Moderating Effects of CRM on e-tail Atmospherics …Moderating Effects of CRM on e-tail Atmospherics-Shopping Behaviour Link: A Case of Modified Mehrabian-Russell Model . Arup Kumar

1 SIT Journal of Management

Vol. 3. No. Special: November 2013, Pp. 1-27

Baksi ISSN: 2278-9111

Moderating Effects of CRM on e-tail Atmospherics-Shopping Behaviour

Link: A Case of Modified Mehrabian-Russell Model

. Arup Kumar Baksi*

Abstract

Proliferation of virtual network-based marketing transactions has triggered a paradigmatic shift

in traditional physical retail environment to electronic-retail or e-tail atmospherics resulting in

the creation of dual-domain. Customer Relationship Management has been found to be critical

in moderating consumer purchase-behavioural pattern relationship. This paper attempts to

explore the probable moderating effects of CRM, if any, on the e-tail atmospherics and the

corresponding shopping behaviour relationship since literature remained inconclusive about the

same. The Mehrabian-Russell (M-R) model, one of the most influential models towards

explaining the effect of physical environment on human behaviour, has been used to identify the

construct relationships with adequate modification suitable to explain effect of virtual e-tail

atmospherics on shopping behaviour and hence proposed and re-nomenclated as the Baksi-

Parida (B-P) model. The study was carried out on the basis of response of the shoppers using

twelve different e-tail services. The results indicated a strong moderating effect of CRM on

enhancement of shopping behaviour under the influence of e-tail atmospherics. The proposed

model took into consideration the components of virtual atmospherics and the robustness was

examined with multivariate statistical analysis. The study, in future, may be extrapolated on a

wider and varied scale of e-commerce applications to obtain a generalized version of the

proposed model.

Key words: e-tail, atmospheric, customer relationship management, shopping behaviour, model

Arup Kumar Baksi *, Assistant Professor, Department of Management Science, Bengal Institute

of Technology & Management, Santiniketan, India, email : [email protected],

[email protected], Phone: +91-9434155575 .




1. Introduction

Electronic commerce has been on the rise since the introduction of World Wide Web browsing

in the 1990s by Tim Berners-Lee. By the end of 20th century, online security had improved and a

steady and uninterrupted connection to the internet was possible via DSL (digital subscriber line)

which enabled e-commerce to function on a new digitized platform. Application of widespread

e-commerce activities saw the steady emergence of virtual retail shops more specifically

nomenclated as electronic retails and acronymed as e-tails. Over the years four major dimensions

of e-tailing were identified: system quality, content quality, reliability and support services.

Academic researchers have also incorporated „service quality‟ as a important aspect of e-tailing

(Santos, 2003) and many of them have related service quality to the success of e-commerce

applications (Aberg and Shahmehri, 2000; Chen et al, 2004; Gefen and Devine, 2001; Madeja

and Schoder, 2003; Page and Lepkowska-White, 2002; Santos, 2003). In the latter half of 21st

century, when „relationship marketing‟ started replacing the conventional „transactional

marketing‟; e-commerce became a critical domain of CRM technology application and vendors

like Gartner, Peoplesoft, Siebel Systems, Amdocs, Epipheny, SAP, Netsuite etc. started

integrating the e-tail sites with pro-customer softwares with an objective to make them more

comfortable to interact with the virtual and intangible environment.

A report on e-commerce market in India namely "e-Commerce Market in India 2013" stated

that a steady rise in the disposable income and proliferation of internet across the country

happened to be the primary market drivers for e-Commerce businesses in India. It is anticipated

that the tier II & III cities will contribute the most in shaping up the demand curve in the ensuing

years. Market research report confirmed opportunities for vendors from the mobile internet and

social media space. The Indian e-Commerce market primarily comprises of five major segments

i.e. online travel, retail, financial services, digital downloads and „other services‟, wherein the

online travel and retail segments dominate the overall pie with a cumulative share of more than

85%. Of all, online retail happens to be the fastest growing segment in the Indian market.

Competition in the market is severe and low consumer loyalty prevailing in the market furthers

the competition by manifolds. Revamped business strategies, consolidations and innovation in

products/service delivery model have become the most eminent trends in the market. Advanced




analytical tools and applications, namely CRM-softwares, have made the job easier for vendors

in India.

Competition in the market is seen to be highly stiff and factors such as low brand loyalty, price

sensitivity and affinity towards discounted offers & services amongst consumers makes the

competition even severe. Advanced CRM analytical tools and applications are being constantly

sought after by players in order to create a better scope in the market.

2. Review of literature

Marketing literatures conceptualized „atmospherics‟ as the conscious designing and arrangement

of servicescape to generate desired level of emotional effects in the shoppers that subsequently

enhances purchase probability (Kotler, 1973). Bitner (1992) identified three dimensions of

atmospherics namely ambient conditions, spatial layout & functionality and sign, symbols &

artifacts. Subsequent research conducted by various researchers proposed an extension of

dimensions of retail atmospherics such as exterior of retail outlet (architectural style and parking

slots), and human elements (employee appearance and customer interaction) (Baker, 1987;

Berman & Evans, 1995; Turley & Milliman, 2000). Environmental psychologists have studied

the interactive relationship between the physical environment and human behaviour for long.

