Quo vadis, e-commerce? Insights on and innovative approaches towards selected current challenges in the e-commerce context

E-commerce has seen a steady increase in usage since its establishment in the 1970s and 80s: By 2025, two-thirds of the world’s population (4,913.9M people) are expected to be e-commerce users. Throughout these decades, e-commerce businesses had to face a variety of different challenges, which, to some extent, determined their survival within their competitive environment. Within this thesis, two selected current phenomena are shed light on with which e-commerce businesses are struggling: A shift within society’s mindset towards environmental awareness and analytical approaches to manage the infinite pool of data about online consumer behavior. Since both research fields have an extremely granular spectrum of different facets, many sub-facets still lack a comprehensive investigation. The overall purpose of this research is thus twofold: (1) Gathering insights on consumers’ sustainable clothing consumption behavior and (2) proposing Artificial Intelligence-driven approaches for analytical problems in the e-commerce context. More specifically, Part A focuses on consumers’ sustainable clothing consumption behavior as the textile industry causes an excessive environmental footprint considering valuable resources as ever inexhaustible and, simultaneously, yields the highest sales among all e-commerce segments. Research Paper No. 1 hence takes a macro-perspective on sustainable clothing consumption behavior by examining the determinants of consumers’ purchase intention for sustainable clothing and factors influencing the intention-behavior gap. Research Paper No. 2 and No. 3 take a deeper dive and provide micro-perspectives on the topic: the impact of specific sustainable clothing attributes on customer satisfaction is investigated (Research Paper No. 2). To complement these findings, the importance of specific sustainable clothing (and online shop) attributes is then compared to the importance of specific conventional clothing (and online shop respectively) attributes (Research Paper No. 3). Within Part B of this thesis, Research Paper No. 4 and No. 5 focus on call center arrivals’ forecasting as call centers still constitute an essential customer touchpoint for e-commerce businesses: Reliable forecasts can enhance customer satisfaction with shortened waiting times and avoid overstaffing (and thus, unnecessary costs). Research Paper No. 4 therefore investigates the trade-off between accuracy and practicability of different machine learning models as these have been neglected by traditional forecasting literature. Research Paper No. 5 draws on these preceding findings and proposes a new dynamic harmonic regression model by incorporating the benefits of both approaches without (i.e., time series models) and with explanatory variables (i.e., machine learning and regression models). Research Paper No. 6 considers another prediction problem, which is particularly inherent to the online context of e-commerce, i.e., online shopping cart abandonment. It investigates the trade-off between accuracy and practicability of machine…