Journal of Retailing and Consumer Services 19 (2012) 419–428

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Store environment’s impact on variety seeking behavior

Geetha Mohan a,n, Bharadhwaj Sivakumaran b,1, Piyush Sharma c,2

a Assistant Professor, SSN School of Management and Computer Applications, Chennai, Indiab Great Lakes Institute of Management, Chennai, Indiac Department of Management and Marketing, Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

a r t i c l e i n f o

Available online 3 May 2012

Keywords:

Variety seeking behavior

Positive effect

Optimum stimulation level

Store environment

Structural equation modeling

89/$ - see front matter & 2012 Elsevier Ltd. A

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esponding author. Tel.: þ91 44 2747 5063; fa

ail addresses: geetham18@gmail.com (G. Moh

reatlakes.edu.in (B. Sivakumaran),

sh@inet.polyu.edu.hk (P. Sharma).

l.: þ91 44 30809210; fax: þ91 44 30809011

l.: þ852 2766 7367; fax: þ852 2765 0611.

a b s t r a c t

This paper explores the influence of store environment on variety seeking behavior with a model

incorporating various components of store environment (music, light, assortment, employee, and

layout) and personality variables, optimum stimulation level (OSL) and deal proneness. Using a mall

survey with shoppers in Dubai, the study establishes that store environment, OSL and deal proneness

affect variety seeking positively. This paper extends extant literature by studying comprehensively the

impact of store environment on variety seeking. This research suggests that retailers need to invest in

the components of store environment to enhance variety seeking. Methodologically, the model

incorporates the Schmid–Leiman factor structure to address the limitations posed by reflective models.

& 2012 Elsevier Ltd. All rights reserved.

1. Introduction

Variety seeking behavior is the tendency of an individual toseek change over time and it continues to attract attention in theretail and shopping context (e.g., Givon, 1984; Sharma et al.,2010a, b). The variety seeking literature deals with issues such asthe determinants of variety seeking (e.g., Van Trijp et al., 1996),models of variety seeking (e.g., McAlister, 1982) and its relation-ship with marketing phenomena like segmentation (Trivedi,1999) and market share (Feinberg et al., 1992).

Prior research shows that various personality, product categoryand situational variables may drive variety seeking. Personalityvariables include optimum stimulation level (Steenkamp andBaumgartner, 1992), deal proneness (Martınez and Montaner,2006), self-monitoring (Ratner and Kahn, 2002) and goal orienta-tion (Wu and Kao, 2011). Product category-level characteristicssuch as involvement (subjective personal relevance of a product)and hedonic features (features in products that cause sensorygratification and give pleasure) also affect variety seeking (VanTrijp et al., 1996). Similarly, situational factors such as positiveaffect (Kahn and Isen, 1993), public scrutiny of choices (Ratner andKahn, 2002) and activation of negative concepts like ‘‘boredom’’(Fishbach et al., 2011) impact variety seeking.

ll rights reserved.

x: þ91 44 22574552.

an),

.

On the other hand, there is growing interest in enhancing storeenvironment as retailers are going the extra mile in making theretail ‘‘experience’’ a key differentiator. A store’s environmentinfluences the quantity of purchase, store liking, time and money(Sherman et al., 1997), quality and evaluation of merchandise(Baker et al., 1994), sales (Milliman, 1982), product evaluation(Wheatley and Chiu, 1977), satisfaction (Bitner, 1990) and storechoice (Darden et al., 1983). However, most studies deal with therole of store environment explore the influence of its variousdimensions independently such as store employees (Hu andJasper, 2006), store convenience and quality (Vahie and Paswan,2006), store trustworthiness (Heijden and Verhagen, 2004), andin-store graphics with social meaning (Hu and Jasper, 2006).

Prior research links store environment to various aspects ofconsumer behavior. For instance, music influences time and moneyspent positively (Milliman, 1982, 1986) and lighting influences thehandling (touching and feeling products) and purchase of itemspositively (Areni and Kim, 1994). Donovan et al. (1994) find thatstore atmosphere drives pleasure and spending of time and moneyin a store. Spies et al. (1997) highlight the importance of a goodstore layout. Baker et al. (2002) show that various aspects of storeenvironment influence store patronage. Store layout, ambience andsales personnel may also influence unplanned buying (Shermanet al., 1997; Geetha et al., 2010).

There is also growing evidence about the influence of certainfacets of store environment on variety seeking behavior. Forexample, assortment drives variety seeking positively (Moraleset al., 2005; Krishen et al., 2010). Maimaran and Wheeler (2008)find that ‘‘exposure to novel visual stimulus arrays of geometricshapes affects consumers’ real choices among products’’. In otherwords, the type of array of products in a store affects variety

G. Mohan et al. / Journal of Retailing and Consumer Services 19 (2012) 419–428420

seeking. Levav and Zhu (2009) find that spatially confined con-sumers make varied choices. However, despite such evidence,most studies link only a few aspects of store environment to thevariety seeking behavior, instead of exploring the overall impactof store environment on variety seeking.

