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RONALD C. CURHAN*
Retai l price, newspaper advertising, display space, and display
locationquali ty were tested at two levels for selected fruits and
vegetables accordingto a fractiona l facto rial research design in
four larg e superm arkets. The resultingimpact on rates of sale was
analyzed for four classes of items; hard fruit,cooking vegetables,
salad vegetables, and soft fruit.
The Effects of Merchandising and TemporaryPromotional Activities
on the Sales of FreshFruits and Vegetables in Supermarkets
Although food chains maintain historical sales rec-ords and have
conducted countless evaluations ofspecific merchandising
activities, it is doubtful thatmore than a few legitimate
experiments have attemptedsystematic measurement of the influence
of variouscombinations of merchandising and temporary promo-tional
activities on sales of supermarket products orclasses of prod ucts.
The effect of such key promotionalvariables as temporary retail
price reductions andadvertising, and of such merchandising
variables asincremental display space and quality of display
loca-tion, has not often been quantified, nor have therelationships
among these variables been determined.Measures of the impact of
these variables on salesand profits are essential to the developm
ent of com pu-ter-based marketing models and management
deci-sion-information systems.The research reported here was
undertaken to pro-vide such measures. Sixteen selected fresh fruits
andvegetables were subjected to merchandising andpromotional
treatments varied according to a fractionalfactorial re search d
esign. This design permits extrapo-
lation of results to products and to combinations ofvariables
other than those tested.Since it is acknowledged among supermarket
man-agers that different classes of products respond dif-
*Ronald C. Curhan is Associate Professor of Marketing,
BostonUniversity. This research was supported by funds from the
COS-MOS Project managed by Case and Company for the
NationalAssociation of Food Chains and the Marketing Science
Institute.
ferently to merchandising and promotional activities,four
categories of merchandise were selected forstudy: hard fruit,
cooking vegetables, salad vege tables,and soft fruit. Physical
characteristics generally varymore across these product categories
than they dowithin these categories. That is, potatoes and
onionsare more alike in their merchandising and
physicalcharacteristics than are potatoes and lettuce or onionsand
cherries. Hard fruit and cooking vegetables, w ithinseason, are
considered almost "staples," whereassalad vegetables and soft fruit
generally are moreperishable; vary more in day-to-day quality,
avail-ability, and price; and require more care in handlingand
refrigeration. These four categories account forvirtually all of
the fresh fruit and vegetables usuallysold in supermarkets.In
addition to noting that response to merchandisingand promotional
activities varies for different produ ctcategories, supermarket
produce m erchandisers repo rtthat differences in response may also
be partly attrib-utable to specific product characteristics such
as:volume classwhether a product is a relatively fast-
selling item or a relatively slow-selling item withinits
merchandise category; price classwhether aproduct sells for a
relatively high price per customerpurch ase, or whether it sells
for a relatively low p rice;and seasonality classwhether a product
is availablethroughout most of the year, or whether availabilityis
limited to a specific season. To consider all combi-nations of
these 3 product characteristics within eachmerchandise category at
only 2 levels would requiretesting of 8 (i.e., 2 x 2 x 2) produc ts
for each ca tegoryor 32 products in all.286
Journal of Marketing ResearcVol. XI (August 1974), 286-94
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EFFECTS OF MERC HAN DISING AN D PRO MOTION S O N SALES 28 7Four
merchandising and temporary promotionalvariables were determined to
be of interest: displayspace, retail price, newspaper advertising,
and displaylocation quality. Each of these variables was testedat
two levels: "normal" and "featured." A featureddisplay consisted of
a space allocation of at least 200%of the space allocated to an
item the period immediately
preceding a test period. Price promotion m eant a priceat least
