STORE ENVIRONMENT’S IMPACT ON VARIETY SEEKING

BEHAVIOR

Geetha Mohan *1

Bharadhwaj Sivakumaran 2

Piyush Sharma 3

April 2012

1 *Corresponding author: Assistant Professor, SSN School of Management and Computer Applications, Chennai, India. Phone: +91-44 2747 5063/Fax: +91-44-22574552. E-mail: geetham18@gmail.com

2 Professor, Great Lakes Institute of Management, Chennai, India. Phone: +91-44-

30809210, Fax: +91-44-30809011, Email: bwaj@greatlakes.edu.in 3 Associate Professor, Department of Management and Marketing, Faculty of Business,

The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. Phone: +852-2766-7367, Fax: +852-2765-0611, Email: mspiyush@inet.polyu.edu.hk

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STORE ENVIRONMENT’S IMPACT ON VARIETY SEEKING BEHAVIOR

ABSTRACT

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.

Keywords: Variety seeking behavior, positive effect, optimum stimulation level, store

environment, structural equation modeling

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1. INTRODUCTION

Variety seeking behavior is the tendency of an individual to seek change over time and it

continues to attract attention in the retail and shopping context (e.g., Givon, 1984; Sharma et al.,

2010a, 2010b). The variety seeking literature deals with issues such as the determinants of

variety seeking (e.g., Van Trijp et al., 1996), models of variety seeking (e.g., McAlister, 1982)

and its relationship with marketing phenomena like segmentation (Trivedi, 1999) and market

share (Feinberg et al., 1992).

Prior research shows that various personality, product category and situational variables

may drive variety seeking. Personality variables include optimum stimulation level (Steenkamp

and Baumgartner, 1992), deal proneness (Martinez and Montaner, 2006), self-monitoring (Ratner

and Kahn, 2002) and goal orientation (Wu and Kao 2011). Product category-level characteristics

such as involvement (subjective personal relevance of a product) and hedonic features (features

in products that cause sensory gratification and give pleasure) also affect variety seeking (Van

Trijp et al., 1996). Similarly, situational factors such as positive affect (Kahn and Isen, 1993),

public scrutiny of choices (Ratner and Kahn, 2002) and activation of negative concepts like

“boredom” (Fishbach et al., 2011) impact variety seeking.

On the other hand, there is growing interest in enhancing store environment as retailers

are going the extra mile in making the retail “experience” a key differentiator. A store’s

environment influences 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 store choice

(Darden et al., 1983). However, most studies deal with the role of store environment explore the

influence of its various dimensions independently such as store employees (Hu and Jasper,

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2006), store convenience and quality (Vahie and Paswan, 2006), store trustworthiness (Heijden

and Verhagen, 2004), and in-store graphics with social meaning (Hu and Jasper, 2006).

Prior research links store environment to various aspects of consumer behavior. For

instance, music influences time and money spent positively (Milliman 1982, 1986) and lighting

influences the handling (touching and feeling products) and purchase of items positively (Areni

and Kim, 1994). Donovan et al. (1994) find that store atmosphere drives pleasure and spending

of time and money in a store. Spies et al. (1997) highlight the importance of a good store layout.

Baker et al. (2002) show that various aspects of store environment influence store patronage.

Store layout, ambience and sales personnel may also influence unplanned buying (Sherman et al.

1997; Geetha et al., 2010).

There is also growing evidence about the influence of certain facets of store environment

on variety seeking behavior. For example, assortment drives variety seeking positively (Morales

et al., 2005; Krishen et al., 2010). Maimaran and Wheeler (2008) find that “exposure to novel

visual stimulus arrays of geometric shapes affects consumers' real choices among products”. In

other words, the type of array of products in a store affects variety seeking. Levav and Zhu

(2009) find that spatially confined consumers make varied choices. However, despite such

evidence, most studies link only a few aspects of store environment to the variety seeking

behavior, instead of exploring the overall impact of store environment on variety seeking.

In line with Mattila and Wirtz (2001), this study defines store environment as a

combination of various constituent variables namely lighting, music, scent, sales personnel and

layout that is in gestalt/composite fashion. We treat variety seeking as “the tendency of an

individual to seek variety or change in choices over time, for the purchases made within the

product class” (Kahn, 1995). Sharma et al. (2010a) show that both impulse buying and variety

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seeking are examples of hedonic and exploratory purchase behaviors with similar socio-

psychological origins, Geetha et al. (2010) suggest that store environment drives impulse buying.

Hence, we propose that store environment is likely to drive variety seeking behavior as well and

this study intends to test this thesis.

Research has also shown that consumer behavior is best explained by considering

personality and situational factors (Russel and Mehrabian 1976). Therefore, we consider some

personality variables as well. We would also consider not just one or two elements of stores that

affect variety seeking but store environment as a whole or in gestalt terms since shoppers look at

stores in this way (Ward et al., 1992, Mattila and Wirtz, 2001). Zimmer and Golden (1988) as

well as Keaveney and Hunt (1992) also refer to the gestalt (or holistic) nature of the store

environment construct.

In view of the above, it is important to study these dimensions collectively as consumers

do not perceive a store in piecemeal (Ward et al., 1992; Bitner, 1992) and it is the total

configuration of cues (gestalt of consumers’ perceptions of store) that influence consumer

responses (Mattila and Wirtz, 2001; Holahan, 1982). Consumers look to the total collection of

cues in the environment to decode meanings and to structure their behaviour accordingly

(Solomon, 1983). Therefore, this paper develops a comprehensive model that links store

environment as a whole (and not just one or two aspects of stores) with variety seeking.

From a managerial viewpoint, this research addresses an important question: does store

environment induce variety seeking? If the answer is yes, retailers then have one more additional

reason (apart from patronage, loyalty, store choice and so on) to invest in store environment. If

the answer is no, the retailer can spend this money on other marketing activities like promotions

and price cuts, rather than investing in the antecedents of store environment.

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2. CONCEPTUAL FRAMEWORK AND HYPOTHESES DEVELOPMENT

This section presents hypotheses linking store environment, select personality variables

and variety seeking behavior. Specifically, the model studies the impact of both store-level

(situational) factors (store environment consisting of lighting, music, scent, layout, assortment

and employees) and two personality variables (deal proneness and optimum stimulation level) on

variety seeking though a mediator, positive affect.

