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Open the MDS data-set

a. Using the PROXSCAL program, assume that the data are ordinal and compute the distance matrix between pairs of sports for the 50 respondents with two-dimensional MDS

b. Which sports are the most similar according to consumer perceptions? Which ones are the most different?

c. What are the differences between this distance matrix and the one produced by ALSCAL (see exercise 1)?

d. Try and change the type of measure, the initial configuration (the latter from the OPTION menu) and the number of dimensions and find the best output according to the STRESS function

e. Repeat the analysis, this time looking for similarities between respondents for the ten sports on two-dimensions

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Volkswagen (VW) Group is featured in the opening chapter case as a firm that engages in excellent strategic planning despite its recent legal/ethical pollution fiasco. VW has four major geographic business segments. Each of these divisions of VW would prepare their own IFE matrices, which would be assimilated to develop an overall corporate IFE matrix. This exercise gives you practice developing divisional IFE matrices and assimilating those into an overall corporate IFE matrix. Instructions Step 1 Review VW’s most recent Annual Report in regards to the company’s four geographic business segments, which are North America, South America, Asia-Pacific, and Europe. Step 2 Review the latest S&P Industry Survey for companies that produce and market automobiles. Step 3 Develop a divisional IFE matrix for each of VW’s business segments. Step 4 Assimilate your divisional IFE matrices into an overall corporate IFE matrix for VW.

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Ms. Amna, a partner in Al Raffah Audit Firm, was deputed as a Practitioner of Mazoon Company to review the internal control system of the company. She conducted the review and after verification of relevant records, concluded that the internal control system of the company is sound. Mazoon company was happy with her work. She also developed friendships with the top officials and employees of the company. The next year, Ms. Amna was appointed as the external auditor of Mazoon company. When she reviewed the books of accounts of the company, it was found out that the company had charged depreciation more than the allowed rates and thereby avoided tax on their business profits. Mr. Omar, the Chairman of Mazoon company demanded her not to mention this in her audit report as this information will affect the market value of his company’s shares. Ms. Farida, the CEO of Mazoon company asked Ms. Amna to modify her report otherwise her fees will be delayed. Ms. Iman, the Internal Auditor of the audit client met Ms. Amna and reminded her about the lunch they had once in the office and humbly requested her not to mention this manipulation in the independent audit report. Ms. Amna is very confused and faces an ethical dilemma in the current situation.

Q1:Describe the threats to the fundamental principles of professional ethics faced by Ms. Amna in the Mazoon company?

Q2: Advise Ms. Amna as to what safeguard she has to take in the current situation.

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Suppose Sanjay Patel is faced with the following pay-off table. He has to choose how many pizzas to make in advance each day before he knows the actual demand. • His choice is between 40, 50, 60 and 70 pizzas. The actual demand can also vary between 40, 50, 60 and 70 with the probabilities as shown in the table – e.g. P(demand = 40) is 0.1. The table then shows the profit or loss – for example, if he chooses to make 70 but demand is only 50, then he will make a loss of $60. Daily Supply Daily demand Probability 40 pizzas 0.10 50 pizzas 0.20 60 pizzas 0.40 70 pizzas 0.30 40 pizzas $80 $80 $80 50 pizzas 60 pizzas 70 pizzas $0 $(80) $(160) $100 $20 $(60) $100 $120 $40 $100 $120 $140 $80 Show calculations for your answers below: a. Assume that Sanjay is Optimistic, what is the number of pizzas that he should make in advance? b. Assume that Sanjay is Conservative, what is the number of pizzas that he should make in advance? C. Assume that Sanjay is neither optimistic nor conservative, what is the number of pizzas that he should make in advance? d. Assume that Sanjay is using the Expected Opportunity Loss criteria, what is the number of pizzas that he should make in advance? e. What is the Expected Value of Perfect Information that Sanjay would be willing to pay?

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