1. Competing Theories for Evaluating Sequences of Events Jason Niggley Presentation AOM 2006

2. About My Research Working with Richard Chase and Sriram Dasu at USC Marshall School of Business Early proposal development with working title: Three Essays on Applying Psychology to Service Operations Contributions: Theoretical contribution of cross discipline research with application of theory to service operations

3. About My Research Research Question: Does order of operations change customer evaluation? Techniques: Behavioral experiment (presented here), survey of customer’s in store and after their experience, secondary data analysis from a casino player’s club card all focused on individuals

4. Agenda Area of Study: Evaluations Current Theory: Weighted Averaging Proposed Theory: Discounted Integration Preliminary Experiment Results Research Design Managerial Insight Future Research

5. Area of Study Evaluation of extended experiences (those with multiple separable parts) after they have happened Examples: Medical visit, going out to eat, theme park, film Psychological so no direct way to measure Clear application in service operations because simply changing the order of operations changes the overall evaluation

6. Weighted Averaging (Peak/End) Memory is like a series of snapshots instead of a film Recency effect Fredrickson and Kahneman 93 (movies)

7. Discounted Backwards Integration Fredrickson and Kahneman’s 1993 study has an alternate hypothesis, discounted backwards integration Unable to explain 2 cases

8. Discounted Backwards Integration Given the importance of the peak experience has been confirmed by other researchers, its placement in the sequence of events should matter but has not been researched yet The importance of the peak should be discounted based on how far in the past it occurred Salience of the peak plays a role also

9. Hypothesis The closer the peak is to the end, the more salient it will be in the memory of the subject and thus have greater effect on their global evaluation

10. Investigative Research Design Participants: 3 students IV: Placement of the peak, Between- subjects, 3 conditions (early, mid, late) Context: Newsvendor problem (Schwitzer and Cachon 2000) Control for similar profit DV: Subjects feelings

11. Relationship between Inventory Outcome and Feelings/Manipulation Check

12. Illustration of Data Analysis Procedures BaselineMean Standard Deviation Average difference between Inventory and Feeling Peak/EndPeakOverall Subject 132.21.750.33(7+2)/2=4.5Middle2 Subject 252.751.710.92(6+1)/2=3.5Beginning2 Subject 343.231.361.42(5+2)/2=3.5End3

13. Results: Hypothesis Supported Showed an effect but could show average is best predictor Not a large enough sample to find statistically significant results Somewhat different than predicted but in the right direction

14. Areas for refinement Screen out those that have experience based on extended trial with subject 1 Kahneman and Tversky’s result that losses loom larger than gains Manipulation check insignificant Random generation allows for patterns over small time period

15. Proposed Experiment Participants: MBA Operations Management Class Same 3 divisions of the IV but a greater controlled variance in value from normal and a greater range possible in order to hopefully control for losses versus gains Same DV Clearer phrasing of the manipulation check

16. Managerial Insight If a forecaster or inventory manager makes a extremely bad or good decision, immediately removing them would emphasize that decision Little impact on the next period other than slight revision Newsvendor equation would not help in this situation (due to the manipulation)

17. Future Research Analyze real world casino data Let the peak vary randomly to find the discount factor Compare various models such as weighted average, Bayesian updating, temporal integration, etc. using the same technique Apply to a service setting with data collected from a chain of wireless cellular telephone stores