Mehrabian and Russell (1974) proposed a theoretical model (see Fig.1) to demonstrate the

impact of physical environment on human behaviour. The Mehrabian-Russell (1974) model

claimed that pleasure and arousal were the two orthogonal dimensions representing individual

emotional or affective responses to a wide range of environments and the model specified a

conditional interaction between pleasure and arousal in determining approach-avoidance

behaviour. Donovan and Rossiter (1982) discovered a positive relationship between pleasure and

arousal dimensions and intentions to remain in a retail setting and spend more money. In

addition, Kenhove and Desrumaux (1997) cited in Ryu K. (2005) stated that the examination

relationship between the emotional states evoked in a retail environment and behavioural

intentions in that environment. In a pleasant environment, an increase in arousal was argued to

increase approach behaviours, whereas in unpleasant environments, an increase in arousal was

suggested to motivate more avoidance behaviours (Donovan & Rossiter, 1982). Similarly, the




researcher is in complete agreement with Ryu K. (2005) and would like to add that the traditional

pleasure-arousal interaction effect might be limited to high target arousal situations.

Fig.1: The Mehrabian-Russell model (1974)

Turley and Milliman (2000) presented the S-O-R paradigm which assumed that physical retail

environments contain stimuli (S) that cause changes to people‟s internal or organismic states (O)

which in turn causes the approach-avoidance response (R) (Mehrabian-Russell model). The de

facto way of connecting environmental cues and shopping outcomes is with the M-B

(Mehrabian-Russell) model that uses the S-O-R paradigm (Donovan and Russell. 1982; Eroglu et

al. 2001; Sautter et al. 2004; Manganari et al. 2009). Eroglu, Macheit and Davis (2001, 2003)

attempted an extension of the S-O-R paradigm (see Fig.2) to e-tailing and provided empirical

support for significant effects of site atmospherics on shoppers‟ behavioural attitudes,

satisfaction level and a host of approach-avoidance behaviours.

Fig.2: S-O-R model adapted by Eroglu et al (2001).

Environmental stimuli

Emotional states

Pleasure Arousal

Dominance

Approach or Avoidance

approach

Online

environmental cues

High task relevant

Low task relevant

Internal states

Affect

Cognition

Shopping outcomes

Approach

Avoidance

Atmospheric

responsiveness

Involvements

Organism Response Stimulus




E-tail atmospherics have been studied widely (Eroglu et al., 2001; Sautter et al., 2004; Fiore

and Kelly, 2007; Manganari et al. 2009; Vrechopoulos, 2010). Furthermore, the way web

designers establish atmospherics online is continually changing and evolving with leaps in web

technology, such as CSS3 (Cascading Style Sheets), which improves upon web‟s layout and

visual capabilities. However, most of the academic releases on the subject do not address the

technical level of establishing atmospherics. Eroglu, Machleit and Davis (2001) observed that

many retail-atmospheric variables (studied in physical environment) namely odour, crowding,

illumination, temperature, textures etc. are irrelevant to virtual e-tail atmosphere. Sautter et al

(2004) identified four distinct dimensions of e-tail atmospherics namely vividness, interactivity,

symbolism and social elements. Vividness is the richness of environmental information

presented to human senses (Shih 1998; Steur 1992). Media (e.g., internet ) vividness is a

function of two dimensions: (1) depth, which is the resolution or fidelity of sensory

information, and (2) breadth, which is the number of sensory dimensions concurrently activated

(Shih 1998; Steur 1992). Although research on vividness confirms its significance in online

shopping (Coyle and Thorson 2001) how the depth and breadth dimensions interact to affect

consumers‟ behaviors is unknown. For example, if a compensatory model underlies media

vividness, then improved performance on one dimension (e.g., breadth–use of more sensory

modes) can compensate for deficient performance on the other dimension (e.g., depth–use of

fewer visual stimuli). Although vividness is a desirable feature of virtual stores, excessive

stimulation–as in physical stores–may overwhelm consumers (Steenkamp and Baumgartner

1992). In conventional advertising, the positive effect of vivid information follows an inverted

U-shaped curve (Keller and Block 1997). If online responses are similar, then e-tailers must

consider the cumulative function of vividness breadth and depth and recognize trade-offs in

expanding the richness and diversity of sensory cues.

As many researchers recognize, there is little agreement on the definition of interactivity

(Heeter 2003; Klein 2003). Within our proposed framework, interactivity is presented as a

design characteristic of virtual store environments. Specifically, interactivity is defined as the

susceptibility and responsiveness of computer-mediated environments to user control (Ariely

2000; Klein 2003; Steur 1992). The effect of interactivity on telepresence (Coyle and

Thorson 2001; Klein 2003) and some evaluative aspects of online buyer performance (Ariely




2000) are well known. Future research should consider the direct effects of interactivity on

a broader range of organismic variables. For example, reduced interactivity may frustrate

consumers and decrease their pleasure (Dailey 2001). Alternatively, opportunity for

enhanced interactivity, regardless of realization, may yield perceptions of greater

navigational ease and enhance pleasure derived from online experiences (Childers et al.

2001). Enhanced control should also boost feelings of dominance/control when interacting with

e-tail websites. In physical stores, symbols serve as “explicit or implicit signals to

communicate about the place to its users” (Bitner 1992, p.66). Such symbols may be more

important when shoppers cannot easily ask for a clerk‟s assistance, as they can in physical

stores. Given the importance of navigation in virtual environments, many symbols are

incorporated expressly for function or design. The extent to which such symbols successfully

facilitate navigation will often be critical to the success of shopping experiences. Signs not

meant to promote navigational ease can be used to indicate site credibility and sponsor

integrity/reputation. Certification and rating services, such as eTrust, Verisign and BizRate,

use graphic brand marks to indicate their stamp of approval on certified sites. Alternative

cues for judging site credibility can derive from design elements such as affiliate linkages

(Putcha 2001) and traffic counters. These and other common web design tools can transmit

important symbolic messages that should be further explored in understanding e-tail

atmospherics.