2.1 Positive Affect and Variety Seeking Behavior

Positive affect refers to the pleasantness of the affective experience. Positive affect

reflects the extent to which a person feels enthusiastic, active, and alert. It makes the consumers

stay longer in the store and induces increased browsing (Beatty and Ferrell, 1998) that could lead

to variety seeking (Kahn and Isen, 1993). Hence:

H1: Positive affect has a positive impact on variety seeking behavior.

2.2 Store Environment

Store environment comprises ambient (e.g., lighting, scent, and music), 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. Design factors consist of

layout and assortment (Baker et al., 2002). Layout refers to the way in which products, shopping

carts, and aisles are arranged, the size and shape of items, and the spatial relationships among

them. Layout also includes space design and allocation, grouping and placements of the

merchandise. Product assortment is the total set of items a retailer offers, reflecting the breadth

and depth of product lines. Social factors refer to other shoppers and salespersons or the relevant

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people in the environment (Baker et al., 2002). Other shoppers are not directly under the control

of the retailer and this study does not include them (all other store-level factors are). At best,

retailers can organize queuing procedures efficiently and schedule cashiers appropriately. 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 respond psychologically and

behaviorally to music. These responses occur predominantly 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 music produces positive affect (Garlin and Owen, 2006).

Well-designed lighting systems can enhance a store’s interior, guide the customer’s eyes to key

sales points, create an atmosphere of excitement and induce positive affect (Smith, 1989).

Lighting and music together evoke positive affect (Yoo et al., 1998). Positive experiences arise if

the store is easy for shoppers to find the product they are looking for, when the layout of the

store seems logical and when there are sufficient signs in the store (Bitner, 1992; Spies et al.,

1997). Layout makes the shopping enjoyable and produces positive affect. Design factors reduce

the perceived stress in shopping (Baker et al., 2002) and evoke positive affect (Yoo et al., 1998).

Consumers prefer a large assortment as it helps them tide over uncertainty about future

preferences (Simonson, 1990). Therefore, they evaluate larger assortments more positively

(Broniarczyk et al., 1998).

Store personnel contribute to entertaining store experiences especially when the staff has

a capacity to offer good service or to allow consumers to shop without being under constant

surveillance (Jones, 1999). Employee responses can significantly influence important consumer

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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 make an extra effort and stretch

beyond ‘normal’ service levels. Sales personnel’s assistance may enhance even the utilitarian

shopper’s enjoyment. Even in brief and mundane encounters the employee induces positive

affect (Mattila and Enz, 2002).

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

2.2.2. Store Environment and Variety Seeking Behavior

Music is an important non-verbal communication that enhances store atmosphere

(Turley and Milliman, 2000). Good music stimulates additional sales (Milliman, 1982). This

additional sale could likely be impulse buying (Geetha et al., 2010) and also, buying more of the

same brand or increasing the variety of the purchases. Moreover, impulse buying is related

positively to variety seeking (Sharma et al., 2010a, 2010b). The pleasantness of the music could

work unconsciously as the consumers spend more time in the store, browsing more and

evaluating different alternatives. Additional browsing increases search and this in turn positively

relates to variety seeking (Steenkamp and Baumgartner, 1992). Browsing also increases impulse

buying, which in turn correlates positively with variety seeking (Sharma et al., 2010a, 2010b,

2011).

Well-designed lighting systems create an atmosphere of excitement, induce positive

mood, make key approach areas safe and visible and guide the customer's eyes to key sales

points (Smith, 1989), thereby providing the consumer an opportunity to view more choices.

Lighting affects the number of items a shopper handles and examines (Areni and Kim, 1994).

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Greater the number of items held, greater is the likelihood of including different alternatives in

consideration sets. Larger consideration sets lead to variety seeking (Sivakumaran and Kannan,

2002). Good lighting leads to greater browsing and greater information search. Greater

information search leads to variety seeking (Steenkamp and Baumgartner, 1992).

Consumers spend more money and time in a store that has a pleasant fragrance and it

makes them more attentive towards the different brands in the store (Spangenberg et al., 1996).

This leads to greater appreciation of different attributes and distinctiveness between brands and

larger consideration sets, leading to variety seeking behavior (Sivakumaran and Kannan, 2002).

A good layout facilitates greater in-store exploration. This helps shoppers in viewing the

assortment within a product category and also the availability of different product categories. A

good layout will also give the impression of a greater amount of merchandise being present than

actually present (Morales et al., 2005). In other words, a good layout leads to greater perceived

variety, which is a key driver of variety seeking (Kahn and Wansink, 2004). Product assortment

is also a determinant of variety seeking (Simonson, 1999). Greater the number of flavors and

brands, greater will be the tendency for consumers to choose variety (Inman, 2001). There is

some recent work that seems to suggest that confining consumers spatially would drive variety

seeking (Levav and Zhu, 2009). However, we argue against this line 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 or sales person’s helpfulness in

making the consumer understand the features of different alternatives could lead to variety

seeking. Employees could stimulate the consumer in exploring the store, guiding him towards

various brands, alternatives and thereby induce variety seeking directly.

H3: Overall perception of store environment has a positive impact on variety seeking.

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2.3.Optimal Stimulation Level (OSL)

Variety seeking research in marketing (Steenkamp and Baumgartner, 1992; Van Trijp et

al., 1996) draws heavily on the personality and psychology bodies of literature to explain

personality-related characteristics of variety seeking. Mostly, this line of research revolves

around the optimum stimulation level (OSL) paradigm (Deci and Ryan, 1985). According to this

view, each individual has his/her own level of stimulation. This stimulation is the

“complexity/arousal that associates with a stimulus”. Once the 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 would engage is in “exploratory behavior”

of which variety seeking is a specific manifestation. Individuals with high optimum stimulation

levels (OSL) are also known to be chronically higher in their arousal level making them indulge

in sensation seeking activities, including variety seeking to achieve their optimum stimulation

level (Steenkamp and Baumgartner, 1992). Thus,

H4: Optimum stimulation level has a positive impact on variety seeking behavior.

2.4. Deal Proneness

Consumers who seek variety consider promotions a salient attribute in their patronage

choice and, as a result, do not intend to re-patronize frequently (Wakefield and Barnes, 1996).