In line with Mattila and Wirtz (2001), this study defines storeenvironment as a combination of various constituent variablesnamely lighting, music, scent, sales personnel and layout that isin gestalt/composite fashion. We treat variety seeking as ‘‘thetendency of an individual to seek variety or change in choicesover time, for the purchases made within the product class’’(Kahn, 1995). Sharma et al. (2010a) show that both impulsebuying and variety seeking are examples of hedonic and explora-tory purchase behaviors with similar socio-psychological origins,Geetha et al. (2010) suggest that store environment drivesimpulse buying. Hence, we propose that store environment islikely to drive variety seeking behavior as well and this studyintends to test this thesis.

Research has also shown that consumer behavior is bestexplained by considering personality and situational factors(Russell and Mehrabian, 1976). Therefore, we consider somepersonality variables as well. We would also consider not justone or two elements of stores that affect variety seeking but storeenvironment as a whole or in gestalt terms since shoppers look atstores in this way (Ward et al., 1992; Mattila and Wirtz, 2001).Zimmer and Golden (1998) as well as Keaveney and Hunt (1992)also refer to the gestalt (or holistic) nature of the store environ-ment construct.

In view of the above, it is important to study these dimensionscollectively as consumers do not perceive a store in piecemeal(Ward et al., 1992; Bitner, 1992) and it is the total configuration ofcues (gestalt of consumers’ perceptions of store) that influenceconsumer responses (Mattila and Wirtz, 2001; Holahan, 1982).Consumers look to the total collection of cues in the environmentto decode meanings and to structure their behavior accordingly(Solomon, 1983). Therefore, this paper develops a comprehensivemodel that links store environment as a whole (and not just oneor two aspects of stores) with variety seeking.

From a managerial viewpoint, this research addresses animportant question: does store environment induce variety seek-ing? If the answer is yes, retailers then have one more additionalreason (apart from patronage, loyalty, store choice and so on) toinvest in store environment. If the answer is no, the retailer canspend this money on other marketing activities like promotionsand price cuts, rather than investing in the antecedents of storeenvironment.

2. Conceptual framework and hypotheses development

This section presents hypotheses linking store environment,select personality variables and variety seeking behavior. Speci-fically, the model studies the impact of both store-level (situa-tional) factors (store environment consisting of lighting, music,scent, layout, assortment and employees) and two personalityvariables (deal proneness and optimum stimulation level) onvariety seeking though a mediator, positive affect.

2.1. Positive affect and variety seeking behavior

Positive affect refers to the pleasantness of the affectiveexperience. Positive affect reflects the extent to which a personfeels enthusiastic, active, and alert. It makes the consumers staylonger in the store and induces increased browsing (Beatty andFerrell, 1998) that could lead to variety seeking (Kahn and Isen,1993). Hence:

H1. Positive affect has a positive impact on variety seekingbehavior.

2.2. Store environment

Store environment comprises ambient (e.g., lighting, scent, andmusic), design (e.g., layout, assortment) and social factors (e.g.,presence and effectiveness of salespersons) (Baker et al., 2002).Ambient factors in turn comprise light, scent and music. Designfactors consist of layout and assortment (Baker et al., 2002).Layout refers to the way in which products, shopping carts, andaisles are arranged, the size and shape of items, and the spatialrelationships among them. Layout also includes space design andallocation, grouping and placements of the merchandise. Productassortment is the total set of items a retailer offers, reflecting thebreadth and depth of product lines. Social factors refer to othershoppers and salespersons or the relevant people in the environ-ment (Baker et al., 2002). Other shoppers are not directly underthe control of the retailer and this study does not include them(all other store-level factors are). At best, retailers can organizequeuing procedures efficiently and schedule cashiers appropri-ately. For the most part, they cannot control crowding in aisles.

2.2.1. Store environment and positive affect

According to Yalch and Spangenberg (1990), shoppers respondpsychologically and behaviorally to music. These responses occurpredominantly at a subconscious level. Music is an important,frequent and common variable that influences mood (Bruner,1990). Research has shown that the presence of pleasant musicproduces positive affect (Garlin and Owen, 2006). Well-designedlighting systems can enhance a store’s interior, guide the custo-mer’s eyes to key sales points, create an atmosphere of excite-ment and induce positive affect (Smith, 1989). Lighting and musictogether evoke positive affect (Yoo et al., 1998). Positive experi-ences arise if the store is easy for shoppers to find the productthey are looking for, when the layout of the store seems logicaland when there are sufficient signs in the store (Bitner, 1992;Spies et al., 1997). Layout makes the shopping enjoyable andproduces positive affect. Design factors reduce the perceivedstress in shopping (Baker et al., 2002) and evoke positive affect(Yoo et al., 1998). Consumers prefer a large assortment as it helpsthem tide over uncertainty about future preferences (Simonson,1990). Therefore, they evaluate larger assortments more posi-tively (Broniarczyk et al., 1998).

