Conversational Agents in Healthcare: Using QCA to Explain Patients’ Resistance to Chatbots for Medication

Müller, Lea; Mattke, Jens; Maier, Christian; Weitzel, Tim

doi:10.1007/978-3-030-39540-7_1

Cited by 12 publications

(14 citation statements)

References 43 publications

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“…Samuelson and Zeckhauser [17] referred to this deliberate choice in favor of the known custom as SQB. The SQB perspective is used in information systems (IS) research to study decision-making mechanisms that cause resistance when confronted with new IS [26] or with options for setting a new course of action triggered by technology, such as paying with bitcoin [28] or managing one's medication using chatbots [29].…”

Section: Research Modelmentioning

confidence: 99%

“…We adopt transition costs (TC) and uncertainty costs (UC) as trade-offs made at the rational decision level [17]. While TC refer to the time and effort considerations between the alternative and the status quo [28,29,30], UC address the information deficit associated with a presented alternative, which requires that patients invest in searching and analyzing information to make an informed decision [29].…”

Section: Research Modelmentioning

confidence: 99%

“…We further consider loss aversion (LA), which belongs to the category of cognitive misperceptions and builds on the notion that "individuals weigh losses heavier than gains in making decisions" [17:35]. Previous IS research assessed the presence of LA in an individual when the costs (transition and uncertainty costs) outweigh the perceived benefits [28,29,30]. Thus, to measure loss aversion, the perceived benefits (BF) of the alternative must be considered.…”

Section: Research Modelmentioning

confidence: 99%

“…Considering that resistance is a complex phenomenon, the causality of it typically does not depend on only one factor or the causal symmetry to reasons that lead to acceptance [28,29]. Thus, we further enhance our theoretical framework in Figure 2 by building on the complexity and configuration theory to leverage principles of conjunction, asymmetry, and equifinality to address the complex causality [34].…”

Section: Figure 2 Configurational Research Modelmentioning

confidence: 99%

See 3 more Smart Citations

The Force of Habit: Examining the Status Quo Bias for Using Mixed Reality in Patient Education

Anton¹,

Schuir²,

Teuteberg³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Using mixed reality (MR) glasses for preoperative patient education (PPE) can help patients understand the purpose and risks of surgical procedures through informative visualizations. However, patients tend to be critical regarding the use of MR glasses in healthcare and often prefer the status quo of healthcare services provided. This study explores the resistance to MR technologies in PPE through the lens of the status quo bias theory by surveying n = 171 participants. We conducted a fuzzy set qualitative comparative analysis revealing configurations that provide a typological understanding of patient resistance. This allows healthcare stakeholders to take more targeted interventions to promote MR adoption. Notably, the results indicate that healthcare providers need to be transparent in communicating the benefits and drawbacks of using MR, as uncertainty costs are the main driver of resistance to MR glasses in PPE.

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Section: Research Modelmentioning

confidence: 99%

Section: Research Modelmentioning

confidence: 99%

Section: Research Modelmentioning

confidence: 99%

Section: Figure 2 Configurational Research Modelmentioning

confidence: 99%

See 2 more Smart Citations

The Force of Habit: Examining the Status Quo Bias for Using Mixed Reality in Patient Education

Anton¹,

Schuir²,

Teuteberg³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…Conversational agents are now studied in different application domains, such as for administering surveys ( Kim et al, 2019 ), for healthcare ( Müller et al, 2019 ), and of course, for learning ( Conversational Agents in Education ), and argumentation support ( Conversational Agents in Argumentation ).…”

Section: Background Knowledge and Related Workmentioning

confidence: 99%

Developing a Conversational Agent’s Capability to Identify Structural Wrongness in Arguments Based on Toulmin’s Model of Arguments

Mirzababaei

Pammer-Schindler

2021

Front. Artif. Intell.

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This article discusses the usefulness of Toulmin’s model of arguments as structuring an assessment of different types of wrongness in an argument. We discuss the usability of the model within a conversational agent that aims to support users to develop a good argument. Within the article, we present a study and the development of classifiers that identify the existence of structural components in a good argument, namely a claim, a warrant (underlying understanding), and evidence. Based on a dataset (three sub-datasets with 100, 1,026, 211 responses in each) in which users argue about the intelligence or non-intelligence of entities, we have developed classifiers for these components: The existence and direction (positive/negative) of claims can be detected a weighted average F1 score over all classes (positive/negative/unknown) of 0.91. The existence of a warrant (with warrant/without warrant) can be detected with a weighted F1 score over all classes of 0.88. The existence of evidence (with evidence/without evidence) can be detected with a weighted average F1 score of 0.80. We argue that these scores are high enough to be of use within a conditional dialogue structure based on Bloom’s taxonomy of learning; and show by argument an example conditional dialogue structure that allows us to conduct coherent learning conversations. While in our described experiments, we show how Toulmin’s model of arguments can be used to identify structural problems with argumentation, we also discuss how Toulmin’s model of arguments could be used in conjunction with content-wise assessment of the correctness especially of the evidence component to identify more complex types of wrongness in arguments, where argument components are not well aligned. Owing to having progress in argument mining and conversational agents, the next challenges could be the developing agents that support learning argumentation. These agents could identify more complex type of wrongness in arguments that result from wrong connections between argumentation components.

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Who Would You Rather Ask for Help? A Comparison Between Two Chatbot Personalities in Information Management

Ganal,

Wiederer,

Bachmann

et al. 2023

Lecture Notes in Computer Science

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Conversational Agents in Healthcare: Using QCA to Explain Patients’ Resistance to Chatbots for Medication

Cited by 12 publications

References 43 publications

The Force of Habit: Examining the Status Quo Bias for Using Mixed Reality in Patient Education

The Force of Habit: Examining the Status Quo Bias for Using Mixed Reality in Patient Education

Developing a Conversational Agent’s Capability to Identify Structural Wrongness in Arguments Based on Toulmin’s Model of Arguments

Who Would You Rather Ask for Help? A Comparison Between Two Chatbot Personalities in Information Management

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