articol Despre Aglomeratie

7/21/2019 articol Despre Aglomeratie

1/8

The differential effects of retail density: An investigation of goods versus service settings

Yue Pan a,,1, Jennifer Christie Siemens b,1,2

a 812 Miriam Hall, University of Dayton, 300 College Park, Dayton, OH 45469-2271, United Statesb 810 Miriam Hall, University of Dayton, 300 College Park, Dayton, OH 45469, United States

a b s t r a c ta r t i c l e i n f o

Article history:

Received 26 December 2008

Accepted 10 February 2010

Keywords:

Crowding

Density

Retail

Goods

Services

Two studies investigate the impact of retail density and time pressure on shoppers' store attitudes and

behavioral intentions. In a goods setting, experiment 1 results show a curvilinear pattern as the level of retail

density increases. That is, individuals prefer a medium level of crowding to a store with low or high

crowding. This inverted-U shaped crowding effect has not previously been tested in the retailing literature.

Experiment 2 employs a service setting, where the relationship between retail crowding and outcome

variables is found to be linear rather than curvilinear, except under conditions of time pressure. In contrast to

the goods setting, individuals have more favorable attitudes and expect to pay more for a service as the level

of crowding increases. Thus, our study ndings suggest that the optimal level of crowding depends on the

type of retail setting.

2010 Elsevier Inc. All rights reserved.

1. Background

Several studies have investigated consumption under conditions

of crowding, suggesting that a crowded environment can lead to

attributions of resource scarcity (Jain, 1987), stress (Stokols, 1978),and negative shopping behavior (Harrell et al., 1980). The crowding

literature seems to converge on an information processing explana-

tion, positing that people experience information overload under high

density conditions (Eroglu and Machleit, 1990). Confronted with

inadequate space, a person feels reduced control over the situation

and experiences stress and arousal that, in turn, eventuate in feelings

of being crowded (Rustemli, 1992).Rompay et al. (2008)suggest that

perceived control mediates the effects of spatial density on pleasure

and approach behaviors, and there is an overall negative relation

between spatial density and perceived control.

Manning Theory in ecological psychology (Barker, 1963), investi-

gates the behavioral and cognitive consequences ofundermanning,

a condition where store personnel are fewer than the number

typically required to maintain a setting at an expected, optimal

level. Extreme undermanning may produce a sense of hopelessness

and frustration, adversely affecting staff attitudes and behaviors

(Vecchio and Sussmann, 1981). Perception of crowding is often

associated with the scarcity of resources (e.g., spatial restrictions,

understaffed store) and its subsequent negative outcomes (e.g., poor

service), feeling cramped, and loss of control and privacy. Retail

crowding has been shown to reduce shopping satisfaction and lead to

avoidance behavior (Eroglu and Machleit, 1990; Machleit et al., 1994;

Pons et al., 2006; Pons and Laroche, 2007).

Conversely, low levels of crowding are also undesirable in shapingshopping expectations. Shoppers often rely on simple, easily acces-

sible social cues for forming inferences about product value when

they lack the time to examine complex information, or when such

information is lacking (Baker et al., 1994; Bloom, 1990). Similarly,

categorization theory states that consumers use available information

(e.g., crowding) to help them draw inferences about possibly missing

information (e.g., price, quality) by comparing the target stimulus

with their category knowledge (Baker, 1998). Thus, shoppers may

gauge the attractiveness of a store by the number of people shopping

there. Similarly, marketing semiotics proposes that the store

environment provides symbols that may characterize product

attributes. A retail outlet with few patrons may present an image of

being overpriced, having poor product quality, or simply unappealing

merchandise. Conceptual and empirical support for the symbolic

nature of store environmental cues abounds in the literature (e.g.,

Baker, 1998; Baker et al., 1988; Bitner, 1990; Grossbart et al., 1990).In

summary, low retail density may incur negative perceptions of the

store and hence less motivation to patronize the retailer. However,

too many shoppers could also be undesirable. Beyond a threshold,

crowding may be seriously dysfunctional, whereby shoppers may

experience high cognitive demands and frustration.

Surprisingly, few controlled studies have investigated medium

levelsof crowding. Instead, research hasfocused on conditions of high

crowding and the subsequent negative consequences of such. Are

there times when a moderately crowded setting can lead to more

favorable results? A medium level of crowding might enhance an

Journal of Business Research 64 (2011) 105112

Corresponding author. Tel.: +937 2291773; fax: +937 229 3788.

E-mail addresses:[email protected](Y. Pan),

[email protected](J.C. Siemens).1 Authors' names are listed alphabetically. Both authors contribute equally to this

study.2 Tel.: +937 229 1086; fax: +937 229 3788.

0148-2963/$ see front matter 2010 Elsevier Inc. All rights reserved.

doi:10.1016/j.jbusres.2010.02.011

Contents lists available at ScienceDirect

Journal of Business Research
http://-/?-http://-/?-http://-/?-mailto:[email protected]:[email protected]://dx.doi.org/10.1016/j.jbusres.2010.02.011http://www.sciencedirect.com/science/journal/01482963http://www.sciencedirect.com/science/journal/01482963http://dx.doi.org/10.1016/j.jbusres.2010.02.011mailto:[email protected]:[email protected]://-/?-http://-/?-http://-/?-


2/8

individual's attitude toward the store, increasing their behavioral

intentions. Although they do not empirically test it, Eroglu et al.

