Why shoppers use their smartphone for an in-store purchase?
Gwarlann de Kerviler*
Assistant Professor of Marketing
IESEG School of Management
Nathalie T.M. Demoulin
Associate Professor of Marketing
IESEG School of Management
Pietro Zidda
Professor of Marketing
University of Namur
Center for Research on Consumption & Leisure (CeRCLe)
* IESEG School of Management, Socle de la Grande Arche, 1 Parvis de La Défense, 92044
Paris La Défense cedex, France, [email protected], +33 (0) 1 55 91 10 10.
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Pourquoi les acheteurs utilisent leur smartphone pour un achat en magasin?
Résumé
Le nombre croissant d’utilisateurs d’un smartphone pour faire du shopping pousse les
distributeurs à évaluer son impact sur le comportement de leurs clients. Cet article étudie les
facteurs d’adoption d’un smartphone comme assistant tout au long du processus d’achat en
magasin. En utilisant diverses théories relatives au comportement du consommateur, nous
identifions trois étapes clés du processus. Sur base de données d’enquêtes, nos analyses
montrent d’une part que les déterminants de l’attitude envers l’utilisation du smartphone
comme assistant varient d’une étape à l’autre et d’autre part que l’effet des influences
sociales, de l’expérience et des conditions de facilitation jouent un rôle différent selon l’étape
du processus.
Mots-clés : processus décisionnel d’achat, marketing mobile, assistant de shopping, adoption
Why shoppers use their smartphone for an in-store purchase?
Abstract
With the increasing number of shoppers using smartphones while shopping, retailers need to
understand how it impacts their customers’ shopping process. This article investigates
motivations to and barriers of smartphone usage for shopping activities. Building on various
consumer behavior theories, we identify three stages on the path to an in-store purchase: pre-
shopping, pre-purchase and purchase. Based on survey data, our analyses highlight that in a
retail setting the drivers of the attitude towards smartphone usage vary across shopping stages
and that social influences, shopper experience and facilitating conditions play differing roles
in each stage.
Key-words: shopping decision process, mobile marketing, shopping assistant, adoption
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Why shoppers use their mobile phone for an in-store purchase?
1. Introduction
Consumers go through multiple channels when shopping (Powers et al., 2012; Vanheems,
2013) and 60% of smartphone users now use their mobiles as a shopping assistant to prepare
and complete purchases in the store (Miller and Washington, 2013). The present study aims at
gaining a better understanding of the attitude towards adoption and usage of smartphones as a
shopping assistant. It is the first to analyze specifically how smartphones could be used for in-
store purchases and it thus provides useful information for retailers to design relevant mobile
apps or mobile websites that are likely to enhance their customers’ shopping experience. This
research also complements past studies, which have solely considered the smartphone either
as a communication tool for SMS advertising (e.g., Yang, Kim and Yoo, 2013) and SMS
promotions (Khajehzadeh, Oppewal and Tojib, 2014), as an informational and decision tool in
stores (Van der Heijden, 2006) or as a substitute channel to e-commerce via PC, to brick-and-
mortar stores or to catalogues (Turban et al., 2004).
