Calculating Quantitative Attractiveness Ratings for the Industries a Company Has

question 10

Multiple Choice

Calculating quantitative attractiveness ratings for the industries a company has diversified into involves

• A)determining each industry's key success factors, calculating the ability of the company to be successful on each industry KSF, and obtaining overall measures of the firm's ability to compete successfully in each of its industries based on the combined KSF ratings.
• B)determining each industry's competitive advantage factors, calculating the ability of the company to be successful on each competitive advantage factor, and obtaining overall measures of the firm's ability to achieve sustainable competitive advantage in each of its industries based on the combined competitive advantage factor ratings.
• C)selecting a set of industry attractiveness measures, weighting the importance of each measure, rating each industry on each attractiveness measure, multiplying the industry ratings by the assigned weight to obtain a weighted rating, adding the weighted ratings for each industry to obtain an overall industry attractiveness score, and using the overall industry attractiveness scores to interpret the attractiveness of all the industries, both individually and as a group.
• D)rating the attractiveness of each industry's strategic and resource fits, summing the attractiveness scores, and determining whether the overall scores for the industries as a group are appealing or not.
• E)identifying each industry's average profitability, rating the difficulty of achieving average profitability in each industry, and deciding whether the company's prospects for above-average profitability are attractive or unattractive, industry by industry.

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Definitions:

Standard Error

Standard error measures the accuracy with which a sample represents a population. It is the standard deviation of the sample's means distribution.

Regression Coefficients

Quantitative measures that represent the relationship between a predictor variable and the response variable in a regression model.

Confidence Intervals

A range of values used to estimate the true value of a population parameter with a given level of confidence.

Multicollinearity

A statistical phenomenon in which several independent variables in a regression model are highly correlated, potentially distorting the results and interpretations.