10% lower than the prevailing "no rm al" price,with "normal" price
being determined on the basisof recent pricing within the chain, as
well as competi-tors' prevailing prices. An item was considered
"ad-ver tise d" if it was included as one of the three
produceproducts customarily featured by the chain in itsweekly
newspaper advertisement. Finally, prime loca-tions were designated
for each test store on the basisof researcher observation of store
traffic patterns andconsultation with store personnel. Generally,
separatefloor tables, ends of large tables, and
high-trafficpositions on wall coun ters qualified as prime
locations;To test each of these merchandising and
temporarypromotional variables, even at only two levels,
wouldrequire 16 (i.e ., 2 x 2 x 2 x 2 ) sep arate te sts ofeach
item. Sufficient items to test product charac-teristics for the 4
merchandise categories would meanthat 4 categories x 8 produc ts x
16 tests or 512separate observationswould be required, withoutany
provision for replication. Clearly such a task isoperationally
impractical. Supermarket managers are,understandably, reluctant to
permit store researchwhich would restrict their freedom of activity
soseverely.To overcome this constraint, a 7 ~ factorial
experi-mental design was selected for this study. Fractional
factorial designs yield information on certain variablesand
combinations of va riables, but not on all variablesor combinations
of variables. Particular fractionalfactorial designs can be chosen
which will maximizethe number of variables and combinations of
variablesfor which information of interest can be obtained.It
should be noted that fractional factorial designs
do not yield "clean" data for each variable or combi-nation of
variables tested, but give results which are"confounded" with
so-called higher-order interactionterms. However, by careful choice
of a fractionalfactorial design, the limitations of this
confoundingcan be minimized [6].For purpo ses of this study, it was
decided to replicatea quarter factorial design within each
merchandisecategory using four separate products. Products
werecarefully chosen according to the fractional factorialdesign
specifications. Thus, in each product category,two high-volume
items and two low-volume items we reselected; two items were high
priced and two werelower priced; and, two items were seasonal
productsand two were nonseasonal.The particular combinations of
characteristics re-quired for these three variables and the
productsselected as experim ental test item s to fulfill thes
erequirements are shown in Table 1. The test itemswere selected by
the cooperating chain in consultationwith the researcher. Every
effort was made to selectitems which met the design requirements
within theconstraints of product availability and other
opera-tional considerations.The extent to which the items selected
actually fulfillthe design requirements may be determined from
Table2. With the exception of pineapple, the volume classconditions
are met within each product category,although other items with
higher rates of sale mighthave been selected in preference to
celery hearts inthe salad vegetable category. Price class
complianceis excellent, with the exception of Hubbard
squash.Compliance for seasonality class is satisfactory, al-though
other items might have been chosen in prefer-ence to tomatoes and
bananas. It is difficult to findfruits which are not seasonal, but
D'Anjou pears andRed Delicious apples both have relatively long
season s.In summary, the items chosen met the test require-ments,
although each selection was not necessarilyoptimal.
The particular fractional factorial research designTable 1
REQUIRED PRODUCT CHARACTERISTICS AND TEST ITEMS CHOSEN
VolumeclassLowHighHighLow
Requiredproduct characteristicsPriceclassLowLowHighHigh
SeasonalityNonseasonal
SeasonalNonseasonal
Seasonal
HardfruitLimesNavelorangesRedDeliciousapplesOtherapples
Test itemsCookingvegetablesEggplant
CornBag redpotatoesHubbardsquash
SaladvegetablesRomainelettuceTomatoes
CelloceleryRedcabbage
SoftfruitD'AnjoupearsBananasPineapple
Redgrapes"Northern Spy, Wealthy, and Jonathan varieties.
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288 JOURN AL OF MAR KETING RESEARCH, AU GU ST 1974Table 2
TEST ITEM PRODUCT CHARACTERISTICS
RequiredvolumeclassLowHighHighLow
"Typical customer= 1 head, etc.).
