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Learning About Computers: An Analysis of Information Search and Technology Choice Tülin Erdem University of California, Berkeley Michael Keane Yale University Sabri Öncü Stanford University Judi Strebel San Francisco State University IO workshop, Duke University October 26, 2004. A Quote.

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  1. Learning About Computers: An Analysis of Information Search and Technology Choice Tülin ErdemUniversity of California, BerkeleyMichael KeaneYale University Sabri ÖncüStanford UniversityJudi StrebelSan Francisco State UniversityIO workshop, DukeUniversity October 26, 2004

  2. A Quote “How should we allocate resources between channels? We really don’t know exactly how consumers learn about technology” Marketing manager for a major computer manufacturer

  3. Motivation • Issues for a major computer manufacturer in 1995/96: 1) What is the process consumers use to search for information? What factors affect this process? • Information sources utilized in technology choice • How do information sources interact • How does accuracy/cost of information provided affect search and technology process 2) How do consumers decide which technology to choose and when to buy • Role of expectations

  4. Objective • Develop and estimate a model of consumer information search and choice behavior in high-tech durable goods markets characterized by three key features: 1) there are two or more technological alternatives, 2) consumers have uncertainty about the quality of each alternative (and/or its suitability to their particular needs), and 3) there is a rapid pace of technological improvement, reflected in a rapid rate of price decline for any given level of quality.  Our application is to the computer market.

  5. Contribution • Develop and estimate a structural dynamic model of INFORMATION SEARCH (ACTIVE LEARNING) and TECHNOLOGY CHOICE • To understand consumer information search and technology behavior in high-tech durable good markets • To investigate the managerial/ consumer welfare implications of changes in cost and variability of information, pricing policy, consumer expectation formation processes • Use data on price expectations to estimate consumer future price expectations (Manski 2003), as well as use data on quality expectations, which facilitate identification (McFadden 1989) • Individual level panel data on “real” consumers

  6. Relevant Previous Work • Information search: • Antecedents (Srinivasan and Ratchford 91) • Patterns (Meyer 82, Hagerty and Aaker 84) • High-Tech Durables • Diffusion of innovation (Bass 69), prelaunch models (Urban, Hauser, Roberts 90), market share models (Bridges et. al. 95), expectations (Sultan and Winer 93) • Consumer Choice • Learning models (Roberts and Urban 88, Erdem 98, Anand and Shachar 00, Ackerberg 02, Ching 02, Crawford and Shum 02, Erdem and Keane 96) • Forward-Looking expectations and consumer trade-offs: Gönül and Srinivasan 96, Erdem, Imai and Keane 03, Melinkov 2000, Song and Chintagunta 2003)

  7. A Dynamic Structural Model of Information Search and Technology Choice • Each period the consumer decides • whether or not to obtain information from several sources • whether and what to buy • Consumers are uncertain about quality levels of alternatives, and changes in prices • over time, they learn about quality levels, • they form expectations about prices

  8. Dynamics arise due to... • The trade-off btw buying today versus delaying the purchase • Computer prices tend to drop over time for a given configuration and uncertainty about quality levels decreases over time  reasons for waiting • Opportunity costs that arises from not having a new computer during the period of delay  incentive to buy now

  9. Data • Computer Purchase Panel: n = 345; Six waves of surveys • By t=6, 98 consumers bought, 102 consumers did not buy, attrition: 145 (42%) • Sample Characteristics • Expertise: 34% Novice, 52% Intermediate, 14% Expert • Past Purchase: 45% First time buyer • Gender: 62% Male • Income: 50% over 50K • Age: Average 39 • Education 58 % college or graduate school degree

  10. Panel Data on... • Search and Choice Information • Information Channels visited each period • Retail Stores • Computer Sources • General Sources: • Advertisements • WOM • Whether s/he had bought a PC (if so, its description/cost) • Ratings about perceived quality of each technology • The individual’s perceived price of the type of configuration considered a) at the present time, b) six months earlier, c) price forecasts

  11. Perceived Quality Construct • Will meet my needs for a long time to come • User friendly • Powerful • A large number of software titles • All components operate together without any problems (Hardware, software, peripherals)