Sales promotion can encourage behavioral responses such as brand switching. Deal proneness is

the consumer tendency to respond to sales promotions (Lichenstein et al., 1995). Deal prone

consumers are more likely to use the promotional offers, in-store ads and other signages that

provide information on the deals available and switch brands that are available on sale. Deal-

prone consumers modify their purchase behavior so as to benefit from the temporary incentives

of a promotion (Wakefield and Barnes, 1996).

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Purchase probabilities increase with the increase in the promotional effort due to greater

preference for the promoted product (Trivedi, 1999). Variety seeking allows the consumers to

use the promotions strategically and to experiment with different brands over time. Deal prone

people use this for “smart buying" by utilizing good deals. Promotion is an important marketing

variable (Trivedi and Morgan, 2003) influencing variety seeking and deal prone consumers are

vulnerable to promotions. Hence, the following hypothesis:

H5: Deal proneness has a positive impact on variety seeking behavior

The above discussion leads us to propose a comprehensive model that Figure 1 summarizes.

< Insert Figure 1 about here >

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 with previous studies (Geetha et al., 2010; Sharma et al., 2010a,

2010b). The survey was conducted in the city of Dubai on shoppers of five popular grocery

stores since variety seeking typically occurs in grocery shopping (e.g. van Trijp et al., 1996). We

chose Dubai since apart from being convenient, it is a city in Asia with facilities and

infrastructure on par with the West. It is also a place where the “East meets the West” (Hosea,

2007). Dubai is a “globalized city” (Mady et al., 2011) with considerable Western influence that

has attracted numerous international visitors (Henderson, 2006). It is a city that has one “of the

world’s most urban multi-cultural environments”. (Sankaran and Demangeot, 2011). A total of

350 shoppers were approached out of which 212 agreed to participate in the study. Shoppers

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were told that this was part of an academic project on “shopper behavior”. Finally this was

pruned to 200 after removing incomplete responses, thus yielding a response rate 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 grocery shoppers. 38% of the shoppers

were women and 62% were men and the average age of the shopper was 28.5. 43% of them

were single and 57% were married. 23% were higher secondary graduates, 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 obtained considerable

diversity.

Shoppers were sampled during morning, afternoon and evening hours on weekdays and

weekends. The locations of the interviews, the times of the day and the days of the week were

rotated in accord with the recommendations of Bush and Hair (1985). In the sample, 38% were

female and 62% male. The mean age was 29. All the interviews were done by one interviewer

who was on holiday on Dubai at that time and who wanted to be involved in academic research.

He was trained in the all aspects of interviewing, in line with Salant and Dillman (1994).

3. 2.Measures

Scale items to measure the constructs are from past research studies. All the constructs

were measured using multi-item scales with the exception of variety seeking. All the scales

display acceptable reliabilities. All pertinent details are in Table 1 and Table 2. Please see Table

1 for all items and reliabilities and Table 2 for inter-correlations.

< Insert Tables 1 and 2 about here >

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The interviewer intercepted potential respondents upon their exit from the store and asked

for their participation in the survey. When they agreed, the interviewer recorded all the purchases

made by each shopper. Next, for each purchase the shoppers were asked if they had switched

from their regular brand or flavor. The number of such switches was counted for each shopper to

arrive at the total number of variety seeking purchases. Operationalization of the dependent

variable is in line with the theoretical definition by the authors and extant research (Inman, 2001;

Kahn, 1995; Menon and Kahn, 1995). The average number of switches was 0.4 per respondent.

Please see table 3 for additional details.

< Insert Table 3 about here >

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 the initial measurement model

after assessing the individual reliability of the constructs are as follows: χ2 (330) =551.89 (p =

0.0); IFI =0.94; CFI= 0.94; NNFl =0.91. Further, the indicators of residuals, RMSEA were 0.04

and RMSR 0.06 respectively.

Since the study measures dependent and independent variables from the same source in a

single survey Common Method Variance (CMV) is possible (Podsakoff et al., 2003). The model

tests for CMV by comparing the fit indices between the measurement model and one in which all

the items load on a latent CMV factor besides their theoretical constructs for all the sub-groups.

This method allows the partitioning of the variance of responses to a specific measure into three

components: trait, method, and random error. The model with the CMV factor shows a much

poorer fit (χ2 = 2416.24, df = 529, χ2/df = 4.56, RMSEA = .13, SRMR = .13, CFI = .24), with a

significant difference in χ2 value between this model and the measurement model (Δχ2 =

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1864.34., Δdf = 141, p = 0.00). Hence, common method variance may not be a significant

problem in this study (Podsakoff et al., 2003).

3.3. Structural Model

We tested for differences in variety seeking across stores and found none. Hence, we

analyzed data for all stores combined. Analysis of the structural model followed the purification

of the measurement model. Chi-square value for the overall model fit is 732.63 for 496 degrees

of freedom (p< 0.001). The second order fit indices for the model are NNFI=0.92; CFI=0.91;

IFI=0.92; RMSR=0.08; RMSEA= 0.04. The fit is approaching “good” but is a little below par.

Fortunately, under such conditions (when there is a second order factor model and when there

are multi-item measures), prior research in psychology (Schmid and Leiman, 1957) and fairly

recent work in the methodological space (Wolff and Preising, 2005) give useful pointers on how

to improve the fit in a theoretically meaningful way. This line of research advocates the Schmid-

Leiman factor structure solution.

3.4. Schmid-Leiman Factor Structure

This is a higher-order factor analysis method that enables the researcher to see

hierarchical structures of the phenomena under study (Schmid and Leiman, 1957). In a second

order factor structure (i.e. when a factor has no indicator variables and the variable drives other

(first order) factors that have indicator variables), and also when the constructs have multi-item

measures, the Schmid-Leiman factor structure can be put to use. In a standard higher order factor

structure, the second order factor is considered an exogenous factor while the first order factors

are endogenous, whereas in the Schmid-Leiman factor structure, the indicator variable drives

both the first order factors and the erstwhile second order factor.

