Searching online and buying offline: Understanding the role of
channel-, consumer-, and product-related factors in
determining webrooming intention
Eugene Cheng-Xi Awa,
Norazlyn Kamal Bashab*,
Siew Imm Ngc,
Jo Ann Hod
a,b,c,dDepartment of Management and Marketing, Universiti Putra
50 days' free access:
To cite: Aw, E.C.-X., Kamal Basha, N., Ng, S. I., & Ho, J. A. (2021).
Searching online and buying offline: Understanding the role of channel-,
consumer-, and product-related factors in determining webrooming
intention. Journal of Retailing & Consumer Services, 58, 102328.
Searching online and buying offline: Understanding the role of
channel-, consumer-, and product-related factors in determining
In recent times, the increasing accessibility of mobile technology has led to changes in
consumers’ purchasing behavior. Despite the gloom and doom hearsay about how
electronic commerce is threatening the existence of brick-and-mortar stores, by some
indications, however, webrooming (i.e., the practice of researching items online, and then
buying them offline) may be an even more common practice among shoppers. Against this
background, this study proposes and empirically validates a comprehensive research model
which incorporates consumer traits (i.e., need for touch, need for interaction, and price-
comparison orientation), channel-related factors (i.e., online search convenience, perceived
usefulness of online reviews, perceived helpfulness of in-store salespeople, and perceived
risk of buying online), and smart shopping perception as antecedents of webrooming
intention. Moreover, this study examines the moderating role of product category in
predicting webrooming intention. A questionnaire-based survey was conducted. A total of
280 useable responses was collected and data was analyzed using partial least square
structural equation modeling. The findings revealed significant direct and/or indirect
effects (through smart shopping perception) of consumer traits and channel-related factors
on webrooming intention. In addition, product category was found to moderate the
relationship between price-comparison orientation, online search convenience, perceived
risk, and webrooming intention. Theoretical and practical implications are discussed.
Keywords: Webrooming; Consumer traits; Shopping channel; Product category; Cross-
channel shopping behavior; Smart shopping perception
The current retail landscape is experiencing a drastic change due to the emergence of
mobile technology. With the proliferation of shopping channels available in the market,
the way consumers shop has been revolutionized, manifesting variation in their information
search, product comparison, and purchasing behaviors (Nam & Kannan, 2020). The era of
which consumer shopping journey is completed within a single channel, whether in
physical or online stores has become obsolete. Instead, consumers are increasingly
exhibiting channel usage patterns of which channels are used in combination. For example,
consumers exhibit showrooming behavior when they visit physical stores to browse for
product information, but end up purchasing online. Alternatively, consumers are also
searching for product information online, only to make the final purchase step in physical
stores, exhibiting webrooming behavior as demonstrated by Flavián, Gurrea, & Orús
(2016). This behavior is rather surprising and contradicts the gloom and doom hearsay on
how online commerce is dominating, and may one day eliminate the existence of physical
stores. Instead, more and more pure play online and pure play offline retailers are adopting
multichannel strategies in their business operations by setting up alternative
Cross-channel shopping behaviors such as showrooming and webrooming, have
been increasingly drawing attention from the retail industry. Consumers “cross” channels
by adopting online channels during the search phase of their consumer decision making
process, but move on to physical stores for the actual purchase, and vice versa. Thus far,
webrooming has been regarded as the most extended cross-channel shopping behavior
exhibited by consumers (Flavián et al., 2016). As a matter of fact, a recent consumer
research report has shown that 74% of consumers engage in webrooming, far more than
the 57% that engages in showrooming behavior (JRNI, 2019). The impact of webrooming
on market retail sales is significant, as digital-influenced offline sales capture a
considerable portion of total retail sales (Forrester Research, 2018; Simpson, Ohri,
Lobaugh, 2016). In general, cross-channel shopping behaviors deprive firms’ control over
consumers’ shopping experience and may cause undesired free-riding behaviors (Chiu,
Hsieh, Roan, Tseng, & Hsieh, 2011; Flavián, Gurrea, & Orús, 2019). To clarify, free-riding
is a phenomenon that describes consumers’ switching of retailers in the process of
switching channel’s during their decision-making process (Heitz-Spahn, 2013), which in
the end, result in loss of customers. For example, consumers may benefit from free access
to online product information by retailer A, but purchase with retailer B when they switch
to offline channel. Hence, while it is inevitable that online sales are eating into the profits
of the high street market, it cannot be denied that webrooming puts a constant pressure on
pure play online retailers and multichannel retailers (Aw, 2019). For instance, online retail
giants, such as Amazon and AliExpress have been dealing with substantial loss of sales
due to webrooming (Ecommerce Nation, 2019).
Understanding the ever-changing consumer purchase journey is a vital step in
fulfilling consumers’ shopping expectation. Particularly, how retailers want to sell their
products has become less relevant; instead, how consumers want to shop and buy, lie at the
root of retail success. Hence, an important research question arises: in the current retailing
landscape where multiple shopping channels are easily accessible and free to follow by
consumers, what specific determinants influence their decisions pertaining to shopping
channel selection, particularly webrooming? Webrooming may come as no surprise a
decade ago where online commerce was not as developed as it is today. However, in
today’s retail environment where online commerce thrives, why are consumers
webrooming instead of purely online shopping? This line of research carries important
implications for retailers: by knowing why consumers research product information online
but switch to physical stores for final purchase, firms can better manage their channels and
design strategies to cater to this prevalent cross-channel shopping behavior, with the
ultimate aim of maximizing the conversion rate from search to purchase (Hu & Tracogna,
2020). Likewise, the academic community has called to action for research in the particular
area (i.e., examining determinants of webrooming) (Lemon & Verhoef, 2016; Santos &
In the present study, we undertake the perspective of Millennials as industry
reports have shown that this young generation tend to be more heavily involved in
webrooming behavior (DigitalCommerce 360, 2016; Koetsier, 2018). The young
generation are known to be digital savvy, and they are seen as the driving force of online
commerce (Hall, Towers, & Shaw, 2017; Ladhari, Gonthier, & Lajante, 2019). Their high
involvement in webrooming indicates an interesting phenomenon that contradict the
general assumption that Millennials are predisposed to making online purchases. Clearly,
it is unwise to simply presume that the younger consumers do not feel the need to shop at
offline retailers. Instead, understanding why these young consumers engage in
webrooming behavior would give an upper hand to retailers in dealing with the increasingly
prevalent cross-channel shopping behavior.
