{"type":"working_paper","volume":72,"project":[{"name":"SFB 901 - A4: SFB 901 - Empirische Analysen in Märkten für OTF Dienstleistungen (Subproject A4)","grant_number":"160364472","_id":"8"},{"_id":"1","name":"SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen in dynamischen Märkten ","grant_number":"160364472"},{"name":"SFB 901 - A: SFB 901 - Project Area A","_id":"2"}],"citation":{"short":"D. van Straaten, V. Melnikov, E. Hüllermeier, B. Mir Djawadi, R. Fahr, Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes, 2021.","ama":"van Straaten D, Melnikov V, Hüllermeier E, Mir Djawadi B, Fahr R. <i>Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes</i>. Vol 72.; 2021.","ieee":"D. van Straaten, V. Melnikov, E. Hüllermeier, B. Mir Djawadi, and R. Fahr, <i>Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes</i>, vol. 72. 2021.","bibtex":"@book{van Straaten_Melnikov_Hüllermeier_Mir Djawadi_Fahr_2021, series={Working Papers Dissertations}, title={Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes}, volume={72}, author={van Straaten, Dirk and Melnikov, Vitalik and Hüllermeier, Eyke and Mir Djawadi, Behnud and Fahr, René}, year={2021}, collection={Working Papers Dissertations} }","chicago":"Straaten, Dirk van, Vitalik Melnikov, Eyke Hüllermeier, Behnud Mir Djawadi, and René Fahr. <i>Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes</i>. Vol. 72. Working Papers Dissertations, 2021.","mla":"van Straaten, Dirk, et al. <i>Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes</i>. 2021.","apa":"van Straaten, D., Melnikov, V., Hüllermeier, E., Mir Djawadi, B., &#38; Fahr, R. (2021). <i>Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes</i> (Vol. 72)."},"date_created":"2023-06-15T08:23:33Z","status":"public","_id":"45616","user_id":"477","author":[{"last_name":"van Straaten","full_name":"van Straaten, Dirk","id":"10311","first_name":"Dirk"},{"id":"58747","first_name":"Vitalik","full_name":"Melnikov, Vitalik","last_name":"Melnikov"},{"first_name":"Eyke","id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke"},{"id":"26032","first_name":"Behnud","full_name":"Mir Djawadi, Behnud","last_name":"Mir Djawadi","orcid":"0000-0002-6271-5912"},{"id":"111","first_name":"René","last_name":"Fahr","full_name":"Fahr, René"}],"year":"2021","title":"Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes","abstract":[{"lang":"eng","text":"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."}],"intvolume":"        72","language":[{"iso":"eng"}],"series_title":"Working Papers Dissertations","date_updated":"2023-07-05T07:27:17Z"}