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Journal of Internet Commerce

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“Showrooming” in Consumer Electronics Retailing:An Empirical Study

Francisco Rejón-Guardia & Cuauhtemoc Luna-Nevarez

To cite this article: Francisco Rejón-Guardia & Cuauhtemoc Luna-Nevarez (2017) “Showrooming”in Consumer Electronics Retailing: An Empirical Study, Journal of Internet Commerce, 16:2,174-201, DOI: 10.1080/15332861.2017.1305812

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Published online: 09 May 2017.

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JOURNAL OF INTERNET COMMERCE 2017, VOL. 16, NO. 2, 174–201 http://dx.doi.org/10.1080/15332861.2017.1305812

“Showrooming” in Consumer Electronics Retailing: An Empirical Study Francisco Rejón-Guardiaa and Cuauhtemoc Luna-Nevarezb

aDepartment of Business and Economics, University of Balearic Islands, Mallorca, Spain; bDepartment of Marketing and Sport Management, Sacred Heart University, Fairfield, Connecticut, USA

ABSTRACT The present study focuses on multichannel retailing strategies and describes the state of consumer behavior regarding “showrooming” (the practice of examining merchandise or products in a retail store and then buying it online). Founded on the theory of planned behavior (TPB), the authors examine the antecedents of showrooming using data collected from a sample of 176 retail consumers. Based on their results, they define perceived control, website compatibility, and subjective norms as the main antecedents of consumer attitudes toward online purchases. Additionally, they state that previous experience and reasons against purchasing online are directly associated with consumers’ intention to purchase on the retailer’s website. Finally, some theoretical conclusions and practical implications for retailers are discussed.

KEYWORDS Consumer electronics; showrooming; structural model

Introduction

The Internet offers several advantages over the traditional in-store retail environment and represents the main source of information for consumers, who visit retailers’ websites before and after an actual purchase (Cetelem 2013). Moreover, the Internet has become the most popular retail channel for many consumers, especially in the leisure, fashion, tourism, and electro-nics industries (Webloyalty 2013). Some recent studies draw attention to two trends that are impacting both the online and offline retail channels, namely webrooming and showrooming, in which the physical store and the Internet exchange roles during the purchase decision-making process. As a result, the Internet is becoming the main sales channel for most retailers, whereas their physical stores are becoming a source of information for many consumers. This phenomenon is not particular to a geographic area or consumer typology; it can actually be considered a more generalized consumption trend (Quint, Rogers, and Ferguson 2013).

The evolving online consumer behaviors require a more in-depth analysis as online purchasing represents a growing trend among consumers; 42% of

CONTACT Cuauhtemoc Luna-Nevarez [email protected] Department of Marketing and Sport Management, Sacred Heart University, 5151 Park Avenue, Fairfield, CT 06825, USA. © 2017 Taylor & Francis Group, LLC

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http://dx.doi.org/10.1080/15332861.2017.1305812

Internet users report making online purchases (Statista 2015a). Global consumer retail sales are estimated at more than 750,000 million dollars (Statista 2015b), and an increase of 300,000 million dollars in sales is estimated for US retail e-commerce in 2015 (Statista 2015c). Furthermore, with the increase in the use of mobile devices such as smartphones and tablets, this trend is expected to grow even more. Other studies revealed that 70% of consumers in the United States, England, and Canada reported having practiced showrooming at least once during 2013, which suggests that retail stores are becoming mere extensions of their own e-commerce websites (Clifford 2012). This phenomenon has been studied by major retailers such as Best Buy and Wal-Mart (Bustillo 2012; Zimmerman 2012). The tendency to practice showrooming is also increased by the difficulty for consumers to evaluate some product attributes on the online environment (Mehra, Kumar, and Rahu 2013).

This research studies the showrooming phenomenon among retail consu-mers. Founded on the theory of planned behavior (TPB), the authors developed and tested a theoretical framework for the antecedents of consumer intentions to practice showrooming in the consumer electronics retail industry. The study focuses on this industry because consumer electronics is one of the most shopped categories online (ComScore 2012), thus it represents a market segment where consumers are more likely to practice showrooming (ComScore 2012; Zaubitzer 2013). The outline of this article is as follows. First, a review of literature, including a description of the showrooming concept and the main antecedent variables used to explain this phenomenon, is presented. Second, the theoretical framework (based on the TPB) is examined. Third, a series of hypotheses based on the proposed frame-work is described. Fourth, the method, analysis, and results of the study are explained. Finally, a discussion of the findings and their managerial implica-tions for retailers is presented, as well as the limitations of the study.

Literature review

Definition and state of showrooming

The main premise of e-commerce is to transform consumer behavior. The reduction of the discrepancies between supply and demand offered by e-commerce enables ubiquitous market space, known as u-commerce (Cox 2004). Consumer behavior is changing (Chiang and Dholakia 2003) due to technological advances such as the Internet, social networks, and mobile devices, which have transformed traditional consumption and allowed retai-lers to reach consumers through new touchpoints (Shankar et al. 2010). These consumers that use different retail channels are known as multichannel buyers (Zaubitzer 2013). The concept of showrooming is used to explain a

JOURNAL OF INTERNET COMMERCE 175

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purchase behavior based on the comparison of products and retailers, and has become a trend in the area of online consumer behavior, as stated by the National Retail Federation (NRF; Smith 2013). According to Richter (2014), showrooming refers to the practice of examining products in traditional retail stores or any other offline expositions and later purchasing the products online. In other words, “showroomers” visit a physical retail store, observe, touch, feel, and try a product, but they do not buy it. An opposite trend also exists and is called webrooming, in which consumers visit retail websites to compare prices, attributes, opinions, and warranties among brands, but purchase the desired product offline, at a physical store. That is, consumers research products online (RO = Research Online), but the final purchase occurs offline (PO = Purchase Offline) (Kramer 2014). A relevant aspect about showrooming is that price plays a decisive role in the purchase decision- making. Consumers are generally attracted to the lower prices on retail websites relative to those at physical stores. This can be explained by the fact that many online retailers do not incur as many expenses as most offline stores do (Kramer 2014).

