2012|
University of Hamburg, university degree in Business Administration with majors in Marketing, Business IT and Industrial Management, Hamburg, Germany
Schalowski, Jan and Christian Barrot (2016): Before and after - A managerial application of social network analysis for the planning and evaluation of viral marketing campaigns, in: Samuelsen, Bendik (ed.): Marketing in the age of data: Oslo.
Schalowski, Jan and Christian Barrot (In press): The Long-term Diffusion of Digital Platforms — An Agent-based Model, in: Krcmar, Helmut, Jane Fedorowicz, Wai Fong Boh, Jan M. Leimeister and Sunil Wattal (ed.): Proceedings of the 40th International Conference on Information Systems: Munich.
Abstract: In recent years, many industries have experienced the rise of digital platforms (e.g., eBay, Uber, or Takeaway.com). A common characteristic of these concepts is that they focus on fragmented markets populated by many small firms, which often show a high fluctuation. However, established diffusion models based on Bass (1969) do not account for fluctuation in the market potential, although the exit of adopters and the entry of new firms could change the diffusion curve significantly. Thus, we propose an extension of the Bass Model to account for the exit and entry of (potential) adopters and empirically test this framework in a real-world setting. Using two decades of adopter data of leading digital platforms and information on the complete market potential, we employ agent-based models to analyze the effects of fluctuation on the platform diffusion. Initial results confirm the existence of high fluctuation and indicate relevant impacts on the diffusion curve.
Schalowski, Jan and Christian Barrot (2017): Call Me Maybe: Imbalanced Dyads in Networks Across Four Communication Channels, in: Bijmolt, Tammo, Koert van Ittersum, Peter Verhoef and Jaap Wieringa (ed.): 46th EMAC Annual Conference: Leaving Footprints, European Marketing Academy: Groningen.
Abstract: This paper critically examines the prevailing practice in empirical studies of social networks to define edges in communication networks as undirected. This approach is based on the assumption that communication is inherently reciprocal and, thus, directionality can be ignored. In contrast, we argue that this assumption of reciprocity does not hold true for regular communication channels, such as telephone or instant massaging. The present study uses large-scale observational data on communication in networks across four distinct channels to investigate the (im-)balance of dyads and its causes. We show that dyads with a dominant direction can make up to 73 % of all edges in a communication network. When analyzing potential causes for imbalanced communication patterns, we find that gender is a key driver for observed asymmetric relationships as females reveal significantly higher outbound shares, whereas high degrees of clustering counteract the phenomenon of imbalance.