For completing an in-store purchase, shoppers go through various activities along the
stages of the shopping process, such as creating a shopping list, querying retailers, searching
for the right products, comparing items and purchasing them (Shankar et al., 2010). Different
benefit-driven motivations influence the choice of a particular channel for each activity or
stage (Balasubramanian, Raghunathan and Mahajan, 2005; Frambach, Roest and Krishnan,
2007). Shoppers search for relevant channel attributes at each stage (Gensler, Verhoef and
Böhm, 2012). In the search stage, consumers gather information, while minimizing search
efforts and costs. Then they evaluate alternatives based on assortment, quality and price. At
the purchase stage, shoppers strive to buy the selected product at the lowest price available,
while avoiding privacy and payment risks. Our contribution is twofold: (1) This paper aims to
compare the specific benefit-driven motivations behind smartphone usage at each stage on the
path to an in-store purchase. It thus extends past research which has limited mobile usage to
in-store information search (Van der Heijde, 2005) or to product purchases (Kumar and
Mukherjee, 2013); (2) For the stages along the path of an in-store purchase, we differentiate
between the retailer choice and the product choice as suggested by Levy and Weitz (2012). In
addition to a “pre-purchase” stage inside the store during which shoppers make their product
choice and a “purchase” stage, we indeed consider a “pre-shopping” stage outside the store,
mainly characterized by the shopper’s choice of store. The pre-shopping stage, ignored in
previous studies on channel preference, is based on Bell, Corsten and Knox’s (2011) work,
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which identifies a preliminary stage outside the store. The store choice stage has often been
considered to be specific in the decision making process (Bell, Ho and Tang, 1998) and to
affect not only where shoppers buy but also what and how much they purchase when in the
store (Briesch, Chintagunta and Fox, 2009). The pre-purchase stage is similar to the stage
analyzed by Van der Heijde (2005) who identified “forming a consideration set of products in
store” as a central step in the purchase decision making process. Understanding shoppers’
perceived benefits and risks associated with smartphone usage at various stages of the
shopping process is crucial for managers who want to enrich their customers’ experience
through mobile marketing.
2. Theoretical background and hypotheses
To determine antecedents of the attitude towards smartphone usage for an in-store purchase,
we based our conceptual model on the theory of planned behavior (TPB) (Taylor and Todd,
1995), which has been proven to be successful in predicting adoption of innovation (Morris,
Venkatesh and Ackerman, 2005). We enriched the model with the risk/reward perspective
regarding technological innovation (Wells et al., 2010) and with the task-technology fit
(Goodhue and Thompson, 1995) to gain a more thorough analysis of motivations and barriers
influencing smartphone usage.
The theory of planned behavior (TPB) is rooted in the technology acceptance literature,
which studies individual reactions to a new technology. The theory of reasoned action (TRA)
has often been applied to analyze acceptance of mobile advertising (Bauer et al., 2005; Zhang
and Mao, 2008) and suggests that intentions are influenced by attitude towards the behavior
and by others’ opinions about that behavior (Fishbein and Ajzen, 1975). TPB broadens TRA
to account for conditions where individuals do not have complete control over their behavior
(Taylor and Todd, 1995).
1.1. Attitude toward smartphone usage and its antecedents
To determine the antecedents of the attitude towards smartphone usage as a shopping
assistant, we rely on Wells et al. (2010) risk/rewards approach, as well as on the task-
technology fit (Goodhue and Thompson, 1995). We identify four smartphone benefits, i.e.
access to information, economic, convenience and social, and two barriers, i.e. privacy and
financial risks. Information benefits are associated with smartphone rapid and easy access to a
large quantity of details regarding stores and their merchandise (Varshney, Vetter and
Kalakota, 2000) without spatial or temporal restrictions. Economic benefits denote
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perceptions of good value for money (Sheth et al., 1991) because shoppers receive
promotional offers and compare prices on their mobile. Helping a shopper to conduct
transactions more conveniently has a positive effect on channel choice (Gensler, Verhoef and
Böhm, 2012) and smartphone usage can enhance shopping convenience –or minimize
required time and effort in one’s shopping– through mobile payment, retrieval of stored
information and access to mobile loyalty cards. Sweeney and Soutar (2001) define social
benefits as the utility derived from enhancing social self-concept. Consumers may express
their social identity when shopping. As such, adoption of mobile services are influenced by
their perceived projected image on others (Laukkanen et al., 2007). Perceived risk
corresponds to a combination of uncertainty plus seriousness of the outcome involved (Bauer,
1967, p. 25) and has a negative influence on choice of a channel (e.g., Gensler, Verhoef and
Böhm, 2012). Transposed to mobile usage and payments, the privacy and financial risks are
linked to potential monetary and psychological losses due respectively to a lower control over
personal information (Featherman and Pavlou, 2003; Hérault and Belvaux, 2014) and to
transaction errors or fraudulent uses of banking account information (Lee, 2008).
