Hello Chijioke E Nwachukwu 

It's not easy to answer to your question. Can you give us more details ? 

It's important to know if you have qualitative or quantitative variable 

This link can be helpfull 

https://onlinecourses.science.psu.edu/stat500/node/67

Good luck 

Quantitative variables will be used data from American customer satisfaction index and content analysis will be used for dependent variable .

Which statistical tool will be appropriate?

Based on your depiction in the question, sounds like it is a quantitative correlational research - if this is correct, think multiple regression using statistical software like SPSS etc. or structural equation modeling (SEM) using software like AMOS, SmartPLS etc can be used. 

If your 9 components of mission statement are merely independent variables (IV) and customer satisfaction is the dependent variable (DV), then you can consider multiple regression.  If your 9 components consist of some IV and some are mediators etc, perhaps you can consider SEM.  The correct data analyses to deploy also depending how you word your hypotheses i.e. some are correlational oriented, some are to measure the differences among variables etc.

Check if cluster analysis could be appropriate.

Dear Chijioke E Nwachukwu,

There are number of tools available to do these types of analysis. But you may use MATLAB, SCILAB, R, SPSS etc. I think in your case you should use multivariate adaptive regression analysis.

Same types of problem statements are described in this paper:

https://www.researchgate.net/publication/303011477_Quality_Assessment_of_Web_Services_Using_Multivariate_Adaptive_Regression_Splines 

Conference Paper Quality Assessment of Web Services Using Multivariate Adapti...

Hello Chijioke.

I think cluster analysis could be appropriate ou multivariate regression analysis. You can use SPSS or R softwares.

Good luck.

Elói

Apologies for being slightly short with my last answer. To do cluster analysis you can use SPSS. Basically there are two different strategies, either you cluster cases ( I imagine this would be the six companies) or your cluster variables (this could be the nine components of the mission statement). The clustering procedure comprises different steps: 1) Select a distance measure (e.g. Euclidian distance) 2) select a clustering algorithm (e.g. Ward's method) 3) fix number of clusters 4) Validate analysis. If you have never done it, a book like Everitt, Landau, Leese, Cluster Aalysis could be helpful. First trty to understand the basics of classification.

I would go for multiple regression analysis

Thanks for your suggestions

Even Excel as lot of data analysis facilities , with easy learning curve and good visualisation. R will have a lot of learning curve. SPSS and SAS has it's own cost.