consumer cross-channel behaviour: is it always planned?

Swinburne Research Bankhttp://researchbank.swinburne.edu.au

Author: Maggioni, I., Sands, S.J., Ferraro, C.R., Pallant, J.I.,Pallant, J.L., Shedd, L. and Tojib, D.

Title: Consumer cross-channel behaviour: is it alwaysplanned?

Year: 2020Journal: International Journal of Retail & Distribution

ManagementPages: 1-19URL: http://hdl.handle.net/1959.3/456745

Copyright: Copyright © 2020 Emerald Publishing Limited.Author's final accepted manuscript is under aCreative Commons Attribution Non-commercialInternational Licence 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/

This is the author’s version of the work, posted here with the permission of the publisher for yourpersonal use. No further distribution is permitted. You may also be able to access the publishedversion from your library.

The definitive version is available at: https://doi.org/10.1108/IJRDM-03-2020-0103

Powered by TCPDF (www.tcpdf.org)

Swinburne University of Technology | CRICOS Provider 00111D | swinburne.edu.au

http://www.tcpdf.org

1

Consumer Cross-Channel Behaviour: Is It Always Planned?

Structured abstract

Purpose. For consumers, cross-channel behaviour is increasingly prevalent. Such behaviour

involves consumers actively engaging in (and deriving benefit) from one channel during a

product search but switching to another channel when making a purchase. Drawing on multi-

attribute utility theory, this study proposes a cross-channel behaviour typology consisting of

three key aspects: channel choice behaviour; functional and economic outcomes; and

consumer-specific psychographic and demographic variables.

Design/methodology/approach. Segmentation analysis conducted via latent class analysis

(LCA) was performed on a sample of 400 US consumers collected via an online survey.

Findings. Cross-channel behaviour is not always intentional. We identify a specific segment

of consumers that most often engage in unplanned, rather than intentional, cross-channel

switching. We find that of all shoppers that engage in cross-channel behaviour, a fifth (20%)

are forced to switch channels at the point of purchase.

Practical implications. Cross-channel behaviour can be mitigated by retailers via a deep

understanding of the driving factors of different configurations of showrooming and

webrooming.

Originality/value. In contrast with existing conceptualisations, this study suggests that cross-

channel behaviour often stems from consumers being ‘forced’ by factors outside of their

control, but within the retailers’ control. This research presents a nuanced approach to

decompose consumer cross-channel behaviour from the consumer perspective as planned,

forced, or opportunistic.

Keywords: cross-channel behaviour; channel switching, showrooming, webrooming

2

Consumer Cross-Channel Behaviour: Is It Always Planned?

1. Introduction

Imagine that next week is the start of the school term and you need school supplies. Your local

brick-and-mortar stationery store has run out of stock, and after a quick online search while

standing in the store you discover what you need via an online retailer with free next-day

delivery. You leave the store and place the order with the online retailer. What is a quick and

simple solution to your dilemma has labelled you as a cross-channel shopper; one of the most

challenging kind of consumers for retailers (Chou et al., 2016). Such apparent exploitation of

retailers by switching between online and physical store channels has become commonplace

(Fornari et al., 2016; Kalyanam and Tsay, 2013; Konuş, Verhoef, and Neslin, 2008), with such

cross-channel behaviour typically perceived as intentional (Flavián, Gurrea, and Orús, 2019;

Santos and Gonçalves, 2019; Schneider and Zielke, 2020). Although, as this example

illustrates, cross channel switching may not always be an intentional behaviour, with some

consumers forced to switch channels due to factors outside their control.

Cross-channel behaviour, or switching channels between search and purchase, is a

common retail behaviour (Neslin et al., 2006) and can involve showrooming or webrooming.

Showrooming relates to the intentional patronage of a physical store (showroom) before

purchasing online (Rapp et al., 2015; Verhoef, Neslin, and Vroomen, 2007), where

webrooming relates to the use of an online channel for product research prior to purchasing

from a physical store (Andrews et al., 2016; Aw, 2019; Arora and Sahney, 2017; Flavián,

Gurrea, and Orús, 2016). Cross-channel behaviour can also be classified as either cross-channel

free-riding or within-firm lock-in (Neslin et al., 2006). Cross-channel free-riding occurs when

a consumer engages with one retailer in the search phase (i.e. to obtain initial information,

search, or make comparisons) and then switches channel and retailer to purchase (Aw, 2019;

Viejo-Fernández, Sanzo Pérez, and Vázquez-Casielles, 2020). Within-firm lock-in is a second

3

form whereby a consumer switches channel but not retailer; with the retailer retaining the

consumer across search and purchase. While this later is good in that it prevents consumer

migration from the retailer, it can still have negative consequences – for instance, channel

conflicts can arise if store-based sales staff lose commission of within a franchise network the

purchase could be attributed to another franchise store, or the corporate (online) store.

It has been reported that more than two-thirds of consumers engage some form of cross-

channel behaviour (IBM, 2017), with many retailers concerned about the subsequent effects on

sales (Machavolu and Raju, 2014; Venkatesan, Kumar, and Ravishanker, 2007). Yet while

studies of this phenomenon continue to increase (e.g. Flavián, Gurrea, and Orús, 2019; Sands

et al., 2010; Santos and Gonçalves, 2019; Schneider and Zielke, 2020), those investigating it

from the consumer perspective remain limited (Fernández, Sanzo Pérez, and Vázquez-

Casielles, 2018; Schneider and Zielke, 2020). Given the importance of a holistic understanding

consumer cross-channel shopping, this study adds to our understanding by taking a customer

view of the channel switching phenomenon in terms of both free-riding and within-firm lock-

in.

In developing a better understanding as to why consumer engage in cross-channel

behaviour, we apply a segmentation lens drawing on multi-attribute utility theory (Wallenius

et al., 2008). In doing so, this study provides a more nuanced understanding of how and why

consumers engage in cross-channel behaviour. We propose a typology of heterogeneous

consumers exists in terms of their engagement in different configurations of showrooming and/

or webrooming behaviours. In doing so, we enhance our understanding of consumer channel

switching; finding three distinct motivations for consumer cross-channel behaviour; those that

intentionally plan to engage in channel switching, those that are forced to switch channels (i.e.

via factors inside or outside the retailer’s control, respectively), and those that are opportunistic

and engage in channel switching if and when an opportunity arises (for instance, are driven by

4

environmental factors, i.e. they stumble across a promotion). Following a review of pertinent

literature, the research methodology is outlined before our segmentation results are presented.

After describing the segments, we conclude with a general discussion and discuss implications

for theory and retail managers.