2. Literature review
Our definition of “webrooming” is in good agreement to that of Flavián et al.’s (2019), in
which webrooming is a two-stage decision-making process. In particular, webrooming
begins with consumers’ product information seeking behavior through online channels,
followed by information verification, and ending with completing their actual purchase in
physical stores. To date, authors of extant webrooming literature has taken the economic
perspective in approaching the phenomenon. Within this line of research perspective,
webrooming is a behavioral outcome that weighs channel-related costs and benefits.
Consumers combine both online and offline channels as a way to minimize associated
shopping costs and to maximize its potential benefits (Gensler, Verhoef, & Böhm, 2012).
For example, Arora and Sahney (2018; 2019) integrated the theory of planned behaviour
and technology acceptance model to explain webrooming behaviour. The authors revealed
that consumers’ perception of search benefits offered by online channels (e.g. low search
cost and access to online reviews) and purchase benefits derived from physical stores (e.g.
touch and feel, immediate possession, and sales-staff assistance) determine consumers
attitude and subsequent webrooming intention. Similarly, Aw (2019) identified that
immediate possession is an important driver of webrooming behavior, particularly to young
consumers who are prone to desire instant gratification. Furthermore, it has been shown
that prices in physical stores tend to be generally higher than that of online, and if such
price discrepancy is over than what is expected, consumers may be reluctant to perform
webrooming behaviour, and instead, will complete the purchase journey online (Aw, 2019;
Manss, Kurze, & Bornschein, 2019). This is understandable as online retailers generally
have fewer overhead costs, and thus are able to offer more competitive prices along with
extra discounts and monetary benefits (Gensler, Neslin, & Verhoef, 2017). Adding to this,
Manss et al. (2019) further highlighted the importance of quality of offerings provided by
pure play online retailers in reducing the likelihood of consumers’ webrooming behavior.
On the other hand, consumers’ shopping channel usage patterns can be determined
by their motivations and goals (Frasquet, Mollá, & Ruiz, 2015; Harris, Riley, & Hand,
2018). Shopping motivation often relates to consumers’ desire to satisfy particular needs
through choice of retail formats (Noble, Griffith, & Adjei, 2006). It has been demonstrated
that consumers’ webrooming behavior is motivated by the need to attain more
comprehensive information, as well as to feel in control of the shopping process (Kang,
2018; Santos & Goncalves, 2019). Apparently, the combination of online search-offline
purchase shopping pattern equips consumers with better knowledge and evaluative ability,
thereby facilitating optimal purchase decision making. A few studies have explored
webrooming behavior in relation to shopping experience. Viejo-Fernández, Sanzo-Pérez,
and Vázquez-Casielles (2019) found that in the retailing context, negative emotion impacts
perceived value more adversely in the occasion of webrooming than showrooming as
webroomers tend to be more involved and have greater expectations towards the selected
retailer. Besides that, Goraya et al. (2020) demonstrated that in high webrooming scenarios,
consumers who perceive high channel integration would patronage the offline store of the
same retailer in the future.
In brief, the existing literature mostly undertook the assumption of consumer
rationality in selecting shopping channels. Although such approach has brought value to
the understanding about determinants of webrooming, it lacks a more comprehensive view
to take the literature further. In the present study, we seek to tap into a less explored
antecedent of webrooming, namely consumer traits. Including consumer traits when
analyzing cross-channel shopping behavior is imperative as consumers’ expectation of a
shopping channel and subsequent behavior is largely dependent upon their personal traits
(Dholakia et al., 2010; Rodríguez-Torrico, Cabezudo, & San-Martín, 2017). Apart from
that, we aim to add to prior webrooming literature by investigating several under-
researched channel-related factors, such as perceived usefulness of online reviews and
perceived helpfulness of in-store salespeople. In addition, adhering to the suggestion of
Flavián, Gurrea, & Orús (2020), we examine consumer experience, as manifested by smart
shopping perception as a mediator that explains the mechanism between the proposed
antecedents (i.e., consumer traits and channel-related factors) and webrooming intention.
Lastly, in order to address concerns raised by prior studies (Arora & Sahney, 2019; Wang,
Lin, Tai, & Fan, 2016), product category is incorporated as a moderator to examine the
boundary condition between proposed antecedents and webrooming intention. To address
the aforementioned gaps, we propose a model integrating channel-, consumer-, and
product-related factors in understanding webrooming.
3. Hypotheses development
The stimulus-organism-response (SOR) model of environmental psychology (Mehrabian
& Russell, 1974) advocates that environmental stimulus (S) trigger internal states of the
individuals (O), which in turns, trigger their behavioral responses (i.e., decisions to avoid
or approach a behavior) (R). The SOR model has been widely applied in the retailing
context to understand the effects of retail environment cues on consumers’ perception and
their subsequent behaviours. For instance, prior studies have evidenced the impact of in-
store environmental cues (e.g., social, design, and ambient) as well as online store attributes
(e.g. content quality and interactivity) on consumers’ behavioral response (Dabbous &
Barakat, 2020; Kumar & Kim, 2014). The reasons for applying SOR model in
understanding webrooming behavior are twofold: first, webrooming involves consumers’
interaction in both online and offline channel environments, prompting them to consider
the associated channel-related variables (stimulus) in making shopping channel decision.