Store personnel contribute to entertaining store experiencesespecially when the staff has a capacity to offer good service or toallow consumers to shop without being under constant surveil-lance (Jones, 1999). Employee responses can significantly influ-ence important consumer responses (Bitner, 1990). For instance,if a store employee helps a shopper find a particular product that(s)he is looking for, the latter in turn becomes happy.

Positive in-store experiences results when salespersons makean extra effort and stretch beyond ‘normal’ service levels. Salespersonnel’s assistance may enhance even the utilitarian shopper’senjoyment. Even in brief and mundane encounters the employeeinduces positive affect (Mattila and Enz, 2002).

H2. Overall perception of store environment has a positiveimpact on positive affect.

2.2.2. Store environment and variety seeking behavior

Music is an important non-verbal communication that enhancesstore atmosphere (Turley and Milliman, 2000). Good music stimu-lates additional sales (Milliman, 1982). This additional sale couldlikely be impulse buying (Geetha et al., 2010) and also, buying moreof the same brand or increasing the variety of the purchases.

G. Mohan et al. / Journal of Retailing and Consumer Services 19 (2012) 419–428 421

Moreover, impulse buying is related positively to variety seeking(Sharma et al., 2010a, b). The pleasantness of the music could workunconsciously as the consumers spend more time in the store,browsing more and evaluating different alternatives. Additionalbrowsing increases search and this in turn positively relates tovariety seeking (Steenkamp and Baumgartner, 1992). Browsing alsoincreases impulse buying, which in turn correlates positively withvariety seeking (Sharma et al., 2010a, b, 2011).

Well-designed lighting systems create an atmosphere of exci-tement, induce positive mood, make key approach areas safe andvisible and guide the customer’s eyes to key sales points (Smith,1989), thereby providing the consumer an opportunity to viewmore choices. Lighting affects the number of items a shopperhandles and examines (Areni and Kim, 1994). Greater the numberof items held, greater is the likelihood of including differentalternatives in consideration sets. Larger consideration sets leadto variety seeking (Sivakumaran and Kannan, 2002). Good light-ing leads to greater browsing and greater information search.Greater information search leads to variety seeking (Steenkampand Baumgartner, 1992).

Consumers spend more money and time in a store that has apleasant fragrance and it makes them more attentive towards thedifferent brands in the store (Spangenberg et al., 1996). This leadsto greater appreciation of different attributes and distinctivenessbetween brands and larger consideration sets, leading to varietyseeking behavior (Sivakumaran and Kannan, 2002).

A good layout facilitates greater in-store exploration. Thishelps shoppers in viewing the assortment within a productcategory and also the availability of different product categories.A good layout will also give the impression of a greater amount ofmerchandise being present than actually present (Morales et al.,2005). In other words, a good layout leads to greater perceivedvariety, which is a key driver of variety seeking (Kahn andWansink, 2004). Product assortment is also a determinant ofvariety seeking (Simonson, 1999). Greater the number of flavorsand brands, greater will be the tendency for consumers to choosevariety (Inman, 2001). There is some recent work that seems tosuggest that confining consumers spatially would drive varietyseeking (Levav and Zhu, 2009). However, we argue against thisline of thought in this paper and propose that a spacious well-designed layout contributes to increased variety seeking.

Timely assistance in finding a product or the alternative orsales person’s helpfulness in making the consumer understandthe features of different alternatives could lead to variety seeking.

Optimum Stimulation

Level

Design Factors

Store Environment

Ambient Factors

DealPronene

SocialFactors

Fig. 1. Proposed model of the impact of store

Employees could stimulate the consumer in exploring the store,guiding him towards various brands, alternatives and therebyinduce variety seeking directly.

H3. Overall perception of store environment has a positiveimpact on variety seeking.

2.3. Optimal stimulation level (OSL)

Variety seeking research in marketing (Steenkamp andBaumgartner, 1992; Van Trijp et al., 1996) draws heavily on thepersonality and psychology bodies of literature to explain person-ality-related characteristics of variety seeking. Mostly, this line ofresearch revolves around the optimum stimulation level (OSL)paradigm (Deci and Ryan, 1985). According to this view, eachindividual has his/her own level of stimulation. This stimulation isthe ‘‘complexity/arousal that associates with a stimulus’’. Oncethe stimulation falls short of an individual’s ‘‘optimum’’ level, s(he) seeks stimulation to restore that to the optimum level. Thus,to attain a satisfactory level of stimulation, a person wouldengage is in ‘‘exploratory behavior’’ of which variety seeking is aspecific manifestation. Individuals with high optimum stimula-tion levels (OSL) are also known to be chronically higher in theirarousal level making them indulge in sensation seeking activities,including variety seeking to achieve their optimum stimulationlevel (Steenkamp and Baumgartner, 1992). Thus,

H4. Optimum stimulation level has a positive impact on varietyseeking behavior.