2. Theoretical background

Past research has examined the role of different factors driving customers’ switching behaviour

in both online and offline contexts, including price perceptions (Bansal, Taylor, and St. James,

2005, Singh and Rosengren, 2020), satisfaction (Ganesh, Arnold and Reynolds, 2000), service

quality (Keaveney and Parthasarathy, 2001). The push-pull-mooring (PPM) theory (Bansal,

Taylor, and St. James, 2005) provides a comprehensive framework for the drivers of consumer

channel switching behaviour. In the case of cross-channel behaviour, push variables are

negative factors at the initial retailer in the first channel of choice, which drive customers away,

pull variables are those elements of the alternative retailer or alternative channel attracting

customers towards them, while mooring variables are intervening factors that could facilitate

or prevent switching from occurring. Among push factors, the role of customer service,

convenience (product delivery or store location), price perceptions, product quality, trust

(Bansal, Taylor, and St. James, 2005; Singh and Rosengren, 2020) have been investigated.

Among the pull word of mouth and alternative attraction (Bansal, Taylor, and St. James, 2005,

Singh and Rosengren, 2020) have been identified as important. While for mooring variables,

or those conditions that prevent switching or create switching obstacles (Bansal, Taylor, and

St. James, 2005), switching costs, prior switching behaviour, attitude to seek variety, and

subjective norms (Bansal, Taylor, and St. James, 2005; Ganesh, Arnold and Reynolds, 2000;

Singh and Rosengren, 2020) have been identified as important. The PPM framework has

originally been used to study switching behaviour among alternative service providers or

5

retailers (Bansal, Taylor, and St. James, 2005; Ganesh, Arnold and Reynolds, 2000; Singh and

Rosengren, 2020). Chou et al. (2016) apply this framework to study both within-channel

switching and cross-channel switching from the online channel to the offline and highlight how

perceived risk and switching barriers have a significant effect on both within-channel switching

behaviour and cross-channel switching, while alternative attractiveness significantly influences

only within-channel switching behaviour.

Most consumers engaging in cross-channel switching seek to maximise utility via

different channels (Chiou, Wu, and Chou, 2012; Heitz-Spahn, 2013). Several factors have

influenced this phenomenon including the increasing availability of products and retailers, the

diversification of shopping channels, and the increasing use of mobile devices for browsing

and purchasing (Bezes, 2016; Sit, Hoang, and Inversini, 2018; Wang, Malthouse, and

Krishnamurthi, 2015). The emergence of cross-channel behavioiur originates from consumers’

increasing need for comparative evaluations before proceeding with a purchase decision.

Among such drivers of showrooming and webrooming, price has consistently been identified

as a primary motivator (Machavolu and Raju, 2014). Although non-price related factors have

also been identified as prevalent among channel switching consumers, such as product quality

and wait time perceptions, which are more positively associated with showrooming (Gensler,

Neslin, and Verhoef, 2017). Consumer emotions have also been indicated as meaningful within

the showrooming context (Sit, Hoang, and Inversini, 2018).

2.1 Utility maximisation and channel choice

At its core, channel choices and switching can be better understood through a consumer utility

lens; that is, consumers choosing channels based on the channels perceived benefit (Konuş,

Verhoef, and Neslin, 2008). Multi-attribute utility theory provides a foundation to explore

consumer channel choices, proposing that individuals make decisions by evaluating choice

6

according to the expected utility of each outcome (Wallenius et al., 2008). In such channel

choice situations, a consumer is faced with a two-step decision process, in which channel to

browse and then in which channel to purchase. At each step, the consumer seeks to maximise

their utility – search value and purchase value. Yet while each decision provides opportunity

for utility maximisation, there is also the risk of a poor choice that is high in costs and low in

benefits, with low overall utility (Conchar et al., 2004).

Functional utility relates to the core performance of a channel, such as its ability to

provide information, and to access certain products. Within a channel choice context,

functional utility could include ease of use or required effort (Heitz-Spahn, 2013; Verhoef et

al., 2007). In the context of cross-channel behaviour, functional aspects such as product-

specific factors can influence such behaviour. For example, showrooming has been recognised

as more likely for products with low purchase frequency, and webrooming for products with

dominant search characteristics and rapid technological change (Heitz-Spahn, 2013; Van Baal

and Dach, 2005). Economic utility relates to not wasting money or time. Within a channel

choice context, there may be perceived differences in price and convenience (Heitz-Spahn,

2013; Verhoef, Neslin, and Vroomen, 2007) or quality and price advantages achieved by

engaging in cross-channel behaviour (Balakrishnan, Sundaresan, Zhang., 2014; Gensler,

Neslin, and Verhoef, 2017).

2.2 Consumer-specific variables

Consumer-specific variables have also been known to affect consumer channel choices across

the path to purchase (Ansari, Mela, and Neslin, 2008; Manss, Kurze, and Bornschein, 2019;

Verhoef, Neslin, and Vroomen, 2007). Consumer decision-making and marketplace interaction

styles have both influenced consumer cross-channel behaviour (Burns, 2006; 2007). In terms

of psychographic variables, a consumer’s tendency for information seeking, defined as a need

7

for product attribute information (Noble, Griffith, and Weinberger, 2005), has been known to

increase consumers’ cognitive evaluation of products. Consistent with a reduction in consumer

uncertainty (Flavián, Gurrea, and Orús, 2016; Gensler, Neslin, and Verhoef, 2017 ), a

consumer’s degree of confidence in shopping (online and offline) can drive cross-channel

switching intentions, referred to as online shopping and store shopping savviness in this study.

In contrast, time-pressured consumers are less likely to engage in cross-channel switching,

being less price-conscious and more likely to conduct search and purchase from the same

channel. Verhoef and Langerak (2001) previously demonstrated that the online channel in

particular is often employed by time-pressured shoppers.

It has also been suggested that a consumer’s level of price- and/or brand-consciousness

influences cross-channel behaviour (Lichtenstein, Netemyer, and Burton, 1990). Price-

conscious consumers are aiming to minimise the price paid for an item, including via

evaluations within a specific channel (Verhoef, Neslin, and Vroomen, 2007) that drive channel

choice (Baker et al., 2002; Montoya-Weiss, Voss, and Grewal, 2003). In contrast, brand-

conscious consumers are less likely to switch channels to find a substitute, even when their

branded item is out of stock.

Moreover, Fernández, Sanzo Pérez, and Vázquez-Casielles (2018) identify the aspects

that differentiate webroomers from showroomers. Specifically, webroomers tend to engage in

longer purchasing processes using multiple online touchpoints to obtain and examine

information in depth, becoming ‘smart shoppers’ hard to influence. Webroomers consider

product characteristics as most important in influencing their purchase decisions and have a

clear idea of the product they will purchase once in a physical store. Conversely, showroomers

appear to be more easily influenced particularly by brands and trends. They also tend to

purchase higher value products although they search for the retailer that offers the desired

quality of product at the lowest price (Fernández, Sanzo Pérez, and Vázquez-Casielles, 2018).

