ARE 112 –Summer 2016
I.
Class Notes #5 Strategy-Change Management-Analysis Project
Class Notes
1.
Checkoff exercises
2.
IBM Book
3.
Read the Duke Hospital Case for next class – copy in class and on Canvas
II. Review
III. Strategic Analytical Lenses
A. The four-p’s from www.netmba.com
 Product
 Price
 Promotion
 Placement
 Target markets
ARE112_Su16_Notes_05_Strategy_Change_Analysis.docx
Page 1 of 8
ARE 112 –Summer 2016
Class Notes #5 Strategy-Change Management-Analysis Project
B. Boston Consulting Group Growth-Share Matrix – for cash generation
 Cash cows
 Stars
 Question marks
 Dogs
 Market share
 Market growth
C. SWOT Analysis: Strengths, Weakness, Opportunities and Threats
D. Balanced Scorecard
 Financial
 Customer
 Internal Business Processes
 Learning and Growth
 Metrics
 Initiatives
ARE112_Su16_Notes_05_Strategy_Change_Analysis.docx
Page 2 of 8
ARE 112 –Summer 2016
Class Notes #5 Strategy-Change Management-Analysis Project
E. Porter’s Five Forces Model
 Suppliers
 Customers or buyers
 Substitutes
 Potential entrants
 Industry members
Back to Anthony’s hierarchy
and the value proposition
F.
IV. Change Management
A. Why change occurs – this is not the same as transformational change – more of general comments
about change but we do see change management in the area of transformational change.
1.
2.
3.
4.
Dissatisfaction with the present situation
External pressures toward change
Momentum toward change
These are not the same as the driving forces for change but
the context of change management
B. Threats to the change process – some examples:
 Degree of change
 Threat to security

