@article{65182, abstract = {{The aggregation of rating metrics in reputation systems is crucial for mitigating information overload by condensing customer rating distributions into singular valence scores. While platforms typically employ technical aggregation functions, such as the arithmetic mean to capture product quality, it remains unclear whether these functions align with customers' innate aggregation patterns. To address this knowledge gap, we designed a controlled economic decision experiment to elicit customers' aggregation principles by analyzing their product ranking decisions and contrasting these with various reference functions. Our findings indicate that, on average, customers aggregate rating information in accordance with the arithmetic mean. However, a granular analysis at the individual level reveals significant heterogeneity in aggregation behavior, with a substantial cluster exhibiting binary patterns that focus equally on negative (1-2 star) and positive (4-5 star) ratings. Additional clusters concentrate on negative feedback, particularly 1-star ratings or 1-2 star ratings collectively. Notably, these inherent aggregation patterns exhibit stability across variations in numerical information presentation and are not significantly influenced by individual characteristics, such as online shopping experience, risk attitudes, or demographics. These findings suggest that while the arithmetic mean captures average consumer behavior, platforms could benefit from offering customizable aggregation options to better cater to diverse user preferences for processing rating distributions. By doing so, platforms can enhance the effectiveness of their reputation systems and improve the overall quality of decision-making for consumers.}}, author = {{van Straaten, Dirk and Mir Djawadi, Behnud and Melnikov, Vitalik and Hüllermeier, Eyke and Fahr, René}}, journal = {{SSRN Electronic Journal}}, publisher = {{Elsevier BV}}, title = {{{Aggregation Processes in Customer Rating Systems - Insights from an Economic Decision Experiment}}}, doi = {{http://dx.doi.org/10.2139/ssrn.6201258}}, year = {{2026}}, } @article{30341, author = {{Hoyer, Britta and van Straaten, Dirk}}, issn = {{2214-8043}}, journal = {{Journal of Behavioral and Experimental Economics}}, keywords = {{General Social Sciences, Economics and Econometrics, Applied Psychology}}, pages = {{101869}}, publisher = {{Elsevier BV}}, title = {{{Anonymity and Self-Expression in Online Rating Systems - An Experimental Analysis}}}, doi = {{10.1016/j.socec.2022.101869}}, volume = {{98}}, year = {{2022}}, } @phdthesis{24886, author = {{van Straaten, Dirk}}, title = {{{Inferring Quality with Reputation Systems - Experimental Evidence on Elicitation Mechanisms and Aggregation Metrics}}}, doi = {{10.17619/UNIPB/1-1189 }}, year = {{2021}}, } @techreport{45616, abstract = {{Aggregation metrics in reputation systems are important for overcoming information overload. When using these metrics, technical aggregation functions such as the arithmetic mean are implemented to measure the valence of product ratings. However, it is unclear whether the implemented aggregation functions match the inherent aggregation patterns of customers. In our experiment, we elicit customers' aggregation heuristics and contrast these with reference functions. Our findings indicate that, overall, the arithmetic mean performs best in comparison with other aggregation functions. However, our analysis on an individual level reveals heterogeneous aggregation patterns. Major clusters exhibit a binary bias (i.e., an over-weighting of moderate ratings and under-weighting of extreme ratings) in combination with the arithmetic mean. Minor clusters focus on 1-star ratings or negative (i.e., 1-star and 2-star) ratings. Thereby, inherent aggregation patterns are neither affected by variation of provided information nor by individual characteristics such as experience, risk attitudes, or demographics.}}, author = {{van Straaten, Dirk and Melnikov, Vitalik and Hüllermeier, Eyke and Mir Djawadi, Behnud and Fahr, René}}, title = {{{Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes}}}, volume = {{72}}, year = {{2021}}, } @techreport{45617, author = {{van Straaten, Dirk}}, title = {{{Incentive Schemes in Customer Rating Systems - Comparing the Effects of Unconditional and Conditional Rebates on Intrinsic Motivation}}}, volume = {{71}}, year = {{2021}}, } @techreport{45618, author = {{van Straaten, Dirk and Fahr, René}}, title = {{{Fighting Fire with Fire - Overcoming Ambiguity Aversion by Introducing more Ambiguity}}}, volume = {{73}}, year = {{2021}}, } @inproceedings{1060, abstract = {{With a growing number of online reviews, it becomes increasingly important for customers and online review platforms to find groups of reviewers who write useful reviews. Customers who review local offline businesses such as restaurants can identify themselves as locals or travelers and thus implicitly assign themselves to a specific reviewer group. This study investigates the relationship between identifying as a local and the perceived usefulness of their online reviews. Using data from Yelp.com, we empirically test hypotheses derived from attribution theory. Our results suggest that neutral and negative reviews by locals tend to be perceived as more useful than reviews by travelers. Positive reviews by locals, however, are not perceived as more useful. These findings provide significant practical implications for online review platforms and local offline businesses.}}, author = {{Neumann, Jürgen and Gutt, Dominik and Kundisch, Dennis and van Straaten, Dirk}}, booktitle = {{Proceedings of the Multikonferenz Wirtschaftsinformatik 2018 (MKWI), Lüneburg, Germany}}, title = {{{When Local Praise Becomes Cheap Talk - Analyzing the Relationship between Reviewer Location and Usefulness of Online Reviews}}}, year = {{2018}}, } @inproceedings{115, abstract = {{Whenever customers have to decide between different instances of the same product, they are interested in buying the best product. In contrast, companies are interested in reducing the construction effort (and usually as a consequence thereof, the quality) to gain profit. The described setting is widely known as opposed preferences in quality of the product and also applies to the context of service-oriented computing. In general, service-oriented computing emphasizes the construction of large software systems out of existing services, where services are small and self-contained pieces of software that adhere to a specified interface. Several implementations of the same interface are considered as several instances of the same service. Thereby, customers are interested in buying the best service implementation for their service composition wrt. to metrics, such as costs, energy, memory consumption, or execution time. One way to ensure the service quality is to employ certificates, which can come in different kinds: Technical certificates proving correctness can be automatically constructed by the service provider and again be automatically checked by the user. Digital certificates allow proof of the integrity of a product. Other certificates might be rolled out if service providers follow a good software construction principle, which is checked in annual audits. Whereas all of these certificates are handled differently in service markets, what they have in common is that they influence the buying decisions of customers. In this paper, we review state-of-the-art developments in certification with respect to service-oriented computing. We not only discuss how certificates are constructed and handled in service-oriented computing but also review the effects of certificates on the market from an economic perspective.}}, author = {{Jakobs, Marie-Christine and Krämer, Julia and van Straaten, Dirk and Lettmann, Theodor}}, booktitle = {{The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION)}}, editor = {{Marcelo De Barros, Janusz Klink,Tadeus Uhl, Thomas Prinz}}, pages = {{7--12}}, title = {{{Certification Matters for Service Markets}}}, year = {{2017}}, } @misc{404, author = {{van Straaten, Dirk}}, publisher = {{Universität Paderborn}}, title = {{{Kooperative Verhandlungen im duopolistischen Wettbewerb - eine spieltheoretische Analyse}}}, year = {{2014}}, }