1.2. Smartphone usage intention and its determinants
In addition to the attitude towards usage, past research has highlighted the role of social
influence (according to Azjen (1987), peer influence to which a person’s behavior is exposed
to) and experience on adoption (e.g., Venkatesh, Thong and Xu, 2012). Former studies also
demonstrate that, because it creates a cognitive lock-in, experience with a specific channel
influences consumers’ channel choice (Frambach, Roest and Krishnan, 2007; Gensler,
Verhoef and Böhm, 2012). Facilitating conditions are consumer perceptions of resources,
constraints and support available to perform a behavior (Taylor and Todd, 1995). A consumer
who has access to a favorable set of facilitating conditions is more likely to use a technology
(Venkatesh, Thong and Xu, 2012) and smartphone usage intentions should be higher when all
necessary resources (e.g., time, money, Wi-Fi or mobile internet connections) are available.
1.3. Stages in the shopping process
The shopping process consists of distinct stages and because goals differ at each stage (e.g.,
Lee and Ariely, 2006), channel preferences are likely to differ across the stages of the
shopping cycle (Frambach, Roest and Krishnan, 2007). According to the task-technology fit
approach (Goodhue and Thompson, 1995), a technology should provide features that fit the
requirements of a task. Thus, smartphone usage for an in-store purchase should be linked to
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specific requirements at each stage of the shopping process.
As detailed in the introduction, the shopping stages for an in-store purchase used in our
study were carefully chosen from a review of the buying process described by Levy and
Weitz (2012), Bell, Corsten and Knox (2011) and Van der Heidjen (2005). We distinguish
between a pre-shopping stage (S1), outside the store, during which shoppers set their
shopping goals and compare different retailers (Bell, Corsten and Knox, 2011), a pre-
purchase stage (S2) consisting of selecting a product in the store (Van der Heidjen, 2005), and
a purchase stage (S3) consisting in buying a selected product in the store.
At S1, when the shopper is outside of the store, the task is to plan efficiently one’s
shopping by narrowing down the consideration set (Balasubramanian, Raghunathan and
Mahajan, 2005). Shoppers compare stores according to a list of criteria, such as prices,
assortment, service or convenience (Bell, Corsten and Knox, 2011) before selecting a
particular retailer (Levy and Weitz, 2012). Mobile technology provides quick access to
information about stores, such as locations, opening hours, parking facilities and merchandise.
Smartphones can also be used to compare store prices with comparison apps and to find
retailers’ mobile coupons, thereby influencing the store choice. Information and economic
benefits increase efficiency in one’s shopping (Mourali, Laroche and Pons, 2005) and should
positively influence attitude. Moreover, experienced users who know how to take advantage
of smartphone functions (e.g., mapping one’s environment, downloading store information)
and who have appropriate resources (e.g., easy access to internet through their mobiles) will
be more inclined to use their smartphones to plan their shopping.
The key task involved in S2, when the shopper is inside the store, consists in selecting
merchandise in the store (Levy and Weitz, 2012; Van der Heijde, 2005). Smartphone
functions can help scan QR codes for product information, access mobile applications, share
information with others about an item or display oneself trying the product. Smartphone usage
inside the store can project an image of a smart shopper using a mobile assistant. However,
using mobile functions may also give retailers access to personal information (e.g., user
profiles, location history, in-store behavior, online activities) and enable them to track
customers in the store with geolocation (Hérault and Belvaux, 2014) or bluetooth
technologies (e.g., iBeacon). Provided that personal privacy is protected, the ability to access
product information and to enhance one’s perceived image is particularly important at S2.
Moreover, access to functionalities such as scanning QR codes or sending pictures requires
some experience which should positively influence intention. Frambach et al. (2007) showed
that as consumers progress in the decision making process, social influences become more
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influential. Finally, facilitating conditions influence usage, as access to the internet through
the retailer’s Wi-Fi or mobile internet is needed.
In S3, the task is to finalize the purchase in the store in a convenient way (Frambach et al.,
2007), as well as to buy the merchandise at the lowest price (e.g., Balasubramanian et al.,
2005). Smartphones can be used as a digital wallet, transferring funds electronically from
(almost) anywhere, anytime, and to pay and redeem coupons at the time of purchase. Such
usage may be perceived as innovative and project an image of a technology savvy person.
