  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.