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In the standard second order factor structure, for instance, factor “design” drives the

indicator variable, “Layout1”. In the new Schmid-Leiman factor structure, the same indicator

variable is driven by both store environment (the erstwhile second order factor) and the “design

factor”. The Schmid-Leiman factor structure is more reflective of reality as well, since when a

shopper thinks about “design”, apart from thinking of the store’s design, (s) he is likely to think

about the overall store as well. This conclusion is line with Ward et al. (1992) and Mattila and

Wirtz (2001). Please see figures 2 and 3 for a pictorial representation of the approach vis-à-vis a

standard second order factor model approach.

< Insert Figures 2 and 3 about here >

4. RESULTS

4.1.Summary of Structural Model Results

After the incorporation of Schmid Leiman Factor Structure, the fit improved and the fit

indices are reported in Table 4. The analysis of the model shows a significant Chi-square value

for the overall model fit (χ2 = 559.95 for 439 degrees of freedom with p < .001). The other

indices of model fit, viz., NNFI = .94, IFI= .93 and CFI = .94 indicate a good fit for the model

which is also supported by the RMSEA = .04 and RMSR= .06.

< Insert Figure 4 and Table 4 about here >

Overall the model shows a good fit for the model (Hu and Bentler, 1999). The model

also satisfies the “1 < χ2/df < 5” criterion of Wheaton et al. (1977). Other fit indices are also

higher than .90 (NNFI = .94, IFI = .93, CFI = 0.94) showing a good fit (Kline, 1998). The R2 for

the dependent variable, variety seeking was 20.9. See Table 4 for a comparison of the standard

second order factor indices and those using the Schmid Leiman approach. The R2 is the measure

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of variance accounted for rather than a model fit (Medsker et al. 1994). Hence it is possible that a

well-fitting model can have a low R2 (Kelloway, pp 28). It is not uncommon where the fit indices

are higher but R2 is low ( Bagozzi and Yi,1988).

The study finds support for most of the hypotheses. Store environment drives variety

seeking (path coefficient = .32, t = 3.24, p < .001) thus supporting H3. OSL has a positive effect

on variety seeking (path coefficient = .27, t = 3.74, p < .001), lending support to H4. Results also

reveal that deal proneness drive variety seeking (path coefficient = .20, t = 2.64, p < .001),

providing support to H5. However, the paths between positive affect and variety seeking (path

coefficient = .07, t = .97, p = .25); and between store environment and positive affect (path

coefficient = -.16, t = -1.50 p = .18) are not significant. Thus, H1 and H2 are not supported.

We used another operationalization of variety seeking namely the proportion of goods

bought on variety. For instance, if a shopper A had bought 10 products and he switched brands

for just one, his VSI (variety seeking index) would be 0.1 while the VSI for a shopper B who

switched brands for 5 out of 10 products would be 0.5. We found near identical results and hence

do not discuss this further.

5. DISCUSSION

The study finds that store environment affects variety seeking behavior directly and also

finds that OSL and deal proneness drive variety seeking (Refer table 5 for a summary of results).

< Insert Table 5 about here >

H1 (positive affect drives variety seeking positively) and H2 (store environment drives

positive affect positively) do not find support. This could be due to the problem of disentangling

the pre-existing and the store induced affect in a shopper. In fact, other research using mall

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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 variety seeking (Isen, 1984; Kahn

and Isen, 1993). However, most of these studies use experiments whereas in a survey this may

not hold good since other factors may come into play. More research is in a non-experimental

context could explain the somewhat unsatisfactory findings regarding affect.

The study makes significant theoretical and managerial contributions. From a theoretical

point of view, this results in the integration of the variety seeking body of research with that on

store environment. Prior research finds only one or two store level variables driving variety

seeking. For instance, Simonson and Winer (1992) show that display format drives variety

seeking. Broniarczyk et al. (1998) and Chernev (2003) demonstrate that assortment is a

determinant of variety seeking. Maimaran and Wheeler (2008) find that the type of array of

products in a store affects variety seeking. Similar to Chernev (2003), Krishen et al. (2010) also

find that perceived assortments in retail kiosks positively impact variety seeking. Thus, while

extant research considers 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 variety seeking. Store environment includes not just assortment, but also

layout (design factors), scent, music and lighting (ambient factors); and presence and

effectiveness of salespersons (social factors). Thus, while being broadly consistent with prior

research, we still extend it.

This study also extends the work of Geetha et al. (2010) by showing that store

environment drives not just impulse buying but also variety seeking. This study also adds to the

work of Sharma et al. (2010a, 2010b, 2011). While they show that some personality level

variables like OSL and Consumer Impulsiveness have an effect on variety seeking, they do not

17

consider store-level factors like employees and music. We demonstrate in line with Russell and

Mehrabian (1976) that consumer behavior is best predicted when one considers both personality

and situational factors. In accordance with the above, in this study, select personality (deal

proneness, OSL) and situational (store environment) variables induce variety seeking. Thus, we

build a comprehensive model of variety seeking of that including both personality and situational

variables. Finally, the study adds to the list of determinants of variety seeking (by showing that

store environment is one) and supplement the list of outcomes of store environment (by showing

that variety seeking is an outcome of store environment). We chose Dubai as the city to collect

data from. Since there is greater tendency to choose variety in the West vis-à-vis the East

(Abdullah and Sivakumaran, 2005), ours is a conservative test and hence, our findings should be

generalizable to the West as well. However, this can empirically tested.

While broadly in consonance with existing research, our work is seemingly at variance

with recent findings in this area. Levav and Zhu (2009) find that spatially confined consumers

make varied choices (while we find that a “good” store environment of which a well-designed

layout is a component). This could be because Levav and Zhu in their studies, instructed subjects

to make choices (in other words, it was mandatory to make choices) while in our study,

respondents were merely asked after they made purchases (choice was optional). Levav and Zhu

also did not consider how subjects’ choices changed over time while we did. Finally, even in

their final study, they did not control for personality variables and other store-level variables like

music and lighting.

Managerially, several implications emerge from the study. Managers need to invest in

store environment, like training store personnel, improving the layout and assortment, making

the lighting attractive and by having appropriate scent and music. Several retailers have a

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tendency to economize and indulge in cost cutting in these areas (ecplaza, 2009; Circuit City, a

retail outlet went bankrupt because of downsizing on employees). This is probably because of

short-term pressures and goals. We calculated direct and indirect effects to see which variables

exerted the most influence on variety seeking. We report them in Table 6.

