Profile
Dominik Gutt is Assistant Professor of Business Information Management at the Department of Technology and Operations Management at Rotterdam School of Management, Erasmus University. He obtained his PhD from Paderborn University in May 2019 and joined RSM in September 2019.
Dominik’s main research interests lie in user-generated content (e.g., electronic word-of-mouth or peer-to-peer video streams), web3 (e.g., NFTs and DAOs), and AI usage (e.g., generative AI, chatbots). Currently, Dominik is mainly teaching analytics, research methods for IS students (in particular, econometrics), and web scraping.
His work has been accepted at well-reputed peer-reviewed journals including Information Systems Research and Management Information Systems Quarterly. His work has also been presented at leading Information Systems and Economics conferences including the National Bureau of Economic Research (NBER) Summer Institute, the Workshop on Information Systems and Economics (WISE), the Conference on Information Systems and Technology (CIST), and the International Conference on Information Systems (ICIS).
Please find my personal website here.
Publications
Article (4)
Academic (4)
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Tsekouras, D., Gutt, D., & Heimbach, I. (2024). The Robo Bias in Conversational Reviews: How the Solicitation Medium Anthropomorphism affects Product Rating Valence and Review Helpfulness. Journal of the Academy of Marketing Science. https://doi.org/10.1007/s11747-024-01027-8
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Neumann, J., Gutt, D., & Kundisch, D. (2023). Reviewing from a Distance: Uncovering Asymmetric Moderations of Spatial and Temporal Distance between Sentiment Negativity and Rating. MIS Quarterly, 47(7), 1709-1726. https://doi.org/10.25300/misq/2022/17037
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Gutt, D., Herrmann, P., & Rahman, MS. (2019). Crowd-Driven Competitive Intelligence: Understanding the Relationship between Local Market Competition and Online Rating Distributions. Information Systems Research, 30(3), 980-994. https://doi.org/10.1287/isre.2019.0845
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Gutt, D., Neumann, J., Zimmermann, S., Kundisch, D., & Chen, J. (2019). Design of Review Systems – A Strategic Instrument to shape Online Reviewing Behavior and Economic Outcomes. Journal of Strategic Information Systems, 28(2), 104-117. https://doi.org/10.1016/j.jsis.2019.01.004
Conference proceeding (1)
Academic (1)
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Müller, M., Neumann, J., Gutt, D., & Kundisch, D. (2020). Toss a Coin to Your Host: How Guests End up Paying for the Cost of Regulatory Policies. In ICIS 2020 Proceedings Article 2092 International Conference on Information Systems. https://aisel.aisnet.org/icis2020/sharing_economy/sharing_economy/13
Working paper (6)
Academic (6)
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Foerderer, J., Gutt, D., & Greenwood, B. (2023). Star Wars: An Empirical Investigation of Star Performer Turnover and Content Supply on Multi-Sided Streaming Platforms. https://doi.org/10.2139/ssrn.4321163
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Kanellopoulos, I., Gutt, D., Tunc, M., & Li, T. (2023). How Do Platform Subsidies Affect Creation, Engagement, and Pricing? Evidence from Non-Fungible Tokens. https://doi.org/10.2139/ssrn.4335127
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Kanellopoulos, I., Gutt, D., & Li, T. (2022). Do Non-Fungible Tokens (NFTs) Affect Prices of Physical Products? Evidence from Trading Card Collectibles (under review). https://doi.org/10.2139/ssrn.3918256
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Kupfer, A., Gutt, D., Kundisch, D., & Zimmermann, S. (2021). On the Effectiveness of Self-Contained Reward Systems to Incentivize User-Generated Content. https://doi.org/10.2139/ssrn.3823280
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Tsekouras, D., Gutt, D., & Heimbach, I. (2021). The Rise of Robo-Reviews – The Effects of Chatbot-mediated Review Elicitation on Online Reviews. https://doi.org/10.2139/ssrn.3754200
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Gutt, D., Neumann, J., Jabr, W., & Kundisch, D. (2020). My Reviews are taken away, what about my Reputation? The Asymmetric Impact of Resetting the Review History on Mobile App Platforms. https://doi.org/10.2139/ssrn.3565937
Courses
Information Systems Research (NLIS)
- Study year: 2024/2025
- Code: BERMASC051
- Level: PhD
Advanced Statistics & Programming
- Study year: 2024/2025, 2023/2024, 2022/2023, 2021/2022, 2020/2021
- Code: BM01BAM
- Level: Master
BIM Research Methods
- Study year: 2024/2025, 2023/2024, 2022/2023, 2021/2022, 2020/2021, 2019/2020
- Code: BM06BIM
- Level: Master
BIM Honours Class
- Study year: 2024/2025, 2023/2024, 2022/2023, 2021/2022, 2020/2021
- Code: BMHONBIM
- Level: Master
BIM Master Thesis
- Study year: 2024/2025, 2023/2024, 2022/2023, 2021/2022, 2020/2021, 2019/2020
- Code: BMMTBIM
- Level: Master
BIM Thesis Clinic
- Study year: 2024/2025, 2023/2024, 2022/2023, 2021/2022, 2020/2021, 2019/2020
- Code: BMRM1BIM
- Level: Master
Past courses
Web Mining and Analytics
- Study year: 2023/2024, 2022/2023, 2021/2022, 2020/2021, 2019/2020
- Code: BMME140
- Level: Master, Master, Master, Master
Web Mining and Analytics
- Study year: 2022/2023
- Code: BMME140-BAM
- Level: Master, Master, Master, Master
Data Modelling & Analytics
- Study year: 2021/2022
- Code: B3T1102
- Level: Bachelor 3, Bachelor 3, Bachelor 3
Business Information Management
- Study year: 2020/2021
- Code: BT1213
- Level: Bachelor 1, Bachelor 3, Pre-master
Business Information Management
- Study year: 2019/2020
- Code: BT1113
- Level: Bachelor 1, Bachelor 3, Pre-master
Featured in the media
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The robo bias in conversational reviews: How the solicitation medium anthropomorphism affects product rating valence and review helpfulness
Advantages, drawbacks, and potential ethical concerns of anthropomorphic technology in customer feedback solicitation. Moderate levels of anthropomorphism lead to increased interaction enjoyment, and high levels increase social…
Thursday, 6 June 2024 -
The robo bias in conversational reviews
How the solicitation medium anthropomorphism affects product rating valence and review helpfulness. Advantages, drawbacks, and potential ethical concerns of anthropomorphic technology in customer feedback solicitation. Moderate…
Thursday, 6 June 2024
Featured on RSM Discovery
Discover how the use of chatbots for collecting online reviews leads to higher ratings but less detailed feedback. Learn why policymakers are considering regulations to prevent bias in online reviews.
Researchers found the correlation between negative words and numerical rating gets stronger the further away reviewers are from the restaurant.
