Koen W. De Bock

2.0k total citations · 1 hit paper
28 papers, 1.3k citations indexed

About

Koen W. De Bock is a scholar working on Marketing, Information Systems and Artificial Intelligence. According to data from OpenAlex, Koen W. De Bock has authored 28 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Marketing, 14 papers in Information Systems and 8 papers in Artificial Intelligence. Recurrent topics in Koen W. De Bock's work include Customer churn and segmentation (17 papers), Consumer Market Behavior and Pricing (14 papers) and Data Mining Algorithms and Applications (10 papers). Koen W. De Bock is often cited by papers focused on Customer churn and segmentation (17 papers), Consumer Market Behavior and Pricing (14 papers) and Data Mining Algorithms and Applications (10 papers). Koen W. De Bock collaborates with scholars based in France, Belgium and Germany. Koen W. De Bock's co-authors include Kristof Coussement, Arno De Caigny, Dirk Van den Poel, Stefan Lessmann, Zhenkun Liu, Lifang Zhang, Jianzhou Wang, Ping Jiang, Xinsong Niu and Tsan‐Ming Choi and has published in prestigious journals such as European Journal of Operational Research, Journal of Business Research and Expert Systems with Applications.

In The Last Decade

Koen W. De Bock

27 papers receiving 1.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees

2018 351 citations Arno De Caigny, Kristof Coussement et al. European Journal of Operational Research profile →

Peers

Countries citing papers authored by Koen W. De Bock

Since Specialization

Citations

This map shows the geographic impact of Koen W. De Bock'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 Koen W. De Bock with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Koen W. De Bock more than expected).

Fields of papers citing papers by Koen W. De Bock

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Koen W. De Bock. 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 Koen W. De Bock. The network helps show where Koen W. De Bock may publish in the future.

Co-authorship network of co-authors of Koen W. De Bock

This figure shows the co-authorship network connecting the top 25 collaborators of Koen W. De Bock. A scholar is included among the top collaborators of Koen W. De Bock 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 Koen W. De Bock. Koen W. De Bock is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

#	Work	Indexed citations
1	Ensemble learning for operations research and business analytics 2025 ·Annals of Operations Research ·Koen W. De Bock, Matthias Bogaert, Philippe du Jardin	0
2	The role of embodiment, experience, and self-image expression in creating continuance intention in the metaverse 2024 ·Technological Forecasting and Social Change ·Yogesh K. Dwivedi, Janarthanan Balakrishnan, Anubhav Mishra, Koen W. De Bock, Adil S. Al-Busaidi	17
3	Explainable profit-driven hotel booking cancellation prediction based on heterogeneous stacking-based ensemble classification 2024 ·European Journal of Operational Research ·Zhenkun Liu, Koen W. De Bock, (unknown)	11
4	Explainable Analytics for Operational Research 2024 ·European Journal of Operational Research ·Koen W. De Bock, Kristof Coussement, Arno De Caigny	2
5	Hybrid black-box classification for customer churn prediction with segmented interpretability analysis 2024 ·Decision Support Systems ·Arno De Caigny, Koen W. De Bock, (unknown)	5
6	Extreme gradient boosting trees with efficient Bayesian optimization for profit-driven customer churn prediction 2023 ·Technological Forecasting and Social Change ·Zhenkun Liu, Ping Jiang, Koen W. De Bock, Jianzhou Wang, Lifang Zhang, Xinsong Niu	46
7	Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda 2023 ·European Journal of Operational Research ·Koen W. De Bock, Kristof Coussement, Arno De Caigny, Roman Słowiński, Bart Baesens, Robert Boute, Tsan‐Ming Choi, Dursun Delen, Mathias Kraus, Stefan Lessmann, Sebastián Maldonado, David Martens, María Óskarsdóttir, Carla Vairetti, Wouter Verbeke, Richard W. Weber	61
8	Exploiting time-varying RFM measures for customer churn prediction with deep neural networks 2023 ·Annals of Operations Research ·(unknown), Kristof Coussement, Koen W. De Bock, Arno De Caigny, Stefan Lessmann	24
9	A decision-analytic framework for interpretable recommendation systems with multiple input data sources: a case study for a European e-tailer 2021 ·Annals of Operations Research ·Kristof Coussement, Koen W. De Bock, (unknown)	5
10	Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach 2020 ·European Journal of Operational Research ·Koen W. De Bock, Kristof Coussement, Stefan Lessmann	48
11	Targeting customers for profit: An ensemble learning framework to support marketing decision-making 2019 ·Information Sciences ·Stefan Lessmann, (unknown), Kristof Coussement, Koen W. De Bock	53
12	Incorporating textual information in customer churn prediction models based on a convolutional neural network 2019 ·International Journal of Forecasting ·Arno De Caigny, Kristof Coussement, Koen W. De Bock, Stefan Lessmann	103
13	Leveraging fine-grained transaction data for customer life event predictions 2019 ·Decision Support Systems ·Arno De Caigny, Kristof Coussement, Koen W. De Bock	21
14	Targeting Customers for Profit: An Ensemble Learning Framework to Support Marketing Decision Making 2018 ·SSRN Electronic Journal ·Stefan Lessmann, (unknown), Koen W. De Bock, (unknown)	6
15	A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees breakdown → 2018 ·European Journal of Operational Research ·Arno De Caigny, Kristof Coussement, Koen W. De Bock	351
16	A framework for configuring collaborative filtering-based recommendations derived from purchase data 2017 ·European Journal of Operational Research ·(unknown), Kristof Coussement, Koen W. De Bock	48
17	The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles 2017 ·Expert Systems with Applications ·Koen W. De Bock	14
18	Advanced Database Marketing 2016 ·UWA Profiles and Research Repository (University of Western Australia) ·Koen W. De Bock	2
19	Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models 2012 ·Expert Systems with Applications ·Koen W. De Bock, Dirk Van den Poel	54
20	GAMens: Applies GAMens, GAMrsm and GAMbag ensemble classifiers. R Package version 1.11. 2010 ·Ghent University Academic Bibliography (Ghent University) ·Koen W. De Bock, Kristof Coussement, Dirk Van den Poel	2

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.

Explore authors with similar magnitude of impact