The social elements in physical stores include crowding and the appearance and/or demeanor

of shoppers and employees (Baker, Grewal, and Parasuraman 1994). Although there is no

"visible presence of other shoppers and employees…in the online retail environment (Eroglu,

Machleit, and Davis 2001, p.179), e-tail websites offer other representations of interpersonal

interaction; specifically, shopping agents and online communities. By definition, a shopping

agent is an interactive tool designed to help shopper‟s process product information and make

purchase decisions online (Häubl and Trifts 2000). Shopping agents can mimic the role of

salespeople in physical stores. Many e-tail site designers create virtual bodies or avatars

that can act as representations of salespersons and/or online shoppers (Morgan 1999). Agents

can facilitate search or support choice (Häubl and Trifts 2000; Sproule and Archer 2000) and

will likely influence customer's affective and cognitive states. Social factors, including




dimensions of salespersons‟ performance, affect customers‟ pleasure and arousal (Baker,

Levy, and Grewal 1992). If, as in traditional servicescapes, consumers self-select how and to

what extent a sales assistant is involved, then they should find that an agent increases their

shopping pleasure and feelings of control in service encounters. Attempts to embody shopping

agents as avatars suggests that people prefer the illusion of human touch even if they self-

create, and thus recognize, the falsity of this perception. This welcomed illusion suggests

various opportunities for studying the effects of social elements in website design. For

example, shopping agents can create trust and build relationships in online environments

(Papadopoulou et al. 2001), which means their availability may reduce consumers‟ assessments

of perceived risk and boost their positive affect toward the website. The use of communication

tools to build online communities may be critical to e-tailing success (McWilliam 2000).

Some scholars are skeptical about online community building and report that community is

unrelated to e-tail effectiveness (Wolfinbarger and Gilly 2002). However, their research has

focused on online purchasers and not shoppers who may use (r)e-tail websites to support or

facilitate purchase in physical stores. Such a focus may underestimate the effects of online

communities on consumers‟ internal states. Organismic states have been conceptualized as

cognitive appraisals in monitoring consumers‟ reactions to website design and effectivity and

pose a critical element for perceived experiential value for the online shoppers resulting in the

manifestation of a specific output behavioural pattern.

The review of literature exposed the lack of study on virtual atmospherics resulting in a cognitive

model like the Mehrabian-Russell model for physical-store atmospheric effects.

2a. Hypothesis formulation and proposed research model

Apropos to the literature reviewed and the research gaps identified thereof, the following

hypotheses were formulated for testing.

H1: Atmospheric stimuli (ASt) has impact on Organismic states of shoppers (OS)

H2: Organismic states of shoppers (OS) do influence Perceived experiential value (PEV)

H3: Perceived experiential value (PEV) has an impact on Behavioural output of shoppers (BO)

H4: Augmentation of CRM technology will produce an enhanced impact of summated




atmospheric stimuli on organismic states of shoppers (OS).

H5: Improved CRM technology will ensure stronger impact of organismic states of shoppers

(OS) on perceived experiential value (PEV)

H6: CRM technology will have increased impact of perceived experiential value (PEV) on

approach/avoidance behaviour of shoppers

The following research model has been proposed with expansion and modification of

Mehrabian-Russell model with the introduction of “Perceived Experiential Value” as a bridging

link between “organismic states of shoppers” (emotional states as described in Mehrabian-

Russell model) and “behavioural output” (approach-avoidance dichotomy as stated in

Mehrabian-Russell model) (see Fig.3):

Fig.3: Proposed research model (Baksi-Parida Model)

Atmospheric Vividness

(AV)

Atmospheric Interactivity

(AI)

Atmospheric Symbolism

(AS)

Atmospheric Social elements

(ASE)

Perceived e-tail service

quality (PEtSQ)

Perceived merchandising

quality (PMQ)

Perceived emotional

arousal (PEA)

Perceived shopping

motivation (PSM)

Perceived

experiential value

(PEV)

Approach/ Avoidance

Atmospheric Stimuli (ASt) Organismic states (OS) Behavioural

output

CRM

technology

PEV




3. Methodology

The objective of this study were (a) to evaluate the impact of e-tail atmospheric stimuli (ASt) on

organismic states (OS) of shoppers, (b) to assess the role of shoppers‟ organismic state to form

their perceived experiential value (PEV), (c) to understand the influence of perceived

experiential value in determining the approach/avoidance behaviour of shoppers, (d) to examine

whether CRM technology, used to operate virtual e-tail environment, had any moderating effect

on the relationship between the major variables (e) to propose a model depicting the e-tail

atmospheric stimuli, organismic states of shoppers, perceived experiential values and

behavioural pattern and to test the robustness of the same. To conduct the study mall-intercept

procedure was adopted whereby the researchers questioned the shoppers, coming out of physical

stores, about their habit of shopping in virtual stores (e-tails). Convenience sampling procedure

was deployed as number of targeted shoppers were demographically assorted and thinly

distributed across the customer traffic in a specific physical store. The study was comprised of

two phases. Phase-I involved a pilot study to refine the test instrument with rectification of

question ambiguity, refinement of research protocol and confirmation of scale reliability was

given special emphasis (Teijlingen and Hundley, 2001). FGI was administered. Cronbach‟s α

coefficient (>0.7) established scale reliability (Nunnally and Bernstein, 1994). The structured

questionnaire thus obtained after refinement contained six sections. Section-1 asked the

respondents about the electronic or web atmospherics of the virtual stores, section-2 was

intended to generate response with-regard-to perceived organismic states of e-tail shoppers,

section-3 questioned the shoppers about their perceived experience in using the e-tail services,

section-4 was designed to understand the behavioural pattern of the e-tail shoppers, section-5

asked the e-tail shoppers about their opinion on technological applications in web-store and

section-6 was designed to generate the demographic profile of the respondents. The second phase

of the cross-sectional study was conducted by using the structured questionnaire. A total number

of 2000 questionnaires were used amongst shoppers who use virtual e-tail services of Flipkart,

Aviance, Naaptol, Xerion Retail Pvt. Ltd, eBayIndia Pvt. Ltd., Myntra.com, Homeshop18.com,

inkfruit.com, infibeam.com and Snapdeal.com, which generated 1202 usable responses with a

response rate of 60.10% (approximately).