2.4. Deal proneness

Consumers who seek variety consider promotions a salientattribute in their patronage choice and, as a result, do not intendto re-patronize frequently (Wakefield and Barnes, 1996). Salespromotion can encourage behavioral responses such as brandswitching. Deal proneness is the consumer tendency to respondto sales promotions (Lichtenstein et al., 1995). Deal prone con-sumers are more likely to use the promotional offers, in-store adsand other signages that provide information on the deals availableand switch brands that are available on sale. Deal-prone consumersmodify their purchase behavior so as to benefit from the temporaryincentives of a promotion (Wakefield and Barnes, 1996).

Positive affect

Variety SeekingBehavior

H2H1

H3

H4

H5

ss

environment on variety seeking behavior.

G. Mohan et al. / Journal of Retailing and Consumer Services 19 (2012) 419–428422

Purchase probabilities increase with the increase in the pro-motional effort due to greater preference for the promotedproduct (Trivedi, 1999). Variety seeking allows the consumersto use the promotions strategically and to experiment withdifferent brands over time. Deal prone people use this for ‘‘smartbuying’’ by utilizing good deals. Promotion is an importantmarketing variable (Trivedi and Morgan, 2003) influencing varietyseeking and deal prone consumers are vulnerable to promotions.Hence, the following hypothesis:

H5. Deal proneness has a positive impact on variety seekingbehavior

The above discussion leads us to propose a comprehensivemodel that Fig. 1 summarizes.

3. Method

3.1. Data collection: Survey technique

A single stage mall intercept method was used to collect data.The data collection process followed was broadly in line withprevious studies (Geetha et al., 2010; Sharma et al., 2010a, b). Thesurvey was conducted in the city of Dubai on shoppers of fivepopular grocery stores since variety seeking typically occurs in

Table 1Scale summary.

Scale

Music (Morrin and Chebat, 2005)The store had a pleasant music

The store had a terrible musica.

Scent (Morrin and Chebat, 2005)The store had a pleasant odor/scent.

The store had an appropriate odor/scent

The store had a terrible odor/scenta.

Light (Smith, 1989; Areni and Kim, 1994; Summers and Hebert, 2001)The store is well-lit.

The store is correctly-lit (Neither too bright nor dull).

Lighting in the store is pleasant.

Assortment (Broniarczyk, Hoyer and McAlister, 1998)The store has a wide variety of products.

The store has many brands in most of the product categories.

The store has different price ranges in different products.

Layout (Dickson and Albaum, 1977)It was easy to move about in the store.

It was easy to locate products/merchandise in the store

Employee (Dickson and Albaum, 1977)The store had knowledgeable employees.

The store had friendly employees.

The store had helpful employees

Positive affect (Watson et al., 1988)I felt excited on this shopping trip

I felt enthusiastic while shopping today

I felt happy during this shopping trip

Optimum stimulation level (Steenkamp and Baumgartner, 1992)I like to experience novelty and change in daily routine.

I would like a job that offers change, variety and travel.

I am continually seeking new ideas and experiences.

I like continually changing activities.

I like to find some new and unfamiliar experiences.

Deal proneness (Lichenstein, Netemeyer and Burton, 1995)Buying products in price-off deals makes me feel good.

When I take advantage of ‘‘buy-one-get-one- free’’ offer, I feel good.

I will sometimes switch brands if I can get something for free when I am purchasing

I like to take advantage of special deals I notice in the store.

a Indicates reverse scored items.

grocery shopping (e.g., van Trijp et al., 1996). We chose Dubaisince apart from being convenient, it is a city in Asia with facilitiesand infrastructure on par with the West. It is also a place wherethe ‘‘East meets the West’’ (Hosea, 2007). Dubai is a ‘‘globalizedcity’’ (Mady et al., 2011) with considerable Western influence thathas attracted numerous international visitors (Henderson, 2006).It is a city that has one ‘‘of the world’s most urban multi-culturalenvironments’’. (Sankaran and Demangeot, 2011). A total of 350shoppers were approached out of which 212 agreed to participatein the study. Shoppers were told that this was part of an academicproject on ‘‘shopper behavior’’. Finally this was pruned to 200after removing incomplete responses, thus yielding a responserate of around 57%. This sample size was adequate for the model(Hoelter, 1983; Anderson and Gerbing, 1984; Iacobucci, 2010).