However, customer confidence is still low when it comes to mobile transactions (Jarvenpaa et
al., 2000). Hence, we expect perceived convenience, economic and social benefits to
positively influence attitude, and perceived risks to negatively influence attitude at this stage.
Consumers feel a strong need to be reassured at the time of payment (Frambach et al., 2007)
leading to social norms’ influence on usage intentions at S3. Even experienced users with
access to various functionalities (e.g., access to mobile wallets) may not be more inclined than
other customers to use their smartphones. Therefore experience and facilitating conditions
should have no impact on usage intention and usage frequency at this stage.
To sum up, we suggest the following conceptual model and hypotheses:
Figure 1. Conceptual model
H1: Stages of the shopping process moderate the effect of perceived benefits and perceived
risks on the attitude towards smartphone usage, such that
H1a: Economic benefits influence attitude more during S1 and S3 in comparison to S2
H1b: Convenience benefits influence attitude more during S3 in comparison to S1 and S2
H1c: Information benefits influence attitude more during S1 and S2 in comparison to S3
H1d: Social benefits influence attitude more during S2 and to S3 in comparison to S1
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H1e: Risks influence attitude more during S2 and S3 in comparison to S1
H2: Stages of the buying process moderate the effect of social influence and experience on
smartphone usage intention, such that:
H2a: Social influence influences usage intention more during S2 and S3 in comparison to S1.
H2b: Experience influences usage intention more during S1 and S2 in comparison to S3.
H3: The effect of facilitating conditions on usage frequency is stronger at S1 and S2 than at
S3.
3. Data collection
In order to collect our data, we used a scenario-based survey approach, each scenario
corresponding to a stage of a shopping process in the context of the purchase of a new
compact camera. 541 consumers with at least one online purchase past experience (48%
women and 52% men, aged 20 to 64 with an average age of 35) were surveyed by means of
an online questionnaire. We first asked our respondents to read a general introduction
clarifying the use of the smartphone for shopping purposes (see Appendix 1). We then asked
them to read the details about the three scenarios (i.e., stages) so that they understood the
whole shopping process and we elicited their intention to use a smartphone as a shopping
assistant at each stage (Venkatesh, Thong and Xu, 2012). It was then made clear that each
respondent would be placed in a single stage for the rest of the questionnaire. We then
randomly assigned respondents to one of the three stages and asked them to answer to the
questions related to that particular stage only. Respondents in S1 were placed in an out-of-
home situation with the task of selecting a retailer to visit for the purchase of a new compact
camera; those in S2 were placed in a store with the need to choose a compact camera to
purchase; and those in S3 were again placed in a store, with a payment task for the chosen
compact camera. Within each scenario, participants were first asked about their attitude
towards using a smartphone as a shopping assistant (Taylor and Todd, 1995) and about their
usage frequency (Venkatesh, Thong and Xu, 2012) at that particular stage. We then elicited,
with respect to using a smartphone as a shopping assistant at that stage, the perceived
economic benefits (Mimouni-Chaabanne and Volle, 2010), convenience benefits (Childers,
2001), informational benefits (TNS, 2013), social benefits (Sweeney and Soutar, 2001),
privacy and financial risks (Featherman and Pavlou, 2003), facilitating conditions and social
influences (Venkatesh, Thong and Xu, 2012) as well as smartphone experience (Murray and
Schlacter, 1990). We also measured a set of controlling variables using the scales available in
the literature (i.e., computer experience, purchase decision involvement, product
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involvement). All constructs but usage frequency were measured on a 7-point agreement
scale. Usage frequency was measured on a 7-point frequency scale, ranging from “never” to
“always”.
4. Results
We analyzed the data using SmartPLS version 2.0.M3 (Ringle et al., 2005) for two stages
related to the measurement model and the structural model. We have respectively 178, 179
and 184 respondents for S1, S2 and S3. First, we tested the measurement model for the entire
sample and for each stage by performing a validity and reliability analysis for each measure in
the structural model by using the appropriate tests (Fornell and Larcker, 1981). As shown in
Appendix 2, the measures are reliable and valid overall. Moreover, we tested for measurement
invariance across stages (Eberl, 2010). Our results show evidence of invariance across stages.
