Abstract
With the advent of artificial intelligence (AI) methods, smart decision support systems (DSSs) are becoming ubiquitous. Such systems help reduce complexity for operators by automating data integration tasks and recommending actions. However, these systems are sometimes flawed. It is not sufficiently understood whether, when and why operators comply with such systems in erroneous or correct cases. We empirically investigate compliance with correct and defective DSSs, the influence of correct and erroneous DSS's on performance and subjective factors related to compliance. In the study, a business game was used as an experimental setting in which 40 users took part. The impact of system correctness on user acceptance, trust, compliance and overall performance was investigated. The results show that the defective system reduces trust in automation (−47%), reduces usefulness (−58%), reduces acceptance (−62%) and reduces overall performance (−32%). Overall, the defective system was less user-friendly (−27%). Nevertheless, users who rated the system's usability higher, outperformed users who rated it lower. Usability is therefore an intermediary that compensates for the negative influence of erroneous DSSs.
| Original language | English |
|---|---|
| Journal | Behaviour and Information Technology |
| Volume | 38 |
| Issue number | 12 |
| Pages (from-to) | 1225-1242 |
| Number of pages | 18 |
| ISSN | 0144-929X |
| DOIs | |
| Publication status | Published - 02.12.2019 |
Funding
The German Research Foundation (Deutsche Forschungsgemeinschaft–DFG) funded this work within the Clusters of Excellence Integrative Production Technology for High-Wage Countries (EXC 128), the subproject Cognition Enhanced Self-Optimizing Production Networks (Schlick et al. 2017) and Internet of Production (EXC 2023, 390621612). Authors owe gratitude to participants for their commitment and dedication to contribute to this research. Also, thanks are devoted to Sebastian Stiller, Quoc Hao Ngo, Marco Fuhrmann and Robert Schmitt for in-depth discussions and valuable feedback on this work. Further thanks go to Julia Offermann-van Heek, Anne Kathrin Schaar, Patrick Halbach, Fabian Comanns and Sabrina Schulte for their research. Finally, thanks to the anonymous reviewers for their critical and very helpful comments on this article. The dataset is publicly available (Brauner et al. 2018).
