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Does pickup service quality explain
BOPIS users’store relationship
performance? The moderating role
of users’service
experience consciousness
Thamaraiselvan Natarajan and Deepak Ramanan Veera Raghavan
Department of Management Studies,
National Institute of Technology Tiruchirappalli, Tiruchirappalli, India
Abstract
Purpose –Building on Stimulus-Organism-Response theory, the current study examines the influence of
pickup service quality in buy-online pickup in-store service (BOPIS) on users’perceived relationship
investment with the mediating role of users’perceived experience quality and relationship proneness. This
research also demonstrates the subsequent impact of BOPIS users’perceived relationship investment on their
relationship performance indicators, like their cross-buying behaviors (breadth), frequency of their purchase
(depth) and longevity of their relationship (length) with the store. The moderating role of BOPIS users’service
experience consciousness in a few proposed relationships was tested.
Design/methodology/approach –The research is descriptive, quantitative and cross-sectional
investigation. The study employed a purposive sampling technique. It was conducted using data collected
using a validated self-administered questionnaire from 786 Indian omnichannel shoppers who have used
BOPIS services in the past. The proposed conceptual model was tested using Partial Least Squares-Structural
Equation Modeling.
Findings –The results indicate that BOPIS users’perceived experience quality and relationship proneness
positively mediate pickup service quality and perceived relationship investment. The users’perceived
relationship investment subsequently significantly positively impacts different dimensions of their
relationship performance with the store (breadth, depth and length). Additionally, BOPIS users’service
experience consciousness has a significant negative moderating effect on the direct relationship between
pickup service quality and different dimensions of relationship performance.
Research limitations/implications –The study is conducted in the Indian population, where omnichannel
retailing is still nascent.
Originality/value –This study addresses the need to investigate the relationship performance indicators of
BOPIS users, like their cross-buying behaviors(breadth), frequency of their purchase(depth) and longevity of
their relationship(length) with the store. This study is the first to show that pickup service quality might
explain the relationship performance of BOPIS users through their perceived experience quality, relationship
proneness and relationship investments. The moderating role of BOPIS users’service experience
consciousness in a few proposed relationships was also tested for the first time.
Keywords Pickup service quality, BOPIS, Experience quality, Relationship proneness,
Perceived relationship investment, Relationship performance
Paper type Research paper
1. Introduction
The Covid-19 pandemic tremendously impacted people’s lives and the retail industry globally
(Gupta and Mukherjee, 2022). Retailers adopted various shrewd responsive strategies to deal
with the dynamic and ever-changing market conditions during the pandemic (Deloitte
Pickup service
quality in
BOPIS
The authors extend their sincere gratitude to the anonymous reviewers and editor-in-chief, Prof.
Dr Alexander Douglas.
Disclosure statement: No potential conflict of interest was reported by the authors.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1754-2731.htm
Received 21 March 2023
Revised 2 June 2023
Accepted 25 July 2023
The TQM Journal
© Emerald Publishing Limited
1754-2731
DOI 10.1108/TQM-03-2023-0091
Digital, 2022). Numerous research studies noted that retailers’novel practices, like buy-
online-and-pickup in-store (BOPIS) during Covid-19, were likely to become the new normal
post-pandemic (Gupta and Mukherjee, 2022). Interestingly, the COVID-19 pandemic
prompted customers and retailers worldwide to embrace BOPIS for health and safety
concerns, resulting in a 500% increase in BOPIS in various retail sectors (Jin et al., 2022).
Indeed, omnichannel retail techniques like the “buy online pickup in-store”(BOPIS) model are
rapidly becoming competitive weapons in the war for customer loyalty (Jin et al., 2022).
Incorporating BOPIS into physical retail locations provides a platform capable of bridging
the gap between the ease of online purchasing and the profitability of in-store shopping
(Natarajan et al., 2023;Serkan Akturk and Ketzenberg, 2022). Store traffic dwindling before
the pre-covid time will trickle down unless stores provide consumers with a compelling value
proposition (McKinsey and Company, 2020). BOPIS could fill in that value proposition for the
customers as it may avoid paying shipping charges with BOPIS, obtain products quickly
without waiting for delivery, handle issues instantly while picking up and escape the
frustration of fumbling around a general store in search of products (Babin et al., 2021;Lee
et al., 2020;Natarajan et al., 2023). Researchers claim that ideally, BOPIS service provides an
instantaneous opportunity for retailers to upsell products to customers during their store
visits. However, it still requires empirical investigation (Babin et al., 2021) on various post–
purchase behaviors. Although BOPIS service has been acknowledged to provide many
benefits to customers and retailers, most of the issues in service delivery occur during the
offline pickup procedure, significantly impacting pickup service quality (Lee et al., 2020).
According to a consumer feedback study by Rosenmayer et al., (2018), the majority (24%) of
omnichannel service failures are “bricks-and-mortar”shopping problems, while just 8% are
“website design”concerns. In the offline channel, where customers physically and more
intimately interact with the store, several service disruptions may arise that might encourage
customers to switch retailers, increasing the defection rate (Lee et al., 2020). Failures in retail
services can have serious consequences (Jin et al., 2022). Retaining BOPIS users is also
difficult because they are highly innovative, value-conscious and young (Lee et al., 2020).
Hence, retailers focusing on their BOPIS users’relationship performance to emphasize the
quality of their pickup service have become imperative (Jin et al., 2022;Lee et al., 2020). So
what is the solution for increasing the retailer relationship performance (increase chances of
customer retention, make them frequent purchasers and motivate them to make cross-
category purchasers)? When we focus on these transactional post–purchase behaviors,
focusing on the internal states of customers, like their perceived investments by the retailer to
retain them play a crucial role in the long run (Menidjel and Bilgihan, 2021). However, the
perception of investment among shoppers could occur through the cumulative assessment of
the quality of customer experience rendered by the store and the shoppers’stable/conscious
tendency to engage in a relationship with the firm, i.e. relationship proneness (Menidjel et al.,
2020). Indeed researchers have given particular emphasis to customer experience quality and
relationship proneness to model (non-transactional behaviors of omnichannel shoppers) like
customer engagement and empowerment driven by channel integration quality (Chen et al.,
2022). Yet, particular emphasis to the offline pickup service quality of the omnichannel
retailers has received scanty attention with regard to these dimensions.
1.1 Need for the study
(1) Researchers have indeed focused on various dimensions of channel integration
quality and given particular emphasis to future purchase behaviors of omnichannel
shoppers like stickiness intention (Lin et al., 2022), impulse buying behavior (Pereira
et al., 2022), patronage intention (Le and Nguyen-Le, 2020;Lim et al., 2022;Nguyen,
2021), loyalty (Gao and Huang, 2021) and repurchase intention (Lee et al., 2019).
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Interestingly, BOPIS users’extra-role/citizenship behavior driven by pickup service
quality was modeled by Natarajan et al. (2023). Yet recent literature points out the
pressing need for investigation on transactional post–purchase behaviors (which
brings in revenue to a store) driven by the pickup service quality of BOPIS service
(Gauri et al., 2021;Lee et al., 2020;Natarajan et al., 2023).
(2) For instance, Natarajan et al. (2023), in their recent study, gave a research direction to
investigate the profitability of the BOPIS users in terms of assessing if they make
cross-category purchases-breadth of relationship and their share of wallet to the store
in every trip for pickup of online ordered merchandise exploring their longevity of
relationship (relationship length). Regarding cross-category purchases (one of the
relationship performance indicators), a recent study by Sharma et al., (2021)
effectively demonstrated the responsiveness-driven cross-buying behavior among
offline retail store customers. However, with booming BOPIS sales, investigating this
behavior in a channel-integrated store context has become essential if it leads to
customers’revisit intentions (Deloitte Digital, 2022).
(3) Though there is an imperative need to investigate the loyalty intentions of these
BOPIS users for generating long-term profits, given the high costs involved in
enabling integrated channel purchases (Babin et al., 2021;Natarajan et al., 2023),
studying their service experience consciousness is also essential (Mishra et al., 2022).
Customers who are highly conscious of service experiences are mostly comparison
shoppers and switch retailers, even when the store’s service quality remains superior
(Mishra et al., 2022). Hence this research intends also to examine the moderating role
of BOPIS users’service experience consciousness on the direct relationship between
pickup service quality in BOPIS and the relationship performance of retailers.
To answer all the gaps identified in the omnichannel retail literature, this research intends to
answer these questions:
(1) To what extent do experience quality and relationship proneness mediate the
pickup service quality in BOPIS and the perceived relationship investment of
retailers?
(2) To what extent does the BOPIS users’perceived relationship investment lead to
better relationship performance for omnichannel retailers (relationship length, depth
and breadth)?
(3) To what extent does the BOPIS users’service experience consciousness moderate the
direct relationship between pickup service quality in BOPIS and the relationship
performance of the BOPIS users of omnichannel retailers?
2. Theoretical background and research hypotheses
2.1 Stimulus-Organism-Response (SOR) framework
The Stimulus-Organism-Response framework recommended by Mehrabian and Russell
(1974) was incorporated into this study. This model illustrates the relationship between an
external stimulus (S), internal consumer states (Organism) and the resultant behavior
(Response).
The S-O-R framework is utilized across several retail studies to perceive different
variables used as stimuli (S), such as omnichannel capability (
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glu, 2021), cross-channel integration (Shankar et al., 2021), consumer perception of
channel integration (Lim et al., 2022), channel integration quality (Sombultawee and
Pickup service
quality in
BOPIS
Tansakul, 2022), convenience (Shankar et al., 2021) and pickup service quality in BOPIS
(Natarajan et al., 2023).
