Analysis of GLM Negative Binomial Coefficients

I have opted to use GLM regression with a Negative Binomial probability distribution family, using the following coding:
glm (DV) (IVs), family(nbinomial) link(log)


I cannot find a tutorial on how to interpret the coefficients when the family is nbinomial. Do I interpret the model the same as nbreg?
For my first IV, the coefficient is: 0.877***

One tutorial has told me that this means for every one unit increase in IV, there is an 88% increase in the DV.
I have also read that for every one unit increase in IV, the expected log count of the DV increases by 0.88.
Are either of these correct? Is there a better way to analyse this?

When running nbreg, I know that you have the option of , irr
Say the coeffient is 2.19487, does this mean that for every percent change in the IV, there is 2.2% increase of the DV?
Is the eform option for glm the same as irr?
I.e.
glm (DV) (IVs), family(nbinomial) link(log) eform

I would much prefer to use GLM nbinomial to nbreg.

Thank you.