8

2.3 In-store mobile usage and cross-channel behaviour

In-store mobile usage is another factor that was identified as relevant in understanding

consumer cross-channel free-riding, particularly among showroomers (Sit, Hoang, and

Inversini, 2018; Viejo-Fernández, Sanzo-Pérez, and Vázquez-Casielles, 2020). Mobile devices

continue to have a significant impact on consumer shopping behaviour, integrating easily into

habitual routines (Wang, Malthouse, and Krishnamurthi, 2015), often motivated by

convenience (saving time) and utilitarian value (saving money) (Pantano and Priporas, 2016).

Mobile usage is particularly high in the search phases of the consumer journey among those

that value access to information (Holmes, Byrne, and Rowley, 2014). In-store mobile usage is

often driven by the ability to access information without having to ask service staff, or to find

information unavailable in the store (e.g. price comparisons, product reviews) (Fuentes and

Svingstedt, 2017). Such information access can be empowering, making consumers feel more

knowledgeable and competent, while enabling them to find better value via cross-channel free-

riding behaviour (Fuentes and Svingstedt, 2017).

In-store mobile usage can therefore stimulate deal-seeking behaviours such as

showrooming (Fuentes and Svingstedt, 2017), with the information search capabilities

resulting in a high proportion of deal-prone consumers in the mobile channel. In addition to in-

store mobile usage increasing shopper confidence by facilitating information search and

opinion-seeking while increasing purchases by distracting consumers from their shopping task.

The intensive use of smartphones and the increasing growth of m-commerce have generated a

new form of showrooming, the so-called “mobile showrooming” (Viejo-Fernández, Sanzo-

Pérez, and Vázquez-Casielles, 2020). The affinity of both showroomers and webroomers with

information seeking results in an increased usage of electronic devices, particularly

smartphones. The usage of smartphones inside the store has traditionally been seen as a threat

9

from many retailers. However, there is still limited and contradictory evidence suggesting a

negative role of smartphone usage inside stores as they could also represent a way to drive

impulsive buying and push product with a higher price (Viejo-Fernández, Sanzo-Pérez, and

Vázquez-Casielles, 2020). Specifically, the real-time and mobility nature of smartphones

allows for convenience, customization and broadcasting (Wu et al., 2010, Huang, Lu, and Ba,

2016), creating “always on” customers that are more likely to engage in impulse buying

behaviour. However, it is important to note that the usage of mobile devices reduces the ability

of performing multiple tasks simultaneously and thus could lead to distraction in consumers

while shopping with negative impact on the ability to accurately complete shopping plans and

increasing the likelihood of unplanned purchases (Sciandra, Inman, and Stephen, 2019).

3. Methodology

3.1 Segmentation variables

To put forward a typology of cross-channel behaviour, this study segmented consumers based

on three key functional aspects: 1) channel choice behaviour; 2) functional and economic

outcomes; and 3) consumer-specific psychographic and demographic variables. In line with

prior exploratory segmentation studies (i.e. Konuş, Verhoef, and Neslin, 2008; Sands et al.,

2016), we conduct an ad hoc analysis with no prior information about the resulting

segmentation, and as such we do not derive or state formal hypotheses about the effect of

attitudinal or behavioural variables. However, we do hypothesise that based on the attitudinal

or behavioural variables in our model, we expect consumers to differ in terms of their cross-

channel behaviour.

Channel choice behaviour (indicator variables). We employ three indicator variables

to assess segment membership: 1) showrooming; 2) webrooming; and 3) in-store mobile

usage. Respondents recalled recent shopping trips (past 3-months) and reported their channel

10

choice behaviours (search and purchase). To assess showrooming, respondents were asked to

identify whether their recent behaviour aligned with visiting a physical store: “to examine a

product, then making the purchase online’; ‘to find a product, but it was not available, so

purchased it online’; or ‘when in the store used a smartphone to compare the price, look up

for information or reviews, and then making the purchase online’. To assess webrooming,

respondents were asked to identify whether their recent behaviour aligned with visiting an e-

commerce store to: ‘examine a product, then making the purchase in a physical store’, ‘find a

product, but it was not available, so purchased it in a physical store’; or ‘after intentionally

comparing prices and reviews, made the actual purchase in a physical store’. Two index

variables were created from the above questioning, to capture the extent respondents engaged

in showrooming or webrooming, with variations between 0 (no showrooming or webrooming

activity) to 3 (all showrooming or webrooming activity). The level of in-store mobile usage

was also assessed via five dichotomous variables that captured different mobile-driven

activities that respondents experienced in store: compare product prices, look up product

information, read product reviews, check product availability online, or check products on

other retailers’ websites. An index variable was then created to capture the intensity of in-

store mobile usage activity, which varied between 0 (no in-store mobile usage) and 5 (high

in-store mobile usage).

Consumer-specific variables (covariates). The way that consumers process information

has been identified as a driving factor of cross-channel behaviour (Fernández, Sanzo Pérez,

and Vázquez-Casielles, 2018; Santos and Gonçalves, 2019). In particular, how they seek and

process price- and non-price-related cues during the purchase experience (Sit, Hoang, and

Inversini, 2018). Hence, this study’s segmentation model includes several consumer-specific

psychographic and demographic variables. In terms of psychographic, information seeking,

online shopping savviness, store shopping savviness, time pressure, brand consciousness and

11

price consciousness were included. The listing of adapted items including their sources as well

as construct validity and reliability are provided in Table 1. In terms of demographics, age and

gender were included.

Functional and economic utility (profile outcome variables). Consumers’ derived

functional and economic utility were profiled as: 1) in-store mobile usage (i.e. showrooming);

2) channel switching; and 3) alternative channel for purchase. The utility derived from in-store

mobile usage only occurs when consumers engage in showrooming (i.e. search in store and

purchase via alternative channel). The level of in-store mobile usage intensity was measured

via a series of dichotomous (yes/no) variables that captured different mobile-driven activities

they might have engaged in within the store, such as comparing product prices, looking up

product information, reading product reviews, checking product availability, and checking

products on other retailer websites. For each activity, the respondents were asked how often

they engaged in it when shopping in store. In terms of the utility derived from channel

switching, the model specifies functional and economic utility for showrooming and

webrooming, respectively. In terms of showrooming, this study assumed the primary drivers

for switching between a physical store and online as either deriving functional utility (i.e. free

shipping, home delivery or product availability) or economic utility (i.e. lower price,

promotional deals or convenience). In terms of webrooming, this study assumed the primary

drivers for switching between an online and physical store were either deriving functional

utility (i.e. urgency, need to touch or try, or product availability) or economic utility (i.e. lower

price, promotional deals or convenience). Lastly, we define showrooming as a resulting

purchase destination as either the same retailer’s online store, a different retailer’s online store,

or an online marketplace. For webrooming, we define this as either the same retailer’s physical

store, a different retailer’s physical store, or a department store.

