Definition and psychometric validation of a measurement index common to website and store images

JBR-08053; No of Pages 20 Journal of Business Research xxx (2014) xxx–xxx

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Journal of Business Research

Definition and psychometric validation of a measurement index common to website and store images☆ Christophe Bèzes ⁎ ISTEC Business School, Paris, France

a r t i c l e

i n f o

Article history: Received 4 March 2013 Received in revised form 10 March 2014 Accepted 13 March 2014 Available online xxxx Keywords: Store image Website image Reflective scale Formative index

a b s t r a c t To mesh together their channels and better handle their multichannel management, retailers must better evaluate and coordinate their various channels. They need more precise and more complete measurement instruments to compare the channels in terms of characteristics perceived by their customers. Using the procedure specific to formative variables, this research results in a particularly comprehensive measurement index that culls 10 channel image dimensions (offering, price, layout, accessibility, promotions, customer service, advice, reputation, institution, connections with other channels) from