SOR model is widely utilized to explain how shopping channel characteristics determine
channel choice, and it has been validated in both online and offline channel behavior
contexts, thus rendering it an appropriate theoretical model to be employed in
understanding webrooming behavior (Arora, Parida, & Sahney, 2020; Miquel-Romero,
Frasquet, & Molla-Descals, 2020; Pantano & Viassone, 2015; Zhang, Ren, Wang, & He,
2018). Second, SOR model goes beyond the assumption of direct impacts of stimulus and
incorporates the internal states of consumers as an explanation to how channel-related
variables elicit behavioral intention, thus aid finer theoretical comprehension of cross-
channel shopping behavior (Arora et al. 2020). In sum, the SOR model offers a nuanced
theoretical ground that enable us to consider the sequential impacts of both online and
offline channel attributes as stimuli on consumers’ arousal of cognitive perception towards
webrooming (i.e., smart shopping perception), and subsequent intention to engage in
In the present study, we contend that there is no singular theory that is able to
holistically explain the webrooming phenomenon, due to the complexity of the consumer
decision making process. To accommodate the diverse factors in the present study, we
integrate the meta-theoretic model of motivation (3M model) (Mowen, 2000), as well as
cognitive fit theory (Vessey, 1991) into the model. The integration of theories is befitting,
as it allows us to holistically explain the phenomenon that would otherwise be unattainable
with single theory used. The 3M model offers an understanding in the effects of underlying
personality traits on behavioral intention through a feedback mechanism (evaluation of the
state of experience), thereby backing up the proposition of consumer traits-smart shopping
perception-webrooming intention linkage (Mowen, 2000). Several studies have evidenced
and advocated to examine consumer traits in relation to shopping channel selection/cross-
channel shopping behavior (Aw, 2020; Goraya et al., 2020; Lemon & Verhoef, 2016;
Rodríguez-Torrico, Cabezudo, & San-Martín, 2017; Sands, Ferraro, Campbell, & Pallant,
Meanwhile, the moderating effect of product category is supported by the 3M
model and cognitive fit theory. To clarify, the purchase of search or experience attribute
dominant products represents a situational variable in the context of retailing (Gehrt & Yan,
2004). The 3M model suggests that consumer traits can be highly situational, implying that
its effects on behavioral intention can vary depending on shopping encounters. In a similar
vein, cognitive fit theory highlights that information representation and the characteristics
of task form “cognitive fit” when they are aligned and matched, which leads to approach
behavior. Retail channels differ substantially in their perceived capability to offer
consumption benefits in the search and purchase stages, and product category determines
how the associate channel attributes are perceived (Alba et al., 1997; Vijayasarahthy, 2002).
Therefore, we argue that the effect of channel-related factors on webrooming intention is
moderated by product category. In other words, the impact of channel-related factors in
determining consumers’ webrooming intention, may be augmented or alleviated depending
upon whether consumers shop for search or experience goods.
Need for touch
Need for touch refers to consumers’ inclination of evaluating product information through
the haptic sensory system (Peck & Childers, 2003). Certain consumers demonstrate a
greater preference to touch products than others. Consumers with high need for touch tend
to be more confident with their purchase judgment if product information can be gained
from physically touching the product. Consumers with high need for touch are likely to
switch from online to offline channels during the purchase stage. This can be explained by
the fact that consumers’ attainment of correct purchase goal is more prominent in the
purchase stage (Lester, Forman, & Loyd, 2006), and haptic evaluation is vital for risk
elimination, thereby evidencing webrooming in process. It is important to note that the
effect of need for touch may be contingent upon product category. As shown in González-
Benito, Martos-Partal, and Martín (2015), prominence of tangible and evaluative attributes
determines the discriminability of a product. In the present study, we argue that the need
for touch has a greater effect in the purchase of experience goods, such as apparels because
goods of this kind tend to possess attributes that require a more intensive direct inspection
(Aw, 2020). On the other hand, search goods such as books, features relatively low
discriminable attributes through haptic sensory, therefore there is less risk of incorrect
purchase. Therefore, we hypothesize:
H1: Need for touch is positively related to webrooming intention.
H1a: Product category moderates the relationship between need for touch and
webrooming intention. The relationship between need for touch and webrooming
intention is stronger for experience goods than search goods.
Need for interaction
Need for interaction denotes consumers’ inclination to underscore a personal contact with
salespeople during shopping encounters (Dabholkar, 1996). Consumers with high need for
interaction are comparatively more uneasy and distrusting of online transaction activities
as they perceive greater risk and uncertainty due to perceived lack of human interaction
(Riquelme & Román, 2014). As a result, consumers with high need for interaction prefer
to shop at physical stores as this would enable them to seek assistance and interaction with
salespeople. Besides family and friends, salespeople represent an important source of
information that often only attainable in physical stores, although this may change
somewhat in the near future (Lee, 2017). These consumers may search for information
online, but more likely to switch to offline stores to make the final purchase. As highlighted
by Arora & Sahney (2019), the assistance of in-store salespeople constitutes an important
factor for consumers’ attitude towards webrooming. Furthermore, in the present study, we
argue that the relationship between need for interaction and webrooming intention is
stronger when purchasing experience goods due to the presence of greater information
asymmetry (Nelson, 1970). To explain, high information asymmetry of experience goods
is characterized by high variability in the offerings and uncertainty about outcomes, which
in turn renders online information to be inadequate, and amplify the additional need for
physical contact with salespeople (Lian & Yen, 2013; Sharma & Khrisnan, 2002;). Hence,
H2: Need for interaction is positively related to webrooming intention.
H2a: Product category moderates the relationship between need for interaction and
webrooming intention. The relationship between need for interaction and webrooming
intention is stronger for experience goods.
According to Heitz-Sphan (2013), price-comparison orientation is defined as consumer's
tendency to acquire knowledge about product prices and make comparison. Price is
undoubtedly an important marketplace cue that exert extensive influence on consumer
behavior (Lichtenstein, Ridgway, & Netemeyer, 1993). Studies indicated that consumers
who are highly price comparison-oriented are likely to search for information online prior
to purchase in physical stores as the Internet enables the price comparison to be done easier
and quicker, and the information acquired facilitates subsequent purchase decision (Flavián
et al., 2016; Santos & Goncalves, 2019). Similarly, Heitz-Sphan (2013) revealed that cross-
channel shopping is often performed for utilitarian purpose, such as costs saving.
Meanwhile, the positive impact of price-comparison orientation on webrooming intention
is posited to be weaker when purchasing experience goods, as similar to the suggestion of
Girard, Korgaonkar, and Silverblatt (2003). Our rationale is that consumers tend to be less
price sensitive for experience goods (Fassnacht & Unterhuber, 2016; Wakefield & Inman,
2003), therefore alleviating the need for price comparison and getting best deal. Based on
the reasoning above, we hypothesize:
H3: Price-comparison orientation is positively related to webrooming intention.
