TY  - JOUR
A1  - Wanner, Jonas
A1  - Herm, Lukas-Valentin
A1  - Heinrich, Kai
A1  - Janiesch, Christian
T1  - The effect of transparency and trust on intelligent system acceptance: evidence from a user-based study
JF  - Electronic Markets
N2  - Contemporary decision support systems are increasingly relying on artificial intelligence technology such as machine learning algorithms to form intelligent systems. These systems have human-like decision capacity for selected applications based on a decision rationale which cannot be looked-up conveniently and constitutes a black box. As a consequence, acceptance by end-users remains somewhat hesitant. While lacking transparency has been said to hinder trust and enforce aversion towards these systems, studies that connect user trust to transparency and subsequently acceptance are scarce. In response, our research is concerned with the development of a theoretical model that explains end-user acceptance of intelligent systems. We utilize the unified theory of acceptance and use in information technology as well as explanation theory and related theories on initial trust and user trust in information systems. The proposed model is tested in an industrial maintenance workplace scenario using maintenance experts as participants to represent the user group. Results show that acceptance is performance-driven at first sight. However, transparency plays an important indirect role in regulating trust and the perception of performance.
KW  - user acceptance
KW  - intelligent system
KW  - artificial intelligence
KW  - trust
KW  - system transparency
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-323829
SN  - 1019-6781
VL  - 32
IS  - 4
ER  - 
TY  - JOUR
A1  - Herm, Lukas-Valentin
A1  - Steinbach, Theresa
A1  - Wanner, Jonas
A1  - Janiesch, Christian
T1  - A nascent design theory for explainable intelligent systems
JF  - Electronic Markets
N2  - Due to computational advances in the past decades, so-called intelligent systems can learn from increasingly complex data, analyze situations, and support users in their decision-making to address them. However, in practice, the complexity of these intelligent systems renders the user hardly able to comprehend the inherent decision logic of the underlying machine learning model. As a result, the adoption of this technology, especially for high-stake scenarios, is hampered. In this context, explainable artificial intelligence offers numerous starting points for making the inherent logic explainable to people. While research manifests the necessity for incorporating explainable artificial intelligence into intelligent systems, there is still a lack of knowledge about how to socio-technically design these systems to address acceptance barriers among different user groups. In response, we have derived and evaluated a nascent design theory for explainable intelligent systems based on a structured literature review, two qualitative expert studies, a real-world use case application, and quantitative research. Our design theory includes design requirements, design principles, and design features covering the topics of global explainability, local explainability, personalized interface design, as well as psychological/emotional factors.
KW  - artificial intelligence
KW  - explainable artificial intelligence
KW  - XAI
KW  - design science research
KW  - design theory
KW  - intelligent systems
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-323809
SN  - 1019-6781
VL  - 32
IS  - 4
ER  -