(2005) suggest that there is a general positive effect of human

crowding on shopping satisfaction after certain mediating variables

are accounted for. They propose that there might be an inverted-U

relationship between crowding and satisfaction, whereby an ex-

tremely crowded or uncrowded condition will be undesirable. While

Michon et al. (2005)measure high, medium, and low crowding, they

focus on the effects of ambient odor as an atmospheric variable. Thepurpose of this research is to further investigate the possibility of a

desirable density level for retail settings, specically whether a

curvilinear relationship between crowding and shopping outcomes

exists.

2. Exploratory study

First, an exploratory study was conducted in foundational business

classes. Students were given class credit as an incentive for

participation (n =48). They received an email from the researcher,

randomly directing them to one of three websites where they read a

brief introduction to the study and were asked to imagine themselves

leisurely browsing the stores. Participants were then shown one

picture of a clothing store, which contained either low, medium, orhigh crowding. Participants were instructed to write down any

thoughts they had about the store and their potential to shop there.

2.1. Stimulus development and pretest

In order to present participants with realistic shopping environ-

ments to evaluate, three photos were developed with varying degrees

of crowding. Using photographs as a manipulation of perceived retail

crowding, a common method in the marketing literature, has been

shown to have ecological validity (Bateson and Hui, 1992; Eroglu and

Machleit, 1990). When choosing the retail establishment, careful

consideration was made regarding the merchandise selection, layout

and location of the store. In order to eliminate bias from prior

experience with a recognized store,the chosenstorewas in a differentstate and was not a chain retailer. The store contained both men's and

women's clothing so that merchandise type would not bias store

perception. Shopping volunteers were recruited for the pictures,

which enabled control over the picture content and layout. Thirteen

pictures of varying levels of crowdedness were used in a pretest.

Pretest subjects (n =89) were presented with all thirteen pictures

and were asked to evaluate them with three, seven-point likert items

with endpoints strongly disagree/strongly agree: The store seems

very crowded to me, The store is very busy,and There are a lot of

customers in the store (Machleit et al., 1994). Three color photos

were chosen based on their signicantly different results for the likert

items. These photos were shot from the same area of the store, with

the same type of merchandise shown in each picture. This allows

control over crowding perceptions due to differences in spatialdensity. These photos show varying degrees of human density (from

one shopper to ve shoppers).

2.2. Exploratory study analyses and results

Half of the respondents exposed to a low crowding picture

(n =8) indicated no interest in shopping in the clothing store. Of the

16 respondents who viewed a highly crowded store, the majority

(n =12) expressed no intention to enter the store. A review of the

responses revealed their negative feelings towards the store (e.g.,

messy, unkempt,and not well-maintained).

In contrast, of the 16 respondents assigned to the medium

crowding level, the majority indicated favorable attitudes (n =13) or

intentions (n =12) toward the store. Positive adjectives such as

sophisticated, classy, organized, and rened were use to

describe the store.

In summary, this exploratory study reveals differences in store

perceptions and purchase intentions across the three crowding levels.

While respondents were not asked to specically examine retail

density, their open-ended responses suggested that crowding level

was an important factor. The preliminary ndings provide the

foundation for a controlled experiment. Specically, the following

hypotheses are offered:H1. The effect of retail density on subjects' intentions to enter a store

will follow an inverted-U shaped trend, where intention to enter is

greatest when the store exhibits a medium level of crowding

compared to either low or high crowding levels.

H2. H2a) The effect of retail density on subjects' intentions to

browse in a store will follow an inverted-U shaped trend,

where intention to browse is greatest when the store

exhibits a medium level of crowding compared to either

low or high crowding levels.

H2b) Furthermore, subjects will intend to browse longer in the

retail store when there is a medium level of crowding

compared to low or high levels.

H3. The effect of retail density on subjects' attitudes toward the store

will follow an inverted-U shaped trend, where attitudes are more

positive when the store exhibits a medium level of crowding


H4. The effect of retail density on subjects' purchase intentions will

follow an inverted-U shaped trend, where intention to purchase is

greatest when the store exhibits a medium level of crowding


However, these proposed relationships are subject to an important

caveat. Reactionsto crowding arenot the same for all people, and may

depend on other environmental factors. Crowding is experienced

when the environment is judged as being dysfunctionally dense; thesejudgments will vary across individuals depending on factors such as

time pressure (Eroglu and Harrell, 1986). Time-constrained shoppers

may be more sensitive to crowded conditions (Eroglu and Machleit

1990), and may view crowding more negatively as it prevents

effective goal achievement. As such, we propose that as the amount of

time pressure increases, the negative impact of retail crowding on

shopping attitudes and intentions is likely to be enhanced.

H5. Time pressure will interact with retail density for all dependent

measures. Under the condition of time pressure, the effect of retail

density on subjects'

H5a) intentions to enter the store,

H5b) intentions to browse in the store,

H5c) attitudes toward the store andH5d) purchase intentions will follow a negative linear trend.

Generally, subjects will generally prefer a less crowded

store under conditions of time pressure.