H3a: Product category moderates the relationship between price-comparison orientation
and webrooming intention. The relationship between price-comparison orientation and
webrooming intention is stronger for search goods.
Online search convenience
Online search convenience manifests the perceived ease and speed of which consumers
can gather product information online (Verhoef, Neslin, & Vroomen, 2007). Online
channels have been favorably viewed as a search channel due to the convenience it offers,
including the ease of navigation, price comparison, and individual-tailored suggestions
(Dekimpe, Geyskens, & Gielens, 2020). Webroomers search and acquire product
information online to facilitate subsequent purchase stage in the physical stores (Fernández,
Pérez, & Vázquez-Casielles, 2018). Similarly, Arora and Sahney (2019) indicated online
search convenience as an important factor that affects consumers’ attitude towards
webrooming. Based upon the well-established technology acceptance model (TAM) which
theorizes perceived ease of use determines usage behavior (Davis, 1989), it can be expected
that online search convenience will influence webrooming intention. Moreover, we argue
that the effect of online search convenience on webrooming intention is greater when
purchasing search goods. Online search convenience tends to be more beneficial for
products that contain information that is more discrete in nature and easily comparable, as
manifested in search goods (Chiang & Dholakia, 2003). On the contrary, grounded in
cognitive fit theory (Vessey, 1991) which asserts that information representation and the
characteristics of task needs to fit in order to elicit approaching behavior, experience goods
generally require more extensive physical inspection, and consequently, the reduced search
cost offered by online channel for experience goods may weigh relatively less. Thus, the
following hypotheses are proposed.
H4: Online search convenience is positively related to webrooming intention.
H4a: Product category moderates the relationship between online search convenience and
webrooming intention. The relationship between online search convenience and
webrooming intention is stronger for search goods.
Perceived usefulness of online reviews
Based upon the information processing literature, consumers search information to gain
confidence in judgment, purchase decision satisfaction, as well as reduce purchase risk
(Tormala et al., 2008; Zhang & Hou, 2017). It is generally believed that consumers rely on
the opinion of others, such as family, friends, as well as other consumers to derive at their
final purchase decisions. Literature has shown that online consumer reviews have emerged
as one of the most relevant sources of information in the modern retail environment,
especially for the young generation of consumers (Aw, 2020; Hall et al., 2017). However,
not all online reviews are equal in terms of quality (Karimi & Wang, 2017), and consumers
are more receptive to useful online reviews. In the present study, we assume that perceived
usefulness of online reviews compels consumers to webroom as a means to alleviate
uncertainty in the offline setting. Flavián et al. (2016) backs up this argument as their study
identifies that reading online reviews can positively aid decision making in offline purchase.
On top of that, we propose that the purchase of search goods can augment the relationship
between perceived usefulness of online reviews and webrooming intention. Prior studies
reveal that usefulness of online reviews in reducing risks and uncertainties is enhanced
when the product involved is search attributed in nature (Hong, Xu, Wang, & Fan, 2017;
Weathers, Sharma, & Wood, 2007), partly because search goods can be evaluated more
objectively. Therefore, we hypothesize:
H5: Perceived usefulness of online reviews is positively related to webrooming intention.
H5a: Product category moderates the relationship between perceived usefulness of online
reviews and webrooming intention. The relationship between perceived usefulness of
online reviews and webrooming intention is stronger for search goods.
Perceived helpfulness of in-store salespeople
Channel theory (Li, Kuo, & Rusell, 1999) suggests that interactivity (e,g., response of
salespeople) determines the choice of shopping channel. Helpful salespeople, characterized
by being knowledgeable and trustworthy can foster positive attitude towards the store or
retailer (Cronin, Brady, & Hult, 2000). A recent study by Fassnacht, Beatty, and Szajna
(2019) shows that consumers are unlikely to showroom, and instead complete their
purchase journey in physical stores if salespeople are perceived as helpful. Past
webrooming studies have found that assistance of in-store salespeople may help to reduce
uncertainty associated with online shopping, thereby reinforce consumers’ positive attitude
towards webrooming (Arora & Sahney, 2018; 2019). As a result, we contend that perceived
helpfulness of in-store salespeople motivates the switching behavior from online search to
offline purchase. We further put forth the potential moderating role of product category in
the relationship between perceived helpfulness of in-store salespeople and webrooming
intention, of which the relationship is expected to be stronger for experience goods. As
indicated by previous studies, experience goods carry the characteristics of high variability
and high uncertainty, which would heighten the importance of salespeople assistance (Aw,
2020; Sharma & Krishnan, 2002). Henceforth, we predict that:
H6: Perceived helpfulness of in-store salespeople is positively related to webrooming
H6a: Product category moderates the relationship between perceived helpfulness of in-
store salespeople and webrooming intention. The relationship between perceived
helpfulness of in-store salespeople and webrooming intention is stronger for experience
Perceived risk of buying online
Prospect theory suggests that consumers tend to be risk averse, and they weigh the amount
of loss more than the amount of gain when making decision in a situation of uncertainty
(Kahneman & Tversky, 1979). Hence, the perceived risk of buying online may outweigh
the perceived benefits associated with online purchase. The notion of perceived risk
characterizes the expected difference in purchase experience and goals, as well as the
potential dissatisfaction with the purchase (Pires, Stanton, & Eckford, 2004). Perceived
risk determines consumers’ shopping channel selection (Wang et al., 2016). Recent studies
indicated that the effects of perceived risk remain prominent even after years of e-
commerce implementation, leading consumers to utilize online channels mainly for search
purpose, only to switch to physical stores for the final purchase (Arora & Sahney, 2019;
Santos & Goncalves, 2019). Moreover, perceived risk of buying online is escalated in the
purchase situation involving experience goods (Lian & Yen, 2013). This is attributed to
the fact that the Internet is unable to replace the physical senses made available by
inspecting products in physical stores, which is an important evaluation criterion for
experience goods (Aw, 2020; Park, Stoel, & Lennon, 2008; Lian & Yen, 2013). Therefore,
we argue that the effect of perceived risk of buying online on webrooming is greater for
H7: Perceived risk of buying online is positively related to webrooming intention.
H7a: Product category moderates the relationship between perceived risk of buying online
and webrooming intention. The relationship between perceived risk of buying online and
webrooming intention is stronger for experience goods.