The study covered a wide demographic profile of groceryshoppers. 38% of the shoppers were women and 62% were menand the average age of the shopper was 28.5. 43% of them weresingle and 57% were married. 23% were higher secondary grad-uates, 64% were under graduates and 13% were post graduates.20% of the sample comprised students, 7% were housewives, 6%were self-employed and 66% were employed. Thus, we obtainedconsiderable diversity.

Shoppers were sampled during morning, afternoon and eveninghours on weekdays and weekends. The locations of the interviews,the times of the day and the days of the week were rotated in

Mean SD

2.0 1.0

2.6 1.0

3.5 .83

3.6 .81

3.2 .77

3.8 .48

4.0 .51

3.9 .48

3.5 .74

3.1 .84

3.0 .79

3.6 .87

3.7 .63

3.2 .80

3.2 .76

3.3 .75

3.1 .75

3.3 .76

3.6 .67

2.9 .96

3.5 .73

3.3 .88

3.2 .85

3.0 .87

3.3 .73

3.4 .74

a different brand. 2.8 .91

3.2 .79

Table 2Correlation matrixa.

Music Scent Light Assortment Layout Employee Positive affect Optimum stimulation level Deal proneness

Music .65Scent .15 .76Light .12 .14 .81Assortment .05 � .01 .01 .70Layout .11 .03 .20 .16 .63Employee .16 � .04 .09 .02 .05 .87Positive affect � .03 .01 � .01 � .02 .03 .04 .80Optimum stimulation level .13 .11 .15 .02 .19 � .11 .15 .60Deal proneness .11 .05 .07 .12 .15 .05 � .06 � .01 .71Average variance extracted .68 .65 .63 .64 .62 .76 .66 .54 .61

a Figures on the diagonal show the construct reliabilities of the respective scales.

Table 3Means of variety seeking purchases.

Minimum Maximum Mean Standard deviation

Variety seeking purchases 0 3 0.4 0.57

G. Mohan et al. / Journal of Retailing and Consumer Services 19 (2012) 419–428 423

accord with the recommendations of Bush and Hair (1985). In thesample, 38% were female and 62% male. The mean age was 29. Allthe interviews were done by one interviewer who was on holidayon Dubai at that time and who wanted to be involved in academicresearch. He was trained in the all aspects of interviewing, in linewith Salant and Dillman (1994).

3.2. Measures

Scale items to measure the constructs are from past researchstudies. All the constructs were measured using multi-item scaleswith the exception of variety seeking. All the scales displayacceptable reliabilities. All pertinent details are in Tables 1 and2. Please see Table 1 for all items and reliabilities and Table 2 forinter-correlations.

The interviewer intercepted potential respondents upon theirexit from the store and asked for their participation in the survey.When they agreed, the interviewer recorded all the purchasesmade by each shopper. Next, for each purchase the shoppers wereasked if they had switched from their regular brand or flavor. Thenumber of such switches was counted for each shopper to arriveat the total number of variety seeking purchases. Operationaliza-tion of the dependent variable is in line with the theoreticaldefinition by the authors and extant research (Inman, 2001; Kahn,1995; Menon and Kahn, 1995). The average number of switcheswas 0.4 per respondent. Please see Table 3 for additional details.

We used structural equation modeling to test the model.The authors followed a 2-step approach of first ‘‘cleaning up’’the measurement model before analyzing the structural one(Anderson and Gerbing, 1988) with EQS 6.1. Assessments of theinitial measurement model after assessing the individual relia-bility of the constructs are as follows: w2 (330)¼551.89 (p¼0.0);IFI¼0.94; CFI¼0.94; NNFl¼0.91. Further, the indicators of resi-duals, RMSEA were 0.04 and RMSR 0.06, respectively.

Since the study measures dependent and independent vari-ables from the same source in a single survey Common MethodVariance (CMV) is possible (Podsakoff et al., 2003). The modeltests for CMV by comparing the fit indices between the measure-ment model and one in which all the items load on a latent CMVfactor besides their theoretical constructs for all the sub-groups.This method allows the partitioning of the variance of responsesto a specific measure into three components: trait, method, andrandom error. The model with the CMV factor shows a much

poorer fit (w2¼2416.24, df¼529, w2/df¼4.56, RMSEA¼ .13,

SRMR¼ .13, CFI¼ .24), with a significant difference in w2 valuebetween this model and the measurement model (Dw2

¼1864.34.,Ddf¼141, p¼0.00). Hence, common method variance may not bea significant problem in this study (Podsakoff et al., 2003).

3.3. Structural model

We tested for differences in variety seeking across stores andfound none. Hence, we analyzed data for all stores combined.Analysis of the structural model followed the purification of themeasurement model. Chi-square value for the overall model fit is732.63 for 496 degrees of freedom (po0.001). The second orderfit indices for the model are NNFI¼0.92; CFI¼0.91; IFI¼0.92;RMSR¼0.08; RMSEA¼0.04. The fit is approaching ‘‘good’’ but is alittle below par. Fortunately, under such conditions (when there isa second order factor model and when there are multi-itemmeasures), prior research in psychology (Schmid and Leiman,1957) and fairly recent work in the methodological space (Wolffand Preising, 2005) give useful pointers on how to improve the fitin a theoretically meaningful way. This line of research advocatesthe Schmid–Leiman factor structure solution.