< Insert Table 6 about here>

It is clear that the effect of store environment is the single largest one (.32). From the

above table, we infer that while the effect of personality variables (OSL and deal proneness)

cumulatively (0.2+.27 = 0.47) 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 while personality variables

are not. Economizing on store environment would be at the cost of variety seeking.

One drawback with using overall store environment as a predictor is that we cannot

identify which element of store environment is more important than the others in inducing

variety seeking. Therefore, we also re-ran the model removing store environment and including

direct paths from music, lighting, employee, assortment and layout, leaving the rest of the model

as it is. While the fit for this model was expectedly lower than our original structural model, it

gave us some insights into the relative importance of store environmental factors on impulse

buying.

Specifically, we found that music, light, scent, employees and layout were relatively less

important compared to assortment. Thus, retail managers may focus on having a wider, deeper

assortment while continuing to improve the other elements of store environment like music and

lighting. Since the Schmid Leiman Factor structure gave a good fit, one cannot draw definitive

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conclusions from the first order model results. Still, the first order factor results suggest useful

pointers to managers.

Variety seeking typically increases the size of shopping basket (Simonson, 1990) and

hence, retailers by investing in store environment can hope to get greater sales. Furthermore,

when variety seeking occurs, non-dominant brands gain share at the expense of dominant ones

(Feinberg et al., 1992; Sivakumaran and Shankar, 2010). In the case of retailers, non-dominant

brands would typically be the store brands (Ailawadi and Harlam, 2004). Hence, retailers could

increase the share of non-dominant store brands by investing in store environment. This is an

attractive proposition since store brands are usually more profitable for retailer’s vis-à-vis

national brands (Ailawadi and Harlam, 2004). Finally, consumers indulge in variety seeking to

show themselves as interesting (Ratner and Kahn, 2002). Investing in store environment would

result in shoppers seeking more variety. They would then be seen as more interesting as a result

(Ratner and Kahn, 2002). Being seen as an interesting person would then enhance a shopper’s

self-esteem.

From a methodological viewpoint, the study incorporates the Schmid-Leiman factor

structure in the model following the suggestion of Geetha et al. (2010) in doing so model fit

improves and is more reflective of reality. Other researchers in marketing and related fields

could use the Schmid Leiman factor structure too if conditions permit (presence of second order

factor model with multi-item measures). We offer a useful pointer to other researchers. In a

nutshell, store environment, along with deal proneness and OSL drives variety seeking behavior.

6. LIMITATIONS AND FUTURE RESEARCH

This study makes some valuable contributions but it also suffers from some limitations.

First, it is not able to demonstrate the effect of in-store browsing on variety seeking behavior as

20

repeated attempts to measure this did not yield results because of poor reliabilities. One of the

reasons for this may be that most scales in marketing were developed in the West and they do not

work reliably in non-Western markets sometimes, especially in retail settings (e.g., Kacen and

Lee 2002; Sharma et al., 2011). Therefore, future research should try to develop and test scales

that show cross-cultural conceptual equivalence and measurement invariance.

Second, this research focuses on store environment and positive affect along with two

individual characteristics (i.e., deal proneness and optimum stimulation level) as the independent

variables. However, many other variables may impact the variety seeking behavior such as

specific shopping trip enjoyment and promotional offers, which future research may incorporate

and test their impact. Similarly, there is mixed evidence about the influence 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 between personality and store

environment factors since the study considers store environment as a higher order factor. Future

research may break this into different components and test the individual importance of each

component like music/lighting with a larger sample and see if music works better for some

shoppers vis-à-vis others. Fourth, we conducted the survey in stores that are very similar to each

other. Hence, it is not possible to see whether music works better in some stores vis-à-vis others.

Future research may cover a more diverse range of stores to explore between-stores differences.

Finally, future research may also explore if store environment has a differential effect on variety

seeking in different product categories. For instance, does a good store environment cause

variety seeking even in categories that are not conducive to variety seeking?

21

REFERENCES

Abdullah , N.H., Sivakumaran, B., 2005. Unraveling cross-cultural differences: Effects of

observability, self-monitoring and desire for unique consumer products on tendency to seek

variety", in Advances in Consumer Research , 6, eds. Yong-Uon Ha and Youjae Yi, Duluth, MN:

Association for Consumer Research, pp.127-135.

Ailawadi, K. L., Harlam, B., 2004. An empirical analysis of the antecedents of retail margins:

The role of store-brand share. Journal of Marketing. 68 (January), 147-165.

Anderson, J. C., Gerbing D. W., 1984. The effect of sampling error on convergence, improper

solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis.

Psychometrika. 49, 155−173.

Anderson, J. C., Gerbing D. W., (1988). Structural equation modeling in practice: A review and

recommended two-step approach. Psychological Bulletin. 103 (3), 411-423.

Areni C.S., Kim, D., 1994. The influence of in-store lighting on consumers’ examination of

merchandise in a wine store. International Journal of Research in Marketing. 11, 117-125.

Bagozzi, R.P., Yi,Y., 1988. On the evaluation of structural equation models. Journal of the

Academy of Marketing Science. 16, 74-94.

Baker, J., Grewal, D., Parasuraman, A., 1994. The influence of store environment on quality

inferences and store image. Journal of Academy of Marketing Science. 22 (4), 328-339.

22

Baker, J., Parasuraman, A., Grewal, D., Voss, G.B., 2002. The influence of multiple store

environment cues on perceived merchandise value and patronage intentions. Journal of

Marketing. 66 (2), 120-141.

Beatty, S. E, Ferrell, E. M., 1998. Impulse buying: Modeling its precursors. Journal of Retailing.

74 (2), 169-191.

Bitner, M. J., 1990. Evaluating service encounters: The effects of physical surroundings and

employee responses. Journal of Marketing. 54 (April), 69-82.

Bitner, M. J., 1992. Servicescapes: The impact of physical surroundings on customers and

employees. Journal of Marketing. 56, 57-71.

Broniarczyk, S. M., Hoyer W.D., McAlister, L.,1998. Consumers' perceptions of the assortment

offered in a grocery category: The impact of item reduction. Journal of Marketing Research. 35

(May), 166-176.

Bruner II, G.C., 1990. Music, Mood, and Marketing. Journal of Marketing. October, 94-104.