3. Experiment 1

3.1. Subjects, design and procedure

Subjects were 270 undergraduates at a Midwestern university(see

Table 1 for demographics). Subjects were randomly assigned to one of

six conditions in a 2 (no time pressure vs. time pressure)3 (high,

medium, and low crowding) between-subjects design. Written

instructions were read by the experimenter to ensure equality of

106 Y. Pan, J.C. Siemens / Journal of Business Research 64 (2011) 105112


3/8

experience across conditions. Subjects read the following shopping

scenario: It is Saturday and you are browsing the stores. You are

thinking of buying something. However, since this day is not the only

stretch of time that you can devote to shopping, you are in no hurry

(no time pressure conditions). For subjects assigned to the timepressure condition, they instead read, Since this day is the only

stretch of time that you can devote to shopping, you are pressed for

time and are in a hurry.They were presented with one of the three

pretested retail photos, and then answered the dependent measures.

3.2. Dependent measures

Attitude towardsthe store wasassessedwith a six-item, seven-point

semantic differential scale. Intentions to enter the store, browse, and

make a purchase were assessed with separate three-item, seven-point

scales (seeTable 2). Participants were also asked to list the length of

time in minutes that they would spend shopping in the store.

The measurement model was assessed for convergent and

discriminant validity. The joint con

rmatory factor analysis (CFA)lends strong support to the convergent validity of all measures (see

Table 2). CFA results show an acceptable model t (2(125)=242.71,

pb0.001; CFI =0.988; RMSEA=0.062; SRMR= 0.067). Thus, the

proposed model (i.e., joint CFA with all constructs included simulta-

neously) provides a reasonable explanation of the observed covari-

ance among the constructs. All estimated item loadings are signicant

(pb0.05) and greater than the recommended 0.6 cutoff (Bagozzi and

Yi, 1988). Furthermore, all constructs show satisfactory levels of

average variance extracted (with all AVE's above the recommended

0.5 level,Fornell and Larcker, 1981) and composite reliability (all CR's

higher than the 0.7 cutoff recommended byNunnally and Bernstein,

1994). The internal validity of the measurement model seems

adequate. We then conduct Fornell and Larcker's (1981) test for

discriminant validity by comparing the AVE estimates for each

construct with the squared correlation between any two constructs.

All AVEs are larger than any squared correlation between any two

constructs. Therefore, discriminant validity is established. Intolerance

for crowding was identied as a potential covariate (Machleit et al.,

2000, 3-item, e.g., I avoid crowded stores whenever possible).

Table 1

Demographic characteristics of respondents.

Experiment 1 (n =270) Experiment 2 (n =403)

Demographic characteristics Frequency/Mean Frequency/Mean

Gender

Female 125 209

Male 144 194

Age 21.2 27.6

Household income

0$20,000 (%) 4 93

$20,001$40,000 (%) 17 14

$40,001$60,000 (%) 28 18

$60,001$80,000 (%) 36 41

$80,001$100,000 (%) 43 67

Over $100,000 (%) 132 155

Table 2

Construct measures and CFA results.

Experiment 1 Experiment 2

Construct, scale items, and source Factor loadings CR AVE Factor loadings CR AVE

Store attitude (Golden et al., 1987) 0.915 0.63 0.922 0.67

The retail store/salon shown in the picture is:

Unfavorablefavorable 0.844 0.869Badgood 0.860 0.883

Not likablelikable 0.866 0.883

Uninterestinginteresting 0.622 0.723

Unpleasantpleasant 0.806 0.824

Low-qualityhigh-quality 0.718 0.698

Intention to enter (based onSpears and Singh, 2004)

How likely would you be to enter the store/salon shown in the picture? 0.960 0.83 0.968 0.91

Denitely notdenitely 0.888 0.931

Not very likelyvery likely 0.931 0.983

Probably notprobably 0.915 0.946

Intention to browse (based onSpears and Singh, 2004)

How likely would you be to browse in the store shown in the picture? 0.971 0.86

Denitely notdenitely 0.913

Not very likelyvery likely 0.942

Probably not

probably 0.923

Purchase intention (based onSpears and Singh, 2004) 0.964 0.84

How likely would you be to buy something from the store shown in the picture?



Probably notprobably 0.901

Crowding perception (Machleit et al., 1994) 0.905 0.73 0.910 0.77

The store/salon seems very crowded to me. 0.831 0.814

The store/salon is very busy. 0.919 0.959

There are a lot of customers in the store/salon. 0.808 0.854

Intention to recommend (based on Spears and Singh, 2004) 0.951 0.87

How likely would you be to recommend this salon to a friend?



Probably notprobably 0.894

107Y. Pan, J.C. Siemens / Journal of Business Research 64 (2011) 105112


4/8

Finally, a manipulation check was conducted to ensure the treatment

of store crowding was effective. A one-way ANOVA was signicant,

and LSD post-hoc tests showed signicant differences across all three

levels of crowding (F (2, 266)=114.68, pb0.001; Mlow crowd =2.2,

Mmid crowd =2.6 and Mhigh crowd =4.8). This indicates that the

crowding manipulation was successful.