3.1 Factor constructs measurement

To develop a measure for perception of e-tail atmospheric stimuli a modified 25-item scale was

used which has been adapted from Kaikkonen (2012) was used. The study further used a 15-item

scale for analyzing the organismic states of e-tail shoppers (perceived e-tail service quality,

perceived merchandising quality, emotional arousal and shopping motivation) adopted from Lin

and Chiang (2010), Sautter et al (2004), Wolfinbarger and Gilly (2002) and Baksi and

Parida(2012). Regarding the measurements of emotion, this research used two constructs,

respectively pleasure and arousal, mentioned by Russell (1978) and developed 3 items for each

construct. The third construct that Russell proposed, dominance, was excluded from our

measurements because some researchers thereafter found little explanatory power of dominance

on emotion (e.g. Donovan and Rossiter, 1982). The measurement of perceived experiential value

used 5-item scale (Sautter et al, 2004 adapted from Brakus‟s (2001) unpublished dissertation of

Columbia University. He developed 25 items for five experiences (i.e. experience of sense,

feel, think, act, and relate) from an exploratory study) and Baker et al (2002) while

conceptualization of behavioural pattern used 4-item scale (in reverse pattern) (Sautter et al,

2004; Sweeny and Wyber, 2002; Donovan and Rossiter, 1982). The moderating effect of CRM-

technology was measured by using a 5-item scale adapted from Baksi and Parida (2012). A 7

point Likert scale (Alkibisi and Lind, 2011) was used to mark the degree of agreeableness of the

targeted hopper about a specific item.

3.2 Reliability and validity test

Exploratory factor analysis (EFA) was deployed using principal axis factoring procedure with

orthogonal rotation through VARIMAX process with an objective to assess the reliability and

validity of all factor constructs. Secondly confirmatory factor analysis (CFA) was used to

understand the convergence, discriminant validity and dimensionality for each construct to

determine whether all the items measure the construct adequately as they had been assigned for.

Finally, LISREL 9.10 programme was used to conduct the Structural Equation Modeling (SEM)

and Maximum Likelihood Estimation (MLE) was applied to estimate the CFA models.

4. Data analysis and interpretation




The demographic data collected from the respondents were presented in Table-1

Table-1: Demographic data of the respondents

Demographic Variables Factors Freq.

ency

%

Gender Male 961 79.92%

Female 241 20.05%

Age

≤ 21 years 29 2.44%

22-32 years 392 32.61%

33-43 years 603 50.16%

44-54 years 152 12.64%

≥ 55 years 26 2.18%

Income

≤ Rs. 14999.00 24 1.99%

Rs. 15000-Rs. 24999.00 297 24.70%

Rs. 25000-Rs. 44999.00 599 49.83%

≥ Rs. 45000.00 282 23.48%

Occupation

Service [govt./prv] 773 64.30%

Self employed 277 23.04%

Professionals 86 7.12%

Student 29 2.44%

Housewives 37 3.1%

Educational qualification

High school 03 0.24%

Graduate 892 74.20%

Postgraduate 278 23.12%

Doctorate & others (CA, fellow etc) 29 2.44%

To assess the reliability and validity of the constructs, the researchers applied exploratory factor

analysis (EFA) using principal axis factoring procedure with orthogonal rotation through

VARIMAX process. The results of the EFA were displayed in Table-2. The Cronbach;s

Coefficient alpha was found significant enough, as it measure >.7 (Nunnally and Bernstein,

1994) for all constructs and therefore it is reasonable to conclude that the internal consistency of

the instruments used were adequate. Each accepted construct displayed acceptable construct

reliability with estimates well over .6 (Hair, Anderson, Tatham and William, 1998). Further to

this the average variance extracted (AVE) surpassed minimum requirement of .5 (Haier et al.,

1998). The KMO measure of sample adequacy (0.911) indicated a high-shared variance and a

relatively low uniqueness in variance (Kaiser and Cerny, 1979). Barlett‟s sphericity test (Chi-




square=1398.116, p<0.001) indicated that the distribution is ellipsoid and amenable to data

reduction (Cooper and Schindler, 1998).

The initial 29 items related to perceived service recovery were reduced to 12 items with items

having factor loading scores of <0.6 were discarded. The items related to repatronization were

limited to 2, while the 4 item customer advocacy scale revealed significant factor loading for all

its items and so did the customer-trust scale (3-item).