Bush, A.J., Hair, J. F., 1985. An assessment of the mall intercept as a data collection method.

Journal of Marketing Research. 22 (May), 158-167.

Chernev, A., 2008. The role of purchase quantity in assortment choice: The quantity-matching

heuristic. Journal of Marketing Research. XLV (April), 171-181.

Darden, W.R., Orhan , E., Darden, D.K.,1983. Comparison and test of three causal models of

patronage intentions. In: William R. Darden and Robert F. Lusch(Eds.), Patronage Behavior and

Retail Management, North-Holland, New York, NY, pp. 29-43.

23

Deci,E. L., Ryan, R.M.,1985. Intrinsic motivation and self determination in human behavior,

Plenum, New York.

Dickson, J., Albaum, G., 1977. A method for developing tailor-made semantic differentials for

specific marketing content areas. Journal of Marketing Research. 14 (1), 87-91.

Donovan, R.J., Rossiter, J.R., Marcoolyn,G., Nesdale. A., 1994. Store atmosphere and

purchasing behavior. Journal of Retailing. 70 (3), 283-294.

ecplaza, 2009 Strategic Cost-Saving of Companies during Recession Retrieved February 21,

2010 from http://www.ecplaza.net/forum/forumpub.do

Feinberg, F.M., Kahn, B.E., McAlister L., 1992. Market share response when consumers seek

variety. Journal of Marketing Research. 29 (May), 227-237.

Fishbach,A., Ratner, R.K., Zhang ,Y., 2011 . Inherently loyal or easily bored? Nonconscious

activation of consistency versus variety-seeking behaviour”. Journal of Consumer Psychology.

21, 38–48.

Garlin, F.V., Owen, K., 2006. Setting the tone with the tune: A meta-analytic review of the

effects of background music in retail settings. Journal of Business Research. 59, 755-764.

Geetha, M., Sivakumaran .B., Sharma, P., 2010.Role of store image in consumer impulse

buying behavior. In: Advances in Consumer Research. Provo, UT: Association for Consumer

Research 11, 194.

Givon, M., 1984.Variety seeking through brand switching. Marketing Science. 3 (1), 1-22.

24

Heijden, H., Verhagen, T., 2004. Online store image: Conceptual foundations and empirical

measurement. Information and Management. 41, 609-617.

Henderson, J.C., 2006. Destination development: Singapore and Dubai compared. Journal of

Travel and Tourism Marketing. 20 (3/4), 33-46.

Hoelter, J. W., 1983.The analysis of covariance structures: Goodness-of-fit indices. Sociological

Methods and Research. 11 (3), 325-344.

Holahan, C., 1982. Environmental Psychology, Random House, New York.

Hosea, 2007. Case Study on Emirates. Brand Strategy. 20-23.

Hu, L., Bentler, P.M., 1999. Cut off criteria for fit indexes in covariance structure analysis:

Conventional criteria versus new alternatives. Structural Equation Modeling. 6 (1), 1-55.

Hu, H., Jasper, C.R.,2006. Social cues in the store environment and their impacts on store

image. International Journal of Retail Distribution Management. 34 (1), 25-48.

Iacobucci, D., 2010. Structural equations modeling: Fit Indices, sample size, and advanced

topics. Journal of Consumer Psychology. 20, 90–98.

Inman, J.J., 2001. The role of sensory specific satiety in attribute level variety seeking. Journal

of Consumer Research. 28 (June), 105-120.

Isen, A.,1984. The influence of positive affect on decision-making and cognitive organization,.

In Advances in Consumer Research, vol 11. eds. T. C. Kinnear, Provo, UT: Association for

Consumer Research 11, 534-537.

25

Jones, M. A., (1999). Entertaining shopping experiences: An exploratory investigation. Journal

of Retailing and Consumer Services. 6, 129-139.

Kacen, J. J., Lee, J. A.,2002. The influence of culture on consumer impulsive buying behavior.

Journal of Consumer Psychology. 12 (2), 163-176.

Kahn, B.E., 1995. Consumer variety seeking among goods and services: An integrative review.

Journal of Retailing and Consumer Services. 2(3), 139-148.

Kahn, B. E., Isen, A. M., 1993. The influence of positive affect on variety seeking among safe

and enjoyable products. Journal of Consumer Research. 20 (September), 257-270.

Kahn, B.E., Wansink, B., 2004. The influence of assortment structure on perceived variety and

consumption quantities. Journal of Consumer Research. 30 (March), 519-533.

Keaveney, S.M., Hunt, K.A., 1992. Conceptualization and operationalization of retail store

image: A case of rival middle-level theories. Journal of the Academy of Marketing Science. 20

(2), 165-175.

Kelloway, E.K., 1998. Using Lisrel for structural equation modeling : A researcher’s guide,

Sage Publication , Thousand Oaks, CA.

Kline, R. B., 1998. Principles and practice of structural equation modeling, Guilford Press, New

York:

26

Krishen, A., Bui ,M., Peter, P., 2010. Kiosk retailing environments: Exploring the role of regret

and variety on consumer behavior. International Journal of Retail and Distribution Management.

38(3), 173-189.

Levav, J., Zhu, R., 2009. Seeking freedom through variety. Journal of Consumer Research. 6

December, 600-610

Lichtenstein, D.R, Netmeyer, R.G., Burton, S., 1995. Assessing the domain specificity of deal

proneness: A field study. Journal of Consumer Research. 22 (December), 314-326.

Mady, T., Cheerier, H., Lee, D., Rahman, K., 2011. Can sentiment toward advertising explain

materialism and vanity in the globalization era? Evidence from Dubai. Journal of Global

Marketing. 24 (5), 453-472.

Maimaran, M., Wheeler, S.C., 2008. Circles, squares, and choice: The effect of shape arrays on

uniqueness and variety seeking. Journal of Marketing Research. 45, 731−740.

Martínez, E, Montaner,T., 2006. The effect of consumer's psychographic variables upon deal-

proneness. Journal of Retailing and Consumer Services. 13 (3), 157-168.

Mattila, A. S, Enz ,C.A., 2002.The role of emotions in service encounter. Journal of Service

Research. 4 (4), 268-277.

Mattila, A.S., Wirtz, J., 2001. Congruency of scent and music as a driver of in-store evaluations

and behavior. Journal of Retailing. 77, 273-289.

