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APPLICATION OF DATAMINING TOOL FOR CLASSIFICATION
OF ORGANIZATIONAL CHANGE EXPECTATIONSule
ÖZMENSerra YURTKORU Beril SIPAHI
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DATA MINING
Data mining is the nontrivial extraction of
implicit, previously unknown, and potentially
useful information from data.
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• DIFFERENT GOALS CALL FOR DIFFERENT TECHNIQUES

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DATAMINING TECHNIQUES

• Datamining techniques can be either
• directed
• or
• undirected.

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DATAMINING TECHNIQUES

• Directed
• Goal is
• to predict, estimate, classify, or characterize
  the behavior of some pre-identified target
  variable

Undirected Goal is to discover structure in
the data set as a whole.
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DATAMINING TECHNIQUES

• Directed
• Classification
• Estimation
• Prediction

• Undirected
• Description Visualization
• Association Rule or Affinity Grouping
• Clustering

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Classification is used to develop a model that
maps a data item into one of several predefined
classes.
CLASSIFICATION
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DECISION TREE ANALYSIS
Builds classification and regression
trees Starts with pre-identified target
variable in other words dependent variable. This
is the initial node Initial node is split into
two or more child nodes Splitting is based on
statistical analysis used by decision tree
algorithms
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DECISION TREE ANALYSIS
Target Variable
Target Variable
Predictive Variables
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DECISION TREE ALGORITHMS
CHAID (Chi square Automatic Interaction Detector)
CRT (Classification and Regression Tree) QUEST
(Quick Unbiased Efficient Statistical Test)
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CHAID Method
CHAID was designed to handle categorical
variables only. SPSS has extended algorithm to
handle nominal, ordinal and continuous dependent
variables.
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Components of CHAID
One or more predictor variables. Predictor
variables can be continuous, ordinal, or
nominal. One target variable. The target
variable can be nominal, ordinal or continuous.
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CHAID Algorithms
A CHAID tree is a decision tree that is
constructed by splitting subsets of the space
into two or more child (nodes) repeatedly,
beginning with the entire data set.
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CHAID Algorithms
To determine the best split at any node, CHAID
merges any allowable pair of categories of the
predictor variable (the set of allowable pairs is
determined by the type of predictor variable
being studied) if there is no statistically
significant difference within the pair with
respect to the target variable.
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CHAID Algorithms
The process is repeated until no non-significant
pair is found. The resulting set of categories of
the predictor variable is the best split with
respect to that predictor variable. This process
is followed for all predictor variables. The
split that is the best prediction is selected,
and the node is split. The process repeats
recursively until one of the stopping rules is
triggered.
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APPLICATION
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AIM OF THE RESEARCH

• The ability to be both receptive and responsive
  to change has becomeimportant in recent years.
• Therefore our aim is to analyze change patterns
  that will help managers and organizations to
  manage the process of change more effectively

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SAMPLE
Our sample is consisted of 253 subjects from 7
private Turkish organizations. The sample is
composed of 44 superiors and 209 subordinates.
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INSTRUMENT

• Multi Scale Organizational Change Questionnaire
• Organizational change questionnaire is composed
  of five scales "Forces of Change", Change
  Strategy", "Means of Change", Resistance to
  Change", and Change Expectation" scales

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TARGET VARIABLE

• Change Expectation

• Employee Development
• Efficiency
• Organization Structure
• Acquisition Divestiture
• Alliances
• Restructuring
• means increase in employee self development
  individual learning, increase in employee
  participation employee suggestions acceptance

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Since organizational change is a process that
takes time, we rather asked if the employees
expected change as a result of the actions taken
within the firm, not whether the organization has
changed or not. This is also important because
if the employees dont believe in the actions
taken, they resist and try to block the change
actions.
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PREDICTOR VARIABLES
Change Forces Change Strategy Means of
Change Resistance to Change
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DATA TYPE
All variables collected are transformed into
dichotomous data, like change expected, not
expected competition exists, do not exist etc.
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CONCLUSION

• If business inputs are forcing an organization
  to change, the expectation of employee
  development change is 90.
• In addition if benchmarking is applied as a
  change means then this percentage increases to
  95.
• like customer demand, bargaining power of
  customers suppliers, information and production
  technology)

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But if the organization is not forced by
business inputs even then there is a chance of
change expectation if improvement in guidance
control is applied (78 expect change).
This increases to 92 with the presence of
force of laws regulations like improvement in
reward system, communication between departments,
quality control, internal control
CONCLUSION
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When there is no force of laws regulations if
benchmarking is applied, the change expectation
rate is 80.
CONCLUSION
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Which emphasizes the importance of benchmarking
in change process. Even when there is no force
to change if the organization is applying
benchmarking (which is actually a proactive
change strategy) even this is enough to trigger
change expectation.
CONCLUSION
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On the other hand if there is no force of
business inputs, there is no improvement in
guidance control, and no force of competition
then 82 of employees dont expect to have a
chance to improve themselves.
CONCLUSION
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As can be seen from the above example every path
has an implication.
CONCLUSION
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What makes this study different from other
applications is the nature of the problem
explored. Decision tree analysis are widely used
in classification of customers for segmentation
purpose and other CRM applications.
IMPLICATION
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However the main purpose in this study is to
identify important variables in change
expectation through classifying the respondents
on the basis of their perceptions about the
change criterion.
IMPLICATION
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Therefore by identifying these respondents on
the basis of the factors effecting their change
expectations, and describing the important
variables is a valuable information for
developing strategies and policies of
organizational change.
IMPLICATION