Finally, we performed ANOVAs to compare respondents across stages in terms of age,
computer experience, smartphone experience, decision making involvement and product
involvement. No significant differences were found. Thus, the three groups can be considered
to be similar.
In order to test our hypotheses, we performed multi-group analyses applied to PLS (Eberl,
2010). Table 1 presents the path coefficients of the model tested for each stage and for the
total sample and their statistical significance (using bootstrapping resampling techniques).
According to Chin (1998), the R-squared values for our main dependent variables can be
considered substantial for attitude (i.e., stage 1: R²= .66; stage 2: R²= .60; stage 3: R² = .56)
and usage intention (i.e., stage 1: R²= .66; stage 2: R²= .54; stage 3: R² = .69) and rather
moderate for usage frequency (i.e., stage 1: R²= .53; stage 2: R²= .51; stage 3: R² = .34). The
GoF of models for the three stages are good (ranging from .71 to .76). Looking at the Q-
square values, the model has predictive relevance (Fornell and Cha, 1994). According to
Henseler et al. (2009), the dependent variable Q-square values in each of the three models are
evaluated as large, i.e. between .58 and .69 for attitude, between .48 and .65 for usage
intention and between .33 to .52 for usage frequency.
To verify H1, H2 and H3, we performed parameter comparisons using t tests as recommended
by Eberl (2010) in multi-group analysis. The effect of economic benefits on attitude is
positive and significant at S1 and S3. The effect at S1 and S3 is significantly higher than at S2
(S1/S2: t=2.33, p< 05; S2/S3: t=1.74, p<.05). Thus, H1a is supported. Convenience benefits
impact attitude only at S3 and the influence is marginally higher than in other stages (S1/S3:
t=1.54, p<.1; S2/S3: t=1.61, p<.1). Thus, H1b is partially supported. Information benefits
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Pre-shopping (S1) Pre-purchase (S2) Purchase (S3) Total sample
Std
coef
t-value Std
coef
t-value Std
coef
t-value Std
coef
t-value
Economic Benefits Attitude 0,36 **
2,76 -0,01
0,10 0,21 * 2,37 0,30
*** 5,30
Convenience Benefits Attitude 0,11
0,94 0,18
1,81 0,34 ***
3,75 0,20 ***
3,47
Information Benefits Attitude 0,36 **
2,58 0,56 ***
6,00 0,09
1,11 0,23 ***
4,38
Social Benefits Attitude 0,07
1,19 0,15 *
1,98 0,22 ***
3,52 0,16
4,40
Risks Attitude 0,01
0,17 -0,20 *
2,15 -0,24 ***
4,39 -0,20 **
6,31
Attitude Usage Intention 0,55 ***
6,12 0,38 ***
5,01 0,65 ***
8,86 0,55 ***
11,34
Social Influences Usage Intention 0,10
1,31 0,25 ***
3,35 0,22 ***
3,27 0,20 ***
4,68
Experience Usage Intention 0,27 ***
3,60 0,27 ***
3,64 0,04
0,67 0,16 ***
3,77
Facilitating Conditions Usage Frequency 0,16 * 2,44 0,16
* 2,38 0,07
1,35 0,15
*** 4,31
Usage Intention Usage Frequency 0,62 ***
10,63 0,62 ***
9,87 0,55 ***
9,57 0,58 ***
15,47
GoF 0,76
0,71
0,71
0,73
*<.05, **<.01, ***<.001 Table 1. Model coefficient estimation results
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(S1/S3: t=1.73, p<.05; S2/S3: t=3.93, p<.01). These results support H1c. Social benefits
significantly increase attitude when customers are in the store (S2 and S3), whereas it does
not at S1. However, the difference between coefficients is only significant between S1 and S3
(S1/S2: t=.89, p>.1; S1/S3: t=1.87, p<.05). H1d is thus partially supported. Risks significantly
decrease attitude at S2 and S3. The parameters significantly differ between S1 and S2 (t=1.93,
p<.05) as well as between S1 and S3 (t=3.19, p<.01). As a result, H1e is confirmed.
