Prototyping Theories with ChatGPT: Experiment with the Technology Acceptance Model

TL;DR

This study evaluates ChatGPT's effectiveness in prototyping theories using the Technology Acceptance Model, demonstrating its ability to generate construct-aligned responses while highlighting limitations like response duplication and biases.

Contribution

It introduces ChatGPT as a novel tool for theory prototyping and assesses its validity and limitations in understanding theoretical constructs.

Findings

ChatGPT can generate responses aligned with TAM constructs.

Response duplication rates were 26.25% and 40% in the studies.

Models showed high validity with R-squared values of 82% and 71%.

Abstract

This research paper presents the findings of two experimental studies that explore the use of ChatGPT as a tool for theory prototyping. The objective of the studies is to assess ChatGPT's ability to comprehend theoretical concepts and differentiate between constructs. During the experiments, duplicated responses were identified in both Study 1 and Study 2, with duplicate response rates of 26.25% and 40% respectively. The results of the experiments indicate that ChatGPT can generate responses aligned with the constructs of the Technology Acceptance Model (TAM). The loading and reliability coefficients demonstrate the validity of the models, with Study 1 achieving an R-squared value of 82% and Study 2 achieving 71%. In Study 2, two items with negative wording exhibited low loadings and were subsequently removed from the model. Both studies exhibit reasonable discriminant validity despite high correlations among the TAM constructs. The experiments reveal potential biases in the generated samples, particularly regarding gender and usage experiences. These biases may impact the responses of constructs and should be considered when interpreting ChatGPT's conceptual capabilities. In sum, ChatGPT shows promise as a tool for theory prototyping, generating relevant responses aligned with theoretical constructs. However, further investigation is needed to address limitations such as duplicated responses, variations in prompts, and the generalizability of findings to different contexts.