3.4. Schmid–Leiman factor structure

This is a higher-order factor analysis method that enables theresearcher to see hierarchical structures of the phenomena understudy (Schmid and Leiman, 1957). In a second order factorstructure (i.e., when a factor has no indicator variables and thevariable drives other (first order) factors that have indicatorvariables), and also when the constructs have multi-item mea-sures, the Schmid–Leiman factor structure can be put to use. In astandard higher order factor structure, the second order factor isconsidered an exogenous factor while the first order factors areendogenous, whereas in the Schmid–Leiman factor structure, theindicator variable drives both the first order factors and theerstwhile second order factor.

In the standard second order factor structure, for instance,factor ‘‘design’’ drives the indicator variable, ‘‘Layout1’’. In the newSchmid–Leiman factor structure, the same indicator variable isdriven by both store environment (the erstwhile second orderfactor) and the ‘‘design factor’’. The Schmid–Leiman factor struc-ture is more reflective of reality as well, since when a shopperthinks about ‘‘design’’, apart from thinking of the store’s design,(s)he is likely to think about the overall store as well. Thisconclusion is line with Ward et al. (1992) and Mattila and Wirtz(2001). Please see Figs. 2 and 3 for a pictorial representation ofthe approach vis-�a-vis a standard second order factor modelapproach.

Music

Light

Light

Light

Employee

Employee

Employee

Lay Out

Lay Out

Music

StoreEnvironment

Music

Employee

Layout

Light

Fig. 2. Typical Schmid–Leiman factor structure.

G. Mohan et al. / Journal of Retailing and Consumer Services 19 (2012) 419–428424

4. Results

4.1. Summary of structural model results

After the incorporation of Schmid Leiman Factor Structure, thefit improved and the fit indices are reported in Table 4. Theanalysis of the model shows a significant Chi-square value for theoverall model fit (w2

¼559.95 for 439 degrees of freedom withpo .001). The other indices of model fit, viz., NNFI¼ .94, IFI¼ .93and CFI¼ .94 indicate a good fit for the model which is alsosupported by the RMSEA¼ .04 and RMSR¼ .06 Fig. 4.

Overall the model shows a good fit for the model (Hu andBentler, 1999). The model also satisfies the ‘‘1ow2/dfo5’’ criter-ion of Wheaton et al. (1977). Other fit indices are also higherthan.90 (NNFI¼ .94, IFI¼ .93, CFI¼0.94) showing a good fit (Kline,1998). The R2 for the dependent variable, variety seeking was20.9. See Table 4 for a comparison of the standard second orderfactor indices and those using the Schmid Leiman approach. TheR2 is the measure of variance accounted for rather than a model fit(Medsker et al., 1994). Hence it is possible that a well-fittingmodel can have a low R2 (Kelloway, 1998: pp 28). It is notuncommon where the fit indices are higher but R2 is low (Bagozziand Yi, 1988).

The study finds support for most of the hypotheses. Store envi-ronment drives variety seeking (path coefficient¼ .32, t¼3.24,po .001) thus supporting H3. OSL has a positive effect on varietyseeking (path coefficient¼ .27, t¼3.74, po .001), lending support toH4. Results also reveal that deal proneness drive variety seeking(path coefficient¼ .20, t¼2.64, po .001), providing support to H5.However, the paths between positive affect and variety seeking(path coefficient¼ .07, t¼ .97, p¼ .25); and between store environ-ment and positive affect (path coefficient¼� .16, t¼�1.50 p¼ .18)are not significant. Thus, H1 and H2 are not supported.

We used another operationalization of variety seeking namelythe proportion of goods bought on variety. For instance, if ashopper A had bought 10 products and he switched brands for

just one, his VSI (variety seeking index) would be 0.1 while theVSI for a shopper B who switched brands for 5 out of 10 productswould be 0.5. We found near identical results and hence do notdiscuss this further.

5. Discussion

The study finds that store environment affects variety seekingbehavior directly and also finds that OSL and deal proneness drivevariety seeking (Refer Table 5 for a summary of results).

H1 (positive affect drives variety seeking positively) and H2(store environment drives positive affect positively) do not findsupport. This could be due to the problem of disentangling thepre-existing and the store induced affect in a shopper. In fact,other research using mall studies also report problems in affect-related hypotheses (e.g., Beatty and Ferrell, 1998; Geetha et al.,2010). Prior research shows that positive affect leads to varietyseeking (Isen, 1984; Kahn and Isen, 1993). However, most of thesestudies use experiments whereas in a survey this may not holdgood since other factors may come into play. More research is in anon-experimental context could explain the somewhat unsatis-factory findings regarding affect.