Mediating role of smart shopping perception
Embedded within the context of this study, smart shopping perception is a concept that
describes consumer experience in terms of minimization of resources and maximization of
outputs derived from the webrooming shopping process (Atkins & Kim, 2012; Flavián et
al., 2020). In this study, we postulate that the smart shopping perception mediates: (1) the
relationship between consumer traits and webrooming intention, and (2) the relationship
between channel-related factors and webrooming intention. Firstly, the crux of our theory
hinges on the argument laid out in 3M model (Mowen, 2000), which asserts that personality
traits play the role as a reference point for outcome appraisal (approach behavior) by
comparing desired state and actual state (evaluative perception). Consumers with different
personality traits determine how they perceive retail channels and their subsequent
behavior (Bosnjak, Galesic, & Tuten, 2007). To illustrate an example in the context of this
study, consumers with high price-comparison orientation are more likely to appraise
webrooming as a “smart” shopping method as it aids them to achieve monetary savings,
which in turn leads to future behavioral intention.
Secondly, we put forth the idea that smart shopping perception mediates the
relationship between channel-related factors and webrooming intention. The integration of
SOR theory and channel theory supports our argument. Channel attributes serve as stimuli
to consumers’ perception towards webrooming, and their subsequent intention. For
example, online search convenience and perceived usefulness of online reviews aid
consumers in their webrooming shopping process, thus foster a sense of smart shopping
perception, as manifested in the perceived ability to save cost and effort as well as making
the right purchase. Indeed, such positive evaluation are likely to prompt consumers to
webroom in the future, which is in good agreement with theory of technology adoption
(Davis, 1989). Based upon the reasoning outlined above, we hypothesize:
H8a: Smart shopping perception mediates the relationship between need for touch and
H8b: Smart shopping perception mediates the relationship between need for interaction
and webrooming intention.
H8c: Smart shopping perception mediates the relationship between price-comparison
orientation and webrooming intention.
H8d: Smart shopping perception mediates the relationship between online search
convenience and webrooming intention.
H8e: Smart shopping perception mediates the relationship between perceived usefulness
of online reviews and webrooming intention.
H8f: Smart shopping perception mediates the relationship between perceived helpfulness
of in-store salespeople and webrooming intention.
H8g: Smart shopping perception mediates the relationship between perceived risk of
buying online and webrooming intention.
-----------------------------Insert Figure 1About Here-----------------------------
Data collection method
We collected the data through a paper-based questionnaire approach. The personal survey
was administered in universities and shopping malls in Malaysia. In order to encourage
participation, a cash voucher was given to respondents who completed and returned the
questionnaire. Due to the difficulty in compiling a complete sampling frame for
webroomers, purposive sampling technique was employed (Sarstedt, Bengart, Shaltoni,
Lehmann, 2018), of which the criteria imposed were that i) respondents should be
Millennials aged 1982- 2004 (Hall et al., 2017) and ii) have webrooming experience in the
past six months. Adopting the approach applied in prior webrooming studies (Aw, 2020;
Santos & Goncalves, 2019), respondents were requested to indicate product purchased in
the most recent webrooming experience. In a two-month data collection period, 300
questionnaires were distributed and 280 responses were deemed usable, after discarding
responses with outliers and straight-lining pattern. The sample size exhibited adequate
statistical power, exceeding the minimum requirement of 183 samples calculated using
G*power, with f2= 0.15, α= 0.05, Power= 0.80 (Faul, Erdfelder, Lang, & Buchner, 2007).
Table 1 demonstrates the demographic characteristics of the respondents.
-----------------------------Insert Table 1 About Here-----------------------------
We adapted established scales from the literature for construct measurements. The
measurements of need for touch and price-comparison orientation were adapted from
Santos and Goncalves (2019). The measurement of need for interaction was adapted from
Dabholkar (1996). The measurements of online search convenience and webrooming
intention were drawn from Arora and Sahney (2019). Perceived usefulness of online
reviews was measured using scale from Park and Lee (2009). Perceived helpfulness of in-
store salespeople was measured using scales from Gensler et al. (2017). Perceived risk was
modelled as a second-order formative construct, with three first-order reflective constructs
(i.e., financial risk, delivery risk, and product risk) using scale from Choi and Lee (2003)
and Hong (2015). Likewise, smart shopping perception was modelled as a second-order
formative construct comprised of three first-order reflective constructs, namely time and
effort savings, making the right purchase, and monetary saving based on the scale of
Flavián et al. (2020). Product category was classified as search (e.g. books) and experience
goods (e.g., cosmestics) based on established literature (Lian & Yen, 2013; Frasquet et al.,
2015), and were coded as 0 and 1 respectively.
Common method bias
In order to counter the potential threat of common method bias in cross-sectional research
design, we employed both procedural and statistical remedies. With regards to procedural
design, the survey was designed in consultation with senior academics specializing in
marketing. Further, pre-test and pilot test were conducted to ensure clarity and conciseness
of the survey questions (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). In addition,
respondents are assured of anonymity and confidentiality. Statistical remedy was first
conducted through the means of Harman Single Factor test, and the result turned out that a
single factor explains 24.381 % of the overall variance, well below the conservative
threshold of 40% (Babin, Griffin, & Hair Jr, 2016). Second, the correlation matrix
procedure (Bagozzi et al., 1991) was executed and the results exhibited highest inter-
construct correlation of 0.688, below the 0.90 threshold value. Thirdly, we performed
another assessment using full collinearity assessment approach (Kock, 2015), and the
results indicated pathological VIF values ranged from 1.320 to 2.370, below the threshold
of 3.3. After careful consideration and assessment, it can be concluded that common
method bias is unlikely a major threat to the present study.