Today, showrooming is a practice that suggests a further step in the pur-chasing process supported by consumers’ common sense. The phenomenon has caught the attention of retailers, who have started to take actions in order to solve this issue. Some actions focus on lowering product prices with the intention of reducing the gap between prices at the physical store and those offered online. According to a survey by Cetelem (2013), 23% of respondents confirmed to know what showrooming is, and 28% of respondents stated to have practiced showrooming in the past.

Marketing Vibes, a consulting firm, presented the following statistics regarding showrooming: (1) There was a rise of 156% in purchases using price comparison between online and offline stores from 2012 to 2014; (2) in 2012, 14% of consumers bought on a competitor’s website (and not on the website of the visited retailer’s store); (3) most buyers who practice showrooming are in the 25–34 age range, whereas 20% of buyers are in the range of 35–44 years old; and (4) 30% of consumers buy a product on Amazon’s website after obtaining information at a retail store (Vibes 2013). According to ComScore (2012), 50% of the customers who engaged in showrooming were between 25–34 years old, 72% of them stated that a reason for this behavior was lower online prices, and 45% of them stated that they wanted to view the product before buying it online. The most purchased items in showrooming were consumer electronics (63%), clothing and accessories (43%), books (29%), and appliances (22%), followed by toys, jewelry/watches, and others (Zaubitzer 2013).

Furthermore, the practice of showrooming has extended to the mobile environment through the use of mobile terminals both inside and outside stores (Kowatsch and Maass 2010). In a Google (2013) report about

176 F. REJÓN-GUARDIA AND C. LUNA-NEVAREZ

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consumers’ online behaviors, it was confirmed that 84% of smartphone buyers use their phones as aids during their purchase decision-making at a retail store. As stated by Parago’s (2013) report, 58% of smartphone adult users and one-third of American adult buyers practice showrooming on a regular basis. Another relevant fact from this report is that Amazon is the top online site where Americans compare smartphones. Moreover, price is a key variable that influences online purchasing behaviors, as 67% of buyers purchase their smartphones at a retail store when the price is similar to Amazon’s price plus a discount.

Among potential multichannel purchasing behaviors, it is important to distinguish loyal-research shoppers from competitive-research shoppers (Neslin and Shankar 2009). Loyal-research shoppers switch channels (i.e., offline to online or vice versa) during the purchasing process but stay with the same retailer all the time. On the other hand, competitive-research shoppers, also called free riders (Umit-Kucuk and Maddux 2010), use one retailer’s channel to gather product information and switch to another retailer’s channel to complete their purchase. This research focuses on competitive showrooming, that is, the practice of using the physical store as a source of information about products or services, and purchasing such products from a different retailer afterward.

Loyal showrooming represents a good opportunity for retailers, but research suggests that only 1.8% of “showroomers” engage in this type of behavior (Van Baal and Dach 2005). According to Neslin and colleagues (2006), loyal showrooming has been criticized because when retailers want to attract more consumers and direct them to their online store, it is likely that consumers switch to a competitor’s online store if the switching cost is low. In the same line, competitive showrooming may have serious conse-quences for retailers by bringing sales down and pressuring them to direct consumers to their online store (website). Thus, competitive showrooming may be considered a threat to the retail industry, particularly to small retailers.

Showrooming as a threat to small retailers

An important question to address regarding showrooming is how small busi-nesses without an online presence may overcome this market trend. Previous research has found a direct relationship between perceived price and the intention to use a distribution channel (Yu, Niehm, and Russell 2011). The price differences between channels and stores represent a critical factor that impacts consumers’ purchase decision-making and the benefits for retailers (Wolk and Ebling 2010). Hence, the practice of showrooming may negatively impact small businesses because specialty stores often offer more sticky prices and higher levels of service, which translates into higher costs that are difficult to handle for small retailers. Therefore, consumers may achieve significant


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savings by “showrooming,” which may be detrimental for small retailers who do not follow a multichannel strategy that includes online distribution.

To counteract the negative impact of showrooming and reduce this trend, retailers should try to reduce the differences between online and offline prices, and reinforce the training programs of their salespeople (Quint et al. 2013). Other authors suggest that retailers should keep price information up-to-date, offer an integrated experience that encompasses both the online and offline channels, ensure the free and quick shipping of products with clear delivery policies, build better relationships with customers, focus more on the consumer’s experience and less on product prices, and avoid making customers wait. In sum, small retailers must revitalize the in-store experience in order to overcome showrooming (Stephens 2013).

Theoretical model and hypotheses

TPB

A review of literature identified three main theories commonly used to predict the acceptance and use of technology: the TPB (Azjen 2011), the Technology Acceptance Model (TAM) (Davis 1986; Xioani and Prybutok 2003), and the Diffusion of Innovations (DOI) Theory (Boateng, Molla, and Heeks 2009). For this research, a theoretical model (based on the TPB) that analyzes some variables influencing retail consumers to practice showrooming was developed.

The TPB was proposed by Azjen (1985) as an extension of the theory of reasoned action (TRA) (Ajzen and Fishbein 1980). The main difference between the theories is that in the TPB, the individual has the possibility of controlling his behavior (Westaby, Versenyi, and Hausmann 2005). The TPB has been extensively used by researchers in the last 20 years and has pro-ven to be ideal to predict a great diversity of human intentions and behaviors in multiple situations (Ajzen and Fishbein 1980; Lutz 2011). Some attempts to improve the TPB have focused on its variables decomposition (DTPB) (Taylor and Todd 1995) and a reformulation of the theory (RTPB) (Ajzen 2002a, 2002b). Such efforts to perfect the predictive theoretical model have proven to be appropriate for innovation environments of technology acceptance and for the use of information systems (Taylor and Todd 1995; Kuo and Young 2008).

