In the last session, we discussed how to use ChatGPT for interpreting a measurement model. In this session, we will focus on using ChatGPT to interpret structural relationships.
Let’s consider a scenario where we have a model with indicators hidden. We are interested in understanding the impact of collaborative culture on organizational performance and organizational commitment. Collaborative culture is the independent variable, organizational commitment is the mediator, and organizational performance is the dependent variable. We have three hypotheses, including one hypothesis to assess the mediating role of organizational commitment.
To interpret the results, we can use ChatGPT to perform bootstrapping. Although it is recommended to use 10,000 iterations, for the purpose of this video, we will use 5,000 iterations. We will calculate bias-corrected and accelerated bootstrap for stable solutions. Since we assume a positive direction of relationship, one-tailed test is appropriate.
Now, let’s analyze the results. We have path coefficients and p-values for each relationship. The impact of collaborative culture on organizational commitment is significant (p < 0.05), as well as the other relationships in the study.
To further interpret these results, we can copy the table to Excel and ask ChatGPT to provide an APA-style report for each hypothesis. We can also provide full names for the constructs to enhance clarity.
In summary, using ChatGPT, we can effectively report the direct relationships from the results of Smart PLS analysis. In the next session, we will explore how to use ChatGPT to summarize mediation analysis results.
Thank you for your attention.
