I'm answering my own question for 2 reason:1) I want to be clear what I've understood is correct or not. 2) If somebody is looking for the same reason he/she should find it here.I hardly found book that gives a clear explanation of interpretation of MDS biplots. I'll also give few references where people can read more about interpretation of MDS ploting to better understand it.
This answer is divided in few parts:
Part 1: The axis of the biplot are the principal components. x-axis has the PC 1, which reflect the max variance in the dataset. y-axis has the PC 2, whichreflect 2nd most variance. E.g. in my example x-axis represent 72% of the variance, while y-axis represent 16% of the variance in the data.
PC1 PC2 PC3 PC4
0.727891 0.166721 0.070320 0.003048
Part 2: The arrows reflect how the variables are loaded in each PCs. E.g. in my example "uncluttered" & "visualization" is highly negatively loaded to PC 2, hence y-axis. Similarly, "no water","fast relief" & "convinient" is highly plositively loaded to PC 2, hence x-axis.This gives us a visualization about how variables are loaded in different PCs.
Safe 0.616967 -0.786989
Highly.efficacious -0.135565 0.990768
Same.side.effect.profile 0.822707 -0.568466
Fast.Relief 0.988621 -0.150428
No.Water 0.990893 0.134648
Convenient 0.989206 0.146534
Convincing 0.763225 -0.646133
Visually.appealing 0.154414 -0.988006
Very.novel 0.900984 0.433853
Noticeable 0.691596 0.722284
Likely.to.be.read 0.887028 -0.461715
Uncluttered 0.031498 -0.999504
Interesting 0.872584 -0.488465
Credible 0.620556 -0.784162
Prescribe.Recommend 0.809955 -0.586492
part 3: Concept points tells us how dissimilar they are from the each other. So, in my example Concept 1 & Concept 2 are very different from rest of them. Concept 2 is both bad in terms of visual appeal as well as convenience. Whereas concept 3 & 4 are more alike. They are also good in terms of visualization as well as convenience.
Reference: 1) Greenacre, M. (2010). Biplots in Practice
2) Everitt & Hothorn: An Introduction to Multivariate Analysis with R(Chapter 4).
3) Hair: Multivariate Data Analysis