The TPB explains that an individual’s behavior is determined by his beha-vioral intentions (BI), attitudes toward the behavior (ATB), social influence (SI), subjective norms (SN), perceived behavioral control (PBC) (Ajzen 1991), previous experiences, beliefs on the behavior (Taylor and Todd 1995), and reasons for and against performing the desired behavior (Westaby et al. 2005). In consumer behavior literature, the TPB models have been


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extensively used in both the offline and online purchasing contexts (George 2004; Hansen, Jensen, and Solgaard 2004; Hsu et al. 2006). According to previous research, the TPB is adequate to explain how consumer attitudes toward practicing showrooming, the influence of subjective norms, and perceived behavioral control may predict consumer intentions to practice showrooming (Luo et al. 2014). The following section describes the main variables used on the proposed model as well as their interrelationships.

Reasons of use

A literature review suggests that reasons are the link between an individual’s beliefs, his global motivations (e.g., attitudes, subjective norms, and perceived control), and his behavioral intentions or intentions of use (Westaby et al. 2005). The theoretical framework proposes that reasons help consumers justify and defend their behaviors by influencing their motivations and global intentions (Westaby et al. 2005). This posits an important theoretical issue as the reason concept has been proven to have a predictive validity in the context of decision-making and judgment (Campion 1991; Pennington and Hastie 1988; Westaby et al. 2005). Therefore, reasons for and against performing a behavior serve as an important link between the attitudes, subjective norms, perceived control, behavioral intentions, and final behavior (Westaby et al. 2005). According to Inks and Mayo (2002), some consumers have reserva-tions about online shopping which can be negatively affecting the acceptance rates of the online channel. For this study, an analysis of previous research was done to determine the main variables making up the reasons for and against online purchasing, as such variables could affect consumers’ attitudes toward showrooming and their intentions to perform this behavior.

This research uses the most relevant behavioral reasons addressed by the e-commerce literature. The main reasons for consumers to purchase (or not to purchase) online include the following: 1. Reduced stress by purchasing online, which has a direct effect on consumers’

behavioral changes and an indirect effect on their decision-making and physical and psychological well-being (Moschis 2007). Research on stress has proved that under stressful conditions, consumers perform behaviors aimed to use resources more efficiently in order to have more control over their environment, such as restraining themselves from making unnecess-ary purchases (Durante and Laran 2016).

2. Reasons related to price differences between distribution channels which suggest the existence of lower online prices (Kannan and Kopalle 2001; Koufaris and Ajit Kambil 2001; Quint et al. 2013). The premise that consumers may purchase products online at lower prices relative to the offline channel becomes a reason for consumers to develop positive attitudes toward online purchasing and intentions to purchase products


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online. Regarding satisfaction, price has the highest impact on online shoppers’ satisfaction, which reinforces the argument that online shopping facilitates finding better deals, as consumers may compare online prices in an easier way (Abdul-Muhmin 2010).

On the other hand, among the reasons that negatively influence consumers’ attitudes and intentions to purchase online, the literature reveals researchers’ interest in studying some reasons against purchasing online, such as 3. Reasons related to the difficulties found by consumers in returning products

purchased online relative to those purchased at a physical store (Ofek, Katona, and Sarvary 2011; Maity and Arnold 2013). In general, loosening up return policies leads to a reduction in the number of products returned to the store, which favors the online purchasing process and improves customer satisfaction, especially for well-known brands (Walsh et al. 2016).

4. Reasons associated with longer delivery times for products purchased online (Li, Lu, and Talebian 2015). On-time delivery is one of the variables with the highest impact on customer evaluations and satisfaction (Dholakia and Zhao 2010), assuming that consumers are willing to pay for shipping costs as long as the product is delivered at the scheduled time (Abdul-Muhmin 2010).

5. Consumers’ need to touch or see the product due to the lack of online infor-mation about its physical attributes (Peterson, Balasubramanian, and Bronnenberg 1997; Burke 2002; Gurrea and Sanclemente 2014). Some research studies demonstrate how touching a product or a product catalog increases consumer persuasion in low-involvement product communica-tions (Peck and Wiggins Johnson 2011). Based on these theoretical contributions, the authors hypothesize the

following:

H1a: The reasons for purchasing online have a positive effect on consumer attitudes toward online purchases.

H1b: The reasons for purchasing online have a positive effect on consumer intentions to purchase on the store’s website.

H2a: The reasons against purchasing online have a negative effect on consumer attitudes toward online purchases.

H2b: The reasons against purchasing online have a negative effect on consumer intentions to purchase on the store’s website.

Compatibility with purchasing at the retail store

The compatibility with a user’s purchasing style measures the similarity between the online purchasing mode and the degree to which consumers believe that their purchase experience is compatible with their current styles, habits, and past experiences at physical stores (Moore and Benbasat 1991). In


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previous studies about compatibility in the e-commerce context, it was demonstrated that compatibility is a key factor for consumers’ adoption of e-commerce, especially when previous purchase experiences did not allow consumers to physically evaluate the product quality (Jarvenpaa and Todd 1996). Hence, there is a need for retailers to offer experiences that are com-patible to the product testing style at physical stores, for instance, using new technologies such as 3-D modeling (Peterson et al. 1997). Therefore, the perception of compatibility and enjoyment of online purchase experiences relative to purchase experiences at a physical store will influence consumers’ attitudes toward purchasing online. That is, the positive attitudes toward an online purchase may encourage consumers to perform the purchasing beha-vior (Jarvenpaa, Tractinsky, and Vitale 2000; Van der Heijden, Verhagen, and Creemers 2003) through the intention of using the retailer’s website to pur-chase (Jiang and Benbasat 2007). Thus, the authors hypothesize the following:

H3a: The compatibility with purchasing at the retail store will positively influence consumer attitudes toward online purchases.

H3b: The compatibility with purchasing at the retail store will positively influence consumer intentions to purchase on the store’s website.

Perceived control

Perceived control has been analyzed from different perspectives in order to explain its influence on consumer behaviors (Koufaris 2002). A literature review revealed that perceived control was incorporated to the TPB to improve the prediction of behavioral intentions in cases of willingness (Ajzen and Madden 1986; Millstein 1996), and it also represents one of the main antecedents of technology use (Teo et al. 2009).