12

3.2 Data collection and sample profile

Data were collected via an online survey administered through Amazon Mechanical Turk

(AMT). AMT has been used across a diversity of research, including experiments and

segmentation studies (Guttentag et al., 2018; Shank, 2016), where subjects have been shown

to be more reliable (Paolacci et al., 2010). Several strategies were also employed to enhance

data quality, including manipulation checks, attention checks, and ‘catch trial’ questions,

(Paolacci, Chandler, and Ipeirotis, 2010; Thomas and Clifford, 2017).

Respondents were asked to recall shopping experiences from the prior three-months

and asked to identify if they had engaged in showrooming or webrooming. Showrooming was

defined as having visited a physical store channel to make a purchase but then switched

channels for the actual purchase. Webrooming was defined as having visited an online store

channel to make a purchase but then switched channels for the actual purchase. Given this

study sought to better understand cross-channel free-riding, consumers who had never engaged

in such showrooming or webrooming behaviours were excluded from the sample. Additional

exclusion criteria were used to filter poor responses and those that did not pass the attention

checks, which led to the screening out of 35 respondents. The final sample comprised 400 US

resident respondents.

3.3 Data analysis

Prior to segmentation analysis, confirmatory factor analysis (CFA) was employed to assess the

validity and reliability of multi-item scale measures used as covariates in the segmentation

model (see Table 1). CFA showed an overall acceptable fit (χ2/df=1.843; CFI=0.896;

GFI=0.865; SRMR=0.070), and the constructs showed an appropriate level of reliability and

validity with composite reliability coefficients larger than 0.70, average variance extracted

(AVE) larger than 0.50, and correlations between the latent variables lower than the AVE

13

square roots (Hair et al., 2010). Composite scores for each variable were obtained by

computing the mean scores across the items.

INSERT TABLE 1

The segmentation analysis was conducted via latent class analysis (LCA) using

LatentGold 5.1 (Vermunt and Magidson, 2002). LCA has various advantages over other

clustering methods such as k-means, including the ability to segment based on multiple variable

types, the inclusion of covariates, and the stronger statistical basis for final solution selection

(Vermunt and Magidson 2002). It has also been employed across a range of multi-channel

shopping research (e.g. Pallant et al., 2017; Sands et al., 2016).

To determine cluster membership, a three-step procedure was applied (Vermunt, 2010),

meaning that the covariates were included without them interfering with the measurement of

the model (i.e. classification was solely based on the indicators). The three stages were: 1)

estimation of the model based on the indicators; 2) probabilistic assignment of subjects to latent

classes (posterior membership probabilities); and 3) estimation of the effects of the covariates

on latent class membership, corrected for the classification error to prevent bias. This procedure

allows for the establishment of the effects (and significance) of covariates, corrected for

measurement errors, while not letting the covariates interfere with the classification based on

the indicators (Vermunt, 2010). The indicator variables and covariates are provided in Table 2.

INSERT TABLE 2

For the segmentation analysis, the convergence criterion was set at 0.000001 (Collins

and Lanza, 2010), with 50 random sets of starting parameters (Masyn, 2013) to reduce the

likelihood of convergence to local maxima (McCutcheon, 2002). The primary goal of LCA is

to identify the most parsimonious model (i.e. smallest number of latent classes), and in this

study it was used to adequately describe the associations between the indicators within the

14

conceptual model. Subsequent models were also estimated with 1 through 10 latent classes to

identify the optimal model, with the corresponding solutions for one shown in Table 3.

INSERT TABLE 3

Multiple criteria were used to select the optimal model. First, the Bayesian Information

Criterion (BIC) was used to compare relative model fit, and the segment profiles were then

considered in terms of over-extraction, class separation and interpretability of results (Masyn,

2013; Wedel and Kamakura, 2012). The preferred solution was determined as the one with the

lowest BIC value, where consistent Akaike Information Criterion (AIC) was considered an

indicator of the optimal model (Collins and Lanza, 2010; Vermunt and Magidson, 2013), which

was the three-segment solution. This solution displayed no evidence of over-extraction, as the

smallest segment was 20% of the sample, and it also enabled meaningful interpretation as the

clusters showed strong class separation. In contrast, solutions with additional segments resulted

in smaller clusters with lower class separation.

Table 4 provides a summary of all indicator and covariate variables (Panel A) and

profiling variables (Panel B) relative to segment membership. A strong positive coefficient

meant those consumers that scored high on a covariate were more likely to appear in that

relevant segment. Within Panel A, significant covariate coefficients were found for information

seeking (Wald=14.134, p<0.001), in-store shopping savviness (Wald=15.63, p<0.001) and age

(Wald=20.92, p<0.01). In contrast, online shopping savviness, time pressure, brand

consciousness and online shopping frequency did not vary by segment. In addition to the

demographic variables included in the LCA, we also collect information on household income

and household composition. ANOVA results show that only household composition is a

significant segment predictor (F-value = 4.52; p < 0.01).

INSERT TABLE 4

15

4. Profiling consumer cross-channel behaviour

In line with our hypothesis, the study reveals that consumer segments differ in terms of their

channel choice behaviour, their resulting functional and economic benefits, and based on their

psychographic and demographic characteristics. Within each of these segments, we find that

showrooming (store to web behaviour) is the more common form of cross-channel switching

behaviour. In the following sections, we provide a detailed profile for each segment.

Planned channel switcher (Segment 1). Slightly more than a fifth (22%) of all shoppers

exert planned, or intentional, cross-channel switching behaviour. Those in this segment exhibit

the highest propensity to engage in cross-channel switching; both in terms of showrooming

(2.9 out of 3) and webrooming (2.5 out of 3). These consumers also display the highest

frequency of in-store mobile usage (4.3), likely a signal of their intention to switch channels.

These consumers use their mobile devise to engage in price comparison primarily (95%).

Product category analysis identifies cross-channel switching occurs most often for consumer

electronics (35%), clothing, footwear and accessories (22%), and food and groceries (17%).

When showrooming, the vast majority (81%) of these consumers switch to purchase

from a competing channel; most often an online marketplace (70%) or a different retail brand’s

online store (11%). However, 19% exhibit within-firm lock-in, switching to purchase from the

online store of the same retailer – hence not posing a significant threat to the brand in terms of

lost revenue to the retailer overall. Often these consumers intentionally undertake

showrooming when in store, using their mobile device to systematically check products on

other retailers’ websites (83%) and check product availability online (75%). Exhibiting an

extremely high degree of cross-channel switching when showrooming, these consumers are

primarily driven by lower prices (69%) and free shipping (45%). These consumers are also

16

more sensitive to deals and coupons, with 20% driven to showroom by the availability of

special promotions.