Regarding the determinants of usage intention, the attitude significantly increases the usage
intention at every stages. As expected, the effect of social influence on usage intention is
significant when customers are in the store (S2 and S3). There are marginally significant
differences between the parameters of S1 and S2 (S1/S2: t=1.43, p<.1) and 3 (S1/S3: t=1.35,
p<.1). H2a is partially confirmed. Experience with the smartphone for shopping activities
positively impacts smartphone usage intention in S1 and S2 and there are significant
differences with S3 (S1/S3: t=2.44, p<.01; S2/S3: t=2.45, p<.01). Thus, it supports H2b.
Facilitating conditions only increase use at the first two stages, but differences between stages
are not significant (S1/S3: t=1.17, p>.1; S2/S3: t=.85, p>.1). H3 is partially supported.
5. Discussion and conclusion
First, reflecting emerging shopping behaviors, we consider smartphones to be a
complementary channel that facilitates in-store purchases. We detail the tasks involved and
identify smartphone functionalities supporting them and induced risks. Second we
demonstrate that perceived risks and benefits are key antecedents of customer attitude towards
smartphone usage for an in-store purchase, that attitude, social norms and experience
influence smartphone usage intentions and that usage intentions and facilitating conditions in
turn determine smartphone usage. Third, because each stage involves a different task calling
for relevant specific channel functions (Konus et al., 2008), we demonstrate the moderating
effect of the shopping stage. More precisely, in the pre-shopping stage (S1), when consumers
are searching for the right store, access to information and savings are the main benefits
driving intentions. Later, in the pre-purchase stage (S2), because users are interested in
finding the right product in the store while maintaining their privacy, information benefits and
risks are particularly important. Finally, during the purchase stage (S3), consumers adopt the
smartphone if they perceive it to be a convenient risk-free payment method and helpful to
minimize costs through mobile coupons. Shoppers use their smartphones to strengthen their
social identity when they are surrounded by other customers in the store (S2 and S3).
Experience and facilitating conditions are influential at S1 and S2 because mobile payment is
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still uncommon even among experienced users with access to all resources.
Our findings can help retailers determine the best mobile strategies. During S1, shoppers
search for information and promotional offers. Retailers could develop mobile applications or
a mobile website that helps them to be more easily located and to promote their offers, their
convenient hours and their services. Retailers could also provide shopping tips and mobile
coupons to be redeemed at the stores. For S2, what matters is to ensure access to product
information, project a positive image of oneself, but also avoid privacy concerns. Retailers
could provide quick answers through the smartphone to queries regarding products, while
protecting privacy. Finally, at S3, retailers should focus on convenience and access to
promotions, while ensuring financial security.
Our results also suggest a need for retailers to develop specific strategies for developing m-
commerce, even towards experienced users. With higher financial security of m-transactions
and new features adding value to mobile payment (e.g., free delivery, direct link with
customers’ accounts or loyalty programs), users might better understand the potential benefits
of paying through the smartphone.
As the goal was to focus on smartphone usage, we did not include other online
environments accessible through laptops and tablets. Further research could investigate
customers’ motivations to use all channels simultaneously or only one depending on
situational factors, such as the purchase level of urgency. Another limitation is the
generalization of our results, as our study is about a purchase for a compact camera and thus
about a single product category. Future studies could investigate other categories for which
the smartphone is often used as a complementary channel to an in-store purchase, such as
household appliances, groceries or fashions, and examine whether the results hold. Another
interesting research could be to investigate the effects of strategies encouraging m-commerce
with dedicated apps or mobile websites and higher perceived financial security. Finally, a
potentially fruitful research direction could be the investigation of the effects of smartphone
versus tablet usage on consumer loyalty to a particular retailer.
