1
Web Appendices
Submitted to
Customer Needs and Solutions
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.
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Web Appendix A
PRODUCT LIST
S. No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
Category
Bath Tissue
Bath Tissue
Bath Tissue
Bath Tissue
Canned Soup
Canned Soup
Canned Soup
Canned Soup
Canned Soup
Canned Soup
Canned Soup
Canned Soup
Canned Soup
Canned Soup
Canned Soup
Milk
Milk
Milk
Milk
Milk
Milk
Milk
Milk
Milk
Milk
Milk
Milk
Milk
Orange Juice
Orange Juice
Orange Juice
Orange Juice
Orange Juice
34
Orange Juice
35
36
37
38
39
40
41
42
43
44
45
46
47
Orange Juice
Orange Juice
Orange Juice
Orange Juice
Orange Juice
Paper Towel
Paper Towel
Paper Towel
Pasta
Pasta
Pasta
Pasta
Pasta Sauce
Brand & Product Description
Angel Soft Bath Tissue, Double Rolls, White
Kleenex 4 Pk Double Roll White Bath Tissue
Schnuck True Soft Premium Bath Tissue Double Roll
Scott Bath Tissue, White
Campbell Home Cooking Chicken with Noodles
Campbell Home Cooking Potato Roast Garlic
Campbell Roasted Chicken
Campbell Roasted Chicken Rice Soup
Campbell Chunky Chicken with Vegetables
Campbell Chunky Vegetables
Progresso Chicken Noodles Soup
Progresso Chicken Rice Soup
Progresso Hearty Tomato
Progresso Soup Beef & Baked Potato
Progresso Vegetable Soup
Pevely Milk, Homogenized
Schnuck Milk, 1% - Half Gallon
Schnuck Milk, 1/2% - Gallon
Schnuck Milk, 2% - Gallon
Schnuck Milk, 2% - Half Gallon
Schnuck Milk, 2% - Quart
Schnuck Milk, Homogenized - Gallon
Schnuck Milk, Homogenized - Half Gallon
Schnuck Milk, Homogenized - Quart
Schnuck Milk, Lite One - Gallon
Schnuck Milk, Skim - Gallon
Schnuck Milk, Skim - Half Gallon
Schnuck Milk, Skim - Quart
Schnuck Orange Juice, Gallon
Schnuck Orange Juice, Select w/Calcium
Florida Natural, Orange Juice
Florida Natural, Orange Juice w/Calcium
Minute Maid Orange Juice, Calcium Fortified
Minute Maid Orange Juice, Premium, Original Calcium Low
Pulp
Schnuck Orange Juice
Tropicana Orange Juice, Premium Grove Stand
Tropicana Orange Juice, Premium Grove Stand
Tropicana Orange Juice, Premium Homestyle
Tropicana Orange Juice, Pure Premium, Calcium Fortified
VIVA Big Roll White Towel
Schnuck Paper Towels White 1 Roll
Schnuck Paper Towels White 6 Roll
R&F Spaghetti
R&F Thin Spaghetti
Schnuck Spaghetti
Schnuck Spaghetti
Brilla Pasta Sauce, Mushroom Garlic
Pack Size
4 ct
4 ct
4 ct
4 ct
18.6 oz
18.8 oz
18.6 oz
18.6 oz
18.8 oz
18.8 oz
19 oz
19 oz
19 oz
18.5 oz
19 oz
64 oz
64 oz
128 oz
128 oz
64 oz
32 oz
128 oz
64 oz
32 oz
128 oz
128 oz
64 oz
32 oz
128 oz
64 oz
64 oz
64 oz
64 oz
128 oz
64 oz
64 oz
96 oz
64 oz
64 oz
1 Each
1 ct
6 ct
16 oz
16 oz
16 oz
32 oz
26 oz
3
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
Pasta Sauce
Pasta Sauce
Pasta Sauce
Pasta Sauce
Pasta Sauce
Pasta Sauce
Pasta Sauce
Pasta Sauce
Pasta Sauce
Pasta Sauce
Soda
Soda
Soda
Soda
Soda
Soda
Soda
Soda
Soda
Soda
Soda
Soda
Soda
Soda
Soda
Soda
Soda
Soda
Soda
Yogurt
Yogurt
Yogurt
Yogurt
Yogurt
82
Yogurt
83
84
85
86
87
88
89
Yogurt
Yogurt
Yogurt
Yogurt
Yogurt
Yogurt
Yogurt
Brilla Pasta Sauce, Tomato Basil
Del Monte D'Italia Pasta Sauce, Four Cheese
Del Monte Pasta Sauce, Garlic & Onion
Del Monte Pasta Sauce, Meat
Hunt's Pasta Sauce, Meat
Hunt's Pasta Sauce, Parmesan
Hunt's Pasta Sauce, Traditional
PREGO GARLIC PARMESAN SAUCE
Prego Pasta Sauce, Meat
Prego Pasta Sauce, Regular
Coke Classic, 2-Liter
Coke Classic, 6-Pack Bottles
Diet Coke, 6-Pack Bottles
Diet Coke, 6-Pack Cans
Diet Coke, Caffeine-Free, 2-Liter
Pepsi, 2-liter
Pepsi, 6 Pk Bottles
Pepsi, 6-Pack Cans
Pepsi, Caffeine-Free, 2-Liter
Diet Pepsi, 2-Liter
Diet Pepsi, 6 Pk Bottles
Diet Pepsi, 6-Pack Cans
Diet Pepsi, Caffeine-Free, 2-Liter