When webrooming, 60% of these consumers switch to purchase from a competing

channel. This is most often a different retail brand’s physical store (40%) but also a department

store (20%). However, 40% exhibit within-firm lock-in and switch to make the purchase from

the online store of the same retailer. The key drivers for their webrooming (online to store)

behaviour is the ability to touch and feel the product (37%), purchase urgency (34%), and

convenience (34%). In terms of psychographic variables, these consumers tend to report

seeking information frequently (mean = 4.5), report being savvy shoppers (mean = 4.3), and

make online purchases frequently (51% weekly or more). In terms of demographics, planned

channel switchers tend to be young, with more than half (52%) aged under 35 years of age and

53% are female. Respondents in this segment are most likely to dwell in households with

family or single parent that have children between the ages of 5 and 12 years of age.

Forced channel switcher (Segment 2). Twenty percent of shoppers engage in cross-

channel switching behaviour when driven by influences outside of their control, such as out-

of-stock products. Product category analysis identifies cross-channel switching occurs most

often for clothing, footwear and accessories (26%), consumer electronics (24%), and food and

groceries (22%). Those in the forced channel switcher segment exert low levels of in-store

mobile usage (0.4 out of 5) and subsequently these consumers display a low degree of cross-

channel behaviour in terms of showrooming (1.1 out of 3) and webrooming (0.9 out of 3).


from a competing channel. This is most often an online marketplace (61%) but also a different

retail brand’s online store (21%). However, 18% exhibit within-firm lock-in and to make the

17

purchase from the online store of the same retailer. The main drivers for showrooming are

product unavailability (41%) and lower prices (42%).


channel. This is most often a different retail brand’s physical store (47%) but also a department


the online store of the same retailer. Webrooming behaviour for those in this segment is

influenced by convenience (28%), purchase urgency (21%), the need to touch and feel a

product (20%), as well as product unavailability (18%). In terms of psychographic variables,

these consumers are less likely to report high levels of information seeking (mean = 3.9), are

less savvy in their shopping (mean = 3.7) and tend to be lower in brand-conscious (mean =

2.8). They also tend to shop online less frequently than the other segments, and then are

generally older than those in other segments. Fifty-eight percent of forced channel switchers

are female. Respondents in this segment are most likely to dwell in households with family or

single parent that have children between the under the age of 4.

Opportunistic channel switcher (Segment 3). The final segment is largest segment (58%)

and represents consumers who display a low to moderate propensity to intentionally exert

cross-channel behaviour. Product category analysis identifies cross-channel switching occurs

most often for clothing, footwear and accessories (34%), consumer electronics (19%), and

cosmetics and toiletries (19%). For these shoppers, the decision to engage in channel switching

is more in-the-moment, or opportunistic.


from a competing channel. This is most often an online marketplace (75%) but also a different

retail brand’s online store (10%). However, 15% exhibit within-firm lock-in and switch to

make the purchase from the online store of the same retailer. In the context of showrooming,

18

this segment shows moderate behavioural intensity (1.8 out of 3), with the key drivers for such

behaviour being lower prices, free shipping and home delivery.


channel. This is most often a department store (36%) but also a different retail brand’s physical


the online store of the same retailer. However, these consumers also convey a low degree of

engagement in webrooming behaviour (1.0 out of 3), which is primarily driven by purchase

urgency, convenience, and ability to touch and feel the product. This segment displays high in-

store mobile usage, mostly to compare prices, read product reviews and search for product

information. When webrooming, they are most likely to switch and purchase from a department

store (36%) or from the same retailer of the website they browsed (34%). In terms of

psychographic variables, no significant differences were identified. In terms of demographics,

56% are female and 18% are 34 years of age or younger. Respondents in this segment are most

likely to dwell in households with family or single parent that have children between the ages

of 5 and 12 years of age.

Figure 1 provides a profile showrooming vs webrooming activity across cross-channel

free-riding and within-firm lock-in across all segments.

INSERT FIGURE 1

5. Discussion and conclusions

Consumer cross-channel switching continues to increase as a prominent phenomenon, with it

generally perceived as consumer-driven and representing a significant threat to bricks-and-

mortar retailers (Daunt and Harris, 2017; Gensler, Neslin, and Verhoef, 2017 ). This study

challenges earlier assumptions that consumer cross-channel switching occurs because the

consumer is price-sensitive, deal-oriented, and disloyal (Chiou, Wu, and Chou, 2012), and

19

highlights that cross-channel behaviour is not always a planned or intentional activity on the

part of consumers. It argues that such behaviour does not always arise from consumer desire

to ‘game’ the retail system and maximise value from trading off benefits of different channels.

By uncovering the heterogenous nature of cross-channel behaviour, this study

distinguishes between intentional behaviour, which is unlikely to be changed through retailer

engagement (i.e. planned showrooming), and unintentional behaviour that could be influenced

by a retailer. It expands on the notion that retail threats from showrooming can be managed

and potentially converted into favourable consumer behaviour (Freeman, 2014; Sit, Hoang,

and Inversini, 2018, Viejo-Fernández, Sanzo-Pérez, and Vázquez-Casielles, 2020).

The results suggest that approximately one fifth (22%) of consumers engaging in cross-

channel switching do so in an intentional, or pre-planned manner. Results indicate that most

cross-channel switching is driven by price, and that these consumers tend to be younger and

have a high degree of in-store mobile usage. In this way, the mobile device is a means for

conducting price comparisons and seeking information from competitor websites to evaluate

purchase decisions. In addition to these planned channel switchers, two additional segments

were identified: the opportunistic channel switcher and the forced channel switcher. These

segments differ in terms of their showrooming, webrooming, and mobile usage intensity.

The largest opportunity for retailers appears to be the forced channel switcher, who

typically is pushed to switch channels due to factors that are often in the retailer’s control (e.g.

out-of-stock products, poor in-store experience, lack of staff). In such forced contexts, retailers

run the risk of these consumers not only switching channels within the same brand, but also

complete the purchase with another brand (i.e. cross-channel free-riding). Hence, it is important

for retailers to understand the reasons for channel switching, including identifying the cause

for both the planned and forced scenarios, to maximise conversion at the browsing stage of the

consumer journey.

20

This study also finds that most consumers engage in cross-channel switching in an

opportunistic manner; driven by environmental factors and often cherry-picking reasons for

channel switching behaviour; including price reductions, free shipping, or home delivery. It

would also appear that when these consumers interact with a retailer (online or in-store), they

have high deal sensitivity and are likely at a tipping point where promotions could have an

impact on their decision to switch. Showrooming also seems to be more common than

webrooming among this segment, with divergent reasons for such behaviours. The main

showrooming drivers are lower prices, free shipping and home delivery, while the main

webrooming drivers are purchase urgency, a need for convenience, and a desire to touch and

feel the product.