  12. A few words on the Data • Search • Individuals search the most intensively in the first period and then search activity declines for all channels; no large differences across channels • Demographics do not affect the search patterns over time but they affect slightly the amount of search in each period (“Intermediate” expertise individuals search the most in each period than experts and novices) • Price Expectations • Similar expectations for Apple versus IBM/Compatible technology • Demographics seem to affect expectations very little (experts expect bigger declines than the rest)

  13. The Model: The Preliminaries • Let Uijrt denote the utility to person i from purchase of technology j, where j=Apple (platform), IBM/compatible (Windows Platform), in dollar amount Pr at time t=1,T. Let Pr for r=1,R be a set of discrete dollar amounts that the consumer may choose to spend on a computer. • Assume that consumers have a utility function defined over the efficiency units of computer capabilities they possess, G, and consumption of an outside good, C. • If a consumer sends Pr dollars on a computer, then his/her consumption of the outside good is Cir = Ii– Pr, where Ii is the consumer’s income.

  14. Preliminaries continued Gijrt = jtPrQj • jtis an index of the efficiency units of computer capabilities that one can purchase by spending one dollar on technology j at time t  can be thought as inverse price indices. • Qj is the per dollar quality of technology j • jt Qj is the efficiency units of computer capabilities that one can purchase by spending one dollar on technology j at time t Gijrt : the efficiency units of computer capabilities that one can purchase by spending Pr dollars on technology j at time t

  15. Utility Specification i indexes individual; j indexes technology; t indexes time. Uijrt: the utility to person i from purchase of technology j in dollar amount Pr at time t.  : risk aversion parameter i : individual specific utility weight : price coefficient jt: an index of the efficiency units of computer capabilities that one can purchase by spending one dollar on technology j at time t Qj : per dollar quality level of technology j. Pr for r=1,..R is a set of discrete dollar amounts the consumer may choose to spend on a computer

  16. Forecasting Future Prices ij,t+1: the inverse of the consumer’s report of his/her expectation of the price decline from t to t+1. E denotes the Expectation operator.

  17. Price Expectations Process If 0=2=0 and 1=1, then consumers simply extrapolate the most recent one period (inverse) price change into the future. If 0=0, 1 > 2|2|, 2 < 0, then consumers expect that whatever acceleration or deceleration that occurred from t-2 to t will continue in the future. If 00, 0 < 1 < 1, 0 < 2 < 1, then the consumer expects the rate of price change to revert to some “natural” rate.

  18. Distribution of Future Prices Point estimates of expected future prices are not sufficient to solve the consumer’s dynamic choice problem  We need to specify the expected distribution of future prices. Assume that agents expect that the distribution of future prices be: Iit : information set that individual i has at time t

  19. Learning about Quality • Consumers lean about quality of each technology through information channel (source) signals in a Bayesian fashion; for each signal we estimate the variability of the information source • Over time, their mean expectations will evolve and the variance of their quality beliefs will shrink (provided that the signals are not very noisy) • We estimate mean quality perceptions for each technology for two segments (discrete mass approach to capture unobserved heterogeneity) but we also use consumer self-reports of quality expectations

  20. Learning about Quality of Technology Consumer Priors about Quality Levels Qj : quality of technology j. Qoj : consumer’s prior expectation of the quality of computers type j 2oj: consumer’s prior uncertainty about about computers of type j

  21. Information Channel Signals k indexes the information source, Sjk : Signal from source k about technology type j. k2 : Variance of the information provided by the k-th signal (inverse of the precision of information provided by k) [accuracy/precision of information]

  22. Expected Quality of Technology z is the expectation error is the variance of consumer beliefs of quality of technology I is the information set , , .

  23. Information obtained from Information Channels S denotes the signal received about the quality of technology x is the random term (noise) associated with the information received

  24. Expectations of Technology Quality & Technology Quality Ratings (8), .

  25. Ordered Probit to Estimate the Cut-off Points for the Signal Levels of L, M and H for Quality of Technology • is the cumulative normal distribution function with mean 0 and variance , ,

  26. Consumer’s Dynamic Choice Problem m indexes the combination of information sources (the m=1,..,32 search options, where 32=25); V denotes “Value function”; P denotes “Purchase”; NP denotes “No=Purchase” Jkm: an indicator for whether source k is included in combination m. ck: the costs of obtaining information from each source k imt: an i.i.d. stochastic shock to the cost of search option m at time t. VP captures the maximum over all possible technology and price choices {j,r} of the expected utilities associated with choices Ui0:per period utility from the current computer (if applicable)