Diet Sprite, 2-Liter
Dr. Pepper .5 Liter
Dr. Pepper Diet .5 Liter
Dr. Pepper, 2-Liter
Mountain Dew, 2-liter
Mountain Dew, 6 Pk Bottles
Dannon Yogurt, Non-Fat Plain
Dannon Yogurt, Plain
Dannon Yogurt, Plain
Dannon Yogurt, Raspberry
Dannon Yogurt, Vanilla
Yoplait Go-Gurt, Portable Yogurt Strawberry & Berry Blue
2.25 Oz
Yoplait Yogurt, Light Apricot Mango Fat Free
Yoplait Yogurt, Light Banana Cream Pie Fat Free
Yoplait Yogurt, Light Blueberry Patch Fat Free
Yoplait Yogurt, Light Strawberries 'N Bananas Fat Free
Yoplait Yogurt, LightWhite Chocolate Strawberry Fat Free
Yoplait Yogurt, Original Cherry Orchard 99% Fat Free
Yoplait Yogurt, Original French Vanilla 99% Fat Free
26 oz
26.5 oz
26.5 oz
26.5 oz
26.5 oz
26 oz
26.5 oz
26 oz
48 oz
26 oz
67.6 oz
48 oz
48 oz
72 oz
67.6 oz
67.6 oz
144 oz
72 oz
67.6 oz
67.6 oz
144 oz
72 oz
67.6 oz
67.6 oz
101.4 oz
101.4 oz
67.6 oz
67.6 oz
144 oz
32 oz
32 oz
6 oz
6 oz
32 oz
18 oz
6 oz
6 oz
6 oz
6 oz
6 oz
6 oz
6 oz
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Web Appendix B
DATA COLLECTION PROCESS
IN EACH WEEKLY SHOPPING CYCLE
Online Store Experiment
Pre-Purchase
Data
Collection
Shopping at the
Online Store
 Budget: $15
 89 SKUs in 9
product categories
Post-Purchase
Data
Collection
Pre-purchase data
Post-purchase data
1. SKUs that respondent plans to
buy in the current visit
2. PRIOR: Today’s expected price
be for a unit of X (for each of
the SKUs indicated in #1)
3. Respondent’s confidence level
on a 10-point scale for today’s
price of one unit of X being
between p1 and p2 (p1 and p2
are upper and lower limits for
10% range around the indicated
price in #2).
4. Next purchase plan
5. POST: Expected price for each
unit of X when respondent buys
next time (X was either planned
to be bought or not planned but
actually bought)
6. Respondent’s confidence level
on a 10-point scale for price of
one unit of X being between p1
and p2 when she buys it next
time (p1 and p2 are upper and
lower limits for 10% range
around the indicated price in
#5).
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Web Appendix C
A snap-shot of the shopping screen
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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:
prob( j will be purchased | pricing condition k ) 
exp( 0k  1k  ln( ptj / p j )
1  exp( 0k  1k  ln( ptj / p j )
(D.1)
where ptj is the current price, p j the average price for product j,  0k an intercept parameter
and 1k 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%
7
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:
PRIORt   0  1  PRIORt 1   2  DIFFt11  3  DIFFt11  {Price Condition}  et1
(D.2)
POSTt   0  1  POSTt 1   2  DIFFt 2  3  DIFFt 2  {Price Condition}  et2
(D.3)
where DIFFt11 is measured by Pricet-1 – PRIORt-1, and DIFFt 2 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.
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TABLE D1: PRICE SENSITIVITIES UNDER PRICE CHANGES
Pricing Condition
Gradual Increase
Sudden Increase
Gradual Decrease
Sudden Decrease
Price Coefficient
Estimate
-2.65**
-4.31**
-0.83**
-0.55**
Std. Error
0.65
1.98
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
Intercept
0.23**
0.03
PRIOR(t-1)
0.77**
0.04
Diff1
0.08**
0.04
1
Diff *{Gradual Increase}
0.11**
0.05
1
Diff *{Sudden Increase}
0.30**
0.07
Parameter
Diff1*{Gradual Decrease}
Diff2*I{Gradual Decrease}
1
Diff *{Sudden Decrease}
0.02**
-0.22**
2
0.06
2
Diff *I{Sudden Decrease}
2
R = 0.58
**
0.04
Estimate
0.78**
0.18**
0.17**
-0.02**
2
Diff *I{Sudden Increase}
0.00**
Intercept
POST(t-1)
Diff2
Diff2*I{Gradual Increase}
R = 0.03
significant at 5% or better
Std. Error
0.05
0.05
0.05
0.02
0.04
-0.06**
0.02
-0.10**
0.04