When decomposing the psychographic drivers of channel switching across the three

segments, the desire for information (information seeking) and one’s perceived in-store

savviness (store savviness) are critically important determinants. In contrast, price- and brand-

consciousness do not appear to be significant segment identifiers, possibly because price- or

brand-conscious shoppers generally pre-select their preferred channel (cheapest or brand-

specific) and remain with it. It would also appear that cross-channel free-riding is not impacted

by time pressure, meaning these consumers generally have enough time available to switch

channels (i.e. wait for delivery or postpone the purchase).

5.1 Theoretical implications

From a theoretical perspective, this study contributes to existing literature on cross-channel

behaviour by deepening the understanding of it from the consumer perspective. It highlights

the different experiences of consumers that search for product information in one channel and

then complete the purchase in an alternative channel. Importantly, these findings showcase the

positive side of cross-channel behaviour by revealing consumer motives for such channel

21

switching. This new consumer perspective in cross-channel behaviour helps to identify points

of pain and moments that cause friction in consumer experiences.

This study proposes a typology of cross-channel behaviour that distinguishes between

the degrees of consumer intention by introducing the planned versus forced concepts. It also

highlights the differences between showrooming and webrooming cross-channel free-riding.

Further, drawing on multi-attribute utility theory, these findings show the differences between

consumer showrooming and webrooming behaviour from a mix of functional and economic

utility drivers. They highlight that the primary driver for showroomer behaviour is economic

(i.e. prices), while it is less clear what drives webroomer behaviour (varies between functional

and economical).

This study also challenges previous theoretical conceptualisations by illustrating that

cross-channel behaviour can be viewed from a positive standpoint. This includes an ability for

retailers to positively leverage such behaviour via a deep understanding of the driving forces

of different configurations of showrooming and webrooming.

5.2 Managerial implications

This research indicates that cross-channel behaviour can be influenced by retailers, which

means managers should reconsider how they approach showrooming and webrooming

behaviours. Managers should take note of this study’s finding that only a small proportion of

cross-channel free-riding (showrooming or webrooming) is planned. Much of this behaviour

appears to be driven by factors within the retailer’s control, such as having products available

or implementing systems to allow out-of-stock items to be readily delivered. This aligns with

this study’s identification of the most common driver of showrooming being when consumers

find the product is not available in store. Such unintentional showrooming behaviour, which is

prevalent across the consumer segments, suggests that retailers can prevent much of it.

22

In the context of webrooming behaviour, this study’s findings have also suggested

that most consumers that switch channels when searching online remain with the same brand.

Webrooming therefore appears to be less of a threat than showrooming where most switch to

purchase from marketplaces rather than from a physical retailer’s online store. In particular,

these results highlight that retail managers need to get the fundamentals right, such as ensuring

products are in store and facilitating staff training on how to minimise the potential of cross-

channel behaviour. Furthermore, it is important for managers to recognise that these channel

switching behaviours are generally connected, meaning that consumers that webroom also

typically showroom. Thus, one type of behaviour should not be targeted; instead, managers

need to cater for all different consumer journeys or behaviours between and across channels.

Table 5 provides a practical guide for managers in understanding and managing

consumer channel switching behaviour.

INSERT TABLE 5

5.3 Limitations and future opportunities

It is important to highlight that this study is cross-sectional and therefore focuses on a specific

moment in time. Other potential limitations and opportunities include this study’s proposed

approach to sampling and under-sampling, which means that under-representation of specific

consumer groups may exist, particularly in terms of race and income. As such, the results may

not be representative of the general on-the-go consumer, so readers should be careful when

generalising them. Second, the focus on two specific forms of research shopping (webrooming

and showrooming) means that other forms of shopping behaviour were not covered.

While this study’s purpose was not to be inclusive of all forms of cross-channel

shopping, future research could consider how other forms such as click and collect or buy

online and pickup in-store (BOPIS) might be applied across the consumer segments. Third,

cross-channel switching has been defined in this study as a situational behaviour, particularly

23

among opportunistic channel switchers. This implies that the segments are not stable and could

differ depending on situational factors (e.g. product involvement or shopping purpose). Future

research could further analyse the role of situational factors in affecting the proposed typology

of cross-channel behaviour. Lastly, future studies might consider specific consumption sectors.

This study adopted a broad perspective in terms of the products and sectors included, and a

focus on more specific categories could assist with determining whether the same behavioural

patterns apply, or if idiosyncratic category effects exist. In this context, it would be especially

interesting to consider how cross-channel behaviour occurs and develops in the case of

services.

24

References

Andrews, M., Goehring, J., Hui, S., Pancras, J. and Thornswood, L. (2016), “Mobile

promotions: A framework and research priorities”, Journal of Interactive Marketing, Vol.

34, pp. 15–24.

Ansari, A., Mela, C. F. and Neslin, S. A. (2008), “Customer channel migration”, Journal of

Marketing Research, Vol. 45 No. 1, pp. 60–76.

Arora, S. and Sahney, S. (2017), “Webrooming behaviour: A conceptual framework”,

International Journal of Retail & Distribution Management, Vol. 45 No. 7/8, pp. 762–

781.

Aw, E. (2019), “Understanding the webrooming phenomenon: Shopping motivation,

channel-related benefits and costs”, International Journal of Retail & Distribution

Management, Vol. 47 No. 10, pp. 1074-1092.

Baker, J., Parasuraman, A., Grewal, D., and Voss, G. B. (2002), “The influence of multiple

store environment cues on perceived merchandise value and patronage intentions”,

Journal of Marketing, Vol. 66 No. 2, pp. 120–141.

Balakrishnan, A., Sundaresan, S. and Zhang, B. (2014), “Browse-and-switch: Retail-online

competition under value uncertainty”, Production and Operations Management, Vol. 23

No. 7, pp. 1129–1145.

Bansal, H. S., Taylor, S. F., and St. James, Y. (2005), “’Migrating’ to new service providers:

toward a unifying framework of consumers’ switching behaviours”, Journal of the

Academy of Marketing Science, Vol. 33, No. 1, pp. 96-115.

Bezes, C. (2016), “Comparing online and in-store risks in multichannel shopping”,

International Journal of Retail & Distribution Management, Vol. 44 No. 3, pp. 284-300.

Boyle, P. and Halfacree, K. (1998), Migration and gender in the developed world,Longmann

Press, New York, NY.

25

Burns, D. J. (2006), “Consumers' decision-making style: Relationships with attitude toward

consumer free-riding activity”, Marketing Management Journal, Vol. 16 No. 1, pp. 148–

157.

Burns, D. J. (2007), “Marketplace interaction styles and consumer free-riding: The

relationship between assertiveness and aggressiveness and attitudes toward consumer

free-riding”, Services Marketing Quarterly, Vol. 28 No. 4, pp. 35–48.

