Think of the arrows as statements about causality. In your model you are saying "Brand Image causes Auditory", also "Auditory causes MAT" and so on. This is the normal approach in SEM with AMOS. You have observed variables, (MAT, MDE, MPE, etc) that are manifestations of unobserved, or latent, constructs (Auditor, Olfactory, etc). In your model, these unobserved constructs are themselves manifestations of a second-order latent construct, Brand Image.

So that is the theory that you are presenting in your model: your observed (or 'manifest') variables are a result of some other construct that we cannot measure directly, the latent construct Brand Image.

Your panel members believe that arrows - the causal links - should point to Brand Image from Auditory, Olfactory, etc. That would be to say that Auditory causes Brand Image (along with the other first-order latent constructs). You can do that only if you have separate independent measures of Brand Image. That is, where you can say that observed variables such as 'value for money', 'prestige', 'innovativeness', and so on (see Plumeyer et al. for many other examples) are manifestations of Brand Image.

Your model is not a structural model of causal relationships among constructs. It is a model where you propose that Brand Image, which we cannot observe directly, produces five perceptions (Auditory, Olfactory, etc., which we also cannot observe directly), and each of these is manifested by about seven observed variables.

This is not wrong as such. It could be useful if you want to show that your measures are good at measuring your constructs.

Having said that, you should also do the following:

* Present the standardised coefficients. This helps you and the reader understand how much stronger some coefficients are than others. Check the statistical significance of the coefficients from your constructs to your manifest variables. You will probably find that you can comfortably use just four or five manifest variables for each construct instead of the seven or eight that you have.

* Before including the second-order latent construct Brand Image to the model, check the measurement model. (correlate each of the first-order constructs with each other first-order construct, and then check the sign and strength of the coefficients to decide if you really need that observed variable in the model)

* Check for common-method bias and decide if you really need some observed variables.

There are lots of 'how-to' tutorials online and YouTube videos to show you how to do these, Shivam Bhardwaj

Good luck.

Reference:

Plumeyer, A., Kottemann, P., Böger, D. et al. Measuring brand image: a systematic review, practical guidance, and future research directions. Review of Managerial Science 13, 227–265 (2019). https://doi.org/10.1007/s11846-017-0251-2