With regard to the organism (O), interestingly, most of the researchers were concerned
about the internal states of omnichannel shoppers like trust (Lim et al., 2022;Lin et al., 2022;
Natarajan et al., 2023), satisfaction (Lim et al., 2022), commitment (Lin et al., 2022;Natarajan
et al., 2023), customer empowerment (Lim et al., 2022;Mishra et al., 2021;Zhang et al., 2018)
and customer experience dimensions (Gao et al., 2021;Sombultawee and Tansakul, 2022).
Lastly, researchers have modeled various response(R) behavioral reactions to
demonstrate how the consumer reacts to the stimulus leading to internal cognitive and
affective processing. Purchase or repurchase of products and services, referral behavior
(word of mouth) (Natarajan and Veera Raghavan, 2023c) and behavior intended to resolve
cognitive dissonance created by a gap between anticipation and performance, such as
complaining or seeking resolution, are possible consumer behaviors. Focusing our discussion
concerning the omnichannel retail context, researchers have shown interest in modeling the
response variables like omnichannel usage intentions (Gao et al., 2021), customer retention
(Mishra et al., 2021), shoppers’word of mouth and repurchase intention (Sombultawee and
Tansakul, 2022), customer engagement (Chen et al., 2022) and citizenship behavior of BOPIS
users (Natarajan et al., 2023).
2.2 Pickup service quality in BOPIS as stimulus(S)
BOPIS users are time-pressed, price-conscious, innovative, and, most importantly, young (Lee
et al., 2020). They consider online purchases risky and are willing to trade off the cost of
traveling to a nearby convenient physical shop of the channel-integrated retailer to offset
product performance risks associated with their online purchases (Lee et al., 2020). They
evaluate how well the store assists customers by making the pickup process easy, fast, quick,
and, most importantly, pleasant, emphasizing “service effectiveness.”Customers also pay
attention to how the company has built its pickup counter to be conveniently visible as they
enter the store (Natarajan et al., 2023). Customers are more concerned with whether the item is
kept in suitable packaging and damage-free (Natarajan et al., 2023).
Regarding product quality discrepancies, the customer sees how well the store responds
to various problems while picking up an online-ordered item. The service quality that the
retailer offers BOPIS consumers during physical store pickup is critical in determining the
firm beneficial post–purchase behaviors of these BOPIS users (Natarajan et al., 2023). Thus,
pickup service quality consists of four dimensions: service effectiveness, problem handling,
ease of access and item quality, as proposed by Lee et al., (2020).
2.3 Perceived customer experience quality, relationship proneness, perceived relationship
investment and consumer service experience consciousness as organism(O)
2.3.1 Experience quality. Customer experience is the experience of receiving a service or
product, which includes the experiences that precede and follow the service/product
delivery (Leonard et al., 2002). Experience quality differs from service quality and is a
customer’s overall perception of their shopping experiences (Deshwal, 2016). It focuses on
the customer’s perceived value (internal) rather than the retail store’s service environment
(external) alone. Customers’perceived experience quality could be classified as their
overall experiences in terms of individual emotions as they participate in consumption
activities and interact with the physical environment of the retailer (Chen et al., 2022). The
overall experience quality perceived by BOPIS service users could encompass the pickup
service counter design, responsiveness of store employees, the mere presence of other
customers during the pickup process and other elements that support the shopping process
in the store.
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2.3.2 Relationship proneness. Relationship proneness indicates a consumer’s conscious
predisposition to engage in relationship with firm quality. RP is critical in developing retailer-
consumer ties (De Wulf et al., 2001). It is defined as “a consumer’s relatively stable and
conscious tendency to engage in relationships with retailers in a particular product category”
(De Wulf et al., 2001). According to research, customers with greater proneness to engage in
relationships with retailers are likelier to be loyal to their stores (Menidjel et al., 2020). We
suspect in this research that superior pickup service quality triggers BOPIS users’innate
relationship proneness, positively evaluating the relationship investments of the store.
2.3.3 Relationship investment. As theorized by Rusbult (1983), the investment model
proposed that the propensity to stay in and feel psychologically committed to a relationship
results from several factors, including relationship investment. Investing time, effort and
other irrevocable resources in a relationship establish psychological bonds that motivate
customers to stay in the relationship and generate an expectation of reciprocation (Menidjel
and Bilgihan, 2021). Customers who perceive advantages from a retailer’s relationship
investment, such as additional efforts, tailored and personalized products and services, and
presents, are likelier to create and sustain long-term solid relationships with their retailer
(Natarajan and Veera Raghavan, 2023b,c). Interestingly, extant literature has demonstrated
that customers perceived relationship investment as the mediator between service quality
and relationship performance of a retailer launching a self-service technology (Chang
et al., 2016).
2.4 Relationship performance (relationship breadth, depth and length) as response(R)
Relationship performance is the customer’s future purchase behavior (Verhoef, 2003).
Customer purchasing activity’s breadth, depth and longevity are all indicators of customer
loyalty (Bolton et al., 2004). This information may assist retailers in better understanding the
loyalty relationship. Relationship performance, as defined by Bolton et al. (2004), covers
relationship length, depth and breadth and is depicted as follows.
Relationship length: According to Bolton et al. (2004), the “duration”of a transaction
with a firm is the length of the relationship. Verhoef (2003) noted that customers react
subjectively to their relationship with a product or service provider. The relationship ties can
be lengthened and extended (Bolton et al., 2004). We propose that customers’intent to make
long-term purchase decisions will be determined by the omnichannel retailer’s quality of the
pickup service.
Relationship depth is determined by “how frequently”consumer purchases at a specific
store (Bolton et al., 2004). This is referred to as “frequency of purchase”in this study. Hence,
relationship depth is defined as customer willingness to frequently shop at the omnichannel
store that offers superior pickup service.
Relationship breadth: According to Bolton et al. (2004), customer “cross-buying
intentions”quantify relational breadth. Customers who cross-purchase in particular stores
help retailers generate unexpected revenue and typically provide additional benefits to that
retailer. Relationship depth is conceptualized in this study as a customer’s desire to engage in
cross-buying activity in omnichannel retail outlets while pickup online-bought products in a
physical store, driven by past superior pickup service encounters with the same store.
Recent literature has also regarded various investments made by retailers to retain
customers as having a significant impact on their relationship performance.
2.5 Relationship between hypotheses and model development
2.5.1 Pickup service quality, experience quality, relationship proneness and relationship
investment. BOPIS users are ready to make an effort to travel and pick up the online ordered
product from the nearby physical store because they are highly concerned about physically
examining the products they ordered online (Jin et al., 2022). These shoppers expect the
Pickup service
quality in
BOPIS
pickup process to be quick, simple and pleasant (Jin et al., 2022;Lee et al., 2020), as they are
primarily motivated to utilize click-and-collect services due to time constraints and,
interestingly, to satisfy their instant gratification needs to procure online ordered goods
(Zhang et al., 2022). The entire BOPIS service blueprint should support these goals of the
BOPIS users. Be it the real-time inventory sharing to the customers, updates on the online
ordered product availability for pickup, maintaining superior packaging quality, shrewd
employee assistance in case of quality disparities, and most importantly, a properly designed
servicescape that supports the easy examination of the products during pickup (Lee et al.,
2020). Store managers today need to pay attention to factors such as lighting, cleanliness and
overall ambiance to make customers feel comfortable during the pickup process, fulfilling the
tangibility quotient of these omnichannel shoppers to facilitate a wholesome channel-
agnostic shopping experience. All these attributes could eventually determine the shoppers’
cumulative assessment of BOPIS users’perceptions of shopping experience quality. Once the
customers enter the store, the shopping experience is mainly contingent on the store’s
physical environment and how far the store managers try to make the customer feel
comfortable shopping (Roy et al., 2020). Recent past literature identifies properly designed
shopping environment can foster an intense feeling of belonging to the store among
customers (Gorji et al., 2021;Roy et al., 2020) and could trigger positive emotions that
encourage purchasing products and services (Singh et al., 2021) in the long run.
To induce positive service consumption emotions, designing the in-store service counter
exclusively to enable easy pickup of online ordered goods is quintessential. It could reduce
unnecessary crowding within the store and enhance the overall quality of the experience
perceived by the customers (Chen et al., 2022). These positive emotions could eventually
motivate the customers to invest in a long-term relationship with the store and its employees
(Natarajan and Veera Raghavan, 2023b,c). Recent literature identifies customers investing
time, effort and other irrecoverable resources in relationships with retailers generates
psychological ties that drive customers to maintain the relationship and establish
reciprocation like loyalty (Wu and Tang, 2022). For instance, when BOPIS users perceive
that the employees at the store are highly responsive and empathetic regarding their
concerns with the return of damaged products, they might confirm that the store has invested
in resources to train the frontline employees in the store, eventually feel that they should
invest themselves with the store (Lee et al., 2020).
Similarly, when there are no discrepancies in the inventory data shared by the retailer on
the availability of the online ordered goods nearby the customer location for pickup,
customers could sense that the retailer has made some investment to build a robust IT
infrastructure to ensure that their shopping process is hassle-free and convenient (Jin et al.,
2022). When the store has diligently maintained the quality of merchandise, it significantly
mitigates the pre-purchase risks. It impacts customer satisfaction (Natarajan et al., 2023),
positively evaluating retailer investments and inducing positive reciprocatory intentions
(Natarajan et al., 2023;Natarajan and Veera Raghavan, 2023b,c). Not just that, if the store
arranges for assistance loading the purchased goods in the customer’s vehicle, it could
naturally give the impression to the customers that the store is interested in having them as
regular customers in the long run (Natarajan et al., 2023). Eventually, out of the repeated
positive confirmation of their pickup service expectations, they tend to gravitate toward the
store and its employees, fostering long-sustaining relationships and strengthening their
innate propensity to involve in relationships (i.e. relationship proneness) toward the store
(Menidjel and Bilgihan, 2021).
