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ItemThe Impact of Displaying Quantity Scarcity and Relative Discounts on Sales and Consumer Returns in Flash Sale E-Commerce( 2022-01-04)Flash sale e-commerce is a very competitive business with low margins due to the high discounts granted. Against this background, merchants pursue the goal of generating as many orders as possible. To achieve this, techniques that stimulate impulse buying behavior are often used. This paper examines two specific instruments that have the potential to contribute to impulse buying, namely, the on-site display of (1) scarcity notifications and (2) the relative discount provided. We use real-world data from a flash sale e-tailer to analyze the impact on customer sales and returns. In that regard, this study is the first to focus specifically on fashion, which is the product category most affected by returns. Furthermore, to synthesize both perspectives, a quantitative model is presented to serve as the basis for a decision support system that enables managers to better deal with the underlying trade-off.
ItemText vs. Image: An application of unsupervised multi-modal machine learning to online reviews( 2022-01-04)Online user-generated reviews provide a unique view into consumer perceptions of a business. Extant research has demonstrated that text mining provides insight from textual reviews. More recently, we haven seen the adoption of image mining techniques to analyze visual content as well. With data comprising of user-generated imagery (UGI) and textual reviews, we propose to perform a combination of text- and image mining techniques to extract relevant attributes from both modalities. The analysis allows for a comparison between textual and visual content in online reviews. For the UGI analysis, we use a Deep Embedded Clustering model and for the User Generated Text Analysis we use a TF-IDF based mechanism to obtain attributes and polarities. The overall goal is to extract maximum information from text and images and compare the insights we gather from both. We analyze if any modality is self-sufficient or better than the other and also if both modalities combine to give similar or contrasting insights.
ItemHuman vs. AI: Investigating Consumers’ Context-Dependent Purchase Intentions for Algorithm-Created Content( 2022-01-04)Increasingly digitalized media consumption is pressuring profitability in the content industry. Technological advancements in the realm of Artificial Intelligence (AI) render the potential to cut costs by applying algorithms to create content. Yet, before implementing algorithm-created content, content providers should be aware of the impact of algorithmic authorship on consumers’ intention to purchase said content. Accordingly, this study investigates user attitudes toward algorithmic content creation and their dependence on the underlying utilitarian or hedonic consumption context. In our online experiment (N=298), we find evidence for a positive effect of algorithmic authorship on consumers’ purchase intention. Even though the overall purchase intention is context dependent, this algorithm appreciation is independent of the content consumption context. Our study thus suggests that consumers appreciate algorithm-created content. Our results thus provide insights into the benefits of leveraging algorithms in order to maintain content providers’ profitability.
ItemArtificial Intelligence (AI) and Digital Transformation in the Insurance Market: A Case Study Analysis of BGL Group( 2022-01-04)Artificial Intelligence (AI) is transforming the insurance industry and there are many examples in the business press and at industry conferences. However, there is a paucity of detailed conceptual analysis and evaluation of AI technology that places AI in a strategic context and considers how different AI applications fit together and form a coherent picture. In this paper, a detailed case study of BGL group, a leading European insurance firm, is presented. A general business process model of insurance companies is used to structure the analysis. Five AI applications are described using an insurance firm-customer data flow diagram, which illustrates the marketing impact of AI technology and shows the nature of the business value creation process. The results are generalized into an AI customer lifecycle model, which has broad applicability to digital transformation projects. The likely future direction of AI in insurance is outlined and further research opportunities are identified.
ItemIntroduction to the Minitrack on Electronic Marketing( 2022-01-04)