Potential reasons could be

1) the moderation (interaction) effects are truly zero in the population or

2) the moderation (interaction) effects are non-zero in the population, but you did not have enough statistical power in your study to reject the null hypothesis.

Lack of power could, for example, be due to too small a sample size. Moderation effects are often small and can require larger samples to be "detectable."

Hello, in the model you constructed, all the predictor variables have insignificant main effects (p-values) on the dependent variable, and the moderating effects are insignificant. You can check whether it is significant by looking at the p-value or confidence interval.

Hii Saara

I'm not sure how many parameters do you have in your study, so I'm making a few generic comments here. First, as stated by @Ghristian Geiser the non-significant moderation effect could be due to low sample size (n=115) if the number of paraments are more than 12 (considering the 1:10 rule of thumb). In that respect, collect more data (based on the desired sample size requirement obtained by power analysis) and re-run the analysis accordingly. However, this technique doesn't guarantee that the outcomes would be any different (as the interaction effects could be truly zero for the population)! Second, try to perform your analysis in SmartPLS (Refer to Becker et al. 2018 for further insights). This software can provide valid results for small samples. Third, if the theoretical/conceptual contexts of your study permit, try to change the model by treating your moderator(s) as mediator(s) and see if you can get any significant indirect effect.

Reference:

Becker, J. M., Ringle, C. M., & Sarstedt, M. (2018). Estimating moderating effects in PLS-SEM and PLSc-SEM: Interaction term generation* data treatment. Journal of Applied Structural Equation Modeling, 2(2), 1-21.

Christian Geiser, Wang Zheng and Sayoni S. thank you so much for your valuable inputs.

@Saara From the results (screenshot attached), it evident that the direct relationships between your IV, moderator and DV are insignificant that too hence there won't be a significant moderation effect. It might be due to low statistical power at low sample size (as pointed out by @Christian).