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International Journal of Social Robotics

Published in:

28-01-2022

Technology Acceptance Model for Lawyer Robots with AI: A Quantitative Survey

Authors: Ni Xu, Kung-Jeng Wang, Chen-Yang Lin

Published in: International Journal of Social Robotics | Issue 4/2022

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Abstract

The rapid growth of artificial intelligence (AI) robots has brought new opportunities and challenges. The linkage between AI robots and humans has also gained extensive attention from the legal profession. This study focuses on the extended AI Robot Lawyer Technology Acceptance Model (RLTAM). A total of 385 valid questionnaires are collected through quantitative research, and the relationships among the five variables in the model are reanalyzed and revalidated. Results show that the “legal use” variable in the original extended model is not a direct key variable for consumers to accept AI robot lawyers, but it has a direct effect on “perceived ease of use” and “perceived usefulness” variables. AI robots still need to respond actively to attain legitimacy. AI robot lawyers with national legal certification and good user interface design provide humans a sense of trust. AI robot lawyers based on the development of extended intelligence theory can form a closely coordinated working model with humans. In addition, consumers indicate that the normalized use of AI robots could be a trend in the legal industry in the future, and the types of legal profession that robots can replace will not be affected by gender differences. Practitioners using AI robot lawyers need to establish a complete liability risk control system. This study further optimizes the integrity of RLTAM and provides a reference for developers in designing AI robots in the future.

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Title: Technology Acceptance Model for Lawyer Robots with AI: A Quantitative Survey
Authors: Ni Xu
Kung-Jeng Wang
Chen-Yang Lin
Publication date: 28-01-2022
Publisher: Springer Netherlands
Published in: International Journal of Social Robotics / Issue 4/2022
Print ISSN: 1875-4791
Electronic ISSN: 1875-4805
DOI: https://doi.org/10.1007/s12369-021-00850-1

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