RequiredpriceclassLowLowHighHigh
RequiredseasonalityclassNonseasonalSeasonalNonseasonalSeasonal
a. Volume ClassAverage Number of Typical Customer Purchases"
PerHardfruit
Limes65Navel oranges785Red Delicious498Other apples293purchase
size was determined
CookingvegetablesEggplant24Co m216Potatoes215Squash110
by researcher observationb. Price Class
10,000 Customer TransactionsSaladvegetables
Romaine80Tomatoes1,300Celery103Red cabbage20and differs by item
(i.e., corn
Average Retail Price per Typical Customer
PurchaseHardfruitLimes150Navel oranges380Red Delicious580Other
apples440
HardfruitLimesNavel orangesRed DeliciousOther apples
CookingvegetablesEggplant410Corn400Potatoes750Squash140
c. Seasonality
ClassCookingvegetablesEggplantCornPotatoesSquash
SaladvegetablesRomaine250Tomatoes390Celery490Red cabbage420
SaladvegetablesRomaineTomatoesCeleryRed cabbage
SoftfruitD'Anjou pears162Bananas1,222Pineapple38Red grapes72
= 5 ears; romaine lettuce
SoftfruitD'Anjou pears400Bananas340Pineapple600Red grapes550
SoftfruitD'Anjou pearsBananasPineappleRed grapes
chosen also determined the specific combinations ofthe four
variables which were tested (i.e., price,advertising, display
space, and location quality). Eighttreatments were called for: four
of which involveddisplay space as an independent variable, four
whichinvolved price, four of which involved
newspaperadvertisements, and four of which involved
locationquality. Treatments were the same for each
testproduct.Under conditions stipulated by the cooperatingchain,
price and advertising had to be consistentchain-wide, whereas
display space and location q ualitycould vary from store to store
within a given testweek. The selection of four test stores m ade it
possibleto replicate in separate pairs of stores two
displayspace/location quality tests for a given item withina given
week under controlled price/advertisingconditions.Given 4 items in
each product category and 8separate merchandising and promotional
combinations
to be tested for each, 32 separate tests were thereforerequired
in each category, and 128 were required forthe total experiment.
Finally, because each test wasreplicated in a second store, a total
of 256 individualobservations was obtained.In order to minimize the
disruption of usual chainoperations, the testing was spread over a
seven-monthperiod. Two categories were tested during the summerof
1972, and the other two were tested during thefollowing fall and
winter. The critical variable wasadvertising. Eight advertisem ents
w ere required du ringeach period. These were spread over 12 weeks
duringthe summer, and over 17 weeks in the fall and winterperiod.
This allowed for "breather" weeks to avoidholidays and permitted
the chain considerable freedomto pursue its normal promotional
strategy.
Test treatment compliance involving pricing andadvertising at
the chain level was exact in 53 of 64instances, or 83%. Test
treatment compliance involv-ing display space and location quality
at the store
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EFFECTS OF MERC HAN DISING AN D PR OMOT IONS O N SALES 289
level was exact in 218 out of 256 instances, or 85%.Combined com
pliance at both the chain and store levelswas exact in 182 of 256
instances, or ml Wo of thespecified instances.It is the
researcher's judgment that sufficient in-formation was available to
establish reasonable valuesfor essentially all the instances in
which actual per-formance differed from specified design. For
example,each test combination having been replicated in twostores,
acceptable substitute information often wasavailable from the
second test store when compliancewas less than perfect in the first
store. Second,although the test design may not have been adheredto
for a particular produc t in a particular w eek, re sultsof similar
promotional activity were sometimes avail-able from w eeks not
designated as test weeks. Finally,in many instances where
compliance deviated fromspecified conditions, the differences were
minor andare judged not to have biased overall results. Effectsof
individual observation s are dissipated in the proce ssof factorial
analysis so that errors of the magnitudelikely to have occurred are
tolerable and probablydo not distort the findings reported.
The experiment was designed to yield informationon the effects
of temporary m erchandising and prom o-tional activity for certain
types of products withinspecified categories of products. For
example, it washoped that the experiment might lead to
conclusionsof the following type: that the effect of a
pricereduction is greater when executed with increased spacethan it
is under conditions of normal space allocationsfor high-volume
items within the salad vegetablecategory. Such a conclusion would,
of course, ac-knowledge that the effect of a price reduction
mightwell be positive under both increased and normal
spacecondition s. The experim ent was not designed to
permitdetailed analysis of the effects of price,
advertising,display space, or location quality per se on
specificfruits or vegetables. Limes, for example, served
torepresent all low-volume, low-priced, nonseasonalproducts within
the hard fruit category. Individualtest products were not subjected
to sufficient treat-ments to permit systematic analysis of the
impact onsales attributable to merchandising and
promotionalvariables.
Sales data for each test item were determined fromspecial
inventory counts and examination of deliveryrecords for each store.
Results are reported as ratiosof observed test week movement per
thousand cus-tomer transactions to ' 'normal average weekly
move-ment" per thousand customer transactions for eachstore. The
determination of this normal average weeklymovement was to some
extent subjective, sincearbitrary computations tend to mask trend
movementsand other aberrations. However, the basis for
normal-ization was identical for each store for each test week
,with rare exception. The ratios obtained for eachidentical pair of
test conditions were then averaged.