3.3. Results

MANOVA with LSDpost-hoccomparisonswas used for all analyses

unless otherwise noted. The overall MANOVA was signicant for both

crowding level (F(10, 468)= 3.32,pb0.001) and time pressure (F(5,

234)=11.48,pb0.001, seeTables 3 and 4). In support ofH1, subjects

exhibited a greater intention to enter the store under the medium

density condition compared to the low or high crowding conditions

(pb0.001). Furthermore, intentions to enter the store increased

between the low and medium density conditions but then decreased

as density reached a high level. Post-hoc comparisons revealed

signicant differences in intentions to enter thestorebetween thelow

and medium crowding conditions, and the medium and highcrowding conditions (p'sb0.001). Intention to enter the store did

not signicantly differ between the low and high crowding condi-

tions. This demonstrates support for the inverted-U relationship

proposed inH1.

Supporting H2a, intentions to browse were greatest under

conditions of medium crowding. Intentions to browse increased

between the low and medium density conditions, but then decreased

in the high crowding condition. Post-hoc comparisons showed a

signicant difference in intentions to browse between the low and

medium crowding conditions and between the medium and high

crowding conditions (p'sb0.001, Fig. 1). Furthermore, individuals

were equally likely to browse in the store under conditions of low and

high crowding (ns).

Intolerance for crowding was signicantly correlated with thedependent measure of intentionto browse, and thus wasassessedas a

covariate. ANCOVA results showed a signicant effect of crowding

level on intention to browse (F (2, 255)=9.31, pb0.001). Respon-

dents intended to browsesignicantly longerwhen crowding wasat a

medium level compared to either low or high crowding levels

(p'sb0.001,Mlow =9 min,Mmed =14 min,Mhigh =11 min). This sup-

portsH2b.

A signicant effect of retail crowding on attitudes toward the store

was also found (pb0.01). Despite an inverted-U shaped pattern,

attitudeswere equally positive in both the low and medium crowding

conditions. Post-hoc comparisons demonstrated that store attitudes

were signicantly different between the medium and high (pb0.001)

and the low and high crowding conditions (pb0.05), but were not

signicantly different between the low and medium crowding

conditions (seeFig. 1).

Finally, the results indicated a signicant effect of retail crowding

on purchase intentions (pb0.001). Post-hoc comparisons showed a

signicant difference between low and medium crowding conditions,

and between the medium and high crowding conditions (p'sb0.001,

Fig. 1), whereas individuals were equally likely to make a purchase

under conditions of low and high crowding (ns). Therefore,H4is also

supported. The overall MANOVA shows no interaction between

crowding and time pressure (p =0.13), refuting H5. Regardless ofwhether subjects were in a hurry, they always preferred the medium-

crowded store.

3.4. Discussion

Although they only examine two levels of human density

crowding,Machleit et al. (2000) lead to speculation about a oor

and ceiling effectof retail crowding. Eroglu et al. (2005) also propose

that some level of human crowding might positively affect shopping

satisfaction. However, neither of these studies assesses the boundaries

of this effect or empirically substantiates the claim. Most prior

research on crowding compares two crowding dimensions: low

versus high (e.g., Eroglu and Machleit, 1990; Rustemli, 1992; Machleit

et al., 2000). These studies have led to the general conclusion that asretail density increases, consumers encounter conditions that nega-

tively affect their shopping experience.

The results of study 1 show that a medium level of crowding may

actually be optimum for retailers. In fact, the effect of retail crowding

on subjects' intentions to enter the store, intentions to browse in the

store, length they intended to browse, and purchase intentions each

exhibited a curvilinear trend, where shopping outcomes became most

positive when retail density was moderate. As retail density reaches a

high level, consumers start to exhibit more negative attitudes and

shopping intentions. These ndings have implications for retailers

who attempt to maximize consumers' shopping experience and their

perceptions of the store. Factors such as store layout and other

atmospherics can affect the level of perceived crowding, and can be

manipulated to create an optimal shopping environment.

Table 4

Experiment 1 mean values.

No time pressure Time pressur e

Dependent measures Low

crowd

Medium

crowd

High

crowd

Low

crowd

Medium

crowd

High

crowd

Store attitude 5.1 5.1 4.3 3.6 4.3 3.8

Purchase intention 3.1 3.7 2.8 3.4 4.1 3.1

Intention to enter 3.7 4.5 3.4 3.6 4.3 3.4

Intention to browse 3.6 4.4 3.6 3.3 4.3 3.3

Length of browse 11.1 14.0 8.7 11.4 14.8 8.5

Note. Higher means indicate more favorable ratings. Length of browse means are

reported in minutes.

Fig. 1.Experiment 1 results.

Table 3

Experiment 1 ANOVA/ANCOVA results.

F-values anddffor dependent variables

Independent variables Store

attitude

Intention

to enter

Purchase

intention

Intention

to browse

Length of

browse

Covariate:

Crowd tolerance 5.95a

(1, 255)

Crowding level 6.04

b

9.14

c

10.86

c

9.52

c

9.31

c

(2, 255) (2, 245) (2, 245) (2, 244) (2, 255)

Time pressure 43.51c 0.46 3.84 1.21 0.04

(1, 255) (1, 245) (1, 245) (1, 244) (1, 255)

Time pressure

crowding

4.51a 0.01 0.25 0.11 0.02

(2, 255) (2, 245) (2, 245) (2, 244) (2, 255)

a pb0.05.b pb0.01.c pb0.001.