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Appendix 1. Survey scenarios and survey flows
S1: [Read Introduction] [Read Stage 1 Scenario] [Measurement of smartphone usage
intention at stage 1] [Measurement of all stage-related constructs (e.g., usage attitude,
benefits, risks, …] [Read Stage 2 Scenario] [Measurement of smartphone usage int. at
stage 2] [Read Stage 3 Scenario] [Measurement of smartphone usage int. at stage 3]
S2: [Read Introduction] [Read Stage 1 Scenario] [Measurement of smartphone usage
intention at stage 1] [Read Stage 2 Scenario] [Measurement of smartphone usage int. at
stage 2] [Measurement of all stage-related constructs (e.g., usage attitude, benefits, risks,
…] [Read Stage 3 Scenario] [Measurement of smartphone usage int. at stage 3]
S3: [Read Introduction] [Read Stage 1 Scenario] [Measurement of smartphone usage
intention at stage 1] [Read Stage 2 Scenario] [Measurement of smartphone usage int. at
stage 2] [Read Stage 3 Scenario] [Measurement of smartphone usage int. at stage 3]
[Measurement of all stage-related constructs (e.g., usage attitude, benefits, risks, …]
INTRODUCTION
This study focuses on your opinion regarding the use of smartphones for shopping. You will
find below a description of the various usages of your smartphone for shopping. Before
entering a store, the smartphone can provide information by connecting you to the Internet or
by using an application (from retailers, from brands, from comparators, etc.). Outside of the
store, information can include the location of a nearby store, opening hours, features and
prices of several products, promotional offers and availability of products in the store and
reviews from consumers or price comparisons. In the store, in addition to the features already
mentioned, scanning a QR code can provide detailed information on a product, access to a
video or instant rewards. Smartphones can also be used to photograph the product and seek
advice from others by sending the photo by MMS or by posting it on social networks. Some
stores also offer applications that facilitate your shopping by providing advice and
information on where to find your product. Finally, when buying the product, it is also
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possible in some stores to pay with your smartphone using applications such as Google
wallet. Smartphones can be used as an electronic credit card. This electronic portfolio allows
you to store your loyalty cards and receive promotional offers from retailers. Your list of
recent transactions can also be accessed at any time.
You are now asked to read the text below and then answer to a set of questions related to it.
Stage 1 – PRE-SHOPPING “outside the store”
Imagine that you want a new compact camera with a powerful zoom, a large screen and at an
attractive price. You're not at home. You do not know which store has the best offer or what
product or brand is suitable for your needs. You begin to gather information on retailers and
their offers in order to select the store to visit and evaluate products.
Stage 2 – PRE-PURCHASE “in store”
Imagine that you want a new compact camera with a powerful zoom, a large screen and at an
attractive price. You're not at home. You do not know which store has the best offer or what
product or brand is suitable for your needs. You begin to gather information on distributors
and their offers to select the store to visit and evaluate products. Now imagine that for this
camera, you now have gathered information on distributors and their offers to select the store
to visit. You have also collected information on products. You have chosen a store where
several options seem to meet your needs. You are now entering the store to select the product
that best suits you by comparing offers and looking for outside opinions.
Stage 3 – PURCHASE “in store”.
The purchase can be either done through the phone or at the store cashier. Imagine that you
want a new compact camera with a powerful zoom, a large screen and at an attractive price.
You're not at home. You do not know which store has the best offer or what product or brand
is suitable for your needs. You begin to gather information on distributors and their offers to
select the store to visit and evaluate products. Now imagine that for this camera, you now
have gathered information on distributors and their offers to select the store to visit. You have
also collected information on products. You have chosen a store where several options seem
to meet your needs. You are now entering the store to select the product that best suits you by
comparing offers and looking for outside opinions. Now imagine that for this camera you
chose a store and checked out the products to choose the one that best suited you. You
decided on a product and you are ready to proceed to purchase. The store where you are has
a system of direct payment by smartphone. Simply approach your smartphone terminal and
validate the payment on your smartphone to pay for your purchase.
