Week 4
1. How can predictive analytics improve performance measurement?
By increasing the organization’s understanding of the key performance drivers that should
be measured.
By enhancing the setting of performance t
...
Week 4
1. How can predictive analytics improve performance measurement?
By increasing the organization’s understanding of the key performance drivers that should
be measured.
By enhancing the setting of performance targets.
By assisting in weighting different performance measures based on their relative
importance.
All answers are true
2. Which of the following is a key attribute of a causal business model?
A) It includes employee, customer, operational, and innovation measures.
B) It is linked to the organization’s strategy.
C) It articulates the hypothesized drivers of financial performance.
D) Both a and c.
E) Both b and c.
F) All answers are correct
3. Which of the following choices are important when designing statistical tests of a
hypothesized causal business model? (Check all that apply)
The department responsible for conducting the analyses (e.g., finance, marketing, etc.).
The expected time lag between changes in nonfinancial performance and resulting changes
in financial performance (e.g., daily, monthly, yearly, etc.).
The unit of analysis (e.g., customers, employees, projects, product lines, locations, divisions,
etc.).
The desired economic outcomes (e.g., profits, revenue growth, contract renewal, retention,
etc.).
The desi + The unit
The desired + The department
The desi + The expected
the unit + the dep
The unit + the exp
The exp + The desi
The exp + The dep
The dep + the des + the expe
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?
A) Organizational barriers are preventing improvements in measure A from translating into
improvements in measure B.
B) Contrary to the company’s 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. Why is the identification of non-linearities important for setting performance targets?
Managers do not understand the concepts of increasing or diminishing returns to
improvements in nonfinancial performance.
Non-linear relationships between measures cannot be accommodated in statistical models.
If improvements in a nonfinancial performance metric are characterized by diminishing
returns to scale (i.e., greater improvements yield increasing smaller or nonexistent financial
returns), setting nonfinancial performance targets that are too high can actually lead to lower
profitability.
It is never appropriate to maximize scores on nonfinancial metrics such as employee or
customer satisfaction.
6. How can statistical analysis of the linkages between nonfinancial metrics and financial
performance be used to make better investment decisions?
The statistical analyses can ensure that the chosen investments in nonfinancial performance
will improve financial results
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.
The information can be used to forecast future cash flows from investments in nonfinancial
performance dimensions.
7. Assume that three nonfinancial 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
company’s objective is increasing overall profits, should it focus more effort on improving
measure X than on improving measures Y and Z?
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. Which of the following is a common technical issue that makes it difficult to use analytics to
link nonfinancial metrics to financial performance?
The high cost of data storage.
The difficulty in using statistical software packages.
The limited number of performance metrics that are tracked by most organizations
Financial and nonfinancial 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.).
9. Which of the following is NOT a common organizational issue that makes it difficult to use
analytics to link nonfinancial metrics to financial performance?
Different parts of the organization do not want to share the data.
Most organizations do not care about nonfinancial performance.
Lack of resources and appropriate skill sets.
Organizational participants do not want to know the answers, which may contradict their
intuition or beliefs.
10. Why should organizational mechanisms be established to ensure that ongoing analyses of
the linkages between nonfinancial metrics and financial performance are conducted?
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
Changes in competitive environments can make earlier analyses obsolete.
All answers are correct.
Ongoing analysis and questioning of results can help refine strategies, actions, and
measures by revealing the lower-level root causes or drivers of performance.
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