From the perspective of flow in online navigation, perceived control can be defined as “the degree of control over one’s actions and the environment” (Koufaris 2002, 208). In the e-commerce context, there is massive information about products available, and consumers have less time for shopping (Koufaris 2002), which causes utilitarian consumers to require more control, less effort, and higher efficacy for their purchases (Jarvenpaa and Todd 1996; Tracy 1998). Therefore, websites must offer consumers a higher control and convenience by providing interfaces that are simple and easy to navigate, facilitate product search and transactions, and allow consumers to easily understand their use and content (Baty and Lee 1995). In this way, a high degree of perceived control must foster an individual’s intention to perform a behavior, increasing his effort and perseverance (Ajzen 2002a). If consumers think that showrooming is difficult to do due to the difficulties found on the store’s website, their degree of perceived control may be lower (Luo et al. 2014). Thus, the perceived control may translate into a behavior only if the


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consumer has the time, abilities, willingness, and other resources needed to perform the behavior. This research considers that the environment perceived by consumers on a store’s website differs significantly from that of the physical store, and that the perceived control variable suggests a measure of self-efficacy in performing a specific behavior, which can vary across different events or actions (Bandura 1982). As a consequence, consumers’ perceived control over the action of performing showrooming will influence their behavioral intentions. Thus, the authors hypothesize the following:

H4a: Consumers who perceive a higher control over performing showrooming will have a more positive attitude toward online purchases.

H4b: Consumers who perceive a higher control over performing showrooming will have a higher intention to purchase on the store’s website.

Online purchase experience and showrooming

Considering that consumer behavior results from learning (Bentler and Speckart 1979), some researchers argue that past consumption behaviors may provide a good prediction of behavioral intentions (Armitage and Conner 2001). Some authors defend the influence of past purchase experiences on consumer attitudes toward online purchases and intentions to purchase online (May So, Danny Wong, and Sculli 2005; Huang and Hsu 2009). Therefore, this research views showrooming as an opportunistic online behavior that increases with consumer learning. Hence, the authors hypothesize the following:

H5a: Consumers’ showrooming experience has a positive influence on attitudes toward online purchases.

H5b: Consumers’ showrooming experience has a positive influence on intentions to purchase on the store’s website.

Subjective norms

Subjective norms refer to the perceived social pressure to perform or not to perform a behavior, and may be defined as the degree to which the people who are important to the user think a specific behavior should be performed (Ajzen 1991). In other words, subjective norms relate to the normative beliefs about people’s expectations. For Ajzen (1991), a person of reference’s opinion is weighed by the motivation that an individual has to comply with the desires of such person. For example, a consumer may utilize a communication medium to obtain information or make a purchase if he considers that other influencing people demand such behavior, or if the user observes other people around him performing the behavior. For this research, subjective norms are


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defined as the degree to which a person perceives other people’s demands, measured by the “relevance” that they give to the person’s use of a specific technology. Previous research establishes a positive relationship between sub-jective norms and purchasing behaviors, and between subjective norms and attitudes. In the TPB, subjective norms are a main antecedent of intentions (Ajzen and Madden 1986). Thus, if most consumers think that practicing showrooming is an acceptable behavior, they will develop a stronger intention toward such behavior (Luo et al. 2014). For that reason, the authors hypothe-size the following:

H6a: Consumers who perceive a higher social pressure from people of reference will have a more positive attitude toward online purchases.

H6b: Consumers who perceive a higher social pressure from people of reference will have a higher intention to purchase on the store’s website.

Attitudes toward online purchasing and consumers’ intentions to purchase on the store’s website

The proposed theoretical framework adopts a cognitive-affective approach to explain the formation of consumers’ attitudes toward purchasing online or at a physical store. This approach has been applied in several e-commerce research studies and is useful to identify human responses in a holistic way (Eagly, Mladinic, and Otto 1994; Kempf 1999; Onurbodur, Brinberg, and Coupey 2000). Literature reveals that consumer intentions toward online purchase behaviors are mainly determined by the attitude toward the online

Figure 1. Proposed theoretical model.


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store or the attitude toward purchasing on a particular website. Thus, positive attitudes toward the purchasing process, a brand, or a product will encourage consumers to perform a purchase behavior and increase the likelihood that

Table 1. Scales. Variables Items Source(s)

Reasons for purchasing online

In regards to online shopping, I think that: .� Shopping online reduces my stress .� Online prices are generally lower than

in-store prices

Peterson et al. (1997), Burke (2002); O’Connor (2003); Moschis (2007); Ofek, Katona, and Sarvary (2011); Maity and Arnold (2013);Quint et al. (2013); Gurrea and Sanclemente 2014; Li, Lu, and Talebian (2015), Kannan and Kopalle (2001); Koufaris and Ajit Kambil (2001)

Reasons against purchasing online

In regards to online shopping, I think that: .� It is more difficult to return a product

purchased online than a product pur-chased in-store

.� Not being able to see the product when purchasing online is a problem for me

.� Shopping at a physical store is quicker than shopping online

Compatibility with purchasing at retail store

.� Evaluating products online is similar to evaluating them at a physical store

.� Evaluating products online is consistent with how I like to evaluate products at the physical store

.� Becoming familiar with products online is similar to becoming familiar with products at the physical store

Jiang and Benbasat (2007)

Perceived control

During my last visit to the online store, I felt: .� Confused .� Calm .� In control .� Frustrated

Koufaris (2002)

Showrooming experience

.� Have you purchased a product on a store’s website after visiting the physical store to see and/or try the product?

Luo et al. (2014)

Social influence: subjective norms

.� People that usually influence my buying behavior think that I should purchase products online

.� People that matter to me think that I should purchase products online

Venkatesh et al. (2003)

Attitudes toward online purchasing

In regards to the store’s website that you are visiting,

.� I like the idea of using Internet to make purchases on the store’s website

.� Using the Internet to purchase products on the store’s website is a good idea

.� I believe that the final outcome of purchasing products online should be positive.

Lee et al. (2006)

Intention to purchase on the store’s website

.� I have the intention to use the website of a store in order to purchase products in the near future

.� My prediction is that I will use the website of a store to purchase products in the near future.

.� I am going to use the website of a store to purchase products in the near future.

