User-Specific Determinants of Conversational Agent Usage: A Review and Potential for Future Research

Riefle, Lara; Benz, Carina

doi:10.1007/978-3-030-86797-3_8

Cited by 2 publications

(1 citation statement)

References 39 publications

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“…This study contributes to the body of knowledge about the determinants of CA continuance usage. Almost half of the existing literature on CA acceptance, adoption, and usage evaluates a specific CA artifact, while only 21% of studies put the user in the center of attention when investigating the determinants of their acceptance and usage of CAs [39]. Most of these user-focused empirical studies did not draw on specific concepts from theory for their evaluations [40][41][42][43], which makes the results hard to compare.…”

Section: Contributions Of This Studymentioning

confidence: 99%

Investigating Patients' Continuance Intention Toward Conversational Agents in Outpatient Departments: Cross-sectional Field Survey

Li¹,

Xie²,

Ye³

et al. 2022

J Med Internet Res

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Background Conversational agents (CAs) have been developed in outpatient departments to improve physician-patient communication efficiency. As end users, patients’ continuance intention is essential for the sustainable development of CAs. Objective The aim of this study was to facilitate the successful usage of CAs by identifying key factors influencing patients’ continuance intention and proposing corresponding managerial implications. Methods This study proposed an extended expectation-confirmation model and empirically tested the model via a cross-sectional field survey. The questionnaire included demographic characteristics, multiple-item scales, and an optional open-ended question on patients’ specific expectations for CAs. Partial least squares structural equation modeling was applied to assess the model and hypotheses. The qualitative data were analyzed via thematic analysis. Results A total of 172 completed questionaries were received, with a 100% (172/172) response rate. The proposed model explained 75.5% of the variance in continuance intention. Both satisfaction (β=.68; P<.001) and perceived usefulness (β=.221; P=.004) were significant predictors of continuance intention. Patients' extent of confirmation significantly and positively affected both perceived usefulness (β=.817; P<.001) and satisfaction (β=.61; P<.001). Contrary to expectations, perceived ease of use had no significant impact on perceived usefulness (β=.048; P=.37), satisfaction (β=−.004; P=.63), and continuance intention (β=.026; P=.91). The following three themes were extracted from the 74 answers to the open-ended question: personalized interaction, effective utilization, and clear illustrations. Conclusions This study identified key factors influencing patients’ continuance intention toward CAs. Satisfaction and perceived usefulness were significant predictors of continuance intention (P<.001 and P<.004, respectively) and were significantly affected by patients’ extent of confirmation (P<.001 and P<.001, respectively). Developing a better understanding of patients’ continuance intention can help administrators figure out how to facilitate the effective implementation of CAs. Efforts should be made toward improving the aspects that patients reasonably expect CAs to have, which include personalized interactions, effective utilization, and clear illustrations.

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Section: Contributions Of This Studymentioning

confidence: 99%

Investigating Patients' Continuance Intention Toward Conversational Agents in Outpatient Departments: Cross-sectional Field Survey

Li¹,

Xie²,

Ye³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

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Individual Differences in Teaming with Artificial Intelligence, Robots, and Virtual Agents in the Workplace

Matthews,

Hancock,

Szalma

et al. 2024

The Oxford Handbook of Individual Differences in Organizational Contexts

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New intelligent technology is rapidly transforming the workplace. Employees face the challenges of managing complex systems powered by artificial intelligence (AI) and capable of autonomous decision-making. This chapter reviews individual difference factors that influence performance, trust, and well-being when the person is teamed with intelligent physical and virtual systems. Conventional personality traits such as the Big Five factors are expected to predict a range of outcomes, as in the traditional workplace. However, personality impacts may be moderated by novel factors such as the comprehensibility of AI, and the transfer of decision authority to the machine. Beyond standard personality, the chapter reviews various traits that capture aspects of the person’s mental model for advanced machines, including anthropomorphic perceptions of robots as teammates or threats.

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User-Specific Determinants of Conversational Agent Usage: A Review and Potential for Future Research

Cited by 2 publications

References 39 publications

Investigating Patients' Continuance Intention Toward Conversational Agents in Outpatient Departments: Cross-sectional Field Survey

Investigating Patients' Continuance Intention Toward Conversational Agents in Outpatient Departments: Cross-sectional Field Survey

Individual Differences in Teaming with Artificial Intelligence, Robots, and Virtual Agents in the Workplace

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