@misc{BruschRappel, author = {Brusch, Ines and Rappel, Nina}, title = {Exploring the Acceptance of Instant Shopping - An Empirical Analysis of the Determinants of User Intention}, series = {Journal of Retailing and Consumer Services}, volume = {54}, journal = {Journal of Retailing and Consumer Services}, issn = {0969-6989}, doi = {10.1016/j.jretconser.2019.101936}, pages = {14}, language = {en} } @misc{Brusch, author = {Brusch, Ines}, title = {Understanding Customer Preferences Using Image Classification - A Case Study}, series = {Proceedings of the 53rd Hawaii International Conference on System Sciences}, journal = {Proceedings of the 53rd Hawaii International Conference on System Sciences}, isbn = {978-0-9981331-3-3}, doi = {10.24251/HICSS.2020.119}, pages = {11}, language = {en} } @misc{ReichsteinBruschMeierzuUmmeln, author = {Reichstein, Thomas and Brusch, Ines and Meier zu Ummeln, Rebecca}, title = {Social Media Marketing in the Event Industry: An Empirical Study to Determine Factors Influencing the Distribution of Events}, series = {Archives of Data Science, Series A}, volume = {6}, journal = {Archives of Data Science, Series A}, number = {2}, issn = {2363-9881}, pages = {16}, abstract = {Digitization offers great potential for many areas, including event marketing. Newsletters and social media are already being used successfully by companies to draw attention to their events. Social media marketing can help to increase the reach of events. In this context, it is important to understand which factors influence the intention to interact and interest in event announcements. Using an image manipulation experiment, we examined the influence of four framework conditions on event announcements: Image (present vs. absent), number of persons interested (high vs. low), title length (long vs. short), and relationship information (present vs. absent). The results showed that event announcements with an image elicit significantly higher intention to interact. In addition, interest increases significantly when relationship information is present in the ad (when there is a basic interest in the event). Furthermore, we analyzed the influence of emotionality of event images. We show that emotionality is positively significantly correlated with the intention to interact and interest.}, language = {en} } @misc{WernerBruschBuerksArndt, author = {Werner, Bastian and Brusch, Ines and B{\"u}rks-Arndt, Larissa}, title = {Versioning, Price Fairness and Purchase Decision - An Empirical Investigation}, series = {Archives of Data Science, Series A}, volume = {6}, journal = {Archives of Data Science, Series A}, number = {2}, publisher = {KIT-Bibliothek}, address = {Karlsruhe}, issn = {2363-9881}, doi = {10.5445/KSP/1000098012/05}, pages = {21}, abstract = {Companies typically offer different variants of a product to address many heterogeneous consumer needs. This involves improving, reducing, cor- recting, or dismantling existing parts of a product, which is called versioning. This also serves to capture the different willingness to pay of consumers. Ac- cording to rational choice theory, consumers weigh benefits relative to their costs in evaluating a product and generate the purchase decision. Consequently, the production method should be irrelevant. The empirical evidence of this study contradicts this thought. Based on Equity theory and Dual-Entitlement theory, a quantitative survey has been carried out. In this context, the four versioning methods were examined to determine whether they appear fair to consumers and how/if they influence their purchasing decisions. The results provide new insights for researchers from a theoretical and practical point of view, e.g., price fairness, and ethical convictions have significant effects on purchasing decisions. Finally, the paper gives some general implications and recommendations for future research.}, language = {en} } @misc{Brusch, author = {Brusch, Ines}, title = {Identification of Travel Styles by Learning from Consumer-generated Images in Online Travel Communities}, series = {Information \& Management}, volume = {59}, journal = {Information \& Management}, number = {6}, issn = {0378-7206}, doi = {10.1016/j.im.2022.103682}, abstract = {"A picture is worth a thousand words": Never has this adage been more meaningful than it is today. Online social media is driving the growth of unstructured image data. Unstructured data must be structured to be informative and thereby contribute to user understanding and revenue generation. Hitherto, companies have only been able to accomplish this through tedious manual work. This paper demonstrates how image data can be analyzed automatically using a combination of image analysis methods and fuzzy cluster algorithms to predict user preferences, which companies can then use to make targeted offers. Several methods, including support vector machines (SVMs) and convolutional neural networks (CNNs), are benchmarked across various cases of image data taken from an online travel community. Depending on the images' diversity either a SVM or a CNN provides the best basis for preference prediction.}, language = {en} }