Time frame

Redistribution of power

Impact of culture

Disturb existing social networks

Loss of existing benefits

Uncertainty regarding change

Threat to position power

Disruption of routine
ARE112_Su16_Notes_05_Strategy_Change_Analysis.docx
Page 3 of 8
ARE 112 – Summer 2016
Class Notes #6 – Change Management and Decisions
C. Power: Used to change beliefs and/or behavior – or attempts to do so
1.
2.
3.
4.
5.
Coercive power → the power to punish
Legitimate power → the power granted by some authority
Expert power → the power of have some specific skill or knowledge not found in others
Referent power → the power from admiration or respect
Reward power → the power from being able to provide a reward to others
NOTE:
To have authority you need to have power.
 Authority: The ability to command, direct, or influence thought, opinions, or behavior.
 But power alone does not give authority – this is a topic in our leadership section later in
the class.
V. Change Methods - Examples
A. Phase method
1. Unfreezing – prepare for change
2. Changes
3. Refreezing – change is stabilize
B. Crossover approach
VI. Change strategies
Strategy
Power Base
Managerial Behavior
Likely Results
Force-Coercion
Use of formal authority
to create change
Legitimate
Reward
Coercive
Direct forcing
Political maneuvering
Faster but may only be
temporary
Rational Persuasion
Creating change thru
rational and empirical
arguments
Expertise
Informational efforts
Highly variable
depending on acceptance
of change
Shared Power
Developing support thru
personal values, beliefs,
and commitments
Referent
Participative efforts
Slower, but able to
internalize the changes
ARE112_Su16_Notes_05_Strategy_Change_Analysis.Docx
Page 4
ARE 112 – Summer 2016
VII.
Class Notes #6 – Change Management and Decisions
Decision Making
A. General comment:
1. “The process
2.
by which managers
3.
respond to opportunities and threats (from the SWOT world)
4.
by analyzing options
5.
and making determinations
6.
about specific organizational goals
7.
and courses of action.”
B. Many decision relate to one of the following:
1. Resource acquisition (or disposal)
2.
Resource allocation or withholding resources
3.
Resources utilization
C. One of several frameworks to analyze decision making:
1. Structured or programmed decisions
2.
Unstructured or unprogrammed decisions
D. Decision outcomes – the justification for a decision:
1.
Decision framework:
a. Inputs
b. Activities or processes
c. Outputs
d. Activities or processes
2.
Outcomes
i. Optimizing outcomes
ii. Satisficing outcomes
b. Somewhere between inputs and outputs, and between outputs and
outcomes is an activity and it is the activity that gets managed
E. Decision environment
1. Bounded rationality
5.
Ambiguous information
2.
Risk
6.
Time constraints
3.
Uncertainty
7.
Information costs
4.
Information symmetry
8.
NOTE: can be causes of conflicts that we
have already discussed
Page 5
ARE112_Su16_Notes_05_Strategy_Change_Analysis.Docx
ARE 112 – Summer 2016
ANOVA – Analysis of Variance and Chi Square and Probit
F. Information technology and the decision-making process – the MIS world – management
information systems
1. Decision support systems – DSS
2.
Group decision support system - GDS
3.
Executive information system – EIS
4.
Delphi technique – support by MIS applications but not a pure MIS application
VIII. Analysis Project
1.
To see the application of statistical analysis to organizational issues.
2.
Data files and instructions including a “how to” video will be posted to SmartSite
IX. ANOVA – Analysis of Variance
A. Purpose: To see an example of fact-based, analysis-based information to then be used in decision
making
B. Converting Data to Information
1. Likert scale to convert qualitative responses to quantitative or measurable values
a. Use a scale of five to seven responses ranging from “strongly disagree to strongly agree” and
usually the middle value is neither agree nor disagree
b. Generally limited to five to nine questions in the survey
c. And need to characterize the respondents by attributes to be tested such as age category,
economic status, education and the like.
2.
Now apply statistical analysis to the data: one example is ANOVA – analysis of variance
3.
Here we want to see if there is a
difference among groups in the
general population. Here is what we
want to test:
Using Hypothesis Testing – ANOVA
A single-factor ANOVA derives its name from
the fact that the test deals with one independent
variable; in this case, we discuss the population
mean. The question that the ANOVA attempts to
ask is this: are the samples really of the same population, or is there enough variance in the sample means to
suggest that the samples were taken from independent populations? The type of test we shall consider in a
moment deals with the comparison of two or more sample means. The sample sizes themselves need not be
equal
6
ARE 112 – Summer 2016
ANOVA – Analysis of Variance and Chi Square and Probit
State the null and alternative hypothesis.
For ANOVA the null hypothesis is stated as:
H0: All population means are equal.
For example, if we are testing three populations, then we would state:
H0: µ1 = µ2 = µ3 or all treatments are the same
The alternative hypothesis is stated as:
Ha: Not all population means are equal or at lease two treatments differ
It is important to note that ANOVA testing by itself does not tell you which mean does not equal the
other means, or the combination of means that are not equal. It will only tell you that at least one
mean does not equal the other means. It is the Test of Homogeneity that gives us the resultant
populations.
MPG
ANOVA
MPG
Between Groups
Within Groups
Total
Sum of
Squares
17.049
8.028
25.077
Tukey HSDa
df
2
12
14
Mean Square
8.525
.669
F
12.742
Sig.
.001
Gas_Code
3
1
2
Sig.
N
5
5
5
Subset f or alpha = .05
1
2
33.980
34.920
36.560
.206
1.000
Means f or groups in homogeneous subsets are display ed.
a. Uses Harmonic Mean Sample Size = 5. 000.
X. Chi-Square
1. Formula:
The statistic
The calculation
(O – E)2

∑
 =
E
2.
The rationale
As the observed frequency gets closer to the expected frequency
(no difference) the value gets smaller.
Data and output:
7
ARE 112 – Summer 2016
ANOVA – Analysis of Variance and Chi Square and Probit
XI. Probit – Probability Unit Model
The model: Yi = β0 + β1 Xi + εi where Yi = 1, 0 a binary response where 1 – Yes, and 0 – No.
2.
Data:
Actual and fitted Accept versus Gender
Analysis:
fitted
actual
0.8
0.6
Accept
3.
1
0.4
0.2
0
0
1
1
Actual and fitted Accept versus Chem
Gender
fitted
actual
0.8
0.6
Accept
1.
0.4
0.2
0
2
4
6
8
Chem
8
10
12
14