Table-2: Measurement of reliability and validity of the variables

Items FL t α CR AVE

Atmospheric Stimuli (ASt)

My e-tail site has an excellent virtual-layout (AV1) 0.698 - 917 0.917 0.833

My e-tail site has excellent grid, freeform and racetrack arrangement (AV2) 0.694 25.00

96

917 0.917 0.833

My e-tail site uses excellent virtual theatrics (AV3) 0.659 20.87

3

917 0.917 0.833

My e-tail site has soothing background colour, well-articulated space distribution,

visible and strategically highlighted fonts and compatible background music(AV4) 0.674 23.65 917 0.917 0.833

M e-tail site is easy to navigate (AV5) 0.701 25.77

5

917 0.917 0.833

My e-tail site offers excellent opportunity to interact with service provider (AV6) 0.721 30.81

6

917 0.917 0.833

My e-tail site offers excellent third-party gateway and interaction (AV7) 0.644 19.73

1

917 0.917 0.833

My e-tail site displays security symbols (AV8) 0.629 18.42

1

917 0.917 0.833

My e-tail site displays graphic brand-marks for easy identification (AV9) 0.652 20.10

4

917 0.917 0.833

My e-tail site displays certification of credentials and affiliate linkages (AV10) 0.709 27.32

1

917 0.917 0.833

My e-tail site displays adequate information about the shopping agents (AV11) 0.661 22.09

9

917 0.917 0.833

My e-tail site informs me about online communities (AV12) 0.663 22.10

1

917 0.917 0.833

Organismic states (OS)

My e-tail site offers excellent visual ambient environment (PEtSQ1) 0.769 - 909 0.909 0.801

My e-tail site offers reliable service and information (PEtSQ2) 0.717 27.87 909 0.909 0.801

My e-tail site assures me about accuracy and promptness of service (PEtSQ3) 0.691 23.67 909 0.909 0.801

My e-tail site takes responsibility in solving problem when I face one (PEtSQ4) 0.665 22.09 909 0.909 0.801

My e-tail maintain and offer quality product and brand (PMQ1) 0.639 21.01 909 0.909 0.801

My e-tail regularly informs me about the latest arrivals and schemes (PMQ2) 0.701 23.89 909 0.909 0.801

I am satisfied with my e-tail service provider (PEA1) 0.643 21.32 909 0.909 0.801

I feel attached to my e-tail service provider (PEA2) 0.616 20.11 909 0.909 0.801

I feel attached to my e-tail environment every time I log in (PEA3) 0.629 21.28 909 0.909 0.801

I feel highly motivated to shop through my virtual e-tail outlet (PSM1) 0.718 27.89 909 0.909 0.801

I feel a strong desire to shop through my virtual e-tail outlet (PSM2) 0.654 21.68 909 0.909 0.801

Perceived experiential value (PEV)

My e-tail site and service provider makes a sensible proposition (PEV1) 0.628 22.39 871 0.871 0.786

My e-tail site and service provider asserts thoughtful disposition of facts (PEV2) 0.678 26.09 871 0.871 0.786

My e-tail atmosphere allow me to relate physical store environment (PEV3) 0.662 25.31 871 0.871 0.786

Behavioural output (BO)

I desire to continue shopping via my e-tail outlet (BO1) 0.621 22.34 839 0.839 0.753

I desire to recommend my e-tail outlet to others (BO2) 0.608 20.18 839 0.839 0.753

I desire to increase the gamut of shopping via my e-tail outlet (BO3) 0.611 20.56 839 0.839 0.753




CRM Technology (CRMT)

My e-tail site is well connected to payment gateways & other networks (CRMT1) 0.762 26.78 912 0.912 0.895

My e-tail site can be accessed by mobile network (CRMT2) 0.729 24.09 912 0.912 0.895

My e-tail site has chronological storage of utility data (CRMT3) 0.692 22.12 912 0.912 0.895

My e-tail site provide me with real-time interactive sessions (CRMT4) 0.764 26.89 912 0.912 0.895

** FL: factor loadings, t: t-value, α: Cronbach’s α, CR: composite reliability, AVE: average variance

extracted

Mean composite scores were obtained to understand the values of Atmospheric stimuli (ASt),

Organismic states of shoppers (OS), Perceived experiential value (PEV) and Behavioural output

(BO) across the scale element fit to the survey instrument. Consecutive regression analyses were

performed to assess the associationship between the variables. The first of the three regression

analysis was performed to examine whether organismic states of shoppers can be predicted on

the basis of atmospheric stimuli applied. Table-3a, 3b and 3c displayed the regression results for

the first regression. The R2 value was found to be .369 suggesting that the independent variable

(ASt) measured 36.9% of the variation in OS which is considered to be significant enough for

the predictability of the model. ANOVA (Table-3b) established that the variation showed by ASt

was significant at 1% level (f=123.469, p<0.01). Regression coefficients confirmed a strong and

positive associationship between ASt & OS (β = .630, t=15.747, p<0.01).

Table-3a: Model summary

Model R R

Square

Adjusted

R Square

Std. Error

of the

Estimate

Change Statistics

R

Square

Change

F

Change df1 df2

Sig. F

Change

1 .607a .369 .366 6.403 .369 123.469 4 1197 .000

a. Predictors (constant): Atmospheric stimuli

b. Dependant variable: Organismic states of shoppers

Table-3b: ANOVA

Model Sum of Squares df Mean Square F Sig.

1

Regression 20251.277 4 5062.819 123.469 .000b

Residual 34648.988 1197 41.005

Total 54900.265 1200

a. Dependant variable: Organismic states of shoppers (OS)

b. Predictors (constant): Atmospheric stimuli




Table-3c: Regression coefficients

Model

Unstandardized

Coefficients

Standardized

Coefficients t Sig.

B Std.