Chatterjee, P. and Kumar, A. (2016), “Consumer willingness to pay across retail channels”,

Journal of Retailing and Consumer Services, Vol. 34, pp. 264–270.

Chiou, J. S., Wu, L. Y. and Chou, S. Y. (2012), “You do the service but they take the order”,

Journal of Business Research, Vol. 65 No. 7, pp. 883–889.

Chou, S. Y., Shen, G. C., Chiu, H. C., & Chou, Y. T. (2016). Multichannel service providers'

strategy: Understanding customers' switching and free-riding behavior. Journal of

Business Research, 69(6), 2226-2232.

Collins, L. M. and Lanza, S. T. (2010), Latent Class and Latent Transition Analysis: With

Applications in the Social, Behavioral, and Health Sciences, Wiley, Hoboken, New

Jersey.

Conchar, M. P., Zinkhan, G. M., Peters, C. and Olavarrieta, S. (2004), “An integrated

framework for the conceptualization of consumers’ perceived risk processing”, Journal of

the Academy of Marketing Science, Vol. 32, pp. 418–436.

Daunt, K. L. and Harris, L. C. (2017), “Consumer showrooming: Value co-destruction”,

Journal of Retailing and Consumer Services, Vol. 38, pp. 166–176.

Fernández, N., Pérez, M. J. and Vázquez-Casielles, R. (2018), “Webroomers versus

showroomers: Are they the same?”, Journal of Business Research, Vol. 92, pp. 300–320.

26

Flavián, C., Gurrea R. and Orús C. (2016), “Choice confidence in the webrooming purchase

process: The impact of online positive reviews and the motivation to touch”, Journal of

Consumer Behaviour, Vol. 15 No. 5, pp. 459–476.

Flavián, C., Gurrea R. and Orús C. (2019), “Feeling confident and smart with webrooming:

Understanding the consumer's path to satisfaction”, Journal of Interactive Marketing,

Vol. 47, pp. 1–15.

Fornari, E., Fornari, D., Grandi, S., Menegatti, M. and Hofacker, C.F. (2016), “Adding store

to web: migration and synergy effects in multi-channel retailing”, International Journal

of Retail & Distribution Management, Vol. 44 No. 6, pp. 658-674.

Freeman, M. (2014), “Showrooming: It’s an opportunity for retail, not a threat”, The

Guardian, available at https://www.theguardian.com/media-network/media-network-

blog/2014/jan/15/showrooming-opportunity-retail-threat (accessed 5 February 2020).

Fuentes, C. and Svingstedt, A. (2017), “Mobile phones and the practice of shopping: A study

of how young adults use smartphones to shop”, Journal of Retailing and Consumer

Services, Vol. 38, pp. 137–146.

Ganesh, J., Arnold, M.J. and Reynolds, K.E. (2000), “Understanding the customer base of

service providers: an examination of the differences between switchers and stayers”,

Journal of Marketing, Vol. 64, pp. 65-87.

Gensler, S., Neslin, S. A. and Verhoef, P. C. (2017), “The showrooming phenomenon: It's

more than just about price”, Journal of Interactive Marketing, Vol. 38, pp. 29–43.

Guttentag, D., Smith, S., Potwarka, L. and Havitz, M. (2018), “Why tourists choose Airbnb:

A motivation-based segmentation study”, Journal of Travel Research, Vol. 57 No. 3, pp.

342–359.

Hair, J. F., Black, W. C., Babin, B. J. and Anderson, R. E. (2010), Multivariate Data Analysis

(7th edition.), Pearson, Harlow.

27

Heitz-Spahn, S. (2013), “Cross-channel free-riding consumer behavior in a multichannel

environment: An investigation of shopping motives, sociodemographics and product

categories”, Journal of Retailing and Consumer Services, Vol. 20 No. 6, pp. 570–578.

Herhausen, D., Binder, J., Schögel, M. and Herrmann, A. (2015), “Integrating bricks with

clicks: Retailer-level and channel-level outcomes of online-offline channel integration”,

Journal of Retailing, Vol. 91 No. 2, pp. 309–325.

Holmes, A., Byrne, A. and Rowley, J. (2014), “Mobile shopping behaviour: Insights into

attitudes, shopping process involvement and location”, International Journal of Retail &

Distribution Management, Vol. 42 No. 1, pp. 25–39.

Huang, L., Lu, X., Ba, S. (2016), “An empirical study of the cross-channel effects between

web and mobile shopping channels”, Information Management, Vol. 53, pp. 265-278.

IBM (2017), “E-commerce statistics & technology trendsetters for 2017”, available at

https://www.ibm.com/developerworks/community/blogs/d27b1c65-986e-4a4f-

a4915e8eb23980be/entry/Ecommerce_Statistics_Technology_Trendsetters_for_20171?la

ng=en (accessed 28 January 2020).

Kalyanam, K. and Tsay, A. A. (2013), “Free riding and conflict in hybrid shopping

environments: Implications for retailers, manufacturers, and regulators”, The Antitrust

Bulletin, Vol. 58 No. 1, pp. 19–68.

Keaveney, S.M. and Parthasarathy, M., (2001), “Customer switching behaviour in online

services: an explanatory study of the role of selected attitudinal, behavioural, and

demographic factors”, Journal of the Academy of Marketing Science, Vol. 29, 374-390.

Konuş, U., Verhoef, P. C. and Neslin, S. A. (2008), “Multichannel shopper segments and

their covariates”, Journal of Retailing, Vol. 84 No. 4, pp. 398–413.

28

Lichtenstein, D. R., Netemyer, R. G. and Burton, S. (1990), “Distinguishing coupon

proneness from value consciousness: An acquisition-transaction utility theory

perspective”, Journal of Marketing, Vol. 54 No. 3, pp. 54–67.

Machavolu, M. S. K. and Raju, K. V. V. (2014), “Showrooming: The next threat to Indian

retail”, MITS International Journal of Business Research, Vol. 1 No. 1, pp. 1–17.

Manss, R., Kurze, K., & Bornschein, R. (2019). What drives competitive webrooming? The

roles of channel and retailer aspects. The International Review of Retail, Distribution and

Consumer Research, 1-33.

Masyn, K. (2013), “Latent class analysis and finite mixture modelling”, Little, T. D. (Ed.),

The Oxford Handbook of Quantitative Methods in Psychology (Vol. 2), Oxford University

Press, Oxford, pp. 551–611.

McCutcheon, A. L. (2002), “Basic concepts and procedures in single-and multiple-group

latent class analysis”, Hagenaars, J. A. and McCutcheon, A. L. (Eds), Applied latent class

analysis, Cambridge University Press, Cambridge, UK, pp. 57–88.

