1.

Question 1

How can predictive analytics improve performance measurement?

1 point

By assisting in weighing different performance measures based on their relative importance.

All answers are correct

By enhancing the setting of performance targets.

By increasing the organizations understanding of the key performance drivers that should be measured.

2.

Question 2

Which of the following is a key attribute of a causal business model?

1 point

A) It includes employee, customer, operational, and innovation measures.

B) It is linked to the organizations strategy.

C) It articulates the hypothesized drivers of financial performance.

Both A and C

Both B and C

A, B, and C are all correct

3.

Question 3

Which of the following choices are important when designing statistical tests of a hypothesized causal business model? (check all that apply)

1 point

The unit of analysis (e.g., customers, employees, projects, product lines, locations, divisions, etc.).

The expected time lag between changes in nonfinancial performance and resulting changes in financial performance (e.g., daily, monthly, yearly, etc.).

The desired economic outcomes (e.g., profits, revenue growth, contract renewal, retention, etc.).

The department responsible for conducting the analyses (e.g., finance, marketing, etc.).

4.

Question 4

Assume that measure A is expected to lead to improvements in measure B. If no statistically significant relationship is found between the two performance measures, what could explain the insignificant relationship?

1 point

A) Organizational barriers are preventing improvements in measure A from translating into improvements in measure B.

B) Contrary to the companys hypothesis, improvements in the performance dimension captured by measure A do not lead to improvements in measure B.

C) Even though the performance dimension captured by measure A is actually a driver of measure B, the method used to calculate measure A is bad (e.g., it uses too few scale points, the questions are misleading, or it asks about performance dimensions that do not drive customers purchase behavior).

D) Either b or c could explain the insignificant relationship.

E) Either a, b, or c could explain the insignificant relationship.

5.

Question 5

Why is the identification of non-linearities important for setting performance targets?

1 point

Managers do not understand the concepts of increasing or diminishing returns to improvements in non-financial performance.

It is never appropriate to maximize scores on non-financial metrics such as employee or customer satisfaction.

If improvements in a non-financial performance metric are characterized by diminishing returns to scale (i.e., greater improvements yield increasing smaller or nonexistent financial returns), setting non-financial performance targets that are too high can actually lead to lower profitability.

Non-linear relationships between measures cannot be accommodated in statistical models.

6.

Question 6

How can statistical analysis of the linkages between non-financial metrics and financial performance be used to make better investment decisions?

1 point

The statistical analyses can ensure that the chosen investments in non-financial performance will improve financial results

The information can be used to forecast future cash flows from investments in non-financial performance dimensions.

Managers can selectively use the information to financially justify any investment they want

The statistical analyses can replace the use of financial justification methods such as net present value and payback period.

7.

Question 7

Assume that three non-financial performance measures (denoted X, Y, and Z and all measured on ten-point scales) are hypothesized to be drivers of future revenues. Statistical analysis reveals that a one-unit increase in X has the largest impact on future revenues. If the companys objective is increasing overall profits, should it focus more effort on improving measure X than on improving measures Y and Z?

1 point

A) Yes.

B) Maybe, but only after considering the difficulty of improving performance on X relative to the difficulty of improving proving performance on Y or Z.

C) Maybe, but only after considering the cost to improve performance on X relative to the cost to improve Y or Z.

D) Both B and C

8.

Question 8

Which of the following is a common technical issue that makes it difficult to use analytics to link non-financial metrics to financial performance?

1 point

Financial and non-financial data that reside in different databases that are incompatible (e.g., have different coding structures, capture data in different levels of granularity, measure the same dimension differently, etc.).

The difficulty in using statistical software packages.

The high cost of data storage.

The limited number of performance metrics that are tracked by most organizations

9.

Question 9

Which of the following is NOT a common organizational issue that makes it difficult to use analytics to link non-financial metrics to financial performance?

1 point

Lack of resources and appropriate skill sets.

Different parts of the organization do not want to share the data.

Organizational participants do not want to know the answers, which may contradict their intuition or beliefs.

Most organizations do not care about non-financial performance.

10.

Question 10

Why should organizational mechanisms be established to ensure that ongoing analyses of the linkages between non-financial metrics and financial performance are conducted?

1 point

All answers are correct.

Changes in competitive environments can make earlier analyses obsolete.

Ongoing analysis and questioning of results can help refine strategies, actions, and measures by revealing the lower-level root causes or drivers of performance.

Performance metrics that previously were key drivers of financial performance may become less important after the company has achieved its performance targets for those dimensions