Presentation on theme: "1 Teaching Cases Decision Behavior Teaching Conference August 3-5, 2005 George Wu University of Chicago gsbww.uchicago.edu/fac/george.wu/teaching/decision/"— Presentation transcript:
1 Teaching Cases Decision Behavior Teaching Conference August 3-5, 2005 George Wu University of Chicago gsbww.uchicago.edu/fac/george.wu/teaching/decision/
2 Some Background “Managerial Decision Making” at University of Chicago Graduate School of Business Hastie, Hsee, Thaler, Wu (following Einhorn, Hogarth, Russo, Schoemaker, etc.) “Core” Class: Regular MBA curriculum 1 of 4 possible classes to satisfy Managerial and Organizational Behavior requirement 12 sections of students annually Required Class: Executive MBA program 3 sections of 85 students annually Course content Classic Lecture <50% Discussion (cases, caselets, interactive lecture, etc.) >50%
3 Why Cases? Why not JDM’s “greatest hits” instead? Not always sufficient for material to be intrinsically interesting Demonstrations often seen as “stupid human tricks” What do cases demonstrate? Descriptive: that judgment and decision making errors can be extremely costly in the real world Prescriptive: that awareness of these errors can lead to profit opportunities or better decisions
5 What is a Case? The case method is built around the concepts of metaphors and simulation. Each case is a description of a real business situation and serves as a metaphor for a particular set of problems. The situations which you face as a manager may differ from the metaphors we have chosen here, but taken together, the cases provide a useful and relevant set of metaphors for marketing situations. The cases were selected to include a wide variety of products and company types so that at least some of them would be relevant to almost all marketing management situations. The case method of management instruction is based upon the belief that management is a skill rather than a collection of techniques or concepts. … Because it is impractical to have the student manager a company, the case provides a vehicle for simulation. Benjamin Shapiro (1984), “An Introduction to Cases”
6 Cases: Dimensions, etc. Characteristics Engaging and Interactive students defend their ideas, argue, question, etc. Action-oriented Facilitation Retention Recall (pattern recognition) Motivating Devices Relevant issues (decision processes components; John Brown) Specific issues (Toro) Parables (AOL) When the rubber hits the road What are you going to do? — Necessity of tradeoffs “Psychological Engineering”
7 Cases: Types, etc. Professional Cases Business Harvard Business School Publishing Other Schools (Stanford, INSEAD, University of Western Ontario Ivey School, etc.) Other Professional Schools… Journalistic Accounts Need not necessarily be domain-relevant “Into Thin Air” (Jon Krakauer) John Brown Cases vs. “Caselets” Caselet unveiled real-time Students prepare case in advance
8 First, a “Caselet”…
10 Case Example: America Online I Pricing Change On 12/1/96, AOL to introduce a new flat-rate charge: $19.95/month for unlimited access AOL has been charging $19.95/month for 20 hours of service, or $9.95/month for five hours (plus $2.95/additional hour) What are the critical uncertainties?
11 Case Example: America Online II What decisions are tied to these uncertainties? What information would you collect?
12 Case Example: America Online III What would you do?
13 America Online: What Happened AOL Planning Began bracing for the expected usage surge in September, adding 12,000 new modems (to 260,000 existing modems) in November (along with more phone lines and computer servers). Steve Case: AOL would “really be challenged to meet that demand and try to avoid busy signals and system sluggishness.” David Gang, VP marketing: “We’re going into territory that nobody on the face of the planet has ever been before.” What happened Peak hour logins skyrocketed from 140,000 in early October to 236,000 the week before Christmas. Average time spent online by AOL subscriber doubled since September, rising to 32 minutes from 14 minutes. Case urged AOL members who also subscribe to other Internet services to use them as back doors into the AOL system.
14 America Online: Epilogue Bob Pittman, President of AOL: Forecasts are fairly reliable if you have a historical precedent in any business or opportunity. They get tricky if you have no historical precedent. No one has ever been our size before in this business. There is nothing else in the world like AOL and there never has been. Who would have thought usage would double. I mean, that’s like saying the ratings of a radio station or a TV station doubled in one month.
15 Debriefing AOL case What works Demonstrates value of following a particular decision process Process helps generate a specific prescription Demonstrates the role of confidence judgments Distinguishes between Primary and Secondary Knowledge Demonstrates some costs of having narrow confidence intervals (overconfidence) Why does this case work? Case versus “caselets”: Latter requires no advanced prep Students thrown into decision situation Exploits hindsight bias
16 Now, a “Case”…
19 The S'No Risk Promotion
20 So What Would You Do?
21 Survey: Should Toro repeat the program?
22 Survey: “Provide some reasons..”
23 Was the program a success?
24 Survey: Was S'No Risk a success?
25 Survey: justification for success rating
26 Options Is there anything else we could do? Give dealers a choice (dealer allowance or S’No Risk) Use selectively (regionally) Use in the future (jumpstart after poor years) Modify program Change dates Change refund schedule Apply only to high-end products
27 Consumer Decision Making Process Who is the “marginal” consumer? What keeps the marginal consumer on the fence? What does S’No Risk do to convert marginal consumer?
28 S’No Risk eliminates Regret Buy Don’t Buy Snow No Snow Without S’No Risk Buy Don’t Buy Snow No Snow With S’No Risk Where’s the “sleight-of-hand”?
29 Other Psychological Biases Exploited Are there other psychological biases at play? Mental Accounting Prospect theory Overweighting of small probabilities Heuristics and biases Anchoring Overestimating chance of winning
30 Chicago Annual Snowfall
31 Improving Promotion? Is there any way to improve the promotion?
32 Chances of Collecting 20%30%40%50%Prob Collecting Total Cost Boston (126) 1.6%0.0%0.8%4.8%7.7%4.0% Denver (69) 0.0% 4.3% 1.7% Kansas City (109) 0.0%1.8%3.7%7.3%12.8%6.4%
33 Improving Promotion? Is there any way to improve the promotion? 10% 20% 30% 40%
34 Other Cases John Brown (Harvard case*) *AOL (Caselet) National Demographics & Lifestyle (Harvard case*) Kodak in China (Chicago case; available on website) Eureka Ranch (Inc. Magazine) “Jump Start your Business” (May 1997) Dave Armstrong (Harvard case*) *Toro (Harvard case*) Into Thin Air (Outside Magazine) Smithkline Beecham (Harvard Business Review) *