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Appendix 2. Scale items, summary statistics, standard loadings, composite reliability and average variance extracted
Constructs and measured items Standard Loadings
Stage 1 (S1) Stage 2 (S2) Stage 3 (S3) Total sample
Attitude towards smartphone usage* (mean=3.88; SD=1.97) α ; CR ; AVE .97; .98; .91 .96; .97; .89 .96; .97; .90 .98; .98; .93
Using a smartphone would be a Bad/Good idea .94 .95 .97 .96
Using a smartphone would be a Foolish/Wise idea .92 .93 .95 .94
I have an Unfavorable/favorable opinion about using a smartphone .96 .96 .96 .96
I have a Negative/ positive opinion about using a smartphone .95 .95 .97 .96
Economic benefits* (mean=3.73; SD=1.80) α ; CR ; AVE .95; .97; .90 .96; .97; .93 .96; .97; .92 .92; .95; .97
Using a smartphone would allow me to do my shopping at a lower financial cost .96 .97 .93 .96
Using a smartphone would allow me to save money .98 .97 .96 .97
Using a smartphone would allow me to take advantage of promotional offers .94 .93 .90 .92
Convenience benefits* (mean=4.29; SD=1.85) α ; CR ; AVE .96; .98; .94 .97; .98; .94 .96; .98; .93 .97; .99; .94
Using a smartphone would allow me to save time .97 .97 .96 .97
Using a smartphone would make my shopping less time consuming .97 .97 .98 .97
Using a smartphone would be a convenient way to do shopping .97 .95 .96 .96
Information benefits* (mean=4.57; SD=1.72) α ; CR ; AVE .97; .98; .94 .96; .98; .93 .97; .98; .94 .97; .98; .94
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Using a smartphone would allow me to get information about stores and products .96 .97 .97 .97
Using a smartphone would allow me to get information about product price comparison .97 .97 .97 .97
Using a smartphone would allow me to get useful info. to make better shopping
decisions .96 .96 .97 .97
Social benefits* (mean=2.92; SD=1.80) α ; CR ; AVE .96; .97; .93 .98; .99; .96 .95; .97; .90 .95; .97; .92
Using a smartphone would help me make a good impression on other people .97 .94 .94 .95
Using a smartphone would help me feel acceptable .98 .95 .96 .97
Using a smartphone would improve the way I am perceived by others .98 .96 .97 .97
Risks* (mean=4.26; SD=1.67) α ; CR ; AVE .94; .95; .79 .94; .89; .63 .95; .96; .83 .95; .96; .83
Using a smartphone would cause me to lose control over my privacy .91 .69 .69 .93
Using a smartphone would lead to a loss of privacy because my personal information
would be used without my knowledge .95 .65 .65 .93
Using a smartphone would lead me to run the risk of internet hackers taking control of
my personal information .71 .96 .96 .84
Using a smartphone would lead to potential fraud of my checking account .91 .89 .89 .89
Using a smartphone would subject my checking account to financial risks .93 .74 .74 .94
Facilitating conditions* (mean=4.58; SD=1.65) α ; CR ; AVE .96; .95; .82 .96; .95; .83 .91; .94; .79 .93; .95; .82
I have the resources necessary to use a smartphone .94 .88 .92 .92
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I have the knowledge necessary to use a smartphone .93 .93 .91 .93
I have the ability to use a smartphone .95 .94 .92 .94
I would be able to use a smartphone without any internet network access issues .82 .80 .88 .84
Social influences* (mean=3.06; SD=1.69) α ; CR ; AVE .95; .97; .90 .95; .97; .90 .93; .96; .88 .93; .97; .92
People who are important to me think that I should use a smartphone .95 .93 .95 .95
People who influence my behavior think that I should use a smartphone .95 .94 .97 .95
People whose opinions that I value prefer that I use a smartphone .94 .95 .96 .95
Usage intention* (mean=3.61; SD=2.06) α ; CR ; AVE .97; .98; .94 .96; .98; .93 .96; .97; .92 .98; .98; .95
I intend to use a smartphone .97 .97 .98 .98
I plan to use a smartphone .95 .96 .97 .96
I will use a smartphone in the future .97 .95 .98 .97
Experience* (mean=3.73; SD=1.88) α ; CR ; AVE .96; .97; .86 .97; .97; .88 .95; .96; .83 .96; .97; .83
I have a great deal of experience with using smartphones when doing shopping .94 .93 .92 .94
I have used to or been exposed to using smartphones when doing shopping in the past .94 .90 .93 .92
I am familiar with the different possibilities of using smartph. when doing shopping .94 .90 .95 .93
I frequently inform myself on the possibilities of using smartph. when doing shopping .92 .90 .89 .91
I am very confident in using a smartphone when doing shopping .95 .92 .95 .94