Partial Least Square Structural Equation Modelling (PLS-SEM)
PLS-SEM was employed as statistical tool to test the proposed hypotheses. As a variance-
based technique, PLS-SEM was chosen in this study for several reasons. Firstly,
PLS-SEM’ s key goal is to predict target constructs, which is in line with the objective of
this study (i.e., to predict the factors influencing webrooming intention). Secondly, the
research model proposed involves higher-order constructs (i.e., perceived risk and smart
shopping perception). PLS-SEM is suitable in such instance as it facilitates the researcher
to obtain latent variable scores for higher-order constructs computation as well as
estimation of formatively measured constructs without additional specification
modifications (Hair, Hult, Ringle, & Sarstedt, 2017). Thirdly, the present study involves
mediation and moderation, which can be precisely and simultaneously tested using PLS-
SEM (Henseler, Ringle, & Sinkovics, 2009). Fourthly, PLS-SEM can handle data with
non-normal data distribution, giving it an upper hand because social science studies often
rely on non-normal data (Hair, Risher, Sarstedt, & Ringle, 2019).
Reflective measurement model assessment
The assessment of reflective measurement model includes checking of indicator loadings,
internal consistency reliability, convergent validity, and discriminant validity. Firstly,
results indicated that all loadings were above cut-off value of 0.708 (Hair et al., 2019).
Secondly, as depicted in Table 2, composite reliability (CR) values for all constructs were
above 0.70, indicating satisfactory internal consistency. Thirdly, average variance
extracted (AVE) were above the threshold of 0.50 (Fornell & Larcker, 1981). Therefore,
convergent validity was established.
Next, discriminant validity was assessed using the heterotrait-monotrait ratio
(HTMT) of the correlations approach (Henseler, Ringle, & Sasterdt, 2015). Table 3 showed
that (HTMT) ratios were below the conservative threshold of 0.85 (Kline, 2011). Following
the suggestion of Franke and Sarstedt (2019), to further confirm the findings, we applied
bootstrapping and results showed that upper bound of the 95% confidence interval of
HTMT was below 0.85, reinforcing the establishment of discriminant validity.
-----------------------------Insert Table 2 About Here-----------------------------
-----------------------------Insert Table 3 About Here-----------------------------
Formative measurement model assessment
A two-stage approach was employed to examine the measurement properties for reflective-
formative higher-order constructs (i.e., perceived risk and smart shopping perception)
(Sarstedt, Hair Jr, Cheah, Becker, & Ringle, 2019). To explain, the latent variable scores
for the lower-order constructs were first derived, and submitted as manifest variables for
the higher-order constructs. Firstly, the redundancy analysis was conducted with a global
item as endogenous construct for both perceived risk and smart shopping perception. Since
the threshold of 0.7 in path coefficient value was not met, we further probed by conducting
bootstrapping with 5000 subsamples as suggested by Sarstedt et al. (2019). Results turned
out that the path coefficient for perceived risk= 0.611 (95% percentile confidence interval:
[0.561; 0.713]) and smart-shopping perception= 0.635 (95% percentile confidence interval:
[0.516; 0.706]) is not significantly different from 0.7, indicating the acceptance of
convergent validity. Secondly, we assessed the collinearity of the formative indicators. As
shown in Table 4, the VIF values for the formative indicators ranged from 1.331 to 2.169,
below the cut-off value of 3 suggested by Hair et al. (2019). Secondly, we examined the
indicator weights, and their respective statistical significance and relevance. All indicators
exhibited significant importance on respective higher order constructs (p< 0.05) expect
delivery risk. Since the p-value is very close to 0.05 significance level, we further assessed
the confidence interval. The 95% confidence interval (CI) provided evidence to its
significance as there was no 0 straddle in between. Additionally, the outer loading of
delivery risk was greater than 0.50, therefore it was retained.
-----------------------------Insert Table 4 About Here-----------------------------
Structural model assessment
Multicollinearity was examined before the assessment of structural relationships. The VIF
values ranged from 1.252 to 1.586, below the threshold of 3 (Diamantopoulos & Siguaw,
2006). Next, the model explained 30% of variance in the key endogenous construct (i.e.,
webrooming intention). Afterwards, we tested the out-of-sample predictive accuracy using
the state-of-the-art measure, PLSpredict. Following the guideline by Hair et al. (2019), the
RMSE and MAE values generated from PLS-SEM was compared to that of naïve linear
regression model (LM) benchmark. None of the key endogenous construct’ indicators
exhibited PLS-SEM’s RMSE and MAE values that are higher than the naïve LM
benchmark, indicating high predictive power (Hair et al., 2019).
Subsequently, we assessed the significance and relevance of path coefficient using
Bias-Corrected and Accelerated bootstrapping, with 5000 subsamples (Hair et al., 2017).
The results were summarised in Table 5. Three consumer traits, need for touch (β = 0.124,
p< 0.05), need for interaction (β = -0.117, p< 0.05), and price-comparison orientation (β =
0.135, p< 0.05) significantly influenced webrooming intention. In terms of channel-related
factors, perceived risk significantly influenced webrooming intention (β = 0.164, p< 0.01).
However, online search convenience, perceived usefulness of online reviews, and
perceived helpfulness of in-store salespeople exhibited non-significant direct effect on
webrooming intention (p> 0.05).
-----------------------------Insert Table 5 About Here-----------------------------
In executing the analysis of mediation, we adhered to the transmittal approach suggested
by (Rungtusanatham, Miller, & Boyer, 2014). To clarify, the main tenet of the transmittal
approach is to “develop the hypothesis that M mediates the effect of X on Y or that X has
an indirect effect on Y through M without needing to articulate hypotheses relating to X to
M and M to Y” (Rungtusanatham et al., 2014, p. 106). As suggested by Hair et al. (2017),
we bootstrapped the indirect effect with 5000 subsamples and check for 95% bias-corrected
confidence interval. As shown in Table 6, three out of seven mediation paths were found
significant as confidence intervals do not straddle a 0 in between. Smart shopping
perception mediated the relationship between predictors (i.e., price-comparison orientation,
online search convenience, perceived usefulness of online reviews) and webrooming
-----------------------------Insert Table 6 About Here-----------------------------
The moderation test returned three significant moderated relationships. We followed the
suggestion of Dawson (2014) by providing interaction plots to better interpret the nature
of significant moderation. First, the relationship between price-comparison orientation and
webrooming intention is stronger for search goods (β = -0.101, p< 0.05). Second, the
relationship between online search convenience and webrooming intention is stronger for
search goods (β = -0.118, p< 0.05). Third, the relationship between perceived risk and
webrooming intention is stronger for experience goods (β = 0.129, p< 0.05).