However, we also suspect that pickup service quality-driven positive evaluation of
relationship investments could be mediated by relationship proneness. According to recent
literature on retail store customers, customers with higher degrees of relationship proneness
are identified to be more receptive to retailers’efforts to establish relationships and hence
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tend to exhibit a positive attitude toward the various relationship investments of the retailer
(Menidjel et al., 2020). Since not all customers desire to form associations and respond to
various customer retention efforts of the retailer in the same way, identifying those customers
predisposed to form long-term relationships has become imperative for retail firms (Menidjel
et al., 2020). We suspect that superior pickup service quality could strengthen the
relationship-prone customers’innate propensity to be involved in value-generating
relationships with the store in the long run. These customers tend to respond favorably to
relationship activities (Menidjel et al., 2020). Recent literature has demonstrated that
multichannel service delivery (information transparency, accessibility and channel
integration) significantly impacts customer relationship proneness and experience quality
(Chen et al., 2022). However, they didn’t emphasize the pickup service quality or demonstrate
the impact of relationship proneness and experience quality on the BOPIS users’perceptions
of various relationship investments of the store. We suspect experience quality and
relationship proneness to mediate pickup service quality and perceived relationship
investment. Hence, we posit
H1. Perceived customer experience quality mediates the pickup service quality and
perceived relationship investment.
H1a. Pickup service quality positively impacts the BOPIS users’perceived experience
quality.
H1b. BOPIS users’perceived experience quality positively impacts shoppers’perceived
relationship investment.
H2. Relationship proneness mediates the pickup service quality and perceived
relationship investment.
H2a. Pickup service quality positively impacts the BOPIS users’relationship proneness.
H2b. BOPIS users’relationship proneness positively impacts shoppers’perceived
relationship investment.
H3. Pickup service quality has a significant positive impact on perceived relationship
investment.
2.5.2 Perceived relationship investment and relationship performance. Retaining BOPIS users
is becoming a considerable challenge for omnichannel retailers (Natarajan et al., 2023). Recent
literary evidence empirically demonstrates that customer-perceived relationship investment
significantly influences retail store customer loyalty (Menidjel and Bilgihan, 2021). Retailers
are ready to make these essential investments to build, nurture customer loyalty and
eventually gain a long-term competitive advantage (Mishra et al., 2021). However, before
making investments to nurture the relationships measuring customer loyalty in-depth
through appropriate indicators is challenging for retailers (Natarajan and Veera Raghavan,
2023b,c). Relationship performance is regarded as one effective proxy measure of customer
loyalty (Chang et al., 2016). It depicts a customer’s interest in purchasing from a focal retailer
and the tendency to recommend that retailer to other customers (Menidjel and Bilgihan, 2021).
Indeed, researchers have regarded the global channel integration quality as the significant
determinant of omnichannel customer patronage and stickiness intentions with a retailer
(Lim et al., 2022;Lin et al., 2022;Mishra et al., 2022). However, they have never emphasized the
pickup service quality of the BOPIS service to study its impact on BOPIS users driven by
their relationship performance. At the same time, a few questions remain unanswered in
omnichannel retail: Are loyal BOPIS users frequent purchasers from the same store they
bought earlier? Are these BOPIS users cross-category purchasers (Natarajan et al., 2023).
Researchers have shown in various contexts that dimensions like trust, social advantages,
Pickup service
quality in
BOPIS
consumer satisfaction, image, relationship duration and responsiveness are various factors
that influence customers’cross-buying behavior with a firm (Evanschitzky et al., 2016;Hong
and Lee, 2014;Sharma et al., 2021). However, we suspect relationship investment to be
antecedent of the cross-buying intentions (one among the relationship performance indicator)
of the BOPIS users. We suspect retailers’initiatives like introducing digital interactive aisles,
notifying customers with hyper-personalized messages of in-store promotional offers/
discounts, cross-product category redeemable digital coupons as part of retailers’
omnichannel service blueprint design could result in the positive evaluation of the
retailers’relationship investments encouraging the BOPIS users to make additional
unplanned cross-category purchases during pickup visits (Natarajan and Veera Raghavan,
2023c). Recent research by Dahana et al. (2022) identifies customers’need for affiliation, or
belongingness augments their desire to be in a relationship with the firm and acknowledged
as a group member by others. This could significantly influence the individuals’emotional
patterns and cognitive processes (Dahana et al., 2022). Customers with strong affiliation
requirements are also expected to be outgoing, view social interaction as an advantage, and
spend their money on products and services from the company with which they
psychologically feel affiliated (Dahana et al., 2022). In the context of omnichannel retail, we
suspect BOPIS users who feel a psychological connection to the store based on their past
BOPIS experiences and their natural inclination to engage in relationships tend to evaluate
the store’s investments in the relationship positively. This positive evaluation often leads to
cross-buying behaviors, where customers tend to make additional purchases across different
channels offered by the retailer through frequent revisits to the store in the long run.
Recent research has identified that cross-buying behavior enables customers to shop by
allowing them to do one-stop shopping and lower overall cost (Mukerjee and Shaikh, 2019).
With increasing retailers embracing omnichannel strategies, customers may be motivated to
make cross-category purchases at a focal store if they feel their previous click-and-collect
experiences are economical and superior for the cumulative cost trade-off to visit the store.
Indeed, customers with positive shopping experiences are believed to return to the store
frequently (Sharma et al., 2021). Added to the positive shopping experience, retailers’
relationship nurturing initiatives like providing a customer with tangible rewards (such as
price discounts, cash incentives, special deals and gifts), preferential treatment (such
as making customers feel important and unique), frequent and personal communication (such
as displaying personal and warm feelings to customers) and preferential treatment (such as
making customers feel important and unique) trigger the loyalty intentions of customers
toward the retailer (Menidjel and Bilgihan, 2021).
Building our argument on relationship quality theory, we suspect by building strong
relationship ties and leveraging the expertise to deliver a superior pickup experience, BOPIS
users could be motivated to continue their purchasing with the same omnichannel retailer
more frequently with the same retailer in thelong run, encourage them tomake cross-category
purchases during their store visits to pick up the online ordered products. Hence, we posit
H4. BOPIS users’perceived relationship investment positively impacts (a) relationship
breadth, (b) relationship depth and (c) relationship length of BOPIS users with
the store.
2.5.3 Moderating role of customer service experience consciousness. Indeed recent service
quality literature has explored both the direct and the indirect impact of service quality on
customer loyalty. For instance, recent research by Ahmed et al. (2023) demonstrated the direct
impact of service quality on customer loyalty in the service context. Interestingly Marcos and
Coelho (2021) showed how customer satisfaction and the perceived value of the service
experience could indirectly impact customer loyalty. Abdel Fattah et al. (2021) demonstrated
how the service quality of insurance companies increases customer satisfaction leading to
TQM
their loyalty to the firm. A firm’s service quality is directly related to the customers’
willingness to pay more for the products and services, their repurchase intentions and word-of-
mouth behavior (Dandis and Al Haj Eid, 2022). However, the service quality-driven customer
relationship performance, like patronage intentions, could be contingent on the omnichannel
shoppers’service experience consciousness (Mishra et al., 2022). BOPIS users are online
shoppers who rely on the physical store to pick up the products. These shoppers are most
likely to do comparison shopping as they are identified as young, innovative, and, most
importantly, utilitarian value seekers in shopping from a store (Lee et al., 2020). They tend to
evaluate the omnichannel retailers’various product assortment and allied service attributes
before purchasing. This is out of the service experience consciousness of these omnichannel
shoppers. Extant research has identified consumer service experience consciousness as the
significant antecedent of comparison shopping (Mishra et al., 2022). Although superior service
quality is linked to customer retention and cross-buying behaviors, with high service
experience consciousness, BOPIS users might indulge in comparison shopping if they are
innately tuned for high service experience consciousness. This might increase the alternative
retailer attractiveness leading to switching intentions among the shoppers (Mishra et al.,2022).
This could weaken the pickup service quality-driven relationship performance. Hence, we
propose that the direct relationships between pickup service quality in BOPIS and relationship
performance (length, breadth and depth) could be weakened with the increase in the BOPIS
users’service experience consciousness. Hence, we posit
H5. Pickup service quality in BOPIS positively impacts (a) relationship breadth, (b)
relationship depth and (c) relationship length of BOPIS users with the store.
H6. Consumer service experience consciousness significantly moderates the direct
relationship between pickup service quality in BOPIS and (a) relationship breadth,
(b) relationship depth and (c) relationship length of BOPIS users with the store.
The conceptual framework for this study was developed based on the hypotheses proposed
(Refer Figure 1).
Figure 1.
Conceptual framework
Pickup service
quality in
BOPIS
3. Research methodology
3.1 Measures used in the study
The data was gathered using Google Forms, an online survey tool, between December 2022
and January 2023. The questionnaire consisted of questions (refer to Table A1). The scale
used to assess the pickup service quality was developed by Lee et al. (2020). Customer
perceived experience quality was adopted from the work of (Chen et al., 2022;Kim and Choi,
2016), relationship proneness was adopted from the work of Chen et al. (2022) and perceived
investment from the work of Chang et al. (2016). Finally, the scales for the constructs of
relationship performance-relationship breadth, depth and length were adapted from the work
of Chang et al. (2016). Data was collected using 5 points Likert scale, with one denoting
“Strongly disagree”and five indicating “Strongly Agree.”According to Malhotra et al. (2017),
Likert-type scales are advised whenever participants agree or disagree with a series of
statements concerning specific stimulus items. Five-point Likert scale has various
advantages, including ease of development and use and participants’preference for
immediate comprehension (Souki et al., 2022). Dedeoglu et al. (2018) support using five-point
Likert scales because they are easier to use and provide a brief appearance for responders.
According to Malhotra et al. (2017), five-point Likert scales are appropriate for data collection
through online surveys, kiosks, mobile devices, mail, phone or personal interviews.
