Kumulative Dissertation, Otto-Friedrich-Universität Bamberg, 2024

Von der genannten Lizenzangabe ausgenommen sind folgende Bestandteile dieser Dissertation:

Artikel 3 „Forecasting E-Commerce Consumer Returns – A Systematic Literature Review“ (S. 97-162) steht unter der CC-Lizenz CC BY.

Lizenzvertrag: Creative Commons Namensnennung 4.0
https://creativecommons.org/licenses/by/4.0/

Artikel 6 „Big data analytics in returns management – Are complex techniques necessary to forecast consumer returns properly?“ (S. 233-244), Artikel 7 „Examining Drivers of Consumer Returns in E-Tailing with Real Shop Data“ (S. 247-271) und
Artikel 8 „The Impact of Displaying Quantity Scarcity and Relative Discounts on Sales and Consumer Returns in Flash Sale E-Commerce“ (S. 273-298), stehen unter der CC-Lizenz CC BY-NC-ND.

Lizenzvertrag: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
https://creativecommons.org/licenses/by-nc-nd/4.0/

Online shopping has experienced substantial growth, with the COVID-19 pandemic further accelerating this trend. Since e-commerce customers cannot physically assess products online, returned items are part of the e-commerce business model. Consumer returns are associated with various operational challenges, are costly for retailers and impact the environment due to the additional transport emissions and resource waste they produce. Thus, retailers must actively manage returns in advance to secure profitability and limit their CO2 footprint. Various data sources (e.g., transaction data) enable retailers to generate helpful information to support these tasks.

Based on these considerations, this dissertation aims to generate data-driven insights into the field of consumer returns. First, an in-depth understanding of the phenomenon is gained through the documentation and exploration of the status quo of consumer returns in Germany. Second, data-driven analytic and predictive approaches are reviewed, applied, and evaluated. Third, influencing factors for consumers’ purchase and return behavior are subsequently examined.

With a total of nine papers published in national and international journals or conference proceedings, this dissertation addresses these issues from a scientific perspective, without losing sight of practical applicability, thus contributing to a better understanding of data analysis in the context of consumer returns management.