Venkatesh et al. (2003)


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such behavior occurs (Jarvenpaa et al. 2000; Van der Heijden et al. 2003; Jiang and Benbasat 2007). Regarding the online environment, Jarvenpaa and collea-gues (2000) found that favorable attitudes toward purchasing on a store’s website increase the likelihood of purchasing at the store, and consequently, the attitude toward the store will influence purchase intentions (Grazioli and Jarvenpaa 2000; Coyle and Thorson 2001). Other studies evaluated purchase intentions through websites and concluded that online purchasing is influenced by utilitarian value, attitudes toward online shopping, availability of information, and hedonic values (Khare and Rakesh 2011). The attitude toward the retail store is defined as the belief that purchasing at the store will be very likely to produce an overall positive result or an overall negative result (Jarvenpaa et al. 2000; Lim et al. 2006).

This research considers the attitude toward purchasing online as a global evaluation of past online purchase experiences. Thus, the authors hypothesize the following:

H7: A more positive attitude toward online purchasing will have a greater influence on consumers’ intention to purchase on the store’s website.

Behavioral intentions (intentions of use)

The behavioral intention (BI) variable is deemed “the factor that captures the intensity with which an individual will try to perform a behavior” (Ajzen 1991, 181). In the TPB, the behavioral intention is the most influencing predictor of a behavior or final use. This was confirmed by Armitage and Conner (2001), who examined 185 empirical studies published up to 1997 and found that the TPB captures between 27% and 39% of the variation of behavior and intention of use. Therefore, among of the TPB constructs, beha-vioral intention has proven to be the best predictor of effective behavior. For the proposed research model, intention to practice showrooming represents the main endogenous variable. Figure 1 shows the theoretical model which reflects all proposed hypotheses, and Table 1 describes the measures utilized.

Methodology

Sample and data collection

One-hundred-and-seventy-six retail consumers (77 male and 99 female) participated in a survey about the state of showrooming in the consumer electronics retail environment. Participants were selected using a stratified non-probabilistic sampling method in which the control variables were age and gender. Respondents were recruited at the entrance of some of the main consumer electronics stores in the province of Granada, Spain. Data were


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collected using questionnaires administered by a group of market research professionals from February to March 2015. Table 2 summarizes the technical details of the study. Participants were between the ages of 18 and 70 years old, with an average of 35 years (see Table 3 for demographic characteristics of the sample).

Survey measures

To assure the content validity of the measures used in the survey, the authors reviewed previous research studies and adapted the scales accordingly. They used a 7-point Likert-type scale, ranging from Strongly Disagree to Strongly Agree. The indicators for the online purchase experience factor were drawn from Hui, Teo, and Tom Lee (2007). The indicators used to measure the

Table 3. Sample demographics. Gender n %

Male 77 43.8 Female 99 56.3 Total 176 100.0

Age 18–25 56 31.8 26–31 30 17.0 32–45 48 27.3 46–57 34 19.3 >58 8 4.5

Income level 2,100 € 36 20.5

Level of education None 3 1.7 Primary school 14 8.0 Secondary school 57 32.4 High school or above 102 58.0 Employment status

Unemployed 17 9.7 Student 50 28.4 Housewife 13 7.4 Employed 66 37.5 Self-employed 28 15.9 Retired 2 1.1

Number of purchases in the last year: X = 8.24; DT = 22.166.

Table 2. Sampling and data collection. Population Retail customers

Population size Sampling method Convenience and quota sampling Questionnaire type Administered by researchers (CAPI) Sample size 176 participants Data collection time January 2015


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compatibility with the purchases at the store were adapted from the scale proposed by Jiang and Benbasat (2007). Regarding the evaluation of reasons for and against online purchasing, due to the lack of empirically tested scales in the literature, the authors used individual items tested in previous studies by Peterson and colleagues (1997); Burke (2002); O’Connor (2003); Moschis (2007); Ofek, Katona, and Sarvary (2011); Maity and Arnold (2013); Quint and colleagues (2013); Gurrea and Sanclemente (2014); Li, Lu, and Talebian (2015); Kannan and Kopalle (2001); and Koufaris and Ajit Kambil (2001). These items included the following, regarding purchasing on a store’s website: (a) purchasing online reduces my stress, (b) online prices are regularly lower than those at the physical store, (c) it is more difficult to return the product compared to an in-store purchase, (d) not seeing the product when buying at the store is a problem for me, and (e) purchasing the product at the physical store is faster. The indicators to measure the social influence of subjective norms were adapted from the scale proposed by Venkatesh and colleagues (2003). As for the measurement of perceived control, the indicators proposed by Koufaris (2002) were adapted to this context. Regarding the scale for attitudes toward the retail store, the authors used the items developed by Lim and colleagues (2006). Finally, to measure the intention of using the web to perform showrooming, the scale proposed by Venkatesh and colleagues (2003) was adapted to the study.

Data analysis and results

Scale validation

The model was tested using structural equation modeling (SEM), which utilizes a component estimation technique based on partial least squares (PLS). Such technique is similar to regression analysis and uses structural models that focus on the theoretical relationships between latent variables and their measures. This method is mainly used for prediction in causal analyses based on variance, unlike other techniques that are based on covari-ance such as LISREL or AMOS (Chin, Marcolin, and Newsted 1996, 25). It has been also extensively used in marketing (Jarvis et al. 2003; Albers 2010; Vinzi et al. 2010). One of its main advantages is that it does not require a large sample size, and it is adequate for the early stages of theory development (Barclay, Higgins, and Thompson 1995; Chin et al. 1996). The characteristics and advantages of this multivariable analysis method have been widely discussed in literature (Sarstedt, Henseler, and Ringle 2011). Given the characteristics of this study and the variables proposed, PLS represents an appropriate technique for the evaluation of the model.