Heralded as the most extended cross-channel shopping behavior, webrooming has pique
the attention of academicians and retail practitioners alike. Retailers of all kinds, either
pure play online, offline, or multichannel retailers need to comprehend and adapt to this
shopping behavior in order to better seize opportunities and optimize their business
performance. Our study contributes to the literature by providing both theoretical and
managerial implications, which are presented in the following sections.
The foundation of the theoretical contributions flowing from this study is aligned with the
call for action by prior literature that seeks to understand why consumers involve in
webrooming behavior (Arora & Sahney, 2019; Flavián et al., 2016; Lemon & Verhoef,
2016). To this end, we contribute to the literature by integrating consumer traits and
channel-related factors in a single framework, and test for their relevance in relation to
Adding to prior literature that emphasized on shopping channel perception, the
present study highlights the fact that consumer traits exert a real bearing on cross-channel
shopping behavior, in particular, webrooming. First, we reveal that haptic information
processing still remains an important goal-directed behavior to reduce perceived
psychological distance (Jha, Balaji, Stafford, & Spear, 2019), even in the Millennials’
shopping journey. Notwithstanding the prevalence of online commerce, Millennial
consumers with high need for touch are insecure and less confident without haptic modality,
thereby motivating them to engage in webrooming behavior (Aw, 2020; Flavián et al., 2016;
Flavián, Gurrea, & Orús, 2017). The insignificant indirect effect can be plausibly explained
by the fact that while webrooming may realize in the right purchase, it may also entail
greater effort (Fernandez et al., 2018; Flavián et al., 2020), and thus the associated benefit
and cost may cancel each other effect out.
The largely unexplored direct effect of need for interaction on webrooming
intention somehow substantiates the idea of Aw (2020) that younger generation consumers
who have low need for interaction probably tend to search online beforehand to maximize
individual control and avoid time-consuming interaction with persistent in-store
salespeople. The insignificant indirect effects may be explained by the less relevance of a
specific smart shopping perception dimension as a direct outcome for the selected
consumer traits in the present study. The trait of low need for interaction among Millennials
may motivate webrooming to avoid protracted interactions with salespeople, instead of
saving money or time as manifested in smart shopping perception, thereby rendering the
relationship to be insignificant. Further research is needed to verify this. Additionally,
price-comparison orientation is identified as a significant predictor of webrooming
intention, substantiating the idea that cross-channel shopping behavior is largely motivated
by utilitarian benefits, and webrooming is an effective means of acquiring price-related
information (Heitz-Spahn, 2013; Santos & Goncalves, 2019).
On top of that, we validated and identified several channel-related factors that were
overlooked by previous studies, such as perceived usefulness of online reviews, as well as
the multi-faceted perceived risk, which were proven to weigh heavily in determining the
perceptions of Millennial consumers. Echoing the finding of previous studies (Flavián et
al., 2016; 2020), we articulate that when consumers perceive online reviews to be helpful,
they are able to derive positive experience from the webrooming process. This includes
perceptions of time/effort savings as well as perception of making the best purchase, which
in turn drive intention to perform webrooming in future shopping endeavors. While
previous webrooming studies (Arora & Sahney 2019; Santos & Goncalves, 2019) had
utilized the unidimensional perceived risk, our multi-dimensional perceived risk modelling
offers a more in-depth insight into what risk facet matters the most in the context of
webrooming. Importantly, our finding identifies three dimensions of risk, namely financial
risk, delivery risk, and product risk which are relevant in the webrooming context. It is
worth noting that product risk carries the greatest weight in forming consumers’ risk
perception, suggesting that consumers are turning away from online purchases mainly due
to concerns regarding the underperformance of product. Delivery risk represents another
relatively less explored yet relevant facet that concerns consumers in their webrooming
In addition, the mediating role of smart shopping perception provides a springboard
for researchers to understand the mechanism of how the predictors proposed leads to
webrooming intention, stressing the indispensable role of consumer shopping experience.
On top of that, this study uncovers product category as a boundary condition that resides
in the relationships between consumer traits (i.e., price-comparison orientation), channel-
related factors (i.e., online search convenience and perceived risk), and webrooming
intention. To our knowledge, this is the first attempt to delve into such exploration, thereby
addressing pertinent concern raised by prior studies that factors influencing webrooming
behavior may vary for product of different kinds (Arora & Sahney, 2019; Flavián et al.,
2020; Santos & Goncalves, 2019).
The results of this study could be beneficial for retailers in dealing with webrooming
behavior. Firstly, the trait of high need for touch steers consumers away from online to
physical stores for better haptic evaluation on the products. In order to mitigate the effect
of need for touch and stimulate online purchase, pure play online retailers are suggested
to leverage on the visual- and haptic-enabling technologies, such as 3D virtual tours,
more fine-grained zoom-in effects, actuators (e.g., haptic glove) and mid-air tactile
sensations (e.g., AirWave), that could facilitate product evaluation and foster enjoyable
online experience (Duarte & e Silva, 2020; Van Kerrebroeck, Willems, & Brengman,
2017). On the contrary, offline retailers who wish to direct consumers to their physical
stores may engage in advertising strategy that stimulate consumers’ desire to try products
physically. Secondly, as need for interaction negatively influences webrooming intention,
offline retailers may need to empower their in-store salespeople with more adaptive
strategies instead of mere pressure selling, which could backfire when dealing with
Millennial consumers. Thirdly, in order to reduce likelihood of webrooming by
consumers with high price-comparison orientation, pure play online retailers who manage
search goods could engage in time-limited price discounts. For example, popup
promotion messages could be presented with a countdown timer to evoke sense of
urgency, and thus increase the likelihood to retain consumers within channel and close
sales. In addition, loyalty program could be executed by pure play online retailers to
reduce consumers’ temptation to switch channel in pursuit of lower price.
