Explanations from Intelligent Systems: Theoretical Foundations and Implications for Practice

Gregor, Shirley; Benbasat, Izak

doi:10.2307/249487

Cited by 359 publications

(272 citation statements)

References 81 publications

Supporting

Mentioning

269

Contrasting

Unclassified

Order By: Relevance

“…Information presentation and information quality are two elementary functions that make it easier for the consumer to deal with the system where it might otherwise even be impossible. A distinguishing feature of intelligent or smart systems is that they contain a knowledge component comparable to a computerized version of human knowledge [43]. In this regard, purposes, explanations and decisions made by an intelligent agent influence consumer acceptance of smart systems and improve user trust in the advice provided [43], [49].…”

Section: P2b: the Perceived Ease Of Use Of The Embedded Components Pomentioning

confidence: 99%

“…A distinguishing feature of intelligent or smart systems is that they contain a knowledge component comparable to a computerized version of human knowledge [43]. In this regard, purposes, explanations and decisions made by an intelligent agent influence consumer acceptance of smart systems and improve user trust in the advice provided [43], [49]. Nevertheless, an appropriate technology stack is necessary to guarantee a high information quality level.…”

Section: P2b: the Perceived Ease Of Use Of The Embedded Components Pomentioning

confidence: 99%

“…Nevertheless, an appropriate technology stack is necessary to guarantee a high information quality level. This technology must fulfill two requirements to prevent information overload and hence reactance: distinguishing important and unimportant information from each other and making system performance transparent to its users [43], [72].…”

Section: P2b: the Perceived Ease Of Use Of The Embedded Components Pomentioning

confidence: 99%

See 2 more Smart Citations

Determinants of Consumers' Perceived Trust in IT-Ecosystems

Wiedmann

Hennigs

Varelmann

et al. 2010

J. theor. appl. electron. commer. res.

View full text Add to dashboard Cite

Digital ecosystems, or IT-ecosystems (ITEs), are composed of multiple and independent entities such as individuals, organizations, services, software, and applications. Together, these elements create a number of new independent systems that operate and communicate with their own infrastructure (man to machine; machine to machine; person to person), sharing one or several missions. A better understanding of how ITEs and their interconnected components create benefits and added value for different types of consumers is of particular importance to the establishment of digital environments and to managing their resources. Considering different components of perceived trust in ITEs, we rely in this paper on a multi-dimensional framework of trust effects that includes system-centric as well as user-centric determinants of trust. Based on our conceptual model, we develop two sets of propositions. The first ones address technological drivers of trust in ITEs, whereas the second set of propositions considers individual as well as social drivers of trust. The model and propositions are discussed with reference to preliminary empirical results as well as to future research steps and business implications.

show abstract

Section: P2b: the Perceived Ease Of Use Of The Embedded Components Pomentioning

confidence: 99%

Section: P2b: the Perceived Ease Of Use Of The Embedded Components Pomentioning

confidence: 99%

Section: P2b: the Perceived Ease Of Use Of The Embedded Components Pomentioning

confidence: 99%

See 1 more Smart Citation

Determinants of Consumers' Perceived Trust in IT-Ecosystems

Wiedmann

Hennigs

Varelmann

et al. 2010

J. theor. appl. electron. commer. res.

View full text Add to dashboard Cite

show abstract

“…At the turn of the century, we find the issue discussed again in the context of knowledge-based systems [10,11]. Recently, we can see a renewed focus in CBR on this track of research.…”

Section: Why Bother To Explain?mentioning

confidence: 99%

“…This goal is most important with knowledge engineers seeking to debug the system and possibly domain experts seeking to verify the reasoning process [10]. It is also reasonable to think that in domains with a high cost of failure it can be expected that the user wishes to examine the reasoning process more thoroughly.…”

Section: Explanation Goalsmentioning

confidence: 99%

Mapping Goals and Kinds of Explanations to the Knowledge Containers of Case-Based Reasoning Systems

Roth–Berghofer

Cassens

2005

Case-Based Reasoning Research and Development

View full text Add to dashboard Cite

Abstract. Research on explanation in Case-Based Reasoning (CBR) is a topic that gains momentum. In this context, fundamental issues on what are and to which end do we use explanations have to be reconsidered. This article presents a prelimenary outline of the combination of two recently proposed classifications of explanations based on the type of the explanation itself and user goals which should be fulfilled. Further on, the contribution of the different knowledge containers for modeling the necessary knowledge is examined.

show abstract