This study operationalized the pickup service quality construct as a reflective–reflective
construct proposed by Lee et al. (2020) in their seminal scale development work. The
subsequent assessment of this higher-order construct relies on a disjoint two-stage approach,
following the guidelines of Sarstedt et al. (2019).
3.2 Data collection
Experts in the field assessed the items’importance and clarity. Before being sent to
participants, the questionnaire was revised and double-checked for linguistic flaws. A few
randomly chosen regular BOPIS users took part in pilot research to examine the
questionnaire’s validity and reliability. We received good reliability test results, and all
measurement items were well-loaded on their respective constructs. Following that, we
excluded the pilot study responses from the primary sample. We added a three-minute video
clip on the questionnaire’s first page explaining the omnichannel services that well-known
channel-integrated omnichannel retailers provide. Participants were requested to watch the
clip before completing the survey to eliminate bias. The criterion set to participate in the
survey was that the respondents should have recently used omnichannel retailers’BOPIS
service. Purposive sampling strategies were utilized in this study to collect data using online
and paper-based self-administered surveys. Data was also collected physically through a
paper-based survey from a few regular shoppers of the well-known omnichannel retailer after
asking them to watch the explanatory video on omnichannel retail. Purposive sampling is the
best technique for studying a specific phenomenon ofinterest in a given population. According
to previous research, “it moves away from any random form of sampling”(Campbell et al.,
2020). Here, the researchers were particularly interested in the respondents who have used the
physical store of the channel-integrated retailerto make an offline pickup for theironline order
and who have experience with the in-store shopping experience in the recent past. Hence, the
purposive sampling technique was suitable for the study. Interestingly, a recent study by Le
and Nguyen-Le (2020) in the omnichannel retail store context also relied on the purposive
sampling technique to study the growing omnichannel purchase behavior driven by
omnichannel customer experiences. The data was collected from omnichannel shoppers in the
Indian population. India has a diversified population, including various demographics,
cultural origins and geographical preferences, with unique purchase retail expectations. The
retail landscape in India is distinct, with a combination of traditional mom-and-pop stores,
TQM
organized retail chains, and the rising presence of e-commerce platforms, makings an
investigation into the omnichannel retail scenario more imperative (IBEF, 2022). Mobile
commerce has also gained tremendous momentum in the country, with people increasingly
utilizing their mobile devices to research items, purchase and engage with brands. According
to Forbes (2022a,b) industry research, India has around 90 million Internet purchasers.
Seventy percent of these customers claim digital trends and channels influence their shopping
decisions (Natarajan and Veera Raghavan, 2023a). Furthermore, 75% of Indian customers
research a product online before purchasing it in-store, indicating that they are omnichannel
shoppers. Croma, Reliance Retail Ventures, Aditya Birla Fashion, and Retail Limited, and
OnePlus have all implemented omnichannel commerce, according to the Indian Brand Equity
Foundation (2022) research. Hence, Indiansaged 18 years or older who had recently purchased
at an omnichannel retailer, ordered online and picked up the merchandise at the retailer’s
physical store were considered for data collection. 1,306 email invites were sent to verified
Indian shoppers provided by a leading omnichannel retailer in India apart from the offline
paper-based surveys. A filter question was asked to enable only respondents who had used the
BOPIS service at least once in the recent past to participate. On the first page of the
questionnaire, respondents were requested to provide their verified email addresses to which
they received the invite to participate in the survey. They were only permitted to complete the
questionnaire once, and the survey form did not allow repeated responses from the same
respondent for each validatedemail address. As part of online data gathering, the researchers
also employed personal social media networks to reach out to additional potential respondents
apart from the offline surveys. Respondents were told that after completing the survey, they
would be entered into a lottery for a restaurant gift card to increase the number of responses.
We got 824 responses, turning theresponse rate to 63.09%. Of 824 collected responses, 38 were
removed by the researchers from further data analysis owing to duplicate responses,
incomplete data, nonengagement or respondents citing single-channel or multichannel
retailers. As a result, the final admissible sample size forthe study was N5786 (see Table 1).
Characteristics Number (N5786) %
Gender
Male 379 48.22
Female 407 51.78
Age Group
18–24 years of age 61 7.76
25–34 years of age 265 33.72
35–44 years of age 245 31.17
45–54 years of age 147 18.70
55–64 years of age 41 5.22
65 and above 27 3.44
Educational Qualification
Higher Secondary 0 0.00
Under Graduation 387 49.24
Post-Graduation 278 35.37
Doctorate 121 15.39
Product category purchased
Convenience goods 254 32.32
Shopping goods 272 34.61
Specialty goods 260 33.08
Source(s): Based on data collected by authors for the study
Table 1.
Demographic details of
respondents
Pickup service
quality in
BOPIS
The sample included males (48.22%) and females (51.78%). Respondents were categorized
based on the products they purchased in the near past using the BOPIS service of any
omnichannel retailer. The researchers also got information on the respondents’often used
channels in their purchases, like the physical store, website, mobile application and social
media store pages of the focal omnichannel retailer. Respondents’past product purchases
were classified as follows: convenience (groceries), shopping (clothing, housewares and
footwear) and specialty (exotic fragrances, luxury watches and jewels) goods.
4. Data analysis and discussion of results
4.1 Measurement model
The study employed Cronbach’s alpha and composite reliability scores to examine the
measure’s reliability. We also examined common method bias and multicollinearity and
carried out a t-test. First, the Kaiser–Meyer–Olkin (KMO) test for sampling adequacy was
performed, and the value was about 0.929, indicating an acceptable sample size for
multivariate data analysis. Additionally, Soper’s (2023) a priori sample size calculator was
used to compute the appropriate minimum sample size necessary for the analysis in Smart
PLS 4.0 software. The expected effect size was set at 0.3 (medium), while the statistical power
level was set to 0.80. The probability threshold for the latent and observable variables
included in the study was fixed at 0.05 (Cohen, 2013). The findings revealed that n5177 was
the minimum number of samples necessary to detect the impact. The study’s recommended
number of samples and model structure is n5183. We obtained 786 valid responses, more
than the acceptable minimum sample size required for the study.
The SRMR (standardized root mean square residual) value is used to assess the quality of
fit. A good fit is defined by Hair et al. (2019) as a value less than 0.08. The saturated model had
an SRMR value of 0.055, and the estimated model had an SRMR value of 0.069, suggesting a
good fit. The RMSEA and NFI values were 0.072 and 0.901, respectively. R
2
values must be
greater than or equal to 0.1 to assess the strength of each structural path in the model.
According to the findings, the model’s predictive ability for endogenous constructs was
significantly predictive as all the R
2
values were more than 0.1, ranging from 0.389 to 0.498.
The Q
2
values range from 0.327 to 0.421, indicating the proposed model’s predictive relevance
and capability since the results demonstrate significance in predicting constructs.
4.1.1 Test for common method bias. Harman’s one-factor test in SPSS 26 program was
relied upon to check for common method bias. The findings indicated that the one-factor
solution explained just 31.66% of the variance, falling well short of the 50% cutoff
(Podsakoff, 2003).
According to Fuller et al., (2016), CMB should be considered a serious concern for research
findings only when the variance of common methods is effectively higher than 50%. Finally,
if the AVEs and scale reliability match the reference values specified in the literature,
Harman’s single-factor test is extremely robust in identifying common method variance
concerns (Sarstedt et al., 2020). Consequently, we conclude that the CMB does not limit the
interpretation of the current study’s findings.
4.2 Reliability and validity
To verify the reflective–reflective measurement model, which includes higher-order construct
pickup service quality, this study employed the disjoint two-stage technique provided by
Sarstedt et al., (2022). The measurement model for lower-order constructs was estimated in
stage one, followed by higher-order construct in stage two. Table 2 shows the reliability and
validity results for the study sample, and no factor loading was less than 0.7 for all of the
items used in the study. The alpha and composite reliability (CR) scores were higher than the
TQM
0.7 thresholds (Hair et al., 2019). Convergent validity was established with all average
variance extracted (AVE) values greater than 0.5. We found no issues with multicollinearity
because the VIF value of each item in the sample was less than 3.3, as Sarstedt et al., (2022)
Constructs Items Mean λSD Alpha CR AVE VIF
Consumer service experience
Consciousness (CSEC)
CSEC1 3.729 0.881 0.987 0.881 0.926 0.807 2.380
CSEC2 3.696 0.904 1.040 2.643
CSEC3 3.691 0.910 0.997 2.386
Employee Assistance (EA) EA1 3.842 0.827 1.052 0.820 0.893 0.736 1.625
EA2 3.698 0.878 1.116 2.116
EA3 3.661 0.868 1.107 1.975
Experience Quality (EQ) EQ1 3.510 0.746 1.102 0.925 0.937 0.625 2.155
EQ2 3.722 0.798 0.913 2.374
EQ3 3.488 0.786 1.051 2.180
EQ4 3.888 0.803 0.946 2.769
EQ5 3.943 0.807 0.949 2.702
EQ6 3.873 0.808 0.990 2.802
EQ7 3.602 0.822 1.056 2.710
EQ8 3.448 0.781 1.058 2.636
EQ9 3.545 0.760 1.032 2.167
Item Quality (IQ) IQ1 3.906 0.857 0.977 0.821 0.893 0.736 1.680
IQ2 3.785 0.860 1.003 1.973
IQ3 3.755 0.857 1.079 1.961
Problem Handling (PH) PH1 3.991 0.830 0.993 0.885 0.921 0.744 2.060
PH2 3.814 0.884 0.923 2.640
PH3 3.847 0.883 0.996 2.673
PH4 4.028 0.852 0.943 2.097
Perceived Investment (PINV) PIV1 3.584 0.847 1.056 0.902 0.927 0.718 2.533
PIV2 3.551 0.861 1.076 2.684
PIV3 3.606 0.836 1.008 2.244
PIV4 3.486 0.848 1.133 2.476
PIV5 3.586 0.844 1.060 2.308
Relationship Breadth (RB) RB1 3.669 0.885 1.060 0.886 0.929 0.814 2.324
RB2 3.652 0.924 1.093 2.838
RB3 3.490 0.898 1.146 2.560
Relationship Depth (RD) RD1 3.816 0.872 0.961 0.855 0.903 0.700 2.709
RD2 3.877 0.865 0.923 2.591
RD3 3.827 0.876 0.890 2.751
RD4 3.383 0.725 1.190 1.356
Relationship Length (RL) RL1 3.821 0.897 0.965 0.892 0.933 0.822 2.611
RL2 3.877 0.909 0.923 2.594
RL3 3.832 0.915 0.894 2.734
Relationship Proneness (RP) RP1 3.599 0.846 1.043 0.900 0.923 0.666 2.790
RP2 3.442 0.834 1.072 2.600
RP3 3.540 0.815 1.034 2.248
RP4 3.517 0.805 1.110 2.148
RP5 3.729 0.803 0.914 2.082
RP6 3.499 0.794 1.037 1.982
Service Effectiveness (SE) SE1 4.072 0.909 0.948 0.960 0.968 0.836 2.545
SE2 4.085 0.912 0.965 2.996
SE3 4.088 0.823 1.019 2.484
SE4 4.064 0.817 1.005 2.892
SE5 4.083 0.936 0.959 2.000
SE6 4.054 0.925 0.956 2.781
Source(s): Based on data collected by authors for the study
Table 2.