  27. Choice Probability for Search Options Pr(Mimt): Probability of search option m for person i at time t Θ: the set of parameters z: expectation errors τ: latent class type

  28. Choice Probabilities for Purchase and No-Purchase • D stands for probability, θ for the set of parameters, z for expectation errors, and τfor latent class type

  29. Likelihood Function • L1 corresponds to information choices • L2 corresponds topurchase decisions • L3 corresponds toreported quality ratings • L4 corresponds toreported price expectations

  30. Likelihood Function II

  31. Parameter Estimates

  32. Parameter Estimates: Price Process Parameters Price process parameters 0=-0.041 , 1= 1.239 , 2= - 0.958, = 0.088 • 2 is negative and larger in absolute value than 1+ 2 consumers expect mean reversion in price declines • If price stayed constant from t-2 to t then consumers would expect a 4.1% price decline from t to t+1. • A steady state expected rate of price decline of roughly 2.5% per two-month period. • Substantial measurement error in consumer’s reports of their own expectations.

  33. Parameter Estimates II • Prior uncertainty is substantial. It is higher for Apple. • Two segments of consumers, first segment’s mean expected quality is higher for IBM/Compatible than Apple; the opposite holds for the second segment. The first segment constitutes 88% of the individuals. • No-purchase utility is higher for individuals • who 1) already own a computer, 2) older people, 3) women and 4) lower income people. Education and experience do not have a statistically significant effect on No-Purchase Utility. • Utility weight is higher • 1) the more experienced they are with computers, 2) the older they are, 3) the less educated they are, and 4) if they are male.

  34. Parameter Estimates III: Variability and Cost of the information Sources • Computer magazines, and general sources and advertising provide the noisiest information, whereas store visits provide the most precise information. • Reading computer articles, followed by store visits, seem to be the most costly sources, whereas obtaining word of mouth information seems to be the least costly one, followed by reading advertisements.

  35. Model Fit: Comparing sample frequencies of search and purchase behavior with baseline simulations based on parameter estimates • The model fits the data quite well. • It slightly overstates the No-Purchase rate in wave 6, and overpredicts purchases in wave 1. • It slightly under predicts the extent of search. • It accurately predicts the relative utilization of each source, as well as the time-path of utilization. • Percentage of people using each information source declines over time both in the data and according to the model. Evidence for duration dependence.

  36. Model Fit: Table 4

  37. Table 4 (con’t)

  38. Model Fit II: Table 5

  39. Table 5 (con’t)

  40. Policy Experiments I: Effects of Expected Price Declines and Learning on Purchase Delay • No expectations of price decline • Relatively small effect of expected price declines • Acceleration of purchases • However, total purchases are almost unchanged at T=12 • Substantial increase in search costs • Lower computer sales overall • Decreases positive duration dependence

  41. Policy Experiments II: Effects of Price Expectations on Price Elasticities of Demand • Temporary %20 price cut • Expectations adjust versus • They are fixed • Elasticity of demand wrt transitory price cur is almost 50% greater if we allow price expectations to adjust rather than holding them fixed • Acceleration of purchases and total purchases • Incremental sales due to the price cut only 4.6% at T=12 but 80% at T=6. • Price elasticity higher for Apple

  42. Policy Experiments III • EFFECT OF DECERASE IN INFORMATION COSTS ON PUCRHASE BEHAVIOR • Decreasing information costs leads to acceleration of purchases • Decreasing information costs affects Apple sales more than IBM/Compatible sales • Advertising has the least effects

  43. Policy Experiments IV • EFFECTS OF INCREASING THE PRECISION OF INFORMATION ON SEARCH AND PURCHASE BEHAVIOR • Decreasing the variability of information sources (increasing the precision) encourages consumers to search slightly more (own variability effects are positive) and accelerates purchases • Most cross-effects are positive (except for negative effect of store visits precision on advertising and general sources) • Increased precision helps Apple relatively more

  44. Conclusions & Future Research • Consumers are forward-looking • Both learning and price expectations affect purchase timing and purchase decision • Ignoring price expectations severely bias price elasticities • Cost and accuracy of information sources affect consumer search behavior • Availability of survey data about expectations facilitate identification • Future research: • Information search: Learning to learn? • Brand choice, consideration set formation

  45. Appendix

  46. Table 6

  47. Table 7

  48. Table 8

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