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Zherui Yang is interested in research topics of digital transformation and business strategy, the value and impact of information technology, business intelligence and big data analytics, e-commerce, and business ecosystem. His works mainly apply field experiments, but he often uses multimethod research approaches to enrich the empirical settings, such as lab and online experiment, machine learning, econometrics, survey, and interview. 

His dissertation focuses on human centric digital transformation. In this research area, he discusses the role of individual heterogeneity in organizational digital transformations and its impact on the effectiveness of digitalization processes. By exploring this topic, he would like to shed light on the significance of human element in the digital world.

In his first paper, he investigates the impact of consent re-elicitation (i.e., GDPR impact) on companies’ digital migration and customers’ opt-in in digitally lagging contexts. Moreover, he proposes new notions of customer heterogeneity, namely digital activeness, and information proactiveness, and examines their roles in effective digital migration and consent authorization. By conducting field experiments, he finds that opt-in for digital migration increases when GDPR-compliant consent is proactively elicited. Also, customers with higher digital activeness are more likely to opt-in, while customers with varying information proactiveness respond divergently, depending on campaign design’s informativeness. The conference version of this paper was accepted for presentation in several significant conferences in the field of Information Systems (IS), such as ICIS 2019, CODE@MIT 2019, CIST 2019, and INFORMS 2019.

In his second dissertation paper, he focuses on digital transformation in a more advanced context where companies adopt machine learning techniques (i.e., life event prediction) and studies the effect of life-event targeting on customers’ conversion in an insurance context. He conducts a field experiment to examine the effectiveness of life-event targeting and an online experiment to explore the underlying mechanism. The findings suggest that customers’ information seeking need mediates the positive effect of life-event targeting on customers’ conversion, while customers with higher choice uncertainty and those with lower knowledge uncertainty are more likely to respond to life-event targeting. The conference version of this paper was nominated as the best paper award runner-up at the Workshop on e-Business (WeB) 2020 and has been accepted for presentations in multiple leading IS conferences, such as SCECR 2020, CSWIM 2021, INFORMS 2021 and CODE@MIT 2022.

In his third dissertation paper, he studies human centric digital transformation in an industrial context. . In a field study, he demonstrates that there is an immediate gain in performance improvement after a short-term AR usage. Specifically, by improving how workers absorb and process all the information, AR enhances workers’ information processing speed and consequently improves task performance. Moreover, workers’ perception of extraneous cognitive load towards how information is presented and their uncertainty avoidance towards working information significantly moderates the positive effect of AR effect. The conference version of this paper has been accepted for presentations in SCECR 2022, CSWIM 2022, INFORMS 2022 and DSI 2022.

Moreover, in one of his other projects, he examines the value of AR on the hearing-impaired. People with disabilities suffer many kinds of injustice. While legal remedies are naturally seen as a key to improving their lives, the role of digital technology in mitigating social injustice should not be overlooked. He explores the impact of AR on reducing social inequality faced by the hearing-impaired and demonstrates that AR can empower them internally by improving their psychological well-being and externally by increasing their communicative effectiveness. By exploring this topic, he would like to emphasize on the role of digital technology in contributing to social issues. 


Academic (2)
  • Jansen, S., & Yang, Z. (2020). Source Data for the Focus Area Maturity Model for Software Ecosystem Governance. Data in Brief, 31, Article 105656.

  • Zhu, H., Liu, H., Ou, C. XJ., Davison, R. M., & Yang, Z. (2017). Privacy preserving mechanisms for optimizing cross-organizational collaborative decisions based on the Karmarkar algorithm. Information Systems, 72, 205-217.

Academic (6)
  • Yang, Z. (2022). Future of work: How Does Augmented Reality (AR) Improve Workers’ Performance. In Proceedings of the 15th China Summer Workshop on Information Management (CSWIM 2022) (pp. 307-312)

  • Yang, Z., & Li, T. (2020). Life-Event Targeting and Customer Uncertainty – Evidence from Field and Online Experiments. In Proceedings of the 19th Workshop on e-Business (WEB-2020)

  • Yang, Z., Cheng, Z., & Li, T. (2019). Still targeting younger customers? A field experiment on digital communication channel migration. In 40th International Conference on Information Systems, ICIS 2019 Article 2822 Association for Information Systems.

  • Yang, Z., & Ou, C. (2017). Investigating the impact of recommendation agents on e-commerce ecosystem. In Proceedings of Americas Conference on Information Systems (AMCIS)

  • Yang, Z., Jansen, S., Gao, X., & Zhang, D. (2017). On the future of solution composition in software ecosystems. In J. A. Banares, J. Altmann, & K. Tserpes (Eds.), Economics of Grids, Clouds, Systems, and Services - 13th International Conference, GECON 2016, Revised Selected Papers (pp. 3-18). Springer Verlag/Kodansha Ltd.

  • Zhu, H., Yang, Z., Ou, C. X. J., Liu, H., & Davison, R. M. (2015). Investigating the impacts of recommendation agents on impulsive purchase behaviour. In ACIS 2015 Proceedings - 26th Australasian Conference on Information Systems Association for Information Systems.

Academic (1)


Information Strategy

  • Study year: 2024/2025, 2023/2024, 2022/2023, 2021/2022, 2020/2021
  • Code: BM01BIM
  • Level: ERIM, Exchange, IM/CEMS, Master

BIM Research Methods

  • Study year: 2024/2025, 2023/2024, 2022/2023, 2020/2021
  • Code: BM06BIM
  • Level: Master

BIM Honours Class

  • Study year: 2023/2024, 2022/2023, 2021/2022, 2020/2021
  • Code: BMHONBIM
  • Level: Master

BIM Master Thesis

  • Study year: 2023/2024, 2022/2023, 2021/2022, 2020/2021, 2019/2020, 2018/2019
  • Code: BMMTBIM
  • Level: Master

BIM Thesis Clinic

  • Study year: 2023/2024, 2022/2023, 2021/2022, 2020/2021, 2019/2020
  • Code: BMRM1BIM
  • Level: Master

Past courses

Business Architecture and Transformation

  • Study year: 2022/2023, 2021/2022, 2020/2021, 2019/2020, 2018/2019
  • Code: BM03BIM
  • Level: ERIM, Exchange, IM/CEMS, Master

BIM Research Methods I - Old style

  • Study year: 2019/2020, 2018/2019
  • Code: BM05BIM
  • ECTS: 2