Item loadings,
reliability and validity
Pickup service
quality in
BOPIS
suggested. Table 3 shows the discriminant validity results. It displays the cross-factor
loadings for all items. The AVE was used to assess the convergent validity of the constructs.
This metric measures the average percentage of variation shared between the latent
CSEC EA EQ IQ PH PIV RB RD RL RP SE
CSEC1 0.881 0.507 0.549 0.546 0.482 0.283 0.440 0.520 0.503 0.517 0.478
CSEC2 0.904 0.539 0.565 0.528 0.511 0.307 0.482 0.598 0.586 0.537 0.433
CSEC3 0.910 0.519 0.583 0.575 0.496 0.361 0.540 0.580 0.543 0.573 0.445
EA1 0.507 0.827 0.539 0.572 0.602 0.278 0.429 0.508 0.496 0.512 0.511
EA2 0.464 0.878 0.533 0.595 0.499 0.324 0.398 0.484 0.457 0.497 0.435
EA3 0.521 0.868 0.551 0.602 0.467 0.329 0.447 0.473 0.421 0.540 0.413
EQ1 0.500 0.498 0.746 0.531 0.480 0.386 0.532 0.567 0.511 0.772 0.436
EQ2 0.470 0.502 0.798 0.532 0.519 0.322 0.456 0.578 0.574 0.764 0.509
EQ3 0.453 0.442 0.786 0.494 0.478 0.390 0.523 0.526 0.486 0.775 0.454
EQ4 0.514 0.532 0.803 0.528 0.568 0.322 0.459 0.584 0.592 0.666 0.552
EQ5 0.515 0.516 0.807 0.509 0.559 0.325 0.455 0.580 0.578 0.680 0.572
EQ6 0.512 0.516 0.808 0.520 0.501 0.337 0.424 0.567 0.580 0.678 0.544
EQ7 0.528 0.514 0.822 0.508 0.480 0.362 0.544 0.593 0.540 0.834 0.455
EQ8 0.533 0.498 0.781 0.505 0.455 0.422 0.560 0.567 0.506 0.808 0.440
EQ9 0.458 0.464 0.760 0.457 0.434 0.329 0.509 0.547 0.506 0.798 0.431
IQ1 0.556 0.578 0.609 0.857 0.596 0.337 0.489 0.546 0.540 0.587 0.533
IQ2 0.502 0.598 0.525 0.860 0.470 0.359 0.445 0.473 0.444 0.501 0.379
IQ3 0.514 0.595 0.516 0.857 0.472 0.338 0.444 0.486 0.457 0.495 0.380
PH1 0.408 0.480 0.488 0.476 0.830 0.193 0.415 0.488 0.477 0.428 0.558
PH2 0.482 0.528 0.563 0.530 0.884 0.305 0.489 0.515 0.509 0.537 0.550
PH3 0.534 0.556 0.544 0.563 0.883 0.263 0.523 0.511 0.500 0.512 0.534
PH4 0.475 0.534 0.577 0.508 0.852 0.220 0.472 0.542 0.541 0.514 0.585
PIV1 0.285 0.297 0.362 0.317 0.221 0.847 0.308 0.327 0.291 0.379 0.250
PIV2 0.290 0.312 0.363 0.350 0.216 0.861 0.306 0.298 0.268 0.368 0.248
PIV3 0.292 0.332 0.386 0.331 0.247 0.836 0.245 0.329 0.318 0.388 0.272
PIV4 0.295 0.262 0.370 0.310 0.218 0.848 0.309 0.305 0.256 0.390 0.236
PIV5 0.342 0.327 0.413 0.386 0.304 0.844 0.343 0.347 0.316 0.410 0.303
RB1 0.532 0.468 0.577 0.515 0.503 0.304 0.885 0.701 0.637 0.574 0.416
RB2 0.487 0.446 0.589 0.492 0.516 0.352 0.924 0.720 0.639 0.597 0.387
RB3 0.464 0.428 0.524 0.449 0.475 0.308 0.898 0.690 0.592 0.537 0.347
RD1 0.556 0.503 0.658 0.529 0.547 0.279 0.612 0.872 0.693 0.643 0.549
RD2 0.546 0.491 0.606 0.515 0.509 0.323 0.642 0.865 0.552 0.557 0.536
RD3 0.540 0.455 0.602 0.484 0.547 0.319 0.613 0.876 0.582 0.548 0.511
RD4 0.467 0.449 0.537 0.435 0.395 0.337 0.720 0.725 0.506 0.567 0.312
RL1 0.558 0.505 0.661 0.532 0.547 0.283 0.617 0.621 0.897 0.645 0.548
RL2 0.551 0.492 0.608 0.517 0.509 0.324 0.642 0.622 0.909 0.560 0.537
RL3 0.542 0.457 0.605 0.488 0.548 0.323 0.618 0.641 0.915 0.550 0.510
RP1 0.518 0.505 0.305 0.497 0.473 0.366 0.541 0.584 0.527 0.846 0.445
RP2 0.538 0.502 0.512 0.504 0.455 0.423 0.555 0.573 0.513 0.834 0.449
RP3 0.467 0.480 0.482 0.480 0.451 0.346 0.509 0.550 0.509 0.815 0.448
RP4 0.503 0.506 0.521 0.522 0.474 0.375 0.522 0.572 0.519 0.805 0.440
RP5 0.490 0.505 0.521 0.535 0.511 0.337 0.451 0.586 0.585 0.803 0.522
RP6 0.446 0.447 0.456 0.491 0.471 0.393 0.517 0.525 0.485 0.794 0.447
SE1 0.454 0.488 0.587 0.458 0.577 0.308 0.368 0.516 0.529 0.533 0.909
SE2 0.459 0.491 0.588 0.452 0.573 0.313 0.366 0.514 0.527 0.538 0.912
SE3 0.454 0.454 0.524 0.479 0.619 0.206 0.424 0.535 0.556 0.473 0.823
SE4 0.458 0.471 0.522 0.485 0.628 0.206 0.431 0.532 0.552 0.468 0.817
SE5 0.471 0.492 0.590 0.463 0.574 0.328 0.377 0.514 0.527 0.536 0.936
SE6 0.460 0.499 0.587 0.460 0.580 0.326 0.374 0.514 0.527 0.533 0.925
Source(s): Based on data collected by authors for the study
Table 3.
Discriminant validity –
cross-loadings
TQM
constructs (Fornell and Larcker, 1981). The HTMT criteria is the geometric mean of the
average correlations of indicators measuring the same construct compared to the mean value
of the indicator correlations across constructs (Sarstedt et al., 2020). As a result, HTMT
accurately calculates the actual correlation between two constructs. As a result, high HTMT
values imply issues with the dependent variables. Henseler et al., (2015) and Sarstedt et al.,
(2020) suggest that HTMT values should be less than 0.90 if the model comprises
conceptually identical constructs. The survey findings show that none of the HTMT values
exceed the required standard, ruling out the multicollinearity issues. All corresponding factor
loadings were higher than their cross-loading values, demonstrating discriminant validity
based on the Heterotrait–Monotrait (HTMT) criteria (Sarstedt et al., 2022); results are shown
in Table 4.
4.3 Validating higher-order constructs
The higher-order construct (pickup service quality) was validated as part of the measurement
model assessment. As Sarstedt et al. (2019) suggested, the higher-order construct was
examined for discriminant validity with the other lower-order constructs in the model. The
higher-order construct’s reliability and convergent validity were established with reliability
values more than 0.70 and AVE values greater than 0.5 (see Table 5). The square root of the
constructs’AVE is greater than their correlation with the other constructs, and HTMT values
are less than 0.90, as per the recommendations of Sarstedt et al. (2019).
4.4 The goodness of the model
The SRMR (standardized root mean square residual) value is used to assess the quality of fit.
A good fit is defined by Hair et al. (2019) as a value less than 0.08. The saturated model had an
SRMR value of 0.055, and the estimated model had an SRMR value of 0.069, suggesting a
good fit. The RMSEA and NFI values were 0.072 and 0.901, respectively. R
2
values must be
greater than or equal to 0.1 to assess the strength of each structural path in the model.
According to the findings, the model’s predictive ability for endogenous constructs was
significantly predictive as all the R
2
values were more than 0.1, ranging from 0.389 to 0.498.