For data analysis, the SmartPLS 2.0.M3 software was used (Ringle, Wende, and Will 2005; Ringle, Wende, and Will 2010). Three types of validity were


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tested, namely content validity, convergent validity, and discriminant validity. Content validity was assessed by using previously tested scales, standard procedures for scale adaptation, and academic experts. The scales showed convergent validity, assessed by the Cronbach’s alpha, composite reliability (CR), and average variance extracted (AVE), which were greater than the minimum accepted values in the literature (0.70, 0.70, and 0.50, respectively) (Fornell and Larcker 1981), with the exception of the reasons for and against showrooming variable, which showed a Cronbach’s alpha lower than 0.70, and the compatibility with the retail store and perceived control variables which showed values near 0.70. Regarding CR, all proposed scales showed values equal to or higher than the minimum recommended values with the exception of the reasons for showrooming. As for the AVE, all variables but perceived control were above the recommended values.

Table 4 provides information about the number of items, Cronbach’s alpha, CR, AVE, and reliability of constructs. All constructs showed convergent validity in the empirical context. Discriminant validity was assessed through the comparison between the square-root of AVE (with the correlations of each latent variable) and the other model constructs, and through the analysis of the correlations within constructs and between indicators and constructs (Fornell and Larcker 1981; Barclay et al. 1995). Results demonstrated that the scales clearly showed discriminant validity. Table 5 shows the correlations within constructs and compares them to the square-root of AVE. Overall, the results firmly support the content validity of the study, convergent validity, and discriminant validity of the scales and scale items.

The correlation matrix did not show any highly correlated variable (the highest correlation between the main constructs is r = 0.507). The common method biased test generally shows a high correlation (r > 0.90) (Pavlou and El Sawy 2006). Moreover, it was very unlikely that collinearity problems occurred, as the PLS algorithm is a reflexive mode for all constructs (Chin et al. 1996). The proposed research model was tested with SmartPLS 2.0.3. In order to estimate path significance, the authors used a bootstrapping procedure with 500 subsamples.

Overall, there was significant evidence to demonstrate convergent validity of the scales, with the exception of the reasons for showrooming variable, which was deleted from the model. The reliability of all items was acceptable with the exception of item 4 of the perceived control scale, which was finally discarded.

Structural model

Following Falk and Miller (1992), it was demonstrated that the R2 of latent variables is higher than 0.1 before accepting or rejecting the proposed hypotheses. Considering that the main objective of PLS is prediction, the


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Tabl

e 4.

Go

odne

ss-o

f-fit

indi

ces

for

the

estim

ated

mod

el.

Fact

or

Indi

cato

r M

ean

Stan

dard

D

evia

tion

λ Cr

onba

ch

alph

a CF

I AV

E

Reas

ons

for

onlin

e sh

oppi

ng

Shop

ping

onl

ine

redu

ces

my

stre

ss

4.06

1.

973

0.

756

0.

079

0.

684

0.

521

Onl

ine

pric

es a

re g

ener

ally

low

er t

han

in-s

tore

pric

es

4.99

1.

481

0.

685

Reas

ons

agai

nst

onlin

e sh

oppi

ng

It is

mor

e di

fficu

lt to

ret

urn

a pr

oduc

t pu

rcha

sed

onlin

e th

an a

pro

duct

pu

rcha

sed

in-s

tore

4.

50

1.76

6

0.64

4

0.57

1

0.77

7

0.54

0

Not

bei

ng a

ble

to s

ee th

e pr

oduc

t whe

n pu

rcha

sing

onlin

e is

a pr

oble

m fo

r me

4.

74

1.85

2

0.78

6 Sh

oppi

ng a

t a

phys

ical

sto

re is

qui

cker

tha

n sh

oppi

ng o

nlin

e

4.73

2.

065

0.

765

Com

patib

ility

with

pu

rcha

sing

at r

etai

l sto

re

Eval

uatin

g pr

oduc

ts o

nlin

e is

simila

r to

eva

luat

ing

them

at

a ph

ysic

al s

tore

3.

60

1.66

0

0.90

1

0.65

4

0.85

0

0.74

0 Ev

alua

ting

prod

ucts

onl

ine

is co

nsist

ent w

ith h

ow I

like

to e

valu

ate

prod

ucts

at

the

phys

ical

sto

re

3.60

1.

691

0.

817

Beco

min

g fa

mili

ar w

ith p

rodu

cts

onlin

e is

simila

r to

bec

omin

g fa

mili

ar w

ith

prod

ucts

at

the

phys

ical

sto

re

Perc

eive

d co

ntro

l Co

nfus

ed

5.16

1.

929

0.

910

0.

640

0.

714

0.

531

Calm

4.

81

1.97

6

0.45

3 In

con

trol

4.

42

2.28

1

0.74

8 Fr

ustr

ated

5.

32

1.90

3

0.29

7 Sh

owro

omin

g ex

perie

nce

Have

you

pur

chas

ed a

pro

duct

on

a st

ore’

s w

ebsit

e af

ter

visit

ing

the

phys

ical

st

ore

to s

ee a

nd/o

r tr

y th

e pr

oduc

t?

4.03

2.

413

1

1

1

1

Soci

al in

fluen

ce: S

ubje

ctiv

e no

rms

Peop

le t

hat

usua

lly in

fluen

ce m

y bu

ying

beh

avio

r th

ink

that

I sh

ould

pur

chas

e pr

oduc

ts o

nlin

e

3.70

1.

749

0.

940

0.

765

0.

890

0.

803

Peop

le t

hat

mat

ter

to m

e th

ink

that

I sh

ould

pur

chas

e pr

oduc

ts o

nlin

e

4.03

1.

782

0.

849

Attit

udes

tow

ard

onlin

e pu

rcha

sing

I lik

e th

e id

ea o

f us

ing

Inte

rnet

to

mak

e pu

rcha

ses

on t

he s

tore

’s w

ebsit

e

4.42

1.

581

0.

875

0.

833

0.

897

0.

743

Usin

g th

e In

tern

et t

o pu

rcha

se p

rodu

cts

on t

he s

tore

’s w

ebsit

e is

a go

od id

ea

4.81

1.

515

0.

857

I bel

ieve

tha

t th

e fin

al o

utco

me

of p

urch

asin

g pr

oduc

ts o

nlin

e sh

ould

be

posit

ive.

4.