These averages were analyzed as described in thenext section to
obtain measures of the effects attrib-utable to different
combinations of the factors tested.FACTORIAL ANALYSIS
Factorial analysis estimates the effect of each factorand
combination of factors tested. Customarily, lowercase letters are
used to represent experimental treat-ments, whereas capital letters
stand for estimates ofeffects. For example, in this study treatment
d repre-sents the test condition where display (factor D) isat high
level (i.e ., by definition 200% of normal space),and all other
factors are at low level. D stands forthe estimate of the increm
ental gain in yield attributableto the presence of factor D
averaged over all levelsof other factors. Note that whether a
specific testcondition is designated as high or low is
immaterial.If, for exa mple, we obtain D = 10 under one
condition,we would obtain D = -10 for the other. Also, thatwhen a
complete factorial is analyzed according toYates's algorithm [4],
the treatment a row will yieldan estimate for effect A, the
treatment ab row willyield an estimate for AB, and so on.
In a fractional factorial analysis each estimate willcontain a
number of terms, the number dependingon the particular fractional
replicate. This study isa quarter factorial, hence each estimated
effect con-tains four terms. The sign of these terms and themanner
in which factors are combined within eachof these terms is
specifically a consequence of theparticular fractional factorial
chosen. Moreover, whenfractional factorial data are analyzed
according toYates's algorithm, the estimates are not
generallycorresponding in capital letter to the small
letteredtreatment. For example, in this study treatment g
(i.e.,high level of location quality and low level of allother
factors) yields the estimated effect D+ ABCD + EFG - ABCDEFG. Terms
such as+ ABCD and -ABCDEFG, so-called higher-orderinteraction
terms, customarily are set equal to zerobecause they probably ar e
zero; they are certainlyincomprehensible and devoid of meaning in
practicalterms. Treatment combinations and estimated effectsfor
factors and combinations of factors are shownin Table 3. To
conserve space, degrees of freedom(1 in each instance except for
error terms) and meansquare columns are omitted. Levels of
significancewere determined by testing F-ratios against tablevalues
for (1 , 32) d.f.
ANALYSIS OF RESULTSOf major interest are the so-called "main
effects"attributable to display space, price, advertising,
andlocation quality (e.g ., - D+ EFG, E- DFG, F- DEG,and G - DEF\
where the three-letter terms are setequal to zero on the assumption
that they, like otherhigher order terms, essentially are zero).
These maineffects are the average o ver all factors, including
high
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EFFECTS OF M E R C H A N D I S I N G AND PRO M O TI O NS ON
SALES 291and low levels of volume class, price class,
andseasonality class. In addition, estimates of
so-called"Interaction Effects," that is the effect of displayspace,
price, advertising, and location qu ality at highand low levels of
volume class, price class, andseasonality class averaged over all
other factors canbe obtained from those two-letter terms which
arepaired with three-letter terms where, again, the three-letter
terms are set equal to zero (e.g., - BD-i- ACD,-CD-\-ABD, AD-BCD,
BE-ACE, CE-ABE,-AE+BCE, BE-ACF, CF-ABF, -AF+BCF,BG- ACG,CG-ABC, and
-AG-h BCG).Estimates involving a single-letter term and a
dou-ble-letter term, a combination which cannot beseparated, were
not of primary interest and so know-ingly were compromised in the
research design. Forexample, it is irrelevant to consider whether
low-volume products sell less than high-volume productsunder all
conditions, as test products were selectedto meet this criterion.
Estimates involving two-letterterms paired with other two-letter
terms also areimpossible to interpret. In spite of this
ambiguity,the experiment was conducted as described becauseit
yielded estimates of effects for variables of crucialinterest, and,
relative to the information sought, thedesign was efficient. As
previously noted, explanationof all terms would have required an
experiment atleast four times the size of the one executed,
anundertaking beyond the tolerance of the cooperatingchain.Display
Space
The study demonstrates that bonus space increasessales for all
categories of products (Table 4). ForTable 4
PERCENT INCREASE IN UNIT SALES FOR "HIGH" LEVELSOF DISPLAY (100%
BONUS SPACE)
Productcharacteristics
Average(Main effect)High volume
Low volumeHigh priceLow priceSeasonalNonseasonal
Hardfruit4 4 "20'''=68"49 3 9"5 8"30
Cookingvegetables
5937"81 60"5 8655 3 "
Saladvegetables
28 14 "4 2"26"302 5 "31
Softfruit49"38"60"74-"24a,b5642 "Main effect significant at the
.25 level or better.
