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

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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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Data Availability

The datasets analyzed during the current study are available in the Questionnaire survey data.xlsx repository, https://drive.google.com/file/d/1eMfZ7IfUkE5nEsjo7YufPXXWb7Ax_Jba/view?usp=sharing.

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Author information

Authors and Affiliations

  1. School of Management, National Taiwan University of Science and Technology, Taipei, Taiwan ROC

    Ni Xu & Kung-Jeng Wang

  2. Department of Architecture, Huaqiao University, Xiamen, People’s Republic of China

    Chen-Yang Lin

Authors
  1. Ni Xu
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  2. Kung-Jeng Wang
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  3. Chen-Yang Lin
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Corresponding author

Correspondence to Kung-Jeng Wang.

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Appendices

Appendix A Complementary Data of Reliability and Validity Analysis

Tables

Table 2 Reliability analysis
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Table 3 Analysis of convergent validity
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Table 4 Discriminant validity analysis
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Confirmatory factor analysis of the proposed model

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Appendix B Complementary Data of Regression Analysis

Table

Table 5 Path analysis significance
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Hypothetical model path analysis

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Xu, N., Wang, KJ. & Lin, CY. Technology Acceptance Model for Lawyer Robots with AI: A Quantitative Survey. Int J of Soc Robotics 14, 1043–1055 (2022). https://doi.org/10.1007/s12369-021-00850-1

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