5/8

A potential limitation ofExperiment 1 istheuseof onlyonetype of

retail environment. While Experiment 1 investigated the effects of

retail density within a store selling a tangible product (clothing),

there may be conditions inherent to a goods setting that are different

from a service setting. For example, in a crowded retail store selling

tangible products, a consumer may make inferences about how

difcult it would be tond what they are looking for, get waited on by

the salesperson, or check out with their purchase. This may explain

why individuals view a high level of retail crowding as undesirable.But do the same inferences occur in service settings? Whether

consumers are purchasing a good or service may determine the

degree of informational value of the store environmental cues.

According to Baker (1998), store environment is more likely to have

a pivotal role in the purchase of services for two reasons. First, the

intangible nature of services makes it harder for consumers to assess

value obtained before purchase and consumption. Second, because a

service involves simultaneous production and consumption,consumers

often have to enter the service facility. Therefore, retail density not only

provides extrinsic cues to consumers, it also becomes an intrinsic cue

that forms parts of the total service experience.

Well-established differences between tangible and intangible

products lead to a general expectation that reasons for patronizing a

store in a service setting might differ from those in a goods setting. For

example, services are generally inseparable from the service provider,

do not have inventory, and are nonperishable. In service settings, one

could make an appointment for service, reducing the likelihoodthat the

level of crowding will affect the timeliness of service. Furthermore, in

goods settings, customers mayfeel like they are essentially competing

for products (i.e., if there are too many people in the store, it might

reduce the chance that they get the product they want). Previous

research suggests that products/stores desirability may decrease as a

resultof productscarcitybecausebuyers cannotachieve a high degreeof

personal uniqueness (e.g.,Gierl et al.,2008; Worchel, 1992). Consumers,

by buying a product, equate themselveswithmany other owners of the

same product. The need to compete with fellow shoppers for a product

may inhibit many from patronizing a crowded retail outlet. However,

the outlook for a crowdedservice provider canbe very different. Ponset

al. (2006) nd that conditions of high human density may enhance,rather than harm, consumer experience in leisure services.

Among the few studies that have mentioned an enhancement of

the service experience as a result of the presence of other consumers

in the service setting (e.g., Eastman and Land, 1997; Holt, 1995; Pons

et al., 2006; Price et al., 1995), all have essentially dealt with

experiential and hedonic consumption (e.g., sports spectatorship,

bars). We suspect that the positive inuence of crowds on consumers'

service experience can be extended beyond the context of hedonic

services, for the following reasons. First, services cannot be invento-

ried and demand may not be easily predicted. Waiting before

receiving a service is a common experience (Baker and Cameron,

1996), and customersmay be more tolerant of crowding in stores that

offer services than those that carry goods. Second, retail density in a

service setting signals the desirability of services provided, with anunderstanding that they do not necessarily need to face the

dysfunctional consequences of crowding. For example, shoppers can

avoid the adverse effects of crowding by setting an appointment and

visiting the store at a less busy time. Third, there is less likelihood that

consumers need to compete for a service. Unlike goods that typically

come in limited supply, services can last forever as long as the

provider continues its operation. In a goods setting, scarcity increases

with each sold unit. Shoppers may undergo pressure to strive for the

goods when they perceive a limitation of available units. Scarcity due

to supply is not a concern for services. Therefore, we propose that

shoppers' adaptation strategy will differ in a highly crowded service

context. Avoidance behavior is less likely to occur, and favorable

feelings towards a service provider are more likely to be evoked.

Hence, the relationship between crowding and satisfaction will be

linear in a service setting, where consumers' affective evaluation

becomes more positive as density increases. Specically, we propose:

H6. In the service setting, the effect of retail density on intentions to

enter the store will follow a linear trend. That is, intentions to enter

the store will increase as the level of retail density increases.

H7. In the service setting, the effect of retail density on intention to

recommend the store to a friend will follow a linear trend. That is,

intention to recommend will increase as the level of retail densityincreases.

H8. In the service setting, the effect of retail density on attitudes

toward the store will follow a linear trend, where attitudes become

more positive as the level of retail density increases.

We chose to again assess the time pressurecrowding interaction

in the service setting. As argued earlier, the negative effects of

crowding may be more pronounced when consumers are under time

constraints than when they are not. Therefore, we propose the

following hypotheses:

H9. In the service setting, time pressure will interact with retail

density. For shoppers under time pressure, the effect of retail densityon their

H9a) intention to enter the store,

H9b) intention to recommend the store, and

H9c) attitudes toward the store will exhibit an inverted-U

shaped trend. That is, subjects will prefer a medium level

of crowding when they are under time constraints.

While attitude toward and intention to enter the store remain

consistent with those tested in Experiment 1, here the variable

intention to recommend was included to assess shoppers' behav-

ioral consequences. This variable replaces two outcome variables (i.e.,

purchase intention and intention to browse in the store) used in

Experiment 1, due to the unique features of a service environment

(e.g., consumers aren't likely to browse in a service setting, andpurchase intention is assumed based on entrance to the store). This

outcome variable is more appropriate for use in a service setting.

4. Experiment 2

4.1. Pretest

A pretest was conducted to test the manipulation of crowding in

the service setting (n =59). As inExperiment 1, three photographs of

a hair salon were selected. Pretest subjects saw a photograph andread

a description of a service scenario ranging from one to four customers.

