Analyzing Likert Scale Data Using Statistical Tools

Hi everyone, I'm doing an MBA dissertation on the topic "Impact of Customer Service Satisfaction on Business Performance".

The hypothesis set is "Customer service satisfaction can positively impact business performance whereas the null hypothesis set is "Customer services satisfaction can negatively impact business performance".

I have used the 5 point likert scale for analyzing the data, option given as Strongly Agree, Agree, Not Sure, Disagree, Strongly Disagree. Now supposing the sample size is 100 and (60) of them opt for Strongly Agree, (20) for agree, (10) for not sure, (5) for disagree, (5) for strongly disagree.

Which statistical tool will be best for analyzing such data? Can i use Anova or Chi square test? If yes, can anyone please show step by step on how to analyze the data. or do i have to change my hypothesis, if yes can you please suggest me with one? Thank You in ADVANCE!!
I think SPSS is best option
Can you guide me to how will i use SPSS?

I have collected the data

46 of the total respondents strongly agreed to customer service can positively impact business performance

32 of them agreed

18 of them replied saying Not sure

8 of them disagreed

and 1 strongly disagreed.

Please guide me on how to proceed further. THANKS


TS Contributor
What you call your hypothesis seemingly is simply the question you asked.
So what do you want to do more than just display the descriptive statistics?
You could report: Within my sample, the median response to the question
was "agreed", and a total of 78% of participants agreed or strongly agreed.

An hypothesis, in contrast, would be something like: "In the population from which
my data were sampled, more than 90% agree", or "In the population, the median
response to the question is totally agree". Such hypotheses which state
precise assumptions could be tested. But I don't know whether you need this.

With kind regards