Error Beta

1 (Constant) 34.970 .399 87.598 0.000

Atmospheric stimuli 19.735 1.253 .630 15.747 .000

a. Dependant variable: Organismic states of shoppers (OS)

Table-4a, 4b and 4c displayed the regression results for the first regression. The R2 value was

found to be .625 suggesting that the independent variable (OS) measured 62.5% of the variation

in PEV which is considered to be significant enough for the predictability of the model. ANOVA

(Table-4b) established that the variation showed by OS was significant at 1% level (f=162.001,

p<0.01). Regression coefficients confirmed a strong and positive associationship between OS &

PEV (β = .891, t=23.241, p<0.01).


Model R R

Square

Adjusted

R Square

Std. Error

of the

Estimate

Change Statistics

R

Square

Change

F

Change df1 df2

Sig. F

Change

1 .791a .625 .622 8.171 .625 142.805 7 1312 .000

a. Predictors (constant): Organismic states of shoppers

b. Dependant variable: Perceived experiential value

Table-4b: ANOVA


1

Regression 27238.381 8 5311.312 162.001 .000b

Residual 38977.213 1095 52.187

Total 66215.594 1163

a. Dependant variable: Perceived experiential value

b. Predictors (constant): Organismic states of shoppers





Model

Unstandardized

Coefficients

Standardized

Coefficients t Sig.

B Std.

Error Beta

1

(Constant) 31.521 .401 87.598 0.000

Organismic states of

shoppers 28.117 1.116 .891 23.241 .000

a. Dependant variable: Perceived experiential value

Table-5a, 5b and 5c displayed the regression results for the first regression. The R2 value was

found to be .183 suggesting that the independent variable PEV measured 18.3% of the variation

in BO which is considered to be moderately significant for the predictability of the model.

ANOVA (Table-4b) established that the variation showed by PEV was significant at 1% level

(f=190.251, p<0.01). Regression coefficients confirmed a strong and positive associationship

between OS & PEV (β = .428, t=13.793, p<0.01).

The regression results supported Hypotheses 1, 2 and 3 (H1, H2 and H3).


Model R R

Square

Adjusted

R Square

Std. Error

of the

Estimate

Change Statistics

R

Square

Change

F

Change df1 df2

Sig. F

Change

1 .428a .183 .182 .24183835 .183 190.251 4 1200 .000

a. Predictors (constant): Perceived experiential value

b. Dependant variable: Behavioural output

Table-5b: ANOVA


1

Regression 11.127 4 11.127 190.251 .000b

Residual 49.596 1200 .058

Total 60.723 1201

a. Dependant variable: Behavioural output

b. Predictors (constant): Perceived experiential value





Model

Unstandardized

Coefficients

Standardized

Coefficients t Sig.

95.0%

Confidence

Interval for B

B Std.

Error Beta

Lower

Bound

Upper

Bound

1

(Constant) .173 .010 16.743 .000 .153 .193

Perceived

merchandising quality .054 .004 .428 13.793 .000 .046 .062

a. Dependant variable: Behavioural output

b. Predictors (constant): Perceived experiential value

Hierarchical regression was deployed to identify the moderating effects of CRM technology on

the relationship between the major constructs. The following regression models were generated:

(i) OS = β0 + β1*ASt +β2*CRMT + β3*ASt*CRMT + εi

where, OS represented organismic states of shoppers, Ast represented atmospheric stimuli and

Ast*CRMT represented binary interaction between atmospheric stimuli and CRM technology.

(ii) PEV = β0 + β1*Ost +β2*CRMT + β3*OS*CRMT+ εi

where, PEV represented perceived experiential value of shoppers, OS represented organismic

states of shoppers and OS*CRMT represented binary interaction between organismic states of

shoppers and CRM technology.

(iii) BO = β0 + β1*PEV +β2*PEV*CRMT + β2*PEV*CRMT + εi

where, BO represented behavioural output of shoppers, PEV represented perceived experiential

value of shoppers and PEV*CRMT represented binary interaction between perceived

experiential value of shoppers CRM technology.

The regression models were displayed in Table-6. For each equation 2 regression models were

established. Model-I represented (a) direct effect of atmospheric stimuli (ASt) and CRM

technology (CRMT) on organismic states of shoppers (OS); (b) direct effect of organismic states

and CRM technology on perceived experiential value and (c) direct effect of perceived

experiential value and CRM technology on behavioural output. Model-II represented binary-

interaction effects of (a) ASt*CRMT on OS, (b) OS*CRMT on PEV and (c) PEV*CRMT on

BO. Standardisation was applied to avoid interference with regression coefficients arising out of

multicollinearity between interaction variables (Irwin and Mcllelan, 2001; Aiken and West,




1991). The VIF (Variance Inflation Factor) corresponding to each independent variable is less

than 5, indicating that VIF is well within the acceptable limit of 10 (Ranaweera and Neely,

2003). Model-I revealed that (i) ASt and CRMT were found to have positive and significant

effect on OS (β = .293**, p˂0.01 and β = .116**, p˂0.01 respectively), (ii) OS and CRMT were

found to have significant and positive effect on PEV ((β = .201**, p˂0.01 and β = .097*, p˂0.05

respectively) and (iii) PEV and CRMT were found to have significant and positive effect on BO

((β = .218**, p˂0.01 and β = .163*, p˂0.05 respectively). Model-II revealed that (i) binary

interaction between ASt and CRMT had positive and significant moderating effect on OS (β =

.312**, p˂0.01), (ii) binary interaction between OS and CRMT had positive and significant

moderating effect on PEV β = .142**, p˂0.01), and (iii) binary interaction between PEV and

CRMT had positive and significant moderating effect on BO ((β = .298**, p˂0.01). Hierarchical

regression results lend support to Hypotheses 4, 5 and 6 (H4, H5 and H6)

Table-6: Hierarchical regression results

Independent Variables Dependent variable: Organismic states of shoppers (OS)

