Price Expectations and Purchase Decisions

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Title of the Paper

Price Expectations and Purchase Decisions: Evidence from an Online Store Experiment

Authors and their Affiliation

Sudipt Roy: Assistant Professor of Marketing, Indian School of Business, Gachibowli, Hyderabad 500032, India. Email: [email protected]; Phone +91-40-23187121.

Tat Chan [contact person]: Associate Professor of Marketing, Olin Business School, Washington University in St. Louis, CB1133, One Brookings Drive, St. Louis, MO 63130. Email: [email protected]; Phone +1-314-935-6096.

Amar Cheema: Professor of Commerce, McIntire School of Commerce, University of Virginia, Rouss & Robertson Halls, 125 Ruppel Dr., Charlottesville VA 22903. Email: [email protected]; Phone: +1-434-535- 2627. Web Appendix A

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DATA COLLECTION PROCESS IN EACH WEEKLY SHOPPING CYCLE Web Appendix C

A snap-shot of the shopping screen Web Appendix D

Simple regressions and results

To investigate whether participants respond to different pricing conditions in a sensible way, we run some simple tests to examine the impacts of various pricing conditions on participants’ behaviors. First, we use a simple logit model to estimate the probability that a product j will be eventually purchased under pricing condition k as the following:

(D.1) where is the current price, the average price for product j, an intercept parameter and the price coefficient. Results are reported in table D1. The price coefficients are significantly negative under both “increase” conditions but are insignificantly different from zero under both

“decrease” conditions. This implies that participants were more price sensitive under ascending price conditions. Expectedly, the coefficient for “Sudden Increase” is more negative than that for “Gradual Increase.”

Second, we test how participants update their price expectations under different conditions. Winer (1986) proposes that price expectations are gradually updated from past prices. Based on his results, a participant’s PRIOR in any week t should be lower than the store price under the ascending price condition, and vice versa under the descending price condition.

These are consistent with our data: PRIORt is smaller than PRICEt for 69% of the observations under the ascending conditions, and only 29% under the descending conditions. Winer’s result also implies that POST should be higher than the store price under the ascending price condition, and vice versa under the descending price condition. However, our data shows that

POSTt is larger than PRICEt for only 29% of observations under the former condition but 73% under the latter condition. The primary reason for this inconsistency is that, for most participants in our experiment POST refers to the price they expect to pay in outside stores.

Hence, if the store price of a product is high under the ascending price condition it is more likely that participants expect to pay a lower price in outside stores.

Finally, we also run two simple price expectation regressions for PRIOR and POST as the following:

where is measured by Pricet-1 – PRIORt-1, and by Pricet – POSTt-1, and {} is an indicator function for the four price change conditions which is equal to 1 if the logical expression inside is true, and 0 otherwise. Table D2 reports the results. The coefficients for DIFF1 and DIFF2 are both positively significant, indicating that participants on average were responsive to the disconfirmation of current price in updating price expectations. The updating coefficients for

PRIOR under gradual price increase, sudden price increase and sudden price decrease are all significant and have the right sign, but only gradual and sudden price decrease will lower their expectations of POST. We note that the adjusted R2 for the PRIOR regression is far higher than the POST regression (0.58 vs. 0.03, respectively), further implying that for most participants POST is the expected price to pay outside; hence, the pricing conditions in store do not have a strong impact on POST updating. Still, the significant coefficients in the regression suggest that at least some participants are using current prices in the online store to update their price expectations about outside stores.

TABLE D1: PRICE SENSITIVITIES UNDER PRICE CHANGES

Pricing Condition Price Coefficient Std. Error Estimate Gradual Increase -2.65** 0.65 Sudden Increase -4.31** 1.98 Gradual Decrease -0.83** 0.55 Sudden Decrease -0.55** 0.83 ** significant at 5% or better

TABLE D2: UPDATING PRICE EXPECTATIONS UNDER PRICE CHANGES

PRIOR Updating POST Updating Parameter Estimate Std. Error Parameter Estimate Std. Error Intercept 0.23** 0.03 Intercept 0.78** 0.05 PRIOR(t-1) 0.77** 0.04 POST(t-1) 0.18** 0.05 Diff1 0.08** 0.04 Diff2 0.17** 0.05 Diff1*{Gradual Increase} 0.11** 0.05 Diff2*I{Gradual Increase} -0.02** 0.02 Diff1*{Sudden Increase} 0.30** 0.07 Diff2*I{Sudden Increase} 0.00** 0.04 Diff1*{Gradual Decrease} 0.02** 0.04 Diff2*I{Gradual Decrease} -0.06** 0.02 Diff1*{Sudden Decrease} -0.22** 0.06 Diff2*I{Sudden Decrease} -0.10** 0.04 R2 = 0.58 R2 = 0.03 ** significant at 5% or better