The study makes significant theoretical and managerial con-tributions. From a theoretical point of view, this results in theintegration of the variety seeking body of research with that onstore environment. Prior research finds only one or two store levelvariables driving variety seeking. For instance, Simonson andWiner (1992) show that display format drives variety seeking.Broniarczyk et al. (1998) and Chernev (2008) demonstrate thatassortment is a determinant of variety seeking. Maimaran andWheeler (2008) find that the type of array of products in a storeaffects variety seeking. Similar to Chernev (2008), Krishen et al.(2010) also find that perceived assortments in retail kioskspositively impact variety seeking. Thus, while extant researchconsiders just a few elements of stores affecting variety seeking,our study demonstrates that store environment taken holistically(a gestalt of store level variables) is a significant driver of varietyseeking. Store environment includes not just assortment, butalso layout (design factors), scent, music and lighting (ambientfactors); and presence and effectiveness of salespersons (socialfactors). Thus, while being broadly consistent with prior research,we still extend it.

This study also extends the work of Geetha et al. (2010) byshowing that store environment drives not just impulse buyingbut also variety seeking. This study also adds to the work ofSharma et al. (2010a, b, 2011). While they show that somepersonality level variables like OSL and Consumer Impulsivenesshave an effect on variety seeking, they do not consider store-levelfactors like employees and music. We demonstrate in line withRussell and Mehrabian (1976) that consumer behavior is bestpredicted when one considers both personality and situationalfactors. In accordance with the above, in this study, selectpersonality (deal proneness, OSL) and situational (store environ-ment) variables induce variety seeking. Thus, we build a com-prehensive model of variety seeking of that including bothpersonality and situational variables. Finally, the study adds tothe list of determinants of variety seeking (by showing that storeenvironment is one) and supplement the list of outcomes of storeenvironment (by showing that variety seeking is an outcome ofstore environment). We chose Dubai as the city to collect datafrom. Since there is greater tendency to choose variety in theWest vis-�a-vis the East (Abdullah and Sivakumaran, 2005), ours isa conservative test and hence, our findings should be general-izable to the West as well. However, this can empirically tested.

Table 4Comparison of second order and Schmid Leiman fit indices.

Indices Second order Schmid–Leiman

NNFI 0.92 0.94

IFI 0.92 0.93

CFI 0.91 0.94

RMSEA 0.04 0.04

RMSR 0.08 0.06

StoreEnvironment

Ambient factors

Design Factors

SocialFactors

Music1

Music2

Light1

Light2

Lay Out2

Lay Out1

Light3

Employee1

Employee2

Employee3

Fig. 3. Typical second order factor structure.

G. Mohan et al. / Journal of Retailing and Consumer Services 19 (2012) 419–428 425

While broadly in consonance with existing research, our workis seemingly at variance with recent findings in this area. Levavand Zhu (2009) find that spatially confined consumers makevaried choices (while we find that a ‘‘good’’ store environmentof which a well-designed layout is a component). This could bebecause Levav and Zhu in their studies, instructed subjects tomake choices (in other words, it was mandatory to make choices)while in our study, respondents were merely asked after theymade purchases (choice was optional). Levav and Zhu also didnot consider how subjects’ choices changed over time while wedid. Finally, even in their final study, they did not control forpersonality variables and other store-level variables like musicand lighting.

Managerially, several implications emerge from the study.Managers need to invest in store environment, like training storepersonnel, improving the layout and assortment, making thelighting attractive and by having appropriate scent and music.

Several retailers have a tendency to economize and indulge incost cutting in these areas (ecplaza, 2009; Circuit City, a retailoutlet went bankrupt because of downsizing on employees). Thisis probably because of short-term pressures and goals. Wecalculated direct and indirect effects to see which variablesexerted the most influence on variety seeking. We report themin Table 6.

It is clear that the effect of store environment is the singlelargest one (.32). From the above table, we infer that while theeffect of personality variables (OSL and deal proneness) cumula-tively (0.2þ .27¼0.47) is higher than that of store environment,the latter has the single largest effect (.32) and is quite high.Hence, retailers could ignore this at their own peril. Moreover,store environment is under the direct control of the retailer whilepersonality variables are not. Economizing on store environmentwould be at the cost of variety seeking.

One drawback with using overall store environment as a predic-tor is that we cannot identify which element of store environmentis more important than the others in inducing variety seeking.Therefore, we also re-ran the model removing store environmentand including direct paths from music, lighting, employee, assort-ment and layout, leaving the rest of the model as it is. While the fitfor this model was expectedly lower than our original structuralmodel, it gave us some insights into the relative importance of storeenvironmental factors on impulse buying.