83

1.46

4

0.85

5

Inte

ntio

n to

pur

chas

e on

th

e st

ore’

s w

ebsit

e I h

ave

the

inte

ntio

n to

use

the

web

site

of a

sto

re in

ord

er to

pur

chas

e pr

oduc

ts

in t

he n

ear

futu

re

4.88

1.

581

0.

970

0.

963

0.

976

0.

932

My

pred

ictio

n is

that

I w

ill u

se t

he w

ebsit

e of

a s

tore

to

purc

hase

pro

duct

s in

th

e ne

ar f

utur

e.

4.85

1.

515

0.

960

I am

goi

ng to

use

the

web

site

of a

sto

re to

pur

chas

e pr

oduc

ts in

the

near

futu

re.

4.69

1.

464

0.

965

CFI =

Com

para

tive

Fit

Inde

x.

189

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Tabl

e 5.

D

iscrim

inan

t va

lidity

of

scal

es (

corr

elat

ions

bet

wee

n co

nstr

ucts

, squ

are-

root

of

AVE,

dia

gona

l in

bold

).

Attit

udes

tow

ard

on

line

pu

rcha

sing

Com

patib

ility

Pe

rcei

ved

co

ntro

l In

tent

ion

of

use

Su

bjec

tive

no

rms

Reas

ons

for

…

Reas

ons

agai

nst

…

Show

room

ing

Attit

udes

tow

ard

onlin

e pu

rcha

sing

0.

862

Com

patib

ility

0.

217

0.

860

Perc

eive

d co

ntro

l 0.

410

0.

160

0.

728

In

tent

ion

of u

se

0.32

4

0.24

3

0.23

1

0.96

5

Subj

ectiv

e no

rms

0.19

1

0.10

1

0.14

1

0.20

7

0.89

6

Reas

ons

for

show

room

ing

0.

213

0.

175

0.

100

0.

499

0.

217

0.

721

Re

ason

s ag

ains

t sh

owro

omin

g

−0.2

67

−0.1

73

−0.2

55

−0.4

62

−0.1

30

−0.5

07

0.73

5 Sh

owro

omin

g

0.15

7

0.15

4

0.01

6

0.44

0

0.12

1

0.33

3

−0.3

14

1

190

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Stone-Geisser (Q2) test was used to assess the predictive relevance of constructs. A positive Q2 demonstrates predictive relevance, that is, the dependent construct value may be predicted from the independent variables proposed in the model (Chin 1998). In this case, it was confirmed that the Q2 values for all constructs provide evidence that the model has predictive relevance, with the exception of the reasons against showrooming construct with a value near 0. (Attitude toward the retail store: Q2 = 0.464; Compatibility: Q2 = 0.231; Perceived control: Q2 = 0.151; Intention of use: Q2 = 0.829; Subjective norms: Q2 = 0.375; Reasons against showrooming: Q2 = 0.090). Finally, Table 6 shows the estimation of the structural model and the coefficients for each proposed hypothesis. Hypotheses H2a, H2b, H3a, H3b, H4a, H5a, H5b, H6a, and H7 were supported at a confidence level of 95%. Figure 2 shows the final model.

Table 6. Structural model estimation. Attitudes: β p value Intention of use: β p value

Reasons against −0.119 (1.978)* 0.024 −0.285 (3.889) 0.000 Compatibility 0.118 (2.109) 0.018 0.098 (1.537) 0.062 Perceived control 0.344 (3.961) 0.000 0.071 (1.338) 0.091 n.s. Showrooming experience 0.082 (1.535) 0.063 0.303 (4.789) 0.000 Subjective norms 0.105 (1.499) 0.067 0.088 (1.978) 0.072 n.s. Attitude — — 0.133 (2.138) 0.016

*t value in parentheses.

Figure 2. Final model.


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Discussion and implications

Since its emergence, the Internet has become one of the main sources of infor-mation for consumers. Today, the literature shows evidence of an existing purchase behavior in which consumers acquire information about a product at the physical store and later purchase the product online. Such behavior represents a threat for retailers who do not use a multichannel strategy or take advantage of the Internet as a marketing channel. This research analyzes the state of the showrooming phenomenon in retailing. For this purpose, the authors developed a theoretical model of consumers’ intention to perform showrooming based on the TPB, which includes several proposed variables and their interrelationships.

A preliminary question that this research addresses is whether consumers know the showrooming term. In sum, 67.3% of respondents stated they did not to know about showrooming, but 48.5% of respondents mentioned they acquire information about products and try them at physical stores, but com-plete their purchase on the stores’ websites or other competitors’ websites.

One of the main contributions of this study refers to the use of the TPB to explain a scarcely researched planned behavior (Richter 2014), namely show-rooming, in which consumers make an online purchase after visiting, observ-ing, and comparing products at a physical store. A second contribution relates to the incorporation of variables not extensively used in literature into the theoretical model, such as the reasons for and against a behavior and the com-patibility with in-store purchasing (Jiang and Benbasat 2007). To summarize, the proposed causal relationships based on reasons for attitudes and purchase intention (H1a and H1b) were not statistically significant, as evidenced in previous research (Westaby and Braithewaite 2003; Westaby et al. 2005). Conversely, the reasons against showrooming were found to be a negative direct antecedent of purchase intention (H2b) and attitudes toward the retail store (H2a) as stated by Westaby and colleagues (2005). Based on these results, retailers should minimize the difficulties (i.e., reasons against showrooming) of consumers regarding product returns, especially through the online channel. Moreover, improving delivery times for online purchased products may lead consumers to perceive that the purchasing process at a physical store is faster (Li, Lu, and Talebian 2015). Finally, the impossibility of consumers to see the product they are planning to buy online represents an important reason for not purchasing online, thus it would be critical for online retailers to include more descriptive graphical information about their products through the use of photographs, videos, and/or 3-D models (Peterson et al. 1997; Burke 2002; Gurrea and Sanclemente 2014).