Fourthly, search convenience appears as an apparent disadvantage for offline retailers, in
comparison to pure play online retailers. Recognizing that consumers prefer a convenient
and frictionless search and purchase process, investment in beacon technologies, such as
mobile apps that enable exact location of product aisle could be a direction for search
goods retailers to look into (Dekimpe et al., 2020). In addition, it would be ideal for
offline retailers to digitally enable the in-stock availability and pricing information, to
minimize disappointment (e.g. chance of not finding the item wanted and paying more
than expected). Fifthly, online consumers reviews have emerged as an indispensable
source of information in the digital age. Retailers not only need to encourage consumers
to write reviews of their positive shopping experience, but to also carefully manage
negative reviews, in order to foster positive perception and trust towards the channels of
retailers. Offline retailers who normally do not have their review resources should take
initiative to strategically integrate selected online consumer reviews and third-party
reviews in their in-store information services (Li, Li, Tayi, & Cheng, 2019). Lastly, as
perceived risk is significantly heightened for experience goods, pure play online retailers
are encouraged to improve their refund and return policies, while at the same time,
facilitating consumers with augmented reality technology as a replacement for
inaccessibility to physical inspection.
Limitation and future research direction
This study has several limitations that offer possibilities for further research. Firstly,
although the present study examined a comprehensive list of predictors in terms of
consumer traits and channel-related factors, there are other factors that remain unexplored
and yet possibly relevant to webrooming. For instance, consumers with high regret
tendencies may be more prone to webrooming. In addition, it would be interesting to
uncover the effective situational cues offered by pure play online and offline retailers that
could reduce or reinforce webrooming behavior as webrooming could be circumstantially
driven (Aw, 2019). Secondly, the importance of channel attributes in webrooming may
vary depending on consumers’ shopping motivation in a specific shopping encounter.
Therefore, future studies could examine the its moderating role of various shopping
encounters and its effect on webrooming intention. Thirdly, due to the reason that actual
behavior is difficult to measure, the present study adopts the self-reported intention
measures, supported by Venkatesh and Davis (2000) which contends that behavioral
intention is a valid predictor for actual behavior. In line with the intention-behavior
discrepancy raised in the literature, we do caution that not all consumers who intend to
webroom will actually perform webrooming behavior. However, the issue does not
undermine the merits of this study as meta-analytic studies have provided evidence that
intention is a reliable proxy for behavior and thus still holds great value for practitioners
and researchers (Armitage, & Conner, 2001; Fishbein & Ajzen, 2010; Sheeran & Webb,
2016; Webb & Sheeran, 2006). To better address the issue, the inclusion of volitional
control should be considered in future studies. Thirdly, this study is based on a cross-
sectional survey design, and therefore the is causal effects are speculative at best. Future
studies could undertake a field experimental design to further extend the findings. Finally,
although the use of Millennial sample is in line with the objective of this study, a more
fruitful insight can be obtained by comparing the model across different generations of
consumers. For instance, the Generation Z, which is posed to be the next generation that
will not only represent a significant percentage of all retail sales, but are also likely to
exhibit cross channel shopping behavior.
Given the evolving technological advancement and proliferation of shopping channels,
consumers increasingly undertake the combination of channels in a single shopping
journey to achieve the optimal shopping experience. The growing popularity of cross-
channel shopping behavior, such as webrooming has undesired impact to the retailers (e.g.,
free-riding) but may also represent opportunities for retailers that are able to cater such
changes in consumer shopping behavior. The current study presents an integrated view of
product-, consumer-, and channel-related factors in examining the determinants of
webrooming behavioral intention. Notably, the results evidenced the effects of consumer
traits (i.e., need for touch, need for interaction, and price-comparison orientation) and
channel-related factors (i.e., online search convenience, perceived usefulness of online
reviews, perceived risk of buying online) on webrooming intention, either in a direct
manner or indirectly through the mediator — smart shopping perception. In addition,
product category moderates the relationship between three determinants (i.e., price-
comparison orientation, online search convenience, and perceived risk of buying online)
and webrooming intention. In sum, the study enriches the understanding of why consumers
webroom and offers some actionable insights for retailers, whether the aim is to cater,
foster, or discourage webrooming.
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Table 1. Demographic profile
18-22 years old
23-27 years old
28-32 years old
33-37 years old
Diploma or equivalent
Income per month
Less than RM2000
RM8001 and Above
Table 2: Reflective Measurement Model: Factor Loadings, CR, and AVE
First Order Constructs
1) Need for touch
2) Need for interaction
3) Price-comparison orientation
4) Online search convenience
6) Perceived usefulness of online
7) Perceived helpfulness of in-store
8) Product risk
9) Delivery risk
10) Financial risk
12) Making the right purchase
13) Monetary saving
14) Webrooming intention
Table 3: Discriminant validity (HTMT)
Notes: DR (Delivery risk), FR (Financial risk), WI (Webrooming intention), MS (Monetary saving), NFI (Need for interaction), NFT
(Need for touch), OSC (Online search convenience), PCO (Price-comparison orientation), PR (Product risk), PU (Perceived
usefulness of online reviews), MRP (Making right purchase), PHS (Perceived helpfulness of in-store salespeople), TES (Time/effort
Table 4. Measurement model of second-order constructs (formative)
Note: **p < 0.01, *p <0.05, n.s.= not significant
Table 5. Results for hypotheses testing
NFI*PC -> WI
PCO*PC -> WI
OCS*PC -> WI
PU*PC -> WI
PHS*PC -> WI
PR*PC -> WI
Notes: WI (Webrooming intention), NFI (Need for interaction), NFT (Need for touch),
OSC (Online search convenience), PCO (Price-comparison orientation), PR (Perceived
risk), PU (Perceived usefulness of online reviews), PHS (Perceived helpfulness of in-
store salespeople), SSP (Smart shopping perception), PC (Product category)
Table 6. Mediation test
NFT-> SSP-> WI
NFI-> SSP-> WI
PCO-> SSP-> WI
OCS-> SSP-> WI
PU-> SSP-> WI
PHS-> SSP-> WI
PR-> SSP-> WI
Notes: WI (Webrooming intention), NFI (Need for interaction), NFT (Need for touch),
OSC (Online search convenience), PCO (Price-comparison orientation), PR (Perceived
risk), PU (Perceived usefulness of online reviews), PHS (Perceived helpfulness of in-
store salespeople), SSP (Smart shopping perception)
Figure1. Research model
Figure 2. Moderating effect of product category in the relationship between price-
comparison orientation and webrooming intention
Figure 3. Moderating effect of product category in the relationship between online search
convenience and webrooming intention
Figure 4. Moderating effect of product category in the relationship between perceived
risk and webrooming intention