Montoya-Weiss, M. M., Voss, G. B. and Grewal, D. (2003), “Determinants of online channel

use and overall satisfaction with a relational, multichannel service provider”, Journal of

the Academy of Marketing Science, Vol. 31 No. 4, pp. 448–458.

Neslin, S. A., Grewal, D., Leghorn, R., Shankar, V., Teerling, M. L., Thomas, J. S., and

Verhoef, P. C. (2006), “Challenges and opportunities in multichannel customer

management”, Journal of Service Research, Vol. 9 No 2, pp. 95-112.

Noble, S. M., Griffith, D. A. and Weinberger, M. G. (2005), “Consumer derived utilitarian

value and channel utilization in a multi-channel retail context”, Journal of Business

Research, Vol. 58 No. 12, pp. 1643–1651.

29

Pallant, J. I., Danaher, P. J., Sands, S. J. and Danaher, T. S. (2017)., “An empirical analysis of

factors that influence retail website visit types”, Journal of Retailing and Consumer


Pantano, E. and Priporas, C.-V. (2016), “The effect of mobile retailing on consumers'

purchasing experiences: A dynamic perspective”, Computers in Human Behaviour, Vol.

61, pp. 548–555.

Paolacci, G., Chandler, J. and Ipeirotis, P. G. (2010), “Running experiments on Amazon

Mechanical Turk”, Judgment and Decision Making, Vol. 5 No. 1, pp. 411–419.

Rapp, A., Baker, T. L., Bachrach, D. G., Ogilvie, J. and Beitelspacher, L. S. (2015),

“Perceived customer showrooming behavior and the effect on retail salesperson self-

efficacy and performance”, Journal of Retailing, Vol. 91 No. 2, pp. 358–369.

Sands, S., Ferraro, C. and Luxton, S. (2010), “Does the online channel pay? A comparison of

online versus offline information search on physical store spend”, International Review of

Retail, Distribution & Consumer Research, Vol. 20 No. 4, pp. 397–410.

Sands, S., Ferraro, C., Campbell, C. and Pallant, J. (2016), “Segmenting multichannel

consumers across search, purchase and after-sales”, Journal of Retailing and Consumer


Santos, S. and Gonçalves, H. M. (2019), “Multichannel consumer behaviors in the mobile

environment: Using fsQCA and discriminant analysis to understand webrooming

motivations”, Journal of Business Research, Vol. 101, pp. 757–766.

Schneider, P. and Zielke, S. (2020), “Searching offline and buying online – An analysis of

showrooming forms and segments”, Journal of Retailing and Consumer Services, Vol.

52, (in press).

Sciandra, M., Inman , J. J. and Stephen, A. T. (2019), “Smart phones, bad calls? The

influence of consumer mobile phone use, distraction, and phone dependence on

30

adherence to shopping plans”, Journal of the Academy of Marketing Science, Vol. 47 No.

4, pp. 574-594.

Singh, R., and Rosengren, S., (2020), “Why do online grocery shoppers switch? An empirical

investigation of drivers of switching in online grocery”, Journal of Retailing and

Consumer Services, Vol. 53

Singley, R. B. and Williams, M. R. (1995), “Free riding in retail stores: an investigation of its

perceived prevalence and costs”, Journal of Marketing Theory and Practice, Vol. 3, pp.

64–74.

Shank, D. B. (2016), “Using crowdsourcing websites for sociological research: The case of

Amazon Mechanical Turk”, The American Sociologist, Vol. 47 No. 1, pp. 47–55.

Sit J. K., Hoang, A. and Inversini A. (2018), “Showrooming and retail opportunities: A

qualitative investigation via a consumer-experience lens”, Journal of Retailing and

Consumer Services, Vol. 40, pp. 163–174.

Sproles, G. B. and Kendall, E. L. (1986), “A methodology for profiling consumers' decision‐

making styles”, Journal of Consumer Affairs, Vol. 20 No. 2, pp. 267–279.

Thomas, K. A. and Clifford, S. (2017), “Validity and mechanical turk: an assessment of

exclusion methods and interactive experiments”, Computers in Human Behavior, Vol. 77,

pp. 184–197.

Van Baal, S. and Dach, C. (2005), “Free riding and customer retention across retailers'

channels”, Journal of Interactive Marketing, Vol. 19 No. 2, pp. 75–85.

Venkatesan, R., Kumar, V. and Ravishanker, N. (2007), “Multichannel shopping: Causes and

consequences”, Journal of Marketing, Vol. 71 No. 2, pp.114–132.

Verhoef, P. C. and Langerak, F. (2001), “Possible determinants of consumers’ adoption of

electronic grocery shopping in the Netherlands”, Journal of Retailing and Consumer

Services, Vol. 8 No. 5, pp. 275–285.

31

Verhoef, P. C., Neslin, S. A. and Vroomen, B. (2007), “Multichannel customer management:

Understanding the research-shopper phenomenon”, International Journal of Research in

Marketing, Vol. 24 No. 2, pp. 129–148.

Vermunt, J. K. (2010), “Latent class modeling with covariates: Two improved three-step

approaches”, Political Analysis, Vol. 18 No. 4, pp. 450–469.

Vermunt, J. K. and Magidson, J. (2002), “Latent class cluster analysis”, Hagenaars, J. A. and

McCutcheon, A.L. (Eds), Applied Latent Class Analysis, Cambridge University Press,

Cambridge, New York, pp. 89–106.

Vermunt, J. K. and Magidson, J. (2013), Technical Guide for Latent GOLD 5.0: Basic,

Advanced, and Syntax, Statistical Innovations Inc, Belmont, MA.

Viejo-Fernández, N., Sanzo-Pérez, M., and Vázquez-Casielles, R. (2020), “Is showrooming

really so terrible? Start understanding showroomers”, Journal of Retailing and Consumer

Services, Vol. 54

Wallenius, J., Fishburn, P. C., Zionts, S., Dyer, J. S., Steuer, R. E. and Deb, K. (2008),

“Multiple criteria decision making, multiattribute utility theory: Recent accomplishments

and what lies ahead”, Management Science, Vol. 54 No. 7, pp. 1336–1349.

Wang, R. J.-H., Malthouse, E. C. and Krishnamurthi, L. (2015), “On the go: How mobile

shopping affects customer purchase behavior”, Journal of Retailing, Vol. 91, pp. 217–

234.

Wedel, M. and Kamakura, W. (2012), Market Segmentation: Conceptual and Methodological

Foundations, Springer, New York, NY

Wu, X., Chen, Q., Zhou, W., Guo, J. (2010), “A review of mobile commerce consumers’

behaviour research: consumer acceptance, loyalty and continuance (2000-2009)”,

International Journal of Mobile Communication, Vol. 8 No. 5, 528-560.

consumer cross-channel behaviour: is it always planned?

Documents