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Services Providers Conjoint

Hotel A $$$ *** Friendly Customized Value___. Hotel B $$$ *** Friendly Standardized Value ___. Hotel C $$$ *** Not Friendly Customized Value ___. Hotel D $$$ *** Not Friendly Standardized Value ___. Hotel E $$$ ** Friendly Customized Value ___. Hotel F $$$ **

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Services Providers Conjoint

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  1. Hotel A $$$ *** Friendly Customized Value___ Hotel B $$$ *** Friendly Standardized Value ___ Hotel C $$$ *** Not Friendly Customized Value ___ Hotel D $$$ *** Not Friendly Standardized Value ___ Hotel E $$$ ** Friendly Customized Value ___ Hotel F $$$ ** Friendly Standardized Value ___ Hotel G $$$ ** NotFriendly Customized Value ___ Hotel H $$$ ** NotFriendly Standardized Value ___ Services Providers Conjoint

  2. Hotel L $$ *** Not Friendly Standardized Value ___ Hotel I $$ *** Friendly Customized Value ___ Hotel J $$ *** Friendly Standardized Value ___ Hotel K $$ *** Not Friendly Customized Value ___ Hotel M $$ ** Friendly Customized Value ___ Hotel N $$ ** Friendly Standardized Value ___ Hotel O $$ ** NotFriendly Customized Value ___ Hotel P $$ ** NotFriendly Standardized Value ___ Services Providers Conjoint

  3. Data for 1 Consumer Scenario Cost Quality Friendly Customized Rating* A 1 1 1 1 2 B 1 1 1 0 4 C 1 1 0 1 6 D 1 1 0 0 8 E 1 0 1 1 9 F 1 0 1 0 13 G 1 0 0 1 11 H 1 0 0 0 16 I 0 1 1 1 1 J 0 1 1 0 3 K 0 1 0 1 5 L 0 1 0 0 7 M 0 0 1 1 10 N 0 0 1 0 14 O 0 0 0 1 12 P 0 0 0 0 15 *1=most preferred

  4. This Conjoint in SAS—Input data reg; input stim $ cost quality friend custom judg; costqual=cost*quality; costfrie=cost*friend; *etc.; cqfc=cost*quality*friend*custom; cards; A 1 1 1 1 2 B 1 1 1 0 4 C 1 1 0 1 6 D 1 1 0 0 8 E 1 0 1 1 9 F 1 0 1 0 13 G 1 0 0 1 11 H 1 0 0 0 16 I 0 1 1 1 1 J 0 1 1 0 3 K 0 1 0 1 5 L 0 1 0 0 7 M 0 0 1 1 10 N 0 0 1 0 14 O 0 0 0 1 12 P 0 0 0 0 15 proc reg; model judg = cost quality friend custom / stb; proc reg; model judg = cost quality friend custom costqual costfrie / stb;run;

  5. This Conjoint in SAS—Output Model: MODEL1 Dep Variable: JUDG Analysis of Variance Sum of Mean Source DF Squares Square F Value Prob>F Model 4 328.25000 82.06250 76.824 0.0001 Error 11 11.75000 1.06818 C Total 15 340.00000 Root MSE 1.03353 R-Square 0.9654 Dep Mean 8.50000 Adj R-Sq 0.9529 C.V. 12.15916 Parameter Estimates Parameter Standard T for H0: Variable DF Estimate Error Parameter=0 Prob>|T| INTERCEP 1 15.375000 0.57776017 26.611 0.0001 COST 1 0.250000 0.51676441 0.484 0.6380 QUALITY 1 -8.000000 0.51676441 -15.481 0.0001 FRIEND 1 -3.000000 0.51676441 -5.805 0.0001 CUSTOM 1 -3.000000 0.51676441 -5.805 0.0001 Variable DF Standardized Estimate INTERCEP 1 0.00000000 COST 1 0.02711631 QUALITY 1 -0.86772183 FRIEND 1 -0.32539569 CUSTOM 1 -0.32539569

  6. Regression for 1 Consumer Regression Results for n Consumers: Utilities Consumer b1(cost) b2(qual) b3(friend) b4(custom) 1 -.027 .868 .325 .325 2 3 ... n

  7. Sectors Studied Experience: Hotels, Fast Food Outlets, Hair Salon, Checking Account Credence: Tax Consultant, Psychotherapist, Physician, Financial Investments

  8. Criticality of Service Need Low: e.g., Imagine that you need to make an appointment to have your hair trimmed in the next couple of days. High: Imagine that you need to make an appointment to have your hair cut and styled for a very important social occasion scheduled for tomorrow.

  9. Price Price Low Criticality Price High Criticality Experience Credence Low High Criticality Experience Credence Selection of Results

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