You first need to think through your research questions and whether there are specific hypotheses that could be tested using the data. (Ideally you would have these hypotheses before collecting data and they would influence your sampling strategy as well as the questions you ask. I think your supervisors should have helped you work through this instead of just telling you to use statistical methods but anyway...)
I know nothing about the subject, but to give examples for illustration, you might hypothesise that:
- Products bought longer ago are less likely to be seen by users as functional.
- Some makes and models are more likely to be seen as functional than others.
- Some makes and models of product continue to be seen as functional for longer than others.
- The probability of buying a new product depends on whether it is perceived as functional, the make and model, the time since buying the original product, and awareness of other options.
(These hypotheses seem very roughly to fit within your research questions but that's up to you to decide. They would normally be derived from theory and prior research in your subject area.)
These hypotheses would commonly be tested using logit or probit models, because those models are well suited to cases where we have a response (dependent) variable that can be either 1 or 0 and are trying to explain how the probability of it being a 1 varies with changes in other ('explanatory' or 'independent') variables. For hypothesis 1-3 the dependent variable is (the probability of) whether the use said the product was functional. For hypothesis 4 the dependent variable is (the probability of) whether the user bought a new product.
MANOVA or ordinary least squares (OLS) linear regression - the two are essentially very similar - would also work, but are arguably less appropriate when the dependent variable is binary and we are interested in probabilities.
Your sample may be too small for some types of hypothesis testing. For instance if there were 50 different make/model combinations in a sample of 70, you will not be able to say very much about a particular make/model.
Before running tests make sure to set up appropriate variables based on the questions. For instance, decide what to do with the 'not answered', which would usually be coded as 'missing' and ignored in statistical analysis, but that could potentially bias the results and you might see the non-answers as somehow informative in themselves. Make/model categories would probably be coded as a series of dummy (0 or 1) variables.
As I said in the comments, I don't think it makes sense to try and examine reliability (by which I think you mean internal consistency) in this case. Internal consistency is where you have a set of questions all aiming to measure some single construct, and that doesn't seem to be the case here.