The Q
2
values range from 0.327 to 0.421, indicating the proposed model’s predictive relevance
and capability since the results demonstrate significance in predicting constructs.
CSEC EA EQ IQ PH PIV RB RD RL RP SE
CSEC 0.898 0.682 0.697 0.716 0.623 0.394 0.615 0.726 0.683 0.677 0.549
EA 0.580 0.858 0.723 0.838 0.714 0.420 0.581 0.679 0.625 0.700 0.596
EQ 0.631 0.631 0.790 0.734 0.693 0.491 0.692 0.809 0.756 0.712 0.656
IQ 0.613 0.688 0.645 0.858 0.699 0.465 0.628 0.698 0.655 0.714 0.568
PH 0.552 0.609 0.631 0.603 0.863 0.316 0.621 0.687 0.662 0.646 0.704
PIV 0.356 0.362 0.448 0.401 0.287 0.847 0.397 0.428 0.380 0.507 0.330
RB 0.547 0.495 0.625 0.537 0.552 0.357 0.902 0.832 0.776 0.707 0.464
RD 0.632 0.569 0.719 0.588 0.597 0.380 0.780 0.837 0.821 0.790 0.634
RL 0.606 0.533 0.687 0.564 0.589 0.343 0.690 0.712 0.907 0.719 0.636
RP 0.599 0.602 0.649 0.619 0.580 0.457 0.632 0.693 0.642 0.816 0.605
SE 0.501 0.528 0.620 0.508 0.645 0.310 0.424 0.568 0.585 0.563 0.915
Note(s): Diagonal and italicized elements are the square roots of the AVE (average variance extracted). Above
the diagonal elements are the HTMT values. Below the diagonal elements are the correlations between the
constructs
Source(s): Based on data collected by authors for the study
Table 4.
Discriminant validity
using the criterion by
Fornell and Larcker
and Heterotrait–
Monotrait
Method (HTMT)
Pickup service
quality in
BOPIS
CSEC EQ PIV PSQ RB RD RL RP HOC Alpha CR AVE
CSEC 0.899 0.697 0.394 0.774 0.615 0.726 0.683 0.677
EQ 0.630 0.790 0.491 0.634 0.692 0.709 0.756 0.604
PIV 0.354 0.449 0.847 0.465 0.397 0.428 0.380 0.507
PSQ 0.671 0.659 0.410 0.831 0.691 0.718 0.784 0.711 PSQ 0.851 0.900 0.691
RB 0.547 0.627 0.357 0.602 0.902 0.732 0.776 0.707
RD 0.631 0.618 0.373 0.698 0.766 0.839 0.724 0.690
RL 0.608 0.688 0.342 0.683 0.691 0.673 0.907 0.719
RP 0.604 0.652 0.458 0.610 0.632 0.689 0.645 0.816
Note(s): (1) Diagonal and italicized elements are the square roots of the AVE (average variance extracted). Above thediagonal elements are the HTMT values. Below the
diagonal elements are the correlations between the constructs (2) PSQ is Higher order construct (HOC)
Source(s): Based on data collected by authors for the study
Table 5.
Discriminant validity
with construct
reliability and
convergent validity for
higher order construct
TQM
4.5 Structural model
The structural model was tested, followed by the measurement model (refer to Table 6), which
depicts the results obtained from the analysis. The results show that pickup service quality
significantly and positively impacts experience quality (EQ) (β50.759, t530.357, p50.000)
and relationship proneness (RP) (β50.710, t528.332, p50.000). Hence hypotheses H1a and
H2a are supported. Subsequently, the experience quality (EQ) positively influenced perceived
investment (PIV) (β50.384, t53.558, p50.000), relationship proneness (RP) positively
influenced perceived investment (β50.339, t52.907, p50.004), and pickup service quality
(PSQ) directly had a significant positive impact on perceived investment (PIV) (β50.235,
t55.209, p50.000). Hence, hypotheses H1b,H2b and H3 are supported. Perceived
investment (PIV) affected relationship breadth (RB) positively (β50.199, t55.456,
p50.000), relationship depth (RD) positively (β50.174, t54.532, p50.000) and relationship
length (RL) positively (β50.194, t53.353, p50.001). Hence, hypotheses H4a, H4b and H4c
are supported. Similarly, the pickup service quality had a direct, positive and significant
impact on relationship breadth positively (β50.276, t54.460, p50.000), relationship depth
positively (β50.349, t56.510, p50.000) and relationship length (β50.348, t56.506,
p50.000). Hence, hypotheses H5a, H5b and H5c are supported. Refer Figure 2.
The moderating effects of consumer service consciousness (CSEC) on direct relationships
between pickup service quality (PSQ) and relationship performance dimensions (breadth,
depth and length) revealed interesting results. The CSEC significantly negatively impacted
the relationship between PSQ and relationship breadth PSQ*CSEC →RB (β50.103,
t53.555, p50.000). In addition, CSEC had a significant negative moderating impact on the
relationship between PSQ and relationship depth PSQ*CSEC →RD (β50.072, t53.358,
p50.001). Similarly, it also had a significant negative moderating impact on the relationship
between PSQ and relationship length PSQ*CSEC →RL (β50.096, t53.780, p50.000).
Hence, hypotheses H6a, H6b and H6c are also supported.
5. Discussion
This study builds the theoretical framework based on the Stimulus-Organism-Response
theory, emphasizing the importance of offline pickup service quality for online orders in an
omnichannel setting. This article advances the findings of Lee et al., (2020) by establishing a
link between pickup service quality in BOPIS and the relationship performance of
Hypotheses Paths Path co-efficient TStatistics pvalues Results
H1a PSQ →EQ 0.759 30.357 0.000 Supported
H1b EQ →PIV 0.384 3.558 0.000 Supported
H2a PSQ →RP 0.710 28.332 0.000 Supported
H2b RP →PIV 0.339 2.907 0.004 Supported
H3 PSQ →PIV 0.235 5.209 0.000 Supported
H4a PIV →RB 0.199 5.456 0.000 Supported
H4b PIV →RD 0.174 4.532 0.000 Supported
H4c PIV →RL 0.194 3.353 0.001 Supported
H5a PSQ →RB 0.276 4.460 0.000 Supported
H5b PSQ →RD 0.349 6.510 0.000 Supported
H5c PSQ →RL 0.348 6.506 0.000 Supported
H6a PSQ*CSEC →RB 0.103 3.555 0.000 Supported
H6b PSQ*CSEC →RD 0.072 3.358 0.001 Supported
H6c PSQ*CSEC →RL 0.096 3.780 0.000 Supported
Source(s): Based on data collected by authors for the study
Table 6.
Direct and moderating
relationships
Pickup service
quality in
BOPIS
omnichannel retailers through customer-perceived experience quality, relationship
proneness and perceived relationship investment. Our research empirically demonstrates
that pickup service quality in BOPIS service positively impacts BOPIS users’relationship
performance (breadth, depth and length of relationship with the store) through perceived
experience quality, relationship proneness and BOPIS user perceived relationship
investments by the retailer to retain them in the long run. Though recent research by
Chen et al. (2022) has shown that dimensions of channel integration quality of omnichannel
retailers significantly impact omnichannel shoppers’relationship proneness and experience
quality, no particular emphasis was given to pickup service quality BOPIS service. This
study differs from the work of Chen et al. (2022) by establishing a significant positive
relationship between pickup service quality in BOPIS service and perceived relationship
investment with experience quality and relationship proneness as mediators. Recent
omnichannel literature has focused on the various dimensions of service quality to
empirically validate omnichannel shoppers’transactional and non-transactional behaviors
(Natarajan and Veera Raghavan, 2023b). Major investigations were made on the dimensions
such as customer empowerment and engagement (Chen et al., 2022), online and offline
purchase intention (Swoboda and Winters, 2021), multichannel purchase intention (Xin et al.,
2021) and omnichannel service usage (Shen et al., 2018). Researchers have indeed given
particular emphasis to future purchase behaviors of omnichannel shoppers like stickiness
intention (Lin et al., 2022), impulse buying behavior (Pereira et al., 2022), patronage intention
(Le and Nguyen-Le, 2020;Lim et al., 2022;Nguyen, 2021), customer loyalty (Gao and Huang,
2021) and repurchase intention (Lee et al., 2019). However, to our knowledge, no study has
tried to empirically demonstrate a more in-depth understanding of the BOPIS users’loyalty
Figure 2.
Structural model
depicting path analysis
TQM
by focusing on their relationship performance as driven by the pickup service quality in the
BOPIS service. That is giving particular emphasis to the frequency of purchases (relationship
depth) made by these BOPIS users with the same store, how long they are associated with the
same store (relationship length) and most importantly, their cross-buying intentions
(relationship breadth). Our research also demonstrates the direct impact of pickup service
quality and the indirect impact through perceived relationship investment on relationship
performance.
Additionally, when we focus on BOPIS users, they are online shoppers who rely on the
physical store for pickup. Recent literature acknowledges online shoppers as comparison
shoppers more prone to be service experience conscious (Mishra et al., 2022). However, to the
best of our knowledge, the moderating role of these BOPIS users’service experience
consciousness on the pickup service quality-driven relationship performance of the BOPIS
users has not gained attention. Our findings show that pickup service quality significantly
impacts BOPIS users’relationship performance, both directly and indirectly, through
perceived investment. However, this pioneering research demonstrates that BOPIS users’
service consciousness significantly negatively moderates the direct relationships between
pickup service quality and relationship performance (breadth, depth and length).