The low crowding condition showed and described only one salon

customer. The medium crowding condition showed and described

one customer waiting to get her nails done and one customer havinghis hair washed.The high crowding condition showed and described

onecustomerwaiting to get her nails done, one getting a haircut, one

getting his hair washed, and still another customer waiting to be

served. Perception of crowding was measured with a three-item,

seven-point likert scale (Cronbach alpha=0.79). Subjects perceived

signicant differences in crowdedness across the three treatment

levels (F(2, 56)=27.64,pb0.001).

4.2. Subjects, design and procedure

Experiment 2 employed a 3 (low, medium, and high crowding)2

(time-pressured vs. non-time-pressured) between-subjects design.

Participants were faculty, staff and students at two Midwestern

universities. Subjects were sent a mail survey to their campus address



6/8

and were instructed to return the completed questionnaire in a self-

addressed, postage-paid envelope. As an incentive to participate,

respondents were told that their names would be entered into a

randomdrawing for one of three $30 gift cards. Of the 750 mailed, 403

completed surveys were returned (53.7%).

Subjects were shown one of the three pretested photos of the hair

salon and were instructed to imagine themselves in the following

scenario:It is a Saturday and you are at a new salon to get a haircut.

You have plenty of time and are in no hurry

(for the no time pressurecondition). Subjects in the time pressure condition read, You're

pressed for time and are in a hurry. After reading the scenario and

viewing the picture, subjects were instructed to complete a survey

that included the dependent measures.

4.3. Dependent measures

The same scale was adapted from Experiment 1 to measure

attitude towards the hair salonand intentions to enter the salon.

Subjects were also asked to evaluate whether they would recommend

the salon to a friend using a 3-item, 7-point likert scale (see Table 2).

We conduct CFA to assess convergent and discriminant validity of

the measurement model. Fit indices provide evidence of a reasonably

good t (2(84)=266.25, pb0.001, CFI =0.982, SRMR= 0.051,

RMSEA=0.074). All items signicantly and substantially load onto

the expected latent construct. AVE and composite reliability are

calculated, with all above the recommended thresholds, conrming

convergent validity of the measurement model. Within the CFA

setting, discriminant validity is assessed by calculating the shared

variance between constructs and verifying that it is lower than the

variances extracted for the constructs involved. All AVEs are larger

than any squared correlation among constructs (largest squared

correlation=0.42), suggesting that discriminant validity is achieved.

4.4. Results

The overall MANOVA with LSD post-hoc comparisons indicates

signicant effects for crowding level (F(6, 786)=4.63, pb0.001),

time pressure (F(3, 393)=13.0,pb0.001), and their interaction (F(6,786)=7.99, p b0.001). This suggests that the two treatment factors,

separately and in combination, affect shoppers' evaluation of the store

and their behavioral intentions.

Gender is signicantly correlated with subjects' intentions to enter

the salon (i.e., females were more likely to enter the salon), but is not

signicantly correlated with the treatment conditions. As such,

gender is used as a covariate in the analyses for intentions to enter

the salon. Age is signicantly correlated with subjects' intentions to

enter, their intention to recommend, and their attitude toward the

salon. Therefore, age is used as a covariate in the analyses for these

variables.

ANCOVA results show that the level of crowding signicantly

affects intention to enter the salon (pb0.001,Table 5). However, this

main effect was qualied by the signicant interaction of time

pressure and crowding level. For the no time pressurecondition, no

signicant differences were found across the three levels of crowding,

despite a linear, upward trend (see Table 6 for means). Thus, H6 is not

supported.

Crowding level did emerge as an important factor for predicting

subjects' intentions to recommend the salon to a friend. Subjects

under no time constraints were more willing to recommend a salon

that was more crowded compared to less crowded (Mlow crowd =3.5,

Mmid crowd =3.9,Mhigh crowd =4.2). Means were signicantly different

between the low, medium and high conditions (p'sb0.05). Therefore,

H7is supported.

Furthermore, signicant differences were found in store attitudes

between the three levels of crowding (p'sb0.05). In contrast to the

curvilinear relationship portrayed in the goods setting, subjects under

no time constraints view the service provider more favorably when

the salon is more crowded, showing an upward, linear relationship.

This supportsH8.

In support ofH9, a signicant interaction of crowding and time

pressure was found across all three ANCOVA models (p'sb0.001). As

predicted, under conditions of time pressure, individuals generally

preferred a salon with medium levels of crowding. Post-hoc

comparisons revealed that under time pressure, intentions to enter

the salon were signicantly more favorable with medium crowding

compared to eitherlow (pb0.05) or high (pb0.001) crowding (Fig. 2).

This supports H9a. Similarly, under time pressure subjects had a more

positive intention to recommend the salon when crowding was at amedium level compared to either low (pb0.05) or high (pb0.001)

levels of crowding, supporting H9b (Fig. 3). Finally, under time

pressure, attitudes were most favorable when the store exhibited a

medium level of crowding compared to low or high (p'sb0.001,

Fig. 4). This supports H9c.

4.5. Discussion

Most studies on retail density associate crowding with negative

affective evaluation of a given dense situation. Only recently, a few

researchers suggest that in the context of some hedonic services (e.g.,

disco, bar, and sports game), crowding may favorably inuence

consumers' service experience (Arnould and Price, 1993; Holt, 1995;

Table 5

Experiment 2 ANCOVA results.

