Current - Issue

1. Vollrath, M. D., & Villegas, S. G. (2022). Avoiding digital marketing analytics myopia: revisiting the customer decision journey as a
strategic marketing framework. Journal of Marketing Analytics, 10(2), 106–113. https://doi.org/10.1057/s41270-020-00098-0
2. Gaur, J., Bharti, K., & Bajaj, R. (2026). Maximizing marketing impact: heuristic vs ensemble models for attribution modeling. Global
Knowledge, Memory and Communication, 75(1–2), 359–377. https://doi.org/10.1108/GKMC-04-2023-0112
3. Molina, E., Tejada, J., & Weiss, T. (2022). Some game theoretic marketing attribution models. Annals of Operations Research, 318(2),
1043–1075. https://doi.org/10.1007/s10479-022-04944-5
4. Agrawal, A., Sheoran, N., Suman, S., & Sinha, G. (2022, April). Multi-touch attribution for complex B2B customer journeys using
temporal convolutional networks. In Companion Proceedings of the Web Conference 2022 (pp. 910–917).
https://doi.org/10.1145/3487553.3524670
5. Tao, J., Chen, Q., Snyder Jr, J. W., Kumar, A. S., Meisami, A., & Xue, L. (2025). A graphical point process framework for understanding
removal effects in multi-touch attribution. Management Science, 71(9), 7312–7332. https://doi.org/10.1287/mnsc.2023.00457
6. Özyurt, Y., Hatt, T., Zhang, C., & Feuerriegel, S. (2022, April). A deep Markov model for clickstream analytics in online shopping. In
Proceedings of the ACM Web Conference 2022 (pp. 3071–3081). https://doi.org/10.1145/3485447.3512027
7. Hatt, T., & Feuerriegel, S. (2022). Detecting user exits from online behavior: A duration-dependent latent state model. arXiv preprint
arXiv:2208.03937. https://doi.org/10.48550/arXiv.2208.03937
8. Marcinkevičs, R., & Vogt, J. E. (2023). Interpretable and explainable machine learning: A methods-centric overview with concrete
examples. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13(3), e1493. https://doi.org/10.1002/widm.1493
9. Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., ... & Herrera, F. (2023). Explainable
Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence. Information Fusion, 99, 101805.
https://doi.org/10.1016/j.inffus.2023.101805
10. Wei, Q., Mu, Y., Guo, X., Jiang, W., & Chen, G. (2024). Dynamic Bayesian network–based product recommendation considering
consumers’ multistage shopping journeys: A marketing funnel perspective. Information Systems Research, 35(3), 1382–1402.
https://doi.org/10.1287/isre.2020.0277
11. De La Torre, S. A., El Mistiri, M., Tung, K., Hekler, E., Klasnja, P., Pavel, M., ... & Marlin, B. (2025). A dynamic Bayesian network
approach to modeling engagement and walking behavior: insights from a yearlong micro-randomized trial (Heartsteps II). Health
Psychology and Behavioral Medicine, 13(1), 2552479. https://doi.org/10.1080/21642850.2025.255247912. Shiguihara, P., Lopes, A. D. A., & Mauricio, D. (2021). Dynamic Bayesian network modeling, learning, and inference: A survey. IEEE
Access, 9, 117639–117648. https://doi.org/10.1109/ACCESS.2021.3105520
13. Drury, K., & Smith, J. Q. (2024). Dynamic Bayesian Networks, Elicitation, and Data Embedding for Secure Environments. Entropy,
26(11), 985. https://doi.org/10.3390/e26110985
14. Gao, W., Fan, H., Li, W., & Wang, H. (2021). Crafting the customer experience in omnichannel contexts: The role of channel integration.
Journal of Business Research, 126, 12–22. https://doi.org/10.1016/j.jbusres.2020.12.056
15. Mahajan, Y., Mahajan, V., & Kapse, M. (2024). Marketing analytics and consumer behavior: A systematic literature review for future
research agenda. Data-Driven Decision Making, 151–167. https://doi.org/10.1007/978-981-97-2902-9_7
16. Chen, X., & Li, Y. (2023). An overview of differentiable particle filters for data-adaptive sequential Bayesian inference. arXiv preprint
arXiv:2302.09639. https://doi.org/10.48550/arXiv.2302.09639