In this context, the null hypothesis $H_0$ states that the proportion of students interested in a meal plan is less than or equal to 40%, and the alternative hypothesis $H_{\text{a}}$ states that it is greater than 40%. A Type II error occurs when we fail to reject the null hypothesis when it is actually false.

In other words, a Type II error happens if the true proportion of students interested in a meal plan is actually greater than 40%, but the survey data leads the university to believe that the proportion is 40% or less. As a result, the university would fail to recognize the demand for a new cafeteria and wouldn't take action when they actually should.

Thus, the consequence of a Type II error would be:

(Choice A) They don't consider building a new cafeteria when they should.

Let me know if you would like any further explanation or have more questions!

Here are some related questions for you to explore:

1. What would a Type I error look like in this context?
2. How can the sample size affect the likelihood of a Type II error?
3. What is the power of a hypothesis test, and how does it relate to Type II errors?
4. What are the practical implications of making a Type II error in business or policy decisions?
5. How can we minimize the risk of a Type II error when designing a survey?

Tip: To reduce the likelihood of a Type II error, increasing the sample size can help make the test more sensitive to detecting differences from the null hypothesis.