Aggregating attribute levels into single attribute in DCE analysis

[kcchia95]

Hello,

I am estimating a discrete choice experiment (DCE) using a Mixed Logit model (preference space) and would appreciate your advice on a modelling decision.
One of the attributes, award, has 5 levels: 4 different types of awards and the absence of an award. The original experimental design was generated with Ngene, the code is as follows, and the b5.dummy represents the award attribute:

Design ? Bayesian D-efficient design for GWC survey (INTERACTION)
;alts = choice1*, choice2*, choice3*, choice4*, choice5
;rows = 32
;eff = (mnl,d,mean)
;bdraws = halton(100)
;block = 4
;model:
U(choice1) = b01[0]
+ b1.dummy[(n,0.1,0)|(n,0.1,0)]*A[1,2,0]
+ iae1[(n,0.1,0)]*A.dummy[1]*E.dummy[1]
+ iae2[(n,0.1,0)]*A.dummy[2]*E.dummy[1]
+ iae3[(n,0.1,0)]*A.dummy[1]*E.dummy[2]
+ iae4[(n,0.1,0)]*A.dummy[2]*E.dummy[2]
+ iae5[(n,0.1,0)]*A.dummy[1]*E.dummy[3]
+ iae6[(n,0.1,0)]*A.dummy[2]*E.dummy[3]
+ iae7[(n,0.1,0)]*A.dummy[1]*E.dummy[4]
+ iae8[(n,0.1,0)]*A.dummy[2]*E.dummy[4]
...
+ b5.dummy[(n,0.1,0)|(n,0.1,0)|(n,0.1,0)|(n,0.1,0)]*E[1,2,3,4,0] ? 5. award
+ b6[(n,-0.1,0)]*F[1,2,3,4]
/ ...

I have collected the data, and I am considering conducting two approaches for the model estimation:
In the first specification, each award type is estimated separately.
In the second specification, I am considering replacing the four award type levels with a single binary variable indicating whether an award is present (1 = award present, 0 = no award), regardless of the specific award type.

My questions are:
1. From a methodological perspective, is it acceptable to aggregate the four award types into a single “presence of award” variable?
2. Are there any statistical or experimental design issues that I should consider, such as the imbalance of the two levels (presence/absence), the "aggregated" attribute, and potential biases?

Thank you very much for your time and advice.

Best regards