Thomas Reutterer

2.5k total citations
39 papers, 1.6k citations indexed

About

Thomas Reutterer is a scholar working on Marketing, Organizational Behavior and Human Resource Management and Artificial Intelligence. According to data from OpenAlex, Thomas Reutterer has authored 39 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Marketing, 8 papers in Organizational Behavior and Human Resource Management and 7 papers in Artificial Intelligence. Recurrent topics in Thomas Reutterer's work include Consumer Market Behavior and Pricing (18 papers), Consumer Retail Behavior Studies (12 papers) and Customer Service Quality and Loyalty (8 papers). Thomas Reutterer is often cited by papers focused on Consumer Market Behavior and Pricing (18 papers), Consumer Retail Behavior Studies (12 papers) and Customer Service Quality and Loyalty (8 papers). Thomas Reutterer collaborates with scholars based in Austria, Germany and United Kingdom. Thomas Reutterer's co-authors include Christoph Teller, Michael Hahsler, Andreas Mild, Martin Natter, Alfred Taudes, Herbert Kotzab, Yasemin Boztuğ, Xiao Ma, Hans Risselada and David A. Schweidel and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Marketing and European Journal of Operational Research.

In The Last Decade

Thomas Reutterer

36 papers receiving 1.5k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Thomas Reutterer Austria 19 826 309 260 254 222 39 1.6k
Reinhold Decker Germany 20 554 0.7× 506 1.6× 150 0.6× 141 0.6× 237 1.1× 89 1.5k
Martin Natter Germany 19 706 0.9× 309 1.0× 191 0.7× 249 1.0× 73 0.3× 62 1.3k
Nishad Nawaz Bahrain 22 297 0.4× 261 0.8× 155 0.6× 272 1.1× 220 1.0× 97 1.5k
Jiayin Qi China 21 354 0.4× 544 1.8× 155 0.6× 241 0.9× 333 1.5× 113 1.6k
Orlando Troisi Italy 22 647 0.8× 354 1.1× 81 0.3× 228 0.9× 151 0.7× 72 1.8k
Ricardo Sellers Rubio Spain 21 426 0.5× 350 1.1× 311 1.2× 123 0.5× 133 0.6× 67 1.5k
Sam K. Hui United States 17 992 1.2× 384 1.2× 275 1.1× 123 0.5× 84 0.4× 36 1.6k
Amit Mittal India 23 374 0.5× 371 1.2× 93 0.4× 415 1.6× 212 1.0× 272 2.1k
Chiehyeon Lim South Korea 18 547 0.7× 199 0.6× 82 0.3× 235 0.9× 100 0.5× 46 1.5k
Anuj Sharma India 24 408 0.5× 452 1.5× 149 0.6× 169 0.7× 485 2.2× 91 2.0k

Countries citing papers authored by Thomas Reutterer

Since Specialization
Citations

This map shows the geographic impact of Thomas Reutterer's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Thomas Reutterer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Reutterer more than expected).

Fields of papers citing papers by Thomas Reutterer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thomas Reutterer. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Thomas Reutterer. The network helps show where Thomas Reutterer may publish in the future.

Co-authorship network of co-authors of Thomas Reutterer

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas Reutterer. A scholar is included among the top collaborators of Thomas Reutterer based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Thomas Reutterer. Thomas Reutterer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Schweidel, David A., et al.. (2024). Moving Beyond ChatGPT: Applying Large Language Models in Marketing Contexts. WU Research. 16(1). 24–29. 1 indexed citations
2.
Reutterer, Thomas, et al.. (2023). Deep Generative Models for Synthetic Data: A Survey. IEEE Access. 11. 47304–47320. 43 indexed citations
3.
Fruchter, Gila E., et al.. (2022). Dynamic Formation of Quality Expectations: Theory and Empirical Evidence. WU Research. 21(1). 35–75. 1 indexed citations
4.
Reutterer, Thomas, et al.. (2022). A deep recurrent neural network approach to learn sequence similarities for user-identification. Decision Support Systems. 155. 113718–113718. 23 indexed citations
5.
Reutterer, Thomas, et al.. (2021). Holdout-Based Empirical Assessment of Mixed-Type Synthetic Data. Frontiers in Big Data. 4. 679939–679939. 27 indexed citations
6.
Reutterer, Thomas, et al.. (2021). The carrot and the stick in online reviews: determinants of un-/helpfulness voting choices. Journal of Business Economics. 92(4). 565–590. 3 indexed citations
7.
Reutterer, Thomas, et al.. (2021). Deep Generative Models for Synthetic Data. ePubWU Institutional Repository (Wirtschaftsuniversität Wien). 2 indexed citations
8.
Wieringa, Jaap E., P.K. Kannan, Xiao Ma, et al.. (2019). Data analytics in a privacy-concerned world. Journal of Business Research. 122. 915–925. 102 indexed citations
9.
Reutterer, Thomas, et al.. (2018). Topic modeling in marketing: recent advances and research opportunities. Journal of Business Economics. 89(3). 327–356. 91 indexed citations
10.
Reutterer, Thomas, et al.. (2016). A data mining framework for targeted category promotions. Journal of Business Economics. 87(3). 337–358. 11 indexed citations
11.
Reutterer, Thomas, et al.. (2013). How to derive consensus among various marketing journal rankings?. Journal of Business Research. 67(5). 998–1006. 25 indexed citations
12.
Teller, Christoph, et al.. (2008). Hedonic and utilitarian shopper types in evolved and created retail agglomerations. The International Review of Retail Distribution and Consumer Research. 18(3). 283–309. 75 indexed citations
13.
Hahsler, Michael, et al.. (2008). A REVIEW OF METHODS FOR MEASURING WILLINGNESS-TO-PAY. SHILAP Revista de lepidopterología. 345 indexed citations
14.
Boztuğ, Yasemin & Thomas Reutterer. (2007). A combined approach for segment-specific market basket analysis. European Journal of Operational Research. 187(1). 294–312. 42 indexed citations
15.
Natter, Martin, Thomas Reutterer, Andreas Mild, & Alfred Taudes. (2007). Practice Prize Report—An Assortmentwide Decision-Support System for Dynamic Pricing and Promotion Planning in DIY Retailing. Marketing Science. 26(4). 576–583. 49 indexed citations
16.
Reutterer, Thomas, Michael Hahsler, & Kurt Hornik. (2007). Data Mining und Marketing am Beispiel der explorativen Warenkorbanalyse. Marketing ZFP. 29(3). 163–180. 4 indexed citations
17.
Teller, Christoph & Thomas Reutterer. (2007). The evolving concept of retail attractiveness: What makes retail agglomerations attractive when customers shop at them?. Journal of Retailing and Consumer Services. 15(3). 127–143. 200 indexed citations
18.
Reutterer, Thomas, Andreas Mild, Martin Natter, & Alfred Taudes. (2006). A dynamic segmentation approach for targeting and customizing direct marketing campaigns. Journal of Interactive Marketing. 20(3-4). 43–57. 65 indexed citations
19.
Hahsler, Michael, Kurt Hornik, & Thomas Reutterer. (2005). Implications of Probabilistic Data Modeling for Rule Mining. ePubWU Institutional Repository (Vienna University of Economics and Business). 3 indexed citations
20.
Buchta, Christian, Sara Dolničar, & Thomas Reutterer. (2000). A Nonparametric Approach to Perceptions-Based Market Segmentation: Applications. 16 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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