''First order interaction significant at the .25 level or
better.'Calculated from Table 3 as follows:
-D+EFG =D =AD-BCD =
AD =D+AD =
-44.344+0-23.9(-24)-044+(-24) = 20
Read as: The estimated effect of bonus display space for
fastselling hard fruit is 20.
example, doubling display space for hard fruit in-creased sales
by 44%. This is not to say that increasedspace will increase sales
of every hard fruit by 44%,however, as this value is an estimate of
the categoryaverage which may or may not hold for individualitems.
Also, the experiment tested only normal spacecompared with double
that amount of space. Thequestion as to whether ra tes of sale
increase uniformlyas space is increased is not addressed. Thus, it
ispossible that a 50% space increase would not yieldany sales
increase, and that a 200% increase wouldnot increase sales
appreciably more than did the100%increase. Other studies (see, for
example, [2]) suggestthat space increases must be noticeable before
theywill stimulate significant sales changes, and that sooneror
later sales reach an upper limit beyond which theyno longer respond
to incremental space allocations.
Slow-selling items are more prone to the effectsof change in
display space than are fast-selling items,although the difference
is significant only for hardfruit. Indications are that cooking
vegetables, saladvegetables, and soft fruit also respond in this
fashion,although the results are not statistically
significant.Strict interpretation of the findings limits
conclusionsto the general statement that the effect of displayspace
is positive and does not vary by volume classfor these categories.
It may be reasoned that normalspace allocations for low-volume
items are often belowthe threshold level necessary to attract
consumernotice, whereas operational considerations ensure thatspace
allocations are above this level for high-volumeproducts. Moreover,
incremental space allocated tolow-volume items may more frequently
be perceivedas unusual by shoppers and thus attract attention
asevidence of promotion.The effect of an increase in space is
larger for ahigh-priced soft fruit than for a low-priced soft
fruit,and for seasonal hard fruit than for nonseasonal hardfruit,
although this latter finding is not statisticallysignificant. Note
that the alternative interpretation ofthese interactions, that is,
holding display space con-stant and attributing the effect observed
to a changefrom a low to a high volume class, a change of
priceclass, or a change of seasonality class is irrationalas these
factors cannot vary for a given item.Price
Contrary to generally held trade op inion, the impactof price
reductions was not statistically significantexcept for soft fruit
(Table 5) and, even in this instan ce,the finding needs
qualification as estimates for signifi-cant interactions were large
relative to the estimatefor the main effect (i.e., from Table 3, E
= 18, AE= 24 and CE= -17; hence the estimated effect fora price
reduction is 18 24, or 42 at high volumeand -6 at low volume.
Likewise the effect for sea-sonality is 18 ( -17 ) , or 1 for
seasonal and 35 fornonseasonal soft fruit). U nder this condition,
e stimates
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292 JOURNAL OF AURKETING RESEARCH, AUGU ST 197Table 5
PERCENT INCREASE IN UN IT SALES FOR "H IG H " LEVELSOF PRICE (1
0% REDUCTION)
ProductcharacteristicsAverage
(Main effect)High volumeLow volumeHigh priceLow
priceSeasonalNonseasonal
Hardfruit1119- 7- 1 3 "15"
Cookingvegetables724- 1 011386
Saladvegetables9126135- 119
Softfruit1842'"- 6 ' "24121'"35. .b
Main effect significant at the .25 level or better."First order
interaction significant at the .25 level or better.of the main
effect, even though significant, must beinterpreted with caution
[4, p. 255]. Price reductionshad a large effect for fast-selling
soft fruit. Perhapsin spite of being less widely popular, the
slow-sellingsoft fruits have particularly " loy al" purc hase rs,
whosedecisions to buy are not significantly affected by price.The
effect of a price reduction did not vary signifi-cantly by price
class within any merchandise category.Nominally positive or
negative values suggest thatthe effect of price reductions is
essentially zero, oreven negative, for seasonal products, whereas
theyprobably are significantly positive for non seaso nal
softfruit. In other words, the novelty associated withseasonal
products may be more important than priceper se. Hard fruit,
cooking vegetables, and saladvegetables may be perceived as
categories where,assuming satisfactory quality, a purchase will be
madewith small regard for retail price. Within these catego-ries,
even if products are on sale, the consum er seem sdisinclined to
purchase a greater quantity than origi-nally intended. High-priced
soft fruit purchases areof a much more discretionary charac ter,
with purchasedecisions more subject to in-store influence. A
per-ceived bargain probably consti tutes such an influence.Finally,
the absolute price level for high-priced soft
Table 6PERCENT INCREASE IN UN IT SALES FOR "H IG H " LEVELS
OF ADVERTISING (FEATURED ITEM)
ProductcharacteristicsAverage(Main effect)High volumeLow
volumeHigh priceLow priceSeasonalNonseasonal
Hardfruit33 2838 50"16"39 27
Cookingvegetables8971107116"62"128-"5 0 "
Saladvegetables518- 8- 818- 1 0 "20"
Softfruit312- 6- 1 01624
Main effect significant at the .25 level or better."First order
interaction significant at the .25 level or better.
