Journal Publications

Using AI to predict service agent stress from emotion patterns in service interactions
Bromuri, S., Henkel, A.P., Iren, D. and Urovi, V. (2020), "Using AI to predict service agent stress from emotion patterns in service interactions", Journal of Service Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JOSM-06-2019-0163

Half human, half machine – augmenting service employees with AI for interpersonal emotion regulation
Henkel, A.P., Bromuri, S., Iren, D. and Urovi, V. (2020), "Half human, half machine – augmenting service employees with AI for interpersonal emotion regulation", Journal of Service Management, Vol. 31 No. 2, pp. 247-265. https://doi.org/10.1108/JOSM-05-2019-0160

Identification and analysis of handovers in organisations using process model repositories
Leyer, M., Iren, D. and Aysolmaz, B. (2020), "Identification and analysis of handovers in organisations using process model repositories", Business Process Management Journal, Vol. 26 No. 6, pp. 1599-1617. https://doi.org/10.1108/BPMJ-01-2019-0041

Cost of Quality in Crowdsourcing
Iren, D., & Bilgen, S. (2014). Cost of Quality in Crowdsourcing. Human Computation, 1(2). https://doi.org/10.15346/hc.v1i2.14

AiOLoS: A model for assessing organizational learning in software development organizations
Chouseinoglou, O., Iren, D., Karagöz, N. A., & Bilgen, S. (2013). AiOLoS: A model for assessing organizational learning in software development organizations. Information and Software Technology, 55(11), 1904-1924.


Conference Proceedings

Acceptance of AI for delegating emotional intelligence: Results from an experiment
Aysolmaz, B., Leyer, M., & Iren, D. (2021). Acceptance of AI for delegating emotional intelligence: Results from an experiment. In Proceedings of the 54th Hawaii International Conference on System Sciences (pp. 6307-6316).

Applying Scrum in Data Science Projects
Baijens, J., Helms, R., & Iren, D. (2020). Applying Scrum in Data Science Projects. In 2020 IEEE 22nd Conference on Business Informatics (CBI) (Vol. 1, pp. 30-38). IEEE.

Preventing Algorithmic Bias in the Development of Algorithmic Decision-Making Systems: A Delphi Study
Aysolmaz, B., Iren, D., & Dau, N. (2020). Preventing Algorithmic Bias in the Development of Algorithmic Decision-Making Systems: A Delphi Study. In Proceedings of the 53rd Hawaii International Conference on System Sciences.

Detecting role inconsistencies in process models
Aysolmaz, B.; Iren, D.; & Reijers, H. A., (2019). "Detecting Inconsistencies in Process Models". in Proceedings of the 27th European Conference on Information Systems (ECIS), Stockholm & Uppsala, Sweden, June 8-14, 2019. ISBN 978-1-7336325-0-8 Research Papers. https://aisel.aisnet.org/ecis2019_rp/27

Leveraging business process improvement with natural language processing and organizational semantic knowledge
Iren, D., & Reijers, H. A. (2017). Leveraging business process improvement with natural language processing and organizational semantic knowledge. In Proceedings of the 2017 International Conference on Software and System Process (pp. 100-108).

Using social media to reveal social and collective perspectives on music
Iren, D., Liem, C. C., Yang, J., & Bozzon, A. (2016). Using social media to reveal social and collective perspectives on music. In Proceedings of the 8th ACM Conference on Web Science (pp. 296-300).

Cost models of quality assurance in crowdsourcing
Iren, D., & Bilgen, S. (2014). Cost models of quality assurance in crowdsourcing. In 2014 IEEE Fifth International Conference on Communications and Electronics (ICCE) (pp. 504-509). IEEE.

Utilization of synergetic human-machine clouds: a big data cleaning case
Iren, D., Kul, G., & Bilgen, S. (2014, June). Utilization of synergetic human-machine clouds: a big data cleaning case. In Proceedings of the 1st International Workshop on CrowdSourcing in Software Engineering (pp. 15-18).

An effort prediction model based on BPM measures for process automation
Aysolmaz, B., İren, D., & Demirörs, O. (2013). An effort prediction model based on BPM measures for process automation. In Enterprise, Business-Process and Information Systems Modeling (pp. 154-167). Springer, Berlin, Heidelberg.