Failure to replicate is the definition of bad science. Science is based on objective, repeatable experiments. If the experiments can't even be reproduced how can they be hypothesized and validated?
Suppose your testing a weight loss drug and in the first experiment subjects lost 5 pounds and the second they only lost 3. On the surface the first study replicates fine, except the second study has a weaker signal so it might not past the statistical threshold chosen before the study. This is fairly common issue as you should expect random noise to amplify a signal in about half of experiments and more than half of all published research.
Also, I don’t think you understand that there is a difference between “replicate” and “reproduce” as those terms are typically used in social and biological science. Reproduction refers to the methods and is simply conducting the experiment again, regardless of the outcome. An experiment should be reproducible, of course—if it isn’t, the paper was poorly written (the experiment might still be ok though).
Replication refers to the results: do we get the same answers as the original experiment? Bad science is one way an experiment might not replicate but other reasons are 1. Just a regular type 1 error in the original study, 2. A type 2 error in the new one, 3. an overlooked methodological difference or mistake somewhere. No. 3 doesn’t mean bad science, it could be just normal scientists making normal mistakes, as they sometimes do.
Also, you don’t “hypothesize” an experiment. You come up with a hypothesis then test it with an experiment. (Sometimes scientists HARK but that’s a different discussion …)
In this case, it sounds like there truly was some cheating going on of the worst kind (data fabrication).
If they're replicating with a follow-on study of some kind, it depends on any number of factors, since it's almost impossible to do a follow-on survey-based or lab experiment or use a similar or newer dataset while accounting for and controlling exactly the same set of conditions (this is one of the issues that I've heard used to distinguish between hard and soft sciences).
E.g., if paper A has an observation count of 200 (insert your preferred number), a replication study could hand out exactly the same survey to another 200 respondents and get similar results that, nonetheless, fail to exactly replicate the earlier findings (or, over a small number of samples, fail to conform to a normal distribution).
This doesn't mean that the original study was "bad science" or that the social sciences in general are "bad", only that we have to temper our expectations and maintain a healthy dose of skepticism.
In order to determine what proportion of studies would fail to reproduce, you would also need to know the number of correct hypotheses in the first place. If 100 correct hypotheses and 900 incorrect hypotheses are tested, your sample of "p<0.05" results would contain 95 correct hypotheses and 45 incorrect hypotheses, so about 1/3 of the results would fail to replicate.
This is why there is such a large push for prepublishing of analysis methods, hypotheses, and experimental design, and later publishing of negative results. The initial distribution of hypotheses is important to know how to interpret the resulting body of work.