F-values anddffor dependent variables

Independent variables Store

attitude

Intention to

enter

Intention to

recommend

Covariate:

Gender 17.58a(1, 393)

Age 45.12a (1, 393) 11.97a (1, 393) 21.14a (1, 393)

Crowding level 14.36a (2, 393) 6.87a (2, 393) 5. 64b(2, 393)

Time pressure 6.2c (1, 393) 2.56 (1, 393) 5.25c (1, 393)

Time pressurecrowding 24.78a (2, 393) 10.64a (2, 393) 6.93a (2, 393)

a pb0.001.b pb0.01.c

pb

0.05.

Table 6

Experiment 2 mean values.

No time pressur e Time pr essure

Dependent measures Low

crowd

Medium

crowd

High

crowd

Low

crowd

Medium

crowd

High

crowd

Store attitude 4.2 4.6 5.0 4.6 5.4 4.0

Intend to recommend 3.5 3.9 4.2 4.4 5.0 4.0

Intention to enter 4.1 4.2 4.4 4.1 4.8 3.4

Note. Higher means indicate more favorable ratings.

Fig. 2.Experiment 2 results for intention to enter.



7/8

Ponset al, 2006). This study explores the consumption setting of a less

experiential, less hedonistic service (hair salon). Findings suggest the

presence of positive consumer responses and the potential contribu-

tion of fellow participants in creating heightened expectation for the

service experience.

Experiment 2suggests that retail density in a service context may

enhance product desirability, especially for those shoppers under no

time pressure. This study sheds light on several distinct differences

between goods and service settings that ultimately affect customers'

interpretation of the crowded retail environment. Unlike goods,

quantitative scarcity due to shortage of supply is not a problem for

services. Theoretically, the supply of a service is endless (whereas a

good can be sold out), as long as the service provider remains in

business. Furthermore, a service can be arranged to be delivered at a

time of a shopper's choice. Therefore, in a service encounter where

crowding exists, shoppers may not pursue the avoidance strategy as

they would do in a goods setting. Shoppers have more cognitive

control, knowing that they are not competing against others for a

commodity of limited availability. Thus, the negative psychological

consequences caused by retail density (e.g., emotional discomfort,

stress) can be largely avoided in a service setting. Furthermore, high

retail density may cultivate a perception of a high demand for the

service, thus increasing the service provider's attractiveness in theview of shoppers. A caveat to these ndings is when the consumer is

pressed for time. When consumers are in a hurry, they typically prefer

a hassle-free retail environment that does not work against

completing a shopping task in a timely manner. Conversely, they

are less favorable towards the store when it exhibits a high level of

crowding. Nevertheless, they still do not favor low levels of crowding,

even when under time pressure.

5. General discussion and future research

Based on the ndings of two experiments, it appears that intrinsic

attributes of goods versus service settings might dominate the result

of a patronage decision. Shoppers rely on heuristic information in

deriving judgments about the desirability of a retail store. Retail

density can be interpreted as a heuristic cue. The effect of retail

density on consumers' store evaluations and purchase intentions,

however, largely hinges upon the retail setting. When confronted

with crowding, shoppers may apply different heuristic decision rules

in a goods vs. a service setting. In a goods setting, a moderately

crowded store proves to generate the most favorable store attitudes

and patronage intentions. However, shoppers seem to be more

tolerant of retail density in a service context, as they, when notpressed for time, tend to favor a crowded store than a less crowded

one.

As there are many options relevant for retailers to generate store

trafc, it seems benecial to identify theconditions under which retail

density should be applied as an instrument for enhancing favorable

store attitudes and patronage behavior. The main result of our

empirical investigations is that retail settings can inuence the

direction of the effect of crowding on store desirability. Retail

executives may nd this information particularly relevant and

interesting, as it could help them determine an optimal crowding

strategy that is most likely to stimulate favorable valuation and

purchase intent.

Another interesting question concerns the effect of the three

crowding levels on store image formation. Perhaps environmental

designs can be devised that could enhance store image and consumer

satisfaction by aiding shoppers to adapt to human and spatial

crowding in situations where store trafc is higher than usual (e.g.,

at peak selling times). Specic decisions such as store layout could

directly affect one's perception of retail density, thereby inuencing

attitudes and behavioral intentions.

Future research may look into the psychological effects of retail

density. Our study suggests different patterns of crowding effects in a

goods and a service setting. A logical continuation of this research is to

investigate consumers' reasoning behind their patronage decisions.

For example, researchers can explore how consumers apply extrinsic

cues to form a value assessment and make a choice decision. This

might be accomplished through the use of open-ended surveys or

interviews with consumers.

Another area of further research is the interaction of productscarcity with retail density effects. Crowdedness in a store can

produce an image of strong demand for a product, which can act as a

double-edged sword. On one hand, consumers may foster a more

favorable attitude, such as high assumed quality, and thereby

increased value of the product. On the other hand, they may perceive

a quantitative scarcity due to demand, as they can view this as an

indication of a high number of other people who will own the same

product. Therefore, a high degree of uniqueness can no longer be

achieved by purchasing this product. An individual may lose their

sense of autonomy or distinctiveness (Vecchio and Sussmann, 1981).

In this case, the negative effect of product scarcity can be dominant.