Model-1:β (t value) Model-2: β (t value) VIF

ASt .293** 2.316

CRMT .116** 2.119

Binary interaction effects

ASt*CRMT .312** 2.674

Adjusted R2

.489 .397

F-value 52.69 57.31

Independent Variables Dependent variable: Perceived experiential value (PEV)

Model-1: β (t value) Model-2: β (t value) VIF

OS .201** 2.501

CRMT .097* 1.689


OS*CRMT .142** 2.316

Adjusted R2

.629 .543

F-value 87.32** 96.19**

Dependent variable: Behavioural output (BO)

Model-1: β (t value) Model-2: β (t value) VIF

PEV .218** 2.344

CRMT .163** 1.945


PEV*CRMT .298** 2.721




Adjusted R2

.492 .499

F-value 98.32** 89.41**

To lend support to the extended and modified model and to assess its robustness of the same

confirmatory factor analysis was deployed with principal component factor analysis. This study

used Cronbach‟s α, lambda loading and squared multiple correlations (SMC) to test the

reliability of the model. Cronbach‟s α were recorded to be consistently greater than .800 while

the lambda loading ranged from 0.81 to 0.95 which indicated the extent to which the ratings of

items depend on the latent variable. As revealed in Table-7, the squared multiple correlations

(SMCs) ranged from 0.66 to 0.94 which is higher than the threshold of 0.5 (Hair et al , 1992).

The average variance extracted (AVE) of each construct was well over 0.50 level, implying that

each manifested variable could well explain the latent variable (Chen and Cherng, 1998).

Table-7: Confirmatory factor analysis to measure the modified model

Items

λ

loadi

ngs

SMC AVE α

Atmospheric stimuli (ASt)

My e-tail site has an excellent virtual-layout (AV1) 0.81 0.91 0.72 .812

My e-tail site has excellent grid, freeform and racetrack arrangement (AV2) 0.88 0.87 0.72 .812

My e-tail site uses excellent virtual theatrics (AV3) 0.82 0.87 0.72 .812

My e-tail site has soothing background colour, well-articulated space distribution,

visible and strategically highlighted fonts and compatible background music(AV4) 0.89 0.91 0.72 .812

M e-tail site is easy to navigate (AV5) 0.87 0.83 0.72 .812

My e-tail site offers excellent opportunity to interact with service provider (AV6) 0.85 0.76 0.72 .812

My e-tail site offers excellent third-party gateway and interaction (AV7) 0.83 0.75 0.72 .812

My e-tail site displays security symbols (AV8) 0.83 0.71 0.72 .812

My e-tail site displays graphic brand-marks for easy identification (AV9) 0.81 0.83 0.72 .812

My e-tail site displays certification of credentials and affiliate linkages (AV10) 0.86 0.66 0.72 .812

My e-tail site displays adequate information about the shopping agents (AV11) 0.92 0.69 0.72 .812

My e-tail site informs me about online communities (AV12) 0.87 0.92 0.72 .812

Organismic states (OS)

My e-tail site offers excellent visual ambient environment (PEtSQ1) 0.91 0.85 0.63 .839

My e-tail site offers reliable service and information (PEtSQ2) 0.90 0.88 0.63 .839

My e-tail site assures me about accuracy and promptness of service (PEtSQ3) 0.88 0.91 0.63 .839

My e-tail site takes responsibility in solving problem when I face one (PEtSQ4) 0.89 0.94 0.63 .839

My e-tail maintain and offer quality product and brand (PMQ1) 0.88 0.83 0.63 .839

My e-tail regularly informs me about the latest arrivals and schemes (PMQ2) 0.87 0.78 0.63 .839

I am satisfied with my e-tail service provider (PEA1) 0.88 0.77 0.63 .839

I feel attached to my e-tail service provider (PEA2) 0.86 0.69 0.63 .839




I feel attached to my e-tail environment every time I log in (PEA3) 0.89 0.76 0.63 .839

I feel highly motivated to shop through my virtual e-tail outlet (PSM1) 0.88 0.80 0.63 .839

I feel a strong desire to shop through my virtual e-tail outlet (PSM2) 0.85 0.81 0.63 .839

Perceived experiential value (PEV)

My e-tail site and service provider makes a sensible proposition (PEV1) 0.95 0.92 0.69 .809

My e-tail site and service provider asserts thoughtful disposition of facts (PEV2) 0.81 0.87 0.69 .809

My e-tail atmosphere allow me to relate physical store environment (PEV3) 0.88 0.81 0.69 .809

Behavioural output (BO)

I desire to continue shopping via my e-tail outlet (BO1) 0.92 0.81 0.58 .821

I desire to recommend my e-tail outlet to others (BO2) 0.91 0.76 0.58 .821

I desire to increase the gamut of shopping via my e-tail outlet (BO3) 0.88 0.93 0.58 .821

CRM Technology (CRMT)

My e-tail site is well connected to payment gateways & other networks (CRMT1) 0.81 0.84 0.71 .803

My e-tail site can be accessed by mobile network (CRMT2) 0.84 0.88 0.71 .803

My e-tail site has chronological storage of utility data (CRMT3) 0.87 0.87 0.71 .803

My e-tail site provide me with real-time interactive sessions (CRMT4) 0.86 0.91 0.71 .803

A number of fit-statistics (see Table-8) were obtained. The GFI (0.990) and AGFI (0.985) scores

for all the constructs were found to be consistently >.900 indicating that a significant proportion

of the variance in the sample variance-covariance matrix is accounted for by the model and a

good fit has been achieved (Baumgartner and Homburg, 1996; Hair et al, 1998, 2006; Hulland,

Chow and Lam, 1996; Kline, 1998; Holmes-Smith, 2002, Byrne, 2001). The CFI value (0.983)

for all the constructs were obtained as > .900 which indicated an acceptable fit to the data

(Bentler, 1992). The RMSEA value obtained (0.059) is < 0.08 for an adequate model fit (Hu and

Bentler, 1999). The probability value of Chi-square (χ2 = 197.06) is more than the conventional

0.05 level (P=0.20) indicating an absolute fit of the models to the data.