Specifically, we found that music, light, scent, employees andlayout were relatively less important compared to assortment.

Light

Employee

Optimum Stimulation

Level

Layout

DealProneness

StoreEnvironment

Music

Light

Light

Light

Employee

Employee

Employee

Layout

Layout

MusicMusic Positive affect

Variety SeekingBehavior

-0.16 N/S

0.07N/S

ScentScent

Scent

Scent

Assortment

Assort

Assort

Assort

0.32

0.27

0.20

Fig. 4. Structural model of the impact of store environment on variety seeking behavior.

Table 5Structural model results of variety seeking behavior.

H Hypotheses Predicted direction Observed direction Path coefficient/p value

H1 Positive affect to variety seeking behavior þ þ .07, ns

H2 Store environment to positive affect þ � � .16, ns

H3 Store environment to variety seeking behavior. þ þ .32a

H4 Optimum stimulation level to variety seeking behavior. þ þ .27a

H5 Deal proneness to variety seeking behavior. þ þ .20a

ns — not significant.a Significant at po .001.

Table 6Direct, indirect and total effects on variety seeking behavior.

Predictor variables Direct effect Indirect effect Total effect

Optimum stimulation level .27 .27

Deal proneness .20 .20

Positive affect .07 .07

Store environment .32 �0.0119 .30

G. Mohan et al. / Journal of Retailing and Consumer Services 19 (2012) 419–428426

Thus, retail managers may focus on having a wider, deeperassortment while continuing to improve the other elements ofstore environment like music and lighting. Since the SchmidLeiman Factor structure gave a good fit, one cannot draw defini-tive conclusions from the first order model results. Still, the firstorder factor results suggest useful pointers to managers.

Variety seeking typically increases the size of shopping basket(Simonson, 1990) and hence, retailers by investing in storeenvironment can hope to get greater sales. Furthermore, whenvariety seeking occurs, non-dominant brands gain share at the

expense of dominant ones (Feinberg et al., 1992; Sivakumaranand Shankar, 2010). In the case of retailers, non-dominant brandswould typically be the store brands (Ailawadi and Harlam, 2004).Hence, retailers could increase the share of non-dominant storebrands by investing in store environment. This is an attractiveproposition since store brands are usually more profitable forretailer’s vis-�a-vis national brands (Ailawadi and Harlam, 2004).Finally, consumers indulge in variety seeking to show themselvesas interesting (Ratner and Kahn, 2002). Investing in store envi-ronment would result in shoppers seeking more variety. Theywould then be seen as more interesting as a result (Ratner andKahn, 2002). Being seen as an interesting person would thenenhance a shopper’s self-esteem.

From a methodological viewpoint, the study incorporates theSchmid–Leiman factor structure in the model following thesuggestion of Geetha et al. (2010) in doing so model fit improvesand is more reflective of reality. Other researchers in marketingand related fields could use the Schmid Leiman factor structuretoo if conditions permit (presence of second order factor model

G. Mohan et al. / Journal of Retailing and Consumer Services 19 (2012) 419–428 427

with multi-item measures). We offer a useful pointer to otherresearchers. In a nutshell, store environment, along with dealproneness and OSL drives variety seeking behavior.

6. Limitations and future research

This study makes some valuable contributions but it also suffersfrom some limitations. First, it is not able to demonstrate the effectof in-store browsing on variety seeking behavior as repeatedattempts to measure this did not yield results because of poorreliabilities. One of the reasons for this may be that most scales inmarketing were developed in the West and they do not workreliably in non-Western markets sometimes, especially in retailsettings (e.g., Kacen and Lee, 2002; Sharma et al., 2011). Therefore,future research should try to develop and test scales that showcross-cultural conceptual equivalence and measurement invariance.

Second, this research focuses on store environment andpositive affect along with two individual characteristics (i.e., dealproneness and optimum stimulation level) as the independentvariables. However, many other variables may impact the varietyseeking behavior such as specific shopping trip enjoyment andpromotional offers, which future research may incorporate andtest their impact. Similarly, there is mixed evidence about theinfluence of affect in store settings (e.g., Beatty and Ferrell, 1998),which further research may try to resolve.

Third, future research can consider the interaction effect betweenpersonality and store environment factors since the study considersstore environment as a higher order factor. Future research maybreak this into different components and test the individual impor-tance of each component like music/lighting with a larger sampleand see if music works better for some shoppers vis-�a-vis others.Fourth, we conducted the survey in stores that are very similar toeach other. Hence, it is not possible to see whether music worksbetter in some stores vis-�a-vis others. Future research may cover amore diverse range of stores to explore between-stores differences.Finally, future research may also explore if store environment has adifferential effect on variety seeking in different product categories.For instance, does a good store environment cause variety seekingeven in categories that are not conducive to variety seeking?

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