1. APPLICATION OF DATAMINING TOOL FOR CLASSIFICATION OF ORGANIZATIONAL CHANGE EXPECTATION Şule ÖZMEN Serra YURTKORU Beril SİPAHİ

2. DATA MINING Data mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data.

3. DIFFERENT GOALS CALL FOR DIFFERENT TECHNIQUES

4. DATAMINING TECHNIQUES Datamining techniques can be either directedor undirected.

5. 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.

6. DATAMINING TECHNIQUES Directed Classification Classification Estimation Estimation Prediction PredictionUndirected Description & Visualization Description & Visualization Association Rule or Affinity Grouping Association Rule or Affinity Grouping Clustering Clustering

7. Classification is used to develop a model that maps a data item into one of several predefined classes. CLASSIFICATION

8. 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

9. Target Variable Predictive Variables Target Variable DECISION TREE ANALYSIS

10. DECISION TREE ALGORITHMS CHAID(Chi square Automatic Interaction Detector) CHAID (Chi square Automatic Interaction Detector) C&RT (Classification and Regression Tree) QUEST (Quick Unbiased Efficient Statistical Test)

11. CHAID Method CHAID was designed to handle categorical variables only. SPSS has extended algorithm to handle nominal, ordinal and continuous dependent variables.

12. Components of CHAID One or more predictor variables. One or more predictor variables. Predictor variables can be continuous, ordinal, or nominal. One target variable. One target variable. The target variable can be nominal, ordinal or continuous.

13. 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.

14. 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.

15. 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.

16. APPLICATION

17. AIM OF THE RESEARCH The ability to be both receptive and responsive to change has become important 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

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

19. 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

20. TARGET VARIABLE Change Expectation Employee Development Employee Development * Efficiency Efficiency Organization Structure Organization Structure Acquisition & Divestiture Acquisition & Divestiture Alliances Alliances Restructuring Restructuring * means increase in employee self development & individual learning, increase in employee participation& employee suggestions acceptance

21. 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 don’t believe in the actions taken, they resist and try to block the change actions.

22. PREDICTOR VARIABLES Change Forces Change Strategy Means of Change Resistance to Change

24. DATA TYPE All variables collected are transformed into dichotomous data, like change expected, not expected; competition exists, do not exist etc.

37. 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) CONCLUSION

38. 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

39. When there is no force of laws & regulations if benchmarking is applied, the change expectation rate is 80%. CONCLUSION

40. 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

41. 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 don’t expect to have a chance to improve themselves. CONCLUSION

42. As can be seen from the above example every path has an implication. CONCLUSION

43. 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

44. 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. 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

45. 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