InformaApp - Publikationen

User- and marketer-generated content items on social media platforms are supposed to have an impact on economic target variables, such as variables measuring consumers' purchase behavior. The position of each content item – and thus the impact on economic variables – changes with newly appearing items. We propose a hierarchy score to capture the dynamics of the content items on social media platforms. In order to mimic the reduced visibility of earlier content items, our hierarchy score computes the position of content items based on the number of text line equivalents of content items above a particular item. Employing the proposed hierarchy score in a dynamic regression framework for data of a large online store yields improved estimates and predictions compared to a variety of other models.

Digital F: Angewandte Wirtschaftswissenschaften S: TC Grafenau

Zeitschriftenartikel

Michael Scholz, V. Dorner, G. Schryen, A. Benlian

A configuration-based recommender system for supporting e-commerce decisions

European Journal of Operational Research, vol. 259, no. 1, pp. 205-215

2017

DOI: 10.1016/j.ejor.2016.09.057

Abstract anzeigen

Multi-attribute value theory (MAVT)-based recommender systems have been proposed for dealing with issues of existing recommender systems, such as the cold-start problem and changing preferences. However, as we argue in this paper, existing MAVT-based methods for measuring attribute importance weights do not fit the shopping tasks for which recommender systems are typically used. These methods assume well-trained decision makers who are willing to invest time and cognitive effort, and who are familiar with the attributes describing the available alternatives and the ranges of these attribute levels. Yet, recommender systems are most often used by consumers who are usually not familiar with the available attributes and ranges and who wish to save time and effort. Against this background, we develop a new method, based on a product configuration process, which is tailored to the characteristics of these particular decision makers. We empirically compare our method to SWING, ranking-based conjoint analysis and TRADEOFF in a between-subjects laboratory experiment with 153 participants. Results indicate that our proposed method performs better than TRADEOFF and CONJOINT and at least as well as SWING in terms of recommendation accuracy, better than SWING and TRADEOFF and at least as well as CONJOINT in terms of cognitive load, and that participants were faster with our method than with any other method. We conclude that our method is a promising option to help support consumers’ decision processes in e-commerce shopping tasks.

Digital Gesund F: Europan Campus Rottal-Inn

Zeitschriftenartikel

B. Wolf, Thomas Spittler, J. Clauss, A. Scholz, P. Friedrich, K. Herzog

Telemedizin - rundum gut betreut. Einsatzgebiete des telemedizinischen Netzwerkes COMES®

4. Bulletin, pp. 33-37

2014

Digital F: Angewandte Wirtschaftswissenschaften S: TC Grafenau

Beitrag (Sammelband oder Tagungsband)

Michael Scholz, V. Dorner, A. Landherr, F. Probst

Awareness, Interest, and Purchase: the Effects of User- and Marketer-Generated Content on Purchase Decision Processes

Proceedings of the 34th International Conference on Information Systems

2013