fruit is relatively high, which suggests that savingmay be more
important in this product category.Advertising
The effect of advertising is significant for hard fruiand
cooking vegetables (Table 6). Advertising has anegligible effect on
sales of salad vegetables and soffruit. This suggests that while
the purchase decisionfor the former two categories is influenced by
advertising, the purchase decision for salad vegetables andsoft
fruit essentially is an in-store decision based onthe appearance,
quality, and value of the productThis is not to say that the
cumulative effect ofadvertising soft fruit and salad vegetables
does nomake a valuable contribution to store image. Thisexperiment
does suggest, however, that sales of produce staples respond more
directly to advertisingstimuli than do discretionary purchase i
tems.
The effect of advertising is positive and significanboth for
high- and low-volume h ard fruit an d, esp ecially, for cooking
vegetables, although the impact igreater for slow-sell ing products
in these categoriesDisplays of low-volume products ordinarily
aredwarfed by larger displays of fast-selling productwithin these
categories. Advertising probably serveto call attention to these
otherwise "invisible" products.Results by price also are m ixed.
The repo rted effecof advertising was greater for high-priced
productwithin the hard fruit and cooking vegetable categoriesbut
still positive for low-priced products. The reverstends to be true
for soft fruit and salad vegetablesalthough the effects are not
significant.
The effect of advertising was very large for season aproducts in
the cooking vegetables category, althoughsignificant and positive
for nonseasonal cooking vegetables and for seasonal and nonseasonal
hard fruias well. Cooking vegetables are relatively
"invisibleproducts" for which advertising probably serves toattract
consumer attention.Care must be exercised in drawing conclusions
abouthe main effect of advertising from the results fosalad
vegetables, since this is another instance w herealthough the
estimate of the interaction is significantthe main effect is not.
The finding that advertisingis not significant either for seasonal
or nonseasonasoft fruit again supports the theory that customer"s
ho p" th is depar tment , ra ther than depend on advertising as a
major source for purchase information.
Location QualityEssentially the same pattern of results prevails
folocation quality as for advertising (Table 7); howeverin this
instanc e it is more difficult to relate the findingto practical
experience. What these f indings seem tosuggest is that customers
will shop the entire salad
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EFFECTS OF MERCH AND ISING A ND PROMO TIONS O N SALES 293Table
7
PERCENT INCREASE IN UN IT SALES FOR " H IG H " LEVELSOF LOCATION
QUALITY (PRIME LOCATION)
ProductcharacteristicsAverage(Main effect)High volumeLow
volumeHigh priceLow priceSeasonalNonseasonal
Hardfruit26213146"6-"44,b
8 "
Cookingvegetables4815"8 1"56407026
Saladvegetables- 8- 2- 1 4- 7- 9- 1 4- 2
Softfruit119"- 1 7 "79- 3 0 "32"
Main effect significant at the .25 level or better."First order
interaction significant at the .25 level or better.
vegetable and soft fruit sections without special in-ducement,
because their shopping strategy is basedon visual perusal of these
categories. However, be-cause hard fruit and cooking vegetables are
viewedas staples, customers venture into display areas forthese
products only insofar as they have specificpurchase intentions.
Nevertheless, if items withinthese categories are given high
traffic locations, cus-tomers' attention is drawn to them and
increased salesresult. Location quality has a larger effect on
salesof low-volume than high-volume cooking vegetables.This finding
further substantiates the observation thatdisplay is an important
factor influencing sales forotherwise "inv isible" low-volume
cooking vegetables.