McAlister, L., 1982.A dynamic attribute satiation model of variety seeking behavior. Journal of

Consumer Research. 9 (September), 141-150.

27

Medsker, G.M., Williams, L.J., Holohan, P., 1994. A review of current practices for evaluating

causal models in organizational behaviour and human resources management research. Journal

of Management. 20, 439–64

Menon, S., Kahn, B.E., 1995. The impact of context on variety seeking in product choices.

Journal of Consumer Research. 22 (December), 285-295.

Milliman, R.E., 1982. Using background music to affect the behavior of supermarket shoppers.

Journal of Marketing. 46 (Summer), 86-91.

Milliman, R.E., 1986. The influence of background music on the behavior of restaurant patron.

Journal of Consumer Research. 13 (September), 286-289.

Morales, A., Kahn, B. E., McAlister, L., Broniarczk, S. M., 2005. Perceptions of assortment

variety: The effects of congruency between consumers’ internal and retailers’ external

organization. Journal of Retailing. 81(2), 159-169.

Morrin, M., Chebat, J., 2005. Person – place congruency: The interactive effects of shopper style

and atmospherics on consumer expenditure. Journal of Service Research. 8(2), 181-191.

Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y., Podsakoff , N. P., 2003.Common method biases in

behavioral research: A critical review of the literature and recommended remedies. Journal of

Applied Psychology. 88(5), 879-903.

Ratner, R.K., Kahn, B.E., 2002. The impact of private versus public consumption on variety-

seeking behavior. Journal of Consumer Research. 29(September), 246-257.

28

Russell , J. A., Mehrabian,A., 1976. Environmental variables in consumer research. Journal of

Consumer Research. 3 (June), 62-63.

Salant, P., Dillman, D. A., 1994. How to conduct your own survey, John Wiley and Sons, New

York:

Sankaran , K., Demangeot, C., 2011. On becoming a culturally plural consumer. The Journal of

Consumer Marketing. 28 (7 ), 540-549

Schmid, J., Leiman, J.M., 1957.The development of hierarchical factor solutions. Psychometrika.

22 (1), 83-89.

Sharma, P., Sivakumaran, B., Marshall, R., 2010a. Impulse buying and variety seeking: A trait-

correlates perspective. Journal of Business Research. 63(3), 276-283.

Sharma, P., Sivakumaran, B., Marshall, R., 2010b. Exploring impulse buying and variety

seeking by retail shoppers: Towards a common conceptual framework. Journal of Marketing

Management. 26 (5-6), 473-494.

Sharma, P., Sivakumaran, B., Marshall, R., 2011. Deliberate self-indulgence versus involuntary

loss of self-control: Toward a robust cross-cultural consumer impulsiveness scale. Journal of

International Consumer Marketing. 23 (3), 229-245.

Sherman, E., Mathur, A., Smith, R. B., 1997. Store environment and consumer purchase

behavior: Mediating role of consumer emotions. Psychology and Marketing. 14(4), 361-379.

Simonson, I., 1990. The effect of purchase quantity and timing on variety-seeking behavior.

Journal of Marketing Research. 27 (2), 150-162

29

Simonson, I., 1999. The effect of product assortment on buyer preference. Journal of Retailing.

75 (3), 347-370.

Simonson, I., Winer, R. S., 1992. The influence of purchase quantity and display format on

consumer preference for variety. Journal of Consumer Research. 19 (June), 133-138.

Sivakumaran, B., Kannan P. K., 2002. Consideration Sets under Variety Seeking Conditions: An

Experimental Investigation. In Advances in consumer research. ed. Provo, UT: Association for

Consumer Research, vol 29: 209-210., eds. Susan M. Broniarczyk and Kent Nakamoto,

Valdosta, GA : Association for Consumer Research, 209-210.

Sivakumaran, B., Shankar,V., 2010. The moderating role of variety seeking in order of entry

effect. Working paper, Great Lakes Institute of Management.

Smith, W., 1989. Trends in retail lighting: An intelligent design approach. International Journal

of Retail and Distribution Management. 17 (5), 30-32.

Solomon, M. R., (1983).The role of products as social stimuli : A symbolic interacationism

perspective. Journal of Consumer Research. 10 December, 319-329.

Spies, K., Hesse, F., Loesch, K., 1997. Store atmosphere, mood and purchasing behavior.

International Journal of Research in Marketing. 14, 1-17.

Spangenberg, E. R., Crowley, A. E, Henderson, P. W., 1996. Improving the store environment:

Do olfactory cues affect evaluations and behaviors? Journal of Marketing. 60(2), 67-80.

Steenkamp, J. E.M., Baumgartner, H., 1992. The role of optimum stimulation level in

exploratory consumer behavior. Journal of Consumer Research. 19 (December), 434- 448.

30

Summers, T. A., Hebert, P.R., 2001. Shedding some light on store atmospherics influence of

illumination on consumer behavior. Journal of Business Research. 54, 145-115.

Trivedi. M., 1999. Using variety –seeking-based segmentation to study promotional response.

Journal Academy of Marketing Science. 27(1), 37-49.

Trivedi, M., Morgan, M.S., 2003. Promotional evaluation and response among variety seeking

segments. Journal of Product and Brand Management. 12 (6), 408-425.

Turley, L.W., Milliman , R. E., 2000. Atmospheric effects on shopping behavior. A review of the

experimental evidence. Journal of Business Research. 49, 193-211.

Vahie, A., Paswan, A., 2006. Private label brand image: Its relationship with store image and

national brand. International Journal of Retail and Distribution Management. 34 (1), 67-84.

Van Trijp, H.C.M., Hoyer, W.D., Inman, J. J., 1996. Why switch? product category- level

explanations for true variety seeking behavior. Journal of Marketing Research. 33 (August),

281-292.

Wakefield, K., Barnes, J. H., 1996. Retailing hedonic consumption: A model of sales promotion

of a leisure service. Journal of Retailing. 72 (4), 409-427.

Ward, J.C., Bitner, M. J., Barnes, J., 1992. Measuring the proto typicality and meaning of

retail environments. Journal of Retailing. 68, 194–222.

Watson, D., Clark, L.A., Tellegen, A., 1988. Development and validation of brief measures of

positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology.

