Competing Theories for Evaluating Sequences of Events

Jason Niggley

Presentation

AOM 2006

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

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

Agenda

Area of Study: EvaluationsCurrent Theory: Weighted AveragingProposed Theory: Discounted IntegrationPreliminary ExperimentResultsResearch DesignManagerial InsightFuture Research

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 measureClear application in service operations

because simply changing the order of operations changes the overall evaluation

Weighted Averaging (Peak/End)

Memory is like a series of snapshots instead of a film

Recency effect Fredrickson and Kahneman 93 (movies)

Discounted Backwards Integration

Fredrickson and Kahneman’s 1993 study has an alternate hypothesis, discounted backwards integration

Unable to explain 2 cases

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

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

Investigative Research Design

Participants: 3 studentsIV: Placement of the peak, Between-

subjects, 3 conditions (early, mid, late) Context: Newsvendor problem (Schwitzer

and Cachon 2000)Control for similar profitDV: Subjects feelings

Relationship between Inventory Outcome and Feelings/Manipulation Check

Illustration of Data Analysis Procedures

  Baseline MeanStandard Deviation

Average difference between Inventory

and Feeling Peak/End Peak Overall

Subject 1 3 2.2 1.75 0.33 (7+2)/2=4.5 Middle 2

Subject 2 5 2.75 1.71 0.92 (6+1)/2=3.5 Beginning 2

Subject 3 4 3.23 1.36 1.42 (5+2)/2=3.5 End 3

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

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 insignificantRandom generation allows for patterns

over small time period

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 DVClearer phrasing of the manipulation

check

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)

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