Results imply that location quality has a positiveimpact for
high-volume soft fruit and a negative impactfor low-volume soft
fruit. A gain, any stateme nts a boutthe main effect of location
quality must be viewedwith caution. This, in turn, may be
interpreted asimplying that, although their shopping strategy is
basedon in-store visual perusal, most customers take "theeasy way
out" and terminate their search processwhen confronted with
purchase opportunities which"satisfice." A smaller number of
customers, on theother hand, more systematically "s h op " the soft
fruitsection and are less influenced by display location.The effect
of location quality is greater for high-priced and seasonal hard
fruit and probably not signifi-cant for low-priced and nonseasonal
hard fruit. Theformer classes of products probably are not
normally
sought out by customers, and so may be expectedto benefit from
display ex posu re. The effect of locationquality is negative for
seasonal soft fruit and positivefor nonseasonal soft fruit,
although the net effect isnegligible. Since most supermarkets
prominantly dis-play seasonal soft fruit, nonseasonal items
probablylack visibility in a category where visual shoppingis
important. Also, we would expect results to besimilar for display
space and location quality, but theydiffer for soft fruit even
though they generally areparallel for other merchandise
categories.
SUMMARYThis experiment has produced quantified data forcertain
variables for which numerical information hasnot hitherto been
available. Further, it has suggestedcertain significant
relationships among these variableswhich have not previously been
apparent. Thus, the
study can reasonably claim to have served its intendedpurpose of
developing information useful to the for-mulation of heuristic
decision rules, which, in turn,are an important prerequisite to the
development ofcomputer-based management
decision-informationsystems.In addition, this study demonstrates
the usefulnessof factorial experimental designs to marketing
re-search. Although cited in texts on marketing researchand
experimentation (see, for example, [1,3,5]), fac-torial designs
have been little used in practice. Inpart, this may be attributed
to the large number oftreatmen ts called for by all but the most
simple factorialdesigns. However, factorial designs are
especiallyappropriate to marketing research because they
yieldinformation on the effects of combinations of vari-ables, as
well as on specific variables. Fractionalfactorial designs,
although they sacrifice some detail,reduce the number of required
treatments while stillproviding measures of selected intera ctions.
They m aybe especially useful when employed as the first stepof a
two-stage research proce ss consisting of a " roug hcut" to
identify important effects and subsequentresearch to "fine tune"
suggested results.
Certain findings of this study may have immediatelyapplicable
operational importance to supermarketmanagers. For example, perhaps
advertising effortshould be concentrated on hard fruits and
cookingvegetables and withheld from salad vegetables andsoft
fruits. Indeed, the behavioral similarities of thetest variables
vis-a-vis hard fruit and cooking vegeta-bles on the one hand, and
salad vegetables and softfruit on the other, have been noted
throughout.However, some words of caution are necessary atthis
point. First, although this experiment was con-ducted in large
supermarkets over an extended periodof time, the usual care should
be taken in attemptingto generalize on the basis of a single
experiment. Theresults may be unique to the specific
circumstancestested, although this is judged to be very
unlikely.Second, the impact of greater or lesser rates of
sale on profit is indeterminate without considerationof direct
product profit contribution for specific prod-ucts and, indeed, for
categories of products to includesubstitutes and compliments. The
merchandisingstrategies suggested by results of this study
shouldnot be implemented without due attention to such
directproduct profit contribution analysis.REFERENCES
1. Banks, Seymour. Experimentation in Marketing. NewYork: McGraw
Hill, 1965.
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William M. and W. T. Tucker. "Vanishing Shelf Chapter 10.Space ,"
Atlantic Economic Review, 9 (October 1961), 5. Gree n, Paul E. and
Donald S.Tull. Research/or Marfeefin9-13. Decisions, second
edition. Englewood Cliffs, N.J.: Pren3. Cox, Keith K. and Ben M.
Enis. Experimentation for tice-Hall, 1970.Marketing Decisions.
Scranton, Pa.: International Text- 6. Holland, Charles W. and David
W. Craven s. "Fractio nabook , 1969. Factorial Experimental Designs
in Marketing Re sea rch ,4. Davies, O. L., ed. Th e Design and
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