89213146 Chapter 8 Atmospherics and Retail Space Retail Management
Moderating Effects of CRM on e-tail Atmospherics …Moderating Effects of CRM on e-tail...
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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 .
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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
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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
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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
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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
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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
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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
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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
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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).
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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
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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-
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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
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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
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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).
Table-4a: 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 .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
Model Sum of Squares df Mean Square F Sig.
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
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Table-4c: Regression coefficients
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).
Table-5a: 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 .428a .183 .182 .24183835 .183 190.251 4 1200 .000
a. Predictors (constant): Perceived experiential value
b. Dependant variable: Behavioural output
Table-5b: ANOVA
Model Sum of Squares df Mean Square F Sig.
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
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Table-5c: Regression coefficients
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,
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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
Binary interaction effects
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
Binary interaction effects
PEV*CRMT .298** 2.721
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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
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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
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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)
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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
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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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