Table-8: Summary of fit indices

Fit indices χ2 df

P GFI AGFI CFI RMR RMSEA

Values 197.06 88 0.000 0.990 0.985 0.983 0.043 0.059

Structural Equation Modeling (SEM) was used to test the relationship among the constructs. All

the 17 paths drawn were found to be significant at p<0.05. The research model holds well (see

Fig.4) as the fit-indices supported adequately the model fit to the data. The double-curved arrows

indicate co-variability of the latent variables. The residual variables (error variances) are

indicated by Є1, Є2, Є3, etc. The regression weights are represented by λ. The co-variances are




represented by β. To provide the latent factors an interpretable scale; one factor loading is fixed

to 1 (Hox & Bechger, 1998).

Fig.4: Structural model showing the path analysis

The SEM disclosed the following direct and indirect and total effects of the independent

variables on dependent variables (see Table-9):

Table-9: Direct, indirect and total effects of independent variables on dependent variables

OS1

ASt OS PEV BO

PEV1

OS3

OS2

ASt4

ASt3

ASt2

ASt1

PEV1

PEV1

BO4

BO3

BO2

BO1

CRMT

CRMT3

CRMT2

CRMT1

λ1=1.00

λ2=0.93

λ3=0.95

λ4=0.91

λ5=0.95

λ6=0.89

λ7=0.91

λ8=0.87

λ9=0.83

λ10=0.82

λ11=0.88

λ12=0.81

λ13=0.86

λ14=0.83

λ15=0.97

λ16=0.93

λ17=0.92

β1=0.93

β2=0.91

β4=0.88

β5=0.95

β7=0.96

β8=0.94

β9=0.91

Є1=1.21

Є2=1.21

Є3=1.21

Є4=1.21

Є7=1.01

Є6=0.93

Є5=0.99

β3=0.91

Є10=1.10

Є9=0.91

Є8=1.12

Є11=1.21

Є14=1.23

Є13=1.09

Є12=1.32

β6=0.91

Є17=1.11

Є16=1.04

Є15=0.99

λ18=0.95

λ19=0.93

λ20=0.91

(β = .312**, p˂0.01)

(β = .142**, p˂0.01)

(β = .298**, p˂0.01)




Related variables Direct

effects Indirect effects

Moderating

effects

Total

effects

ASt OS 0.95 0.95

OS PEV 0.93 0.93

PEV BO 0.91 0.91

ASt OS PEV 0.88 (0.95*0.93) 0.88

OS PEV BO 0.84 (0.93*0.91) 0.84

ASt OS PEV BO 0.803 (0.95*0.93*0.91) 0.80

CRM

ASt OS

(β = .312**,

p˂0.01

CRM

OS PEV

(β = .142**,

p˂0.01)

CRM

PEV BO

(β = .298**,

p˂0.01)

5. Conclusion

Atmospherics play a critical role in service transactions as it injects a bundle of stimuli to the

prospective shoppers with an objective to arouse their organismic states and subliminal

perception, which in turn, is likely to manifest as positive approach to shopping decision. The

growth and proliferation of virtual-network based services or e-tail services have witnessed

usage of e-tail atmospherics targeted to generate the same level of arousal in shoppers. The

Mehrabian-Russell (M-R) model has been useful towards explaining the effect of physical

environment on human behaviour. This study has attempted to establish a model (Baksi-Parida

model) that will adequately explain the effects of virtual e-tail atmospherics on shopping

behaviour. Further to this the study explored the extent to which CRM-technology, a pivotal

dimension of CRM application and extremely relevant to e-tail atmosphere, can moderate the




relationship between e-tail atmospherics and shopping behaviour. The study confirmed the

moderating effects of CRM technology on e-tail atmospheric stimuli-organismic arousal of

shoppers‟ desire-perceived experiential value-behavioural output link with perceived experiential

value of shoppers as the new component added to the existing Mehrabian-Russell model to make

it fit to explain service transactions in new (electronic) format. The proposed Baksi-Parida model

also holds good as the model constructs fit the data thereby establishing a cause and effect

relationship between the variables and depicted the direct and indirect effects of the same.

The study has significant managerial implication as the growth of virtual markets will throw up

new challenges to the managers to attract customers on the basis of virtual-store or e-tail layouts

not only on the basis of their design and looks but also on the basis of their sheer ability to

interact and reciprocate to queries, ability to stack relevant data, images and graphics, efficiency

to provide prompt and adequate information to shoppers and keeping provision for shoppers to

navigate through e-tail virtual store by retaining aesthetics. The study is critical for relationship

managers too as they will be using business analytic softwares and technology to identify the

relevant touch-points between e-tail atmospherics and shoppers and their impact on the same.

The study has limitations to electronic-retail sites only and in future extrapolations of the Baksi-

Parida model can be made in other electronic services to understand the nature of relationship

shared by e-tail atmospherics and shopping behaviour.

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Documents

Moderating Effects of CRM on e-tail Atmospherics …Moderating Effects of CRM on e-tail Atmospherics-Shopping Behaviour Link: A Case of Modified Mehrabian-Russell Model . Arup Kumar