This study confirms the important role of the compatibility between the online purchase and the purchase at a physical store in the practice of showrooming. This variable was found to have a direct positive relationship


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with the attitude toward the retail store, as proposed by Jiang and Benbasat (2007). This finding supports the argument that online retailers should mimic the processes and aesthetics of their physical stores so that consumers may perceive that the online purchase environment is similar the purchase environment at the physical store. To extend the compatibility across chan-nels, the authors suggest the creation of websites oriented to simplicity and usability, the use of virtual shopping carts, and the categorization of products using a structure similar to that used by department stores.

With regard to the antecedent variables analyzed in the model, perceived control has been confirmed as the main antecedent of attitudes toward the retail store (H3a), as proposed in literature (Westaby et al. 2005). This finding implies that if retailers want to increase the positive attitudes toward purchasing on their websites, consumers’ perceived control should be increased. To do so, it is recommended that retailers improve their websites’ usability, with a main focus on facilitating consumers’ navigation on the websites. The results demon-strate that there is not a direct relationship between perceived control and intention to use the website, based on the rejection of hypothesis H4b. Thus, perceived control has a greater influence on attitudes relative to purchase inten-tions, which contradicts the arguments proposed by Luo and colleagues (2014).

Regarding consumers’ previous experience with showrooming, the authors found it the main antecedent of intentions of use (H5b) and on a lower scale, an antecedent of the attitude toward purchasing online (H5a) (May So et al. 2005; Huang and Hsu 2009). This confirms that learning based on past behaviors may be helpful to predict intentions of future behaviors (Conner and Armitage 1998). The results suggest to retailers that their customers’ prac-tice of showrooming increases the chances of repeating this behavior. Thus, the authors recommend an in-depth analysis of customers’ showrooming behaviors in order to understand their unmet needs and reasons for perform-ing this behavior. With this information, a retailer may direct customers to its own website, increase their loyalty, and reduce their intention to visit other competitors’ websites.

Social influence through subjective norms demonstrates that such norms are important to improve consumers’ attitudes toward online purchases (H6a) but not to influence their online purchase behavior (H6b), which con-tradicts the argument proposed by Luo and colleagues (2014). An explanation for this result is that for some consumers, the online purchase process represents a lonely act in front of a computer or mobile device, such that their final purchase decision is not influenced by others but their opinions still effect their attitudes considerably (Ickler et al. 2009). Hence, controlling the influence of others, especially online actors such as bloggers, analysts, and journalists, may be interesting to understand the effect of subjective norms on showrooming. Finally, as demonstrated in e-commerce literature that uses the TPB, the attitude toward online purchases is directly and positively


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associated to the intention of web use (H7) (Armitage and Conner 2001). A summary of all hypotheses and results is shown in Table 7.

As for the implications of this research, a relevant question is, how can a small retail store compete against showrooming? Most small retailers have focused on customer service. The authors provide the following recommenda-tions: (1) to improve the customer experience by offering an aesthetic and highly-usable website that includes a detailed analysis of products and price comparisons, and integrating the online and offline channels effectively (e.g., free Wi-Fi, discounts on mobile purchases); (2) to increase consumers’ perceived control (which would subsequently improve their attitudes toward the retail store) by enhancing the product return process, increasing the “tangibility” of the product, showing graphic information about products that is as realistic as possible, and reducing product delivery times; (3) to maximize the compatibility between the online purchase and the purchase at the physical store by maintaining the web environment as similar as possible to the retail store environment, using virtual shopping carts, categorizing products effectively, offering similar payment procedures, and using bloggers and other “influencers” to provide advice on the online channel; and (4) to increase purchase likelihood by identifying and targeting loyal customers, understanding their reasons for purchasing, reducing their reasons against purchasing, and improving their overall attitudes toward the store.

Table 7. Summary of hypotheses. Hypotheses Decision

H1a: The reasons for purchasing online have a positive effect on consumer attitudes toward online purchasing.

Not supported

H1b: The reasons for purchasing online have a positive effect on consumer intentions to purchase on the store’s website.

Not supported

H2a: The reasons against purchasing online have a negative effect on consumer attitudes toward online purchasing.

Supported

H2b: The reasons against purchasing online have a negative effect on consumer intentions to purchase on the store’s website.

Supported

H3a: The compatibility with purchasing at the retail store will positively influence consumer attitudes toward online purchasing.

Supported

H3b: The compatibility with purchasing at the retail store will positively influence consumer intentions to purchase on the store’s website.

Supported

H4a: Consumers who perceive a higher control over performing showrooming will have a more positive attitude toward online purchasing.

Supported

H4b: Consumers who perceive a higher control over performing showrooming will have a higher intention to purchase on the store’s website.

Not supported

H5a: Consumers’ showrooming experience has a positive influence on attitudes toward online purchasing.

Supported

H5b: Consumers’ showrooming experience has a positive influence on intentions to purchase on the store’s website.

Supported

H6a: Consumers who perceive a higher social pressure from people of reference will have a more positive attitude toward online purchasing.

Supported

H6b: Consumers who perceive a higher social pressure from people of reference will have a higher intention to purchase on the store’s website.

Not supported

H7: A more positive attitude toward online purchasing will have a greater influence on consumers’ intention to purchase on the store’s website.

Supported


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Limitations and future research

This research has some limitations that could be addressed in future research. First, a different scale for the reasons for and against using the website variable is recommended because the validity scores achieved in this research were not high. Second, the sample size used in this research is relatively small. Thus, future research should consider increasing the sample size and representative-ness by extending participants’ selection to other geographic areas. A larger sample size would allow researchers to use other calculation techniques based on covariance such as those used by SEM estimation models in LISREL or AMOS. Lastly, the use of PLS did not allow the authors to use adjustment indices for the model, which can be addressed in future research studies.

Regarding future lines of research, the authors suggest extending this study to other product categories. For example, it would be interesting to analyze potential differences in the practice of showrooming across different product categories, such as frequently purchased products, seasonal products, and/or experiential products. Furthermore, it would be important to analyze the impact of demographic differences on the practice of showrooming, as demo-graphic variables appear in literature as some of the main moderators of the TPB. Moreover, further research could examine cross-cultural differences in the practice of showrooming by replicating this study in other geographic areas.

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