54 (June), 1063-1070.

31

Wheatley, J. J., Chiu J. S. Y., 1977. The effects of price, store image, and product and

respondent characteristics on perceptions of quality. Journal of Marketing Research. 14, 181-

186.

Wheaton, B., Muthén, B., Alwin, D., Summers, G., 1977. Assessing reliability and stability in

panel models. In D. R. Heise (Eds.), Sociological Methodology. Jossey-Bass Inc., San

Francisco, 84-136.

Wolff, H.G., Preising, K., 2005. Exploring item and higher order factor structure with the

Schmid-Leiman solution: Syntax codes for SPSS and SAS. Behavior Research Methods. 37 (1),

48–58.

Wu, P.H., Kao, D.T., 2011. Goal orientation and variety seeking behaviour: The role of decision

task. Journal of Economic Psychology. 32 (1), 65-72.

Yalch, R., Spangenberg E., 1990. Effects of store music on shopping behavior. The Journal of

Services Marketing. 4 (1), 31-39.

Yoo, C., Park, J., MacInnis, D. J., 1998. Effects of store characteristics and in-store emotional

experiences on store attitude. Journal of Business Research. 42 (3), 253-263.

Zimmer, M.R., Golden, L.L., 1998. Impressions of retail stores: A content analysis of consumer

images. Journal of Retailing. 64 (Fall), 265-293

32

Fig. 1.Proposed model of the impact of store environment on variety seeking behavior.

Optimum Stimulation

Level

Design Factors

Store Environment

Positive affect

Variety Seeking Behavior

H2 H1

H3

H4

H5

Ambient Factors

Deal Proneness

Social Factors

33

Fig. 2.Typical Schmid-Leiman factor structure.

Music2

Light1

Light2

Light3

Employee1

Employee2

Employee3

Lay Out1

Lay Out2

Music1

Store Environment

Music

Employee

Layout

Light

34

Fig. 3.Typical second order factor structure.

Store Environment

Ambient factors

Design Factors

Social Factors

Music1

Music2

Light1

Light2

Lay Out2

Lay Out1

Light3

Employee1

Employee2

Employee3

35

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

Light

Employee

Optimum Stimulation

Level

Layout

Deal Proneness

Store Environment

Music2

Light1

Light2

Light3

Employee1

Employee2

Employee3

Layout1

Layout2

Music1 Music Positive

affect

Variety Seeking Behavior

-.16 N/S

.07 N/S

Scent Scent1

Scent2

Scent 3

Assortment

Assort1

Assort2

Assort3

.32

.27

. 20

36

Table 1 Scale summary

Scale Mean SD Music (Morrin and Chebat 2005) The store had a pleasant music The store had a terrible music*.

2.0 2.6

1.0 1.0

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 /scent.*

3.5 3.6 3.2

.83 .81 .77

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.

3.8 4.0 3.9

.48 .51 .48

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.

3.5 3.1 3.0

.74

.84

.79

Layout (Dickson and Albaum 1977) It was easy to move about in the store. It was easy to locate products/ merchandise in the store

3.6 3.7

.87 .63

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

3.2 3.2 3.3

.80 .76 .75

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

3.1 3.3 3.6

.75

.76

.67

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.

2.9 3.5 3.3 3.2 3.0

.96

.73

.88

.85

.87

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

3.3 3.4 2.8

3.2

.73 .74 .91

.79

* Indicates reverse scored items.

Table 2 Correlation matrix* Music Scent Light Assortment Layout Employee Positive

Affect Optimum

stimulation level

Deal proneness

Music .65 Scent .15 .76 Light .12 .14 .81 Assortment .05 -.01 .01 .70 Layout .11 .03 .20 .16 .63 Employee .16 -.04 .09 .02 .05 .87 Positive Affect -.03 .01 -.01 -.02 .03 .04 .80 Optimum Stimulation Level

.13 .11 .15 .02 .19 -.11 .15 .60

Deal proneness .11 .05 .07 .12 .15 .05 -.06 -.01 .71 Average Variance Extracted

.68 .65 .63 .64 .62 .76 .66 .54 .61

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

1

Table 3 Means of variety seeking purchases Minimum Maximum Mean Standard

DeviationVariety Seeking

Purchases 0 3 0.4 0.57

Table 4 Comparison 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

1

Table 5 Structural model results of variety seeking behavior

H# Hypotheses Predicted Direction

Observed Direction

Path Coefficient// p value

H1 Positive Affect To Variety Seeking Behavior

+ + .07, ns

H2 Store Environment To Positive Affect

+ - -.16, ns

H3 Store Environment To Variety Seeking Behavior.

+ + .32*

H4 Optimum Stimulation Level To Variety Seeking Behavior. + + .27*

H5 Deal Proneness To Variety Seeking Behavior.

+ + .20*

* Significant at p < .001 ns – not significant

2

Table 6 Direct, 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

3

Authors Bios

Dr. Geetha Mohan is an Assistant Professor at the SSN College of Engineering, Chennai, India.

She earned her PhD in Marketing at the Indian Institute of Technology, Madras. Her research

interests include impulse buying and variety seeking behaviors. She has published her research in

the European Journal of Marketing, Journal of Indian Business Research among others

Prof. Bharadhwaj Sivakumaran is Professor of Marketing at the Great Lakes Institute of

Management, Chennai, India. He earned his PhD in Marketing at the University of Maryland,

USA. His research interests include variety seeking and impulse buying behavior, consideration

sets, order of entry, and cross-cultural differences in consumer behavior and consumer

promotions. He has published his research in the Journal of Business Research, Journal of

Marketing Management, and Journal of International Consumer Marketing among others.

Dr. Piyush Sharma is an Associate Professor in the Department of Management and Marketing at

The Hong Kong Polytechnic University, Hong Kong (mspiyush@polyu.edu.hk). He earned his

PhD in Marketing at Nanyang Technological University, Singapore. His research interests

include self-regulation and self-regulatory failure, cross-cultural consumer behavior, and services

and international marketing. He has published his research in the Journal of the Academy of

Marketing Science, Journal of International Business Studies, Journal of Service Research,

Journal of Business Research, Journal of Services Marketing, Journal of Marketing Management,

Journal of International Consumer Marketing, and Journal of Euromarketing among others.