6. Theoretical implications
(1) How SOR theory is employed to model pickup service quality as
stimulus(S): SOR theory has been relied upon widely by researchers in the past
omnichannel retail context to study customer satisfaction (
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Urg€
upl€
u and Yumurtacı
H€
useyino
glu, 2021), word of mouth (Natarajan and Veera Raghavan, 2023c), and
revisit intentions (Sombultawee and Tansakul, 2022), impulse buying intentions
(Pereira et al., 2022), customer patronage intentions (Lim et al., 2022) and loyalty
intentions (Gao et al., 2021) as a global construct, driven by channel integration
capabilities and quality of retailers’services. However, in an attempt to give a more in-
depth understanding of customer loyalty, our research differs in modeling the
different relationship performance indicators like relationship length, depth and
breadth as (response) (R) variables driven by pickup service quality stimulus (S). We
have given specific focus to BOPIS service and internal states(O) of the BOPIS
users(relationship proneness, relationship investment and experience quality).
(2) Relationship proneness and customer experience quality as mediators:
We have established the mediating impact of relationship proneness and customer
experience quality as internal states (organisms) between pickup service quality and
perceived retailer investment to study the relationship performance in depth. Indeed
researchers have shown interest in modeling omnichannel shoppers’non-
transactional behaviors like customer engagement and empowerment
driven by channel integration quality with the mediating role of customer
relationship proneness and experience quality (Chen et al., 2022). However, our
research differs from this work by relying on SOR theory to model the BOPIS users’
relationship performance driven by pickup service quality influenced relationship
investment, which mainly encompasses transactional post–purchase behaviors
of the BOPIS users like the relationship length of their purchases, their cross-category
purchases and their frequency to bring in revenue to the store.
(3) Relationship investment as an antecedent of BOPIS users’relationship
performance: Our study extends the social exchange theory and relationship
quality theory to the omnichannel retail context by studying the BOPIS users’
Pickup service
quality in
BOPIS
relationship performance driven by relationship investment. Interestingly, recent
work by Menidjel and Bilgihan (2021) demonstrated the direct impact of perceived
relationship investment on customer loyalty as a global construct. However, we
differed from this work to empirically demonstrate that the relationship investment
perceived by BOPIS users subsequently impacts different relationship performance
dimensions differently (which gives an in-depth understanding of customer loyalty to
the academicians and managers).
(4) Relationship breadth as response variable (R): Based on SOR theory, our
current research has modeled the relationship breadth-the cross-buying intentions.
Researchers have shown interest in studying the cross-buying behaviors of
customers in the offline retail store driven by retailer responsiveness (Sharma
et al., 2021), driven by catalog coupons (Ravula et al., 2020), and driven by consumer
motives, store patronage and marketing efforts (Dahana et al., 2022). However, our
study differs from these published studies by demonstrating the pickup service
quality of BOPIS service to impact relationship breadth significantly –the cross-
buying intentions of BOPIS users in omnichannel retail.
(5) Relationship depth and length as a response(R): Researchers in the recent past
have shown interest in modeling the managerially significant behaviors of
omnichannel shoppers’like their revisit intentions in convenience stores (Gibson
et al., 2022), patronage intentions driven by channel integration quality (Le and
Nguyen-Le, 2020;Lim et al., 2022;Nguyen, 2021), stickiness intention (Lin et al., 2022)
and repurchase intention (Lee et al., 2019) driven by channel integration quality.
However, a dearth of research gives importance to pickup service quality to decipher
the BOPIS users’frequency of visit post one pickup service encounter (relationship
depth) and longevity of purchasing with the same store (relationship length) in an
omnichannel retail context. Our study fills this gap by modeling relationship
performance as users’responses.
(6) The moderating role of consumer service experience consciousness as
(organism): Researchers have examined the moderating effect of consumer service
experience consciousness (CSEC) on omnichannel shoppers’satisfaction and
empowerment-driven patronage intentions (Mishra et al., 2022). Nevertheless, CSEC
has been reported to be a significant determinant of comparison shopping in online
retail (Osakwe and Chovancov
a, 2015). But no study has extended this interesting
construct to the pickup service quality literature, focusing on BOPIS users (who are
online shoppers) and who are acknowledged to be service experience-conscious
shoppers (Jin et al., 2022). Hence, our significant contribution to the SOR theory lies in
studying the moderating impact of Consumer service experience consciousness on
the direct relationship between pickup service quality in BOPIS and the relationship
performance dimensions of BOPIS users. The moderation analysis findings give
valuable insights into the relationship between BOPIS users’relationship
performance and their interest in alternatives.
7. Managerial implications
This research demonstrates that retailers must invest in pickup service quality attributes like
(service effectiveness, problem handling, ease of access to pickup counters and product
quality) as identified by Lee et al. (2020), to effectively trigger the cross-buying intentions and
increase the duration of BOPIS users’association with the store raising their revisit
intentions. Our results show that BOPIS users’perceptions about service experience quality
TQM
and their innate propensity to involve in relationships play a significant role in fostering
positive perceptions about relationship investments. However, cumulative assessment of the
BOPIS users’pickup service experience quality could encompass the interesting store
attributes that shoppers evaluate during pickup visits beyond the prerequisites of pickup
service quality. Interestingly, to augment the overall experience quality and boost the BOPIS
users’relationship proneness, retailers could benchmark the best practices of well-known
retailers, like endless aisle introduced by True Religion, to provide an outstanding in-store
shopping experience quality to shoppers who visit the store for pickup. Indeed employee
assistance aided with properly deploying a handy technological interface plays a crucial role
in determining the BOPIS users’overall experience quality appraisal. For instance, this
retailer’s Apple Watch app has an exciting feature that allows sales associates to view
inventory fully, browse, filter, and swiftly identify the precise size, style, color and wash they
believe the customer would want. After locating the desired products, the employee can
“cast" the merchandise picture from the watch to a high-definition monitor for the consumer
to examine. The picture has a bar code, which allows sales associates to complete the sale by
scanning it. Additionally, pickup service quality impacting the customers’innate propensity
to involve in a long-term relationship with the store could be contingent on unexpected
benefits or rewards to BOPIS consumers during their pickup experience (Natarajan and
Veera Raghavan, 2023b,c). Price discounts, small gifts, personalized notes or samples of new
products could be included with the focal online ordered merchandise. The element of
surprise bestows the feel of being in retailers’best interest and becomes a pillar for a more
intimate connection with the retailer, parallelly improving the overall perception of the
quality shopping experience. Yet, the relationship performance of the BOPIS customers with
a store is highly contingent on how they evaluate the various relationship investments made
by the store to retain them. Referral programs to bring in new customers could be considered
an effective investment retailer could make. Retailers should leverage the BOPIS users’data
across channels to identify the customers who are highly prone to involve in long-term
relationships based on their duration of association with the store. To increase the customers’
frequency and longevity of purchases with the store, managers could implement a referral
program in which BOPIS customers are rewarded for recommending friends and family to
use the BOPIS service. For example, Costco recently launched the “Refer a Friend”program,
which allows the customer and his friend a $10 Costco Shop Card*, which may be used at both
the Costco brick-and-mortar store and purchases made on the stores’website. Ideally, by
mobilizing their resources and investing in these programs, managers could capitalize on the
customers who are relationship prone to promote omnichannel service delivery actively,
thereby expanding the customer base. Managers should ensure specific product categories
are eligible for membership card purchases creating a need to make perennial purchases.
Ultimately all the relationship investments made by the retailer are with an earnest
intention to promote cross-category purchases and increase the customer visit frequency to
the store bringing in revenue to the store in the long run (Natarajan and Veera Raghavan,
2023c). Retailers could introduce some interesting “physical store only”product variants with
lucrative exclusive offers that attract customers to visit the store. Once they enter the store,
servicescape design with interactive self-service technologies also plays a crucial role in
service encounter satisfaction. For instance, managers could leverage the benefits of
introducing the virtual rails in-store, as Marks and Spencer did, to create awareness about the
availability of the various fashion products through typical videos playing on big screens,
enabling customers to browse the life-sized imagery of the prettiest dresses and trousers.
This could encourage the BOPIS users who visit the store for product pickup to browse the
product categories on virtual screens, motivating them to make cross-category purchases. To
boost the likelihood of additional purchases, the pickup counter or lockers, for example, might
be placed next to the stock of products currently on sale, or the best-selling items might be
Pickup service
quality in
BOPIS
exhibited in the pickup waiting area (Lee et al., 2020). For instance, retailers may install some
intriguing, easy-to-use locating kiosks (allowing the shoppers to navigate the products with
offers and exciting discounts on the day they visit the store for pickup) in the parking lot.
These strategies might naturally encourage the customers to navigate some other products
apart from their focal purchase and try them out as soon as they enter the store. Retailers
should segment BOPIS users based on their purchasing behavior and preferences, identify
segments more likely to engage in cross-category purchases and tailor product availability
accordingly (Natarajan and Veera Raghavan, 2023c). Within those segments, retailers should
identify customers with high relationship proneness and target them with creative
promotional campaigns highlighting cross-category product purchase benefits across
online and offline channels. However, demand forecasting and product assortment planning
to enable cross-category product purchases are tedious. But if done right, extending the
findings in offline retail by Sharma et al. (2021) to channel integrated store context, these
BOPIS users who are naturally concerned about the total costs involved in any purchases
would feel that they are benefitted economically from their one visit to the store fulfilling
different product needs at one single destination, which could eventually increase their
frequency of revisiting to store. But this may not be true for the customers who are high in
service experience consciousness, who tend to switch to a retailer when their basic
expectations in omnichannel service delivery are not appropriately fulfilled. Its crucial for
retail managers to investigate these segments with ever increasing service failures in BOPIS
orders.
