I will create a combination of functional and project-based organizational structures. I have selected the combination due to the following rationale.
- There are different types and categories of iPhone applications. So, the development of each application is a project. Accordingly, competent professionals will be used and they will move from one project to another project.
- Selling of applications as well as taking care of HR operational aspects of the business will require people with expertise in these functional areas also. So, getting good sales as well as HR retention is the top priority for the business.
- A combination of these two structures will create a mix of back end and front end without any departmental inclination. So, the company will succeed.
Opting for other structures will either focus on applications or sales. But, it will be ineffective as sales will not happen without applications and applications alone cannot generate good sales without proper marketing.
Besides, there can be role confusion and conflict of interest with other organizational structure such as line only or staff only structures. Even, the selection of project-only or functional-only organizational structures will create problems and employee turnover will also increase.
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Based on the percentage of readers who own a particular make of the car and the random sample, we can infer that there is sufficient evidence at a 0.02 level to support the executive claim.
<h3>What is the evidence to support the executive's claim?</h3>
The hypothesis is:
Null hypothesis : P = 0.55
Alternate hypothesis : P ≠ 0.55
We then need to find the test statistic:
= (Probability found by marketing executive - Probability from publisher) / √( (Probability from publisher x (1 - Probability from publisher))/ number of people sampled
= (0.46 - 0.55) / √(( 0.55 x ( 1 - 0.55)) / 200
= -2.56
Using this z value as the test statistic, perform a two-tailed test to show:
= P( Z < -2.56) + P(Z > 2.56)
= 0.0052 + 0.0052
= 0.0104
The p-value is 0.0104 which is less than the significance level of 0.02. This means that we reject the null hypothesis.
The Marketing executive was correct.
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