An empirical testing of how scarcity effects moderate the effect of

retail density on store patronage would provide many interesting

insights.

References

Arnould EJ, Price LL. River magic: extraordinary experience and extended serviceencounter. J Consum Res 1993;20:2445.

Bagozzi R, Yi Y. On the evaluation of structural equation models. J Acad Mark Sci1988;16:7494.

Baker J. Examining the informational value of store environments. In: Sherry JF, editor.ServiceScapes. Chicago: NTC/Contemporary Publishing; 1998. p. 5579.

Baker J, Berry L, Parasuraman A. The marketing impact of branch facility design. J RetailBank 1988:3342 July.

Baker J, Cameron M. The effects of the service environment on affect and consumerperception of waiting time: anintegrative review and research propositions. J AcadMark Sci 1996;24(4):33849.

Baker J, Grewal D, Parasuraman A. The inuence of store environment on qualityinferences and store image. J Acad Mark Sci 1994;22(4):328 59.

Barker R. Ecology and motivation. Nebr Symp Motiv 1963;8:1-50.

Fig. 3.Experiment 2 results for Intention to recommend salon.

Fig. 4.Experiment 2 results for Attitude toward salon.



8/8

Bateson J, Hui M. The ecological validity of photographic slides and videotapes insimulating the service setting. J Consum Res 1992;19(2):271 81.

Bitner MJ. Evaluating service encounters: the effects of physical surroundings andemployee responses. J Mark 1990;54:6982 April.

Bloom PN. Transmitting signals to consumers for competitive advantage. Bus Hor 1990:5866 July/August.

Eastman ST, Land AM. The best of both worlds: sports fans nd good seats at the bar. JSport Soc Issues 1997;21:15678.

Eroglu S, Harrell G. Retail crowding: theoretical and strategic implications. J Retail1986;62:34663.

Eroglu SA, Machleit KA. An empirical study of retail crowding: antecedents and

consequences. J Retail 1990;66(2):201

20.Eroglu SA, Machleit KA, Barr TF. Perceived retail crowding and shopping satisfaction:the role of shopping values. J Bus Res 2005;58(8):114653.

Fornell C, Larcker DF. Evaluating structural equation models with unobservablevariables and measurement error. J Mark Res 1981;28:39 50 February.

Gierl H, Plantsch M, Schweidler J. Scarcity effects on sales volume in retail. Intl RevRetail, Distrib Consum Res 2008;18(1):4561.

Golden LL, Albaum G, Zimmer M. The numerical comparative scale: an economicalformat for retail image measurement. J Retail 1987;63(4):393410.

Grossbart S, Hampton R, Rammohan B, Lapidus RS. Environmental dispositions andcustomer response to store atmospherics. J Bus Res 1990;21(3):225 41.

Harrell GD, Hutt MD, Anderson JC. Path analysis of buyer behavior under conditions ofcrowding. J Mark Res 1980;17:4551.

Holt D. How consumers consume: a typology of consumption practices. J Consum Res1995;22:1-21.

Jain U. Effects of population density and resources on the feeling of crowding andpersonal space. J Soc Psychol 1987;127(3):1318.

Machleit KA, Kellaris JJ, Eroglu SA. Human versus spatial dimensions of crowdingperceptions in retail environments: a note on their measurement and effect onshopper satisfaction. Mark Lett 1994;5(2):18394.

Machleit KA, Eroglu SA, Powell Mantel S. Perceived retail crowding and shoppingsatisfaction: what modies the relationship. J Consum Psychol 2000;9(1):2942.

Michon R, Chebat JC, Turley LW. Mall atmospherics: the interaction effects of the mallenvironment on shopping behavior. J Bus Res 2005;58:57683.

Nunnally JC, Bernstein IH. Psychometric theory. New York: McGraw-Hill; 1994.Pons F, Laroche M, Mourali M. Consumer reactions to crowded retail settings: cross-

cultural differences between North America and the Middle East. Psychol Mark2006;23(7):55572.

Pons F, Laroche M. Cross-cultural differences in crowd assessment. J Bus Res 2007;60(3):26976.Price LL, Arnould EJ, Deibler SL. Consumers' emotional responses to service encounters:

the inuence of the service provider. Intl J Serv Ind Mgmt 1995;6:3463.Rompay T, Van JL, Galetzka M, Pruyn A, Garcia JM. Human and spatial dimensions of

retail density: revisiting the role of perceived control. Psychol Mark 2008;25(4):31935.

Rustemli A. Crowding effects of density and interpersonal distance. J Soc Psychol1992;132(1):518.

Spears N, Singh SN. Measuring attitude toward the brand and purchase intentions. JCurr Iss Res Advert 2004;26(2):5366.

Stokols D. A typology of crowding experience. In: Baum A, Epstein YM, editors. Humanresponse to crowdingHillsdale, NJ: Earlbaum; 1978. p. 21955.

Vecchio R, Sussmann M. Stafngsufciency and jobenrichment:supportfor an optimallevel theory. J Occup Behav 1981;2(3):17787.

Worchel S. Beyond a commodity theory analysis of censorship: when abundance andpersonalism enhance scarcity effects. Basic Appl Soc Psychol 1992;13(1):7992.


articol Despre Aglomeratie

Documents

Transcript of articol Despre Aglomeratie