Based on their omnichannel interactions with the retailer, BOPIS users could be profiled as
low, medium and high in-service experience consciousness. Through social proof features
such as client testimonials, ratings or reviews on product quality and packaging of BOPIS
orders, retailers can showcase positive customer experiences and meticulously engage these
BOPIS users, mitigating their need to comparison shop, naturally bringing down the erosion
of the customer base. Additionally, retailers could capitalize on the natural tendency of the
service experience-conscious BOPIS shoppers to create content in online space encouraged to
engage in activities such as surveys, polls and other retailer-related conversations after they
become members and foster a sense of community membership among these omnichannel
shoppers for long-term rewarding relationships (Natarajan and Veera Raghavan, 2023b). For
instance, Walmart introduced the customer spark community that allows its customers to
voluntarily participate in surveys, discussions and other activities to provide feedback and
share their opinions about Walmart products, services and the overall shopping experience
(Natarajan and Veera Raghavan, 2023c). The retailer has acknowledged this to make
informed business decisions, improve its goods and services, and improve the overall
customer experience by harnessing the insights obtained from the Customer Spark
Community. Retailers could gamify the entire process, introducing prizes or incentives such
as gift cards or registration into sweepstakes in exchange for their participation. Actively
soliciting feedback on various attributes of pickup service and eventually acknowledging
customers for their contributions to co-create service experiences could result in positive
customer evaluation of relationship investments made by the store, leading to increased
revisit intentions of the customers (Natarajan and Veera Raghavan, 2023c).
8. Limitations and future research directions
There are certain limitations to the study that may necessitate more investigation. The study
used a cross-sectional technique in the Indian context to generalize the findings. Future
research might test the same model across several age cohorts with varying BOPIS usage
experiences across different countries where the omnichannel shopping trend is expanding at
different rates, relying on a larger sample size. Researchers could do some experimental
TQM
studies to see if endless aisle digital signages in stores encourage BOPIS users to make cross-
category purchases during store visits. Researchers could rely on the actual behavioral data
of the shoppers to get more insights about the profitability of BOPIS users in the long run.
Exploring the service experience consciousness of BOPIS users across low and high-
involvement products they purchase might give exciting insights. Researchers could also
explore the moderating role of employee citizenship behavior in impacting the relationship
performance of BOPIS users. They could also investigate if the retailer’s credibility and brand
image could offset the pre-purchase risk, reduce the need for product quality examination and
eventually motivate customers to use other omnichannel purchase options like buy online
deliver from the store (BODS).
9. Conclusion
Our study advances the omnichannel literature by bringing in a new understanding of BOPIS
users’relationship performance (length, breadth and depth) driven by the pickup service
quality of channel-integrated omnichannel stores. Retailers need to concentrate on the
various attributes of the pickup service quality of their integrated stores that directly impact
the customers’perception of the relationship investment made by the store through
customers’relationship proneness and experience quality. As this research demonstrates, the
job of retail store managers does not stop with ensuring superior pickup service; they should
emphasize its effect on overall experience quality and relationship proneness to decipher the
relationship performance of customers. Our research also demonstrates the need for service
experience consciousness-based segmentation of BOPIS users, essential for decoding their
relationship performance.
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Pickup service
quality in
BOPIS
Appendix
Main constructs Indicators
Item
codes Questions asked in survey Sources
Pick-up Service
quality in BOPIS
(PSQ)
Service
Effectiveness
(SE)
SE1 The store assigns enough staff at the
pick-up counter to offer good service
to BOPIS customers
Lee et al. (2020)
SE2 Employees are able to serve me
quickly
SE3 Employees are ready to serve me
immediately, when I arrive at the
pick-up counter
SE4 The store dedicates sufficient
resources for pleasant pick-up
experience of customers
SE5 Once it is my turn to be served at the
pick-up counter, it takes no time for
me to receive what I ordered
SE6 The store provides adequate
services to enhance the convenience
of BOPIS customers particularly
Problem
Handling (PH)
PH1 During pick-up, the store directly
and immediately handles problems
including the ones that were caused
during online shopping
PH2 The way the pick-up counter in this
store handles order discrepancies is
satisfactory
PH3 When I face a problem, employees
are sympathetic
PH4 Employees quickly handle problems
when they occur
Ease of Access
(EA)
EA1 The pick-up counter is easy to find
EA2 The pick-up counter is located for
easy access
EA3 This store allows me to pick up my
items as instructed online
Item Quality (IQ) IQ1 The store ensures that pick-up
packages are never damaged
IQ2 The store ensures that pick-up items
are never damaged
IQ3 The store keeps pick-up items in the
proper storage conditions
(continued )
Table A1.
Operationalization of
constructs table
TQM
Main constructs Indicators
Item
codes Questions asked in survey Sources
Experience Quality (EQ) EQ1 Service employees who assisted me
in pickup process at this store
provide thoughtful services
Chen et al.
(2022) and Kim
and Choi (2016)
EQ2 Contacting service providers makes
me feel relieved
EQ3 Service employees serve me in a
friendly and kindly manner during
my pickup process at this store
EQ4 I feel, It is a happy time when I am in
this store for my pickup of online
ordered products
EQ5 I feel, I learn some information about
products when I pick them up
EQ6 I like to interact with this store
EQ7 I would say that the experience of
picking up online ordered product at
this store is excellent
EQ8 I believe that one would get a
superior experience at this store
EQ9 I think that the total experience
procedure at this omnichannel
retailer is excellent
Relationship Proneness (RP) RP1 Generally, I like to be a regular
customer of a store
Chen et al.
(2022)
RP2 Generally, I want to be a regular
customer of a store
RP3 I’m usually a person who likes to
visit the same omnichannel store
RP4 Generally, even if I have to work
hard, I am willing to visit the same
omnichannel store
RP5 I want to keep purchases from this
store
RP6 Even if the retailer is far away, I’m
willing to shop online and pickup
from this store
Consumer Service experience
Consciousness (CSEC)
CSEC1 If I am dissatisfied with an
omnichannel service provider, I will
stop using the service
Mishra et al.
(2022)
CSEC2 I am particular about customer
service whenever I am buying
anything
CSEC3 A retailer who fails to deliver on his/
her promise puts me off from buying
items online or offline
(continued )Table A1.
Pickup service
quality in
BOPIS
Main constructs Indicators
Item
codes Questions asked in survey Sources
Perceived Investment (PIV) PIV1 This omnichannel retailer offering
Buy-online-pickup instore service
that I usually use makes efforts to
increase regular customers’loyalty
Chang et al.
(2016)
PIV2 This omnichannel retailer offering
Buy-online-pickup instore service
that I usually use makes various
efforts to improve its ties with
regular customers
PIV3 This omnichannel retailer offering
Buy-online-pickup instore service
that I usually use really cares about
keeping regular customers
PIV4 I believe this omnichannel retailer
offering Buy-online-pickup instore
service that I usually use really puts
some effort into maintaining a
relationship with me
PIV5 I believe this omnichannel retailer
offering Buy-online-pickup instore
service that I usually use cares about
satisfying my needs
Relationship
Performance (RP)
Relationship
Breadth (RB)
RB1 It is convenient to conduct one-stop
shopping at this omnichannel retail
store offering click-and-collect
service that I usually use
Chang et al.
(2016)
RB2 This omnichannel retailer offering
click-and-collect service that I
usually use can fulfill all of my
various needs in one place
RB3 This omnichannel retailer offering
click-and-collect service that I
usually use can satisfy my need to
purchase related products/services
Relationship
Depth (RD)
RD1 I will continue to do business with
this omnichannel store offering
click-and-collect service that I
usually use even if its prices increase
somewhat
RD2 I will not take some of my business
to a competitor omnichannel store
that offers better prices
RD3 I will not choose another
omnichannel retailer offering click-
and-collect service for purchasing in
the future
Relationship
Length (RL)
RL1 I will continue to be a loyal customer of
this omnichannel retailer offering click-
and-collect service that I usually use
RL2 I consider this omnichannel retailer
offering click-and-collect service that
I usually use to be my first choice to
do business with
Source(s): Developed by authors based on various published questionnaire sources as cited above
Table A1.
TQM
About the authors
Dr Thamaraiselvan Natarajan is a Professor in the Department of Management Studies at the National
Institute of Technology, Tiruchirappalli. He is serving as the Registrar i/c currently for the NIT-Trichy.
His areas of expertise are services marketing, advertising and promotions management, marketing
research, strategic marketing, social media marketing, marketing metrics and analytics. His research
interests include self-service technologies, brand leveraging strategies, customer satisfaction analytics,
services branding, and social and online media. He has published more than 60 scholarly articles in
many international journals ranked A, B and C in the Australian Business Deans Council list, like the
Journal of Retailing and Consumer Services, The TQM journal, Journal of Promotion Management,
Benchmarking: An International Journal, Journal of Food Products Marketing, Technology in Society,
Asia Pacific Journal of Business Administration, Journal of Services Research, International Journal of
Business Information Systems and Journal of Modeling in Management.
Deepak Ramanan Veera Raghavan is a full-time Ph.D. scholar in the Department of Management
Studies, National Institute of Technology, Tiruchirappalli. He has completed his B. Tech in
Biotechnology engineering from the school of Bio and Chemical engineering, Sathyabama Institute of
Science and Technology, Chennai. He is an MBA graduate in Marketing and Operations discipline from
Thiagarajar School of Management, Madurai Kamaraj University, Madurai. He is a Six Sigma Green
Belt certified management professional. As a sales and marketing intern, he has industrial experience
working with the FMCG giant in India, Britannia Industries. He has twice qualified for the National
Eligibility Test (Lectureship) in management studies. He has been an ardent follower of scholarly retail
and consumer behavior studies publications. His broad research interest is omnichannel retailing,
technology-enabled marketing and integrated marketing communication. His work on BOPIS users’
citizenship behavior was published in The TQM journal earlier. He is investigating and writing various
manuscripts on value co-creation and customer engagement behaviors among omnichannel shoppers,
focusing on developing economies. Deepak Ramanan Veera Raghavan is the corresponding author and
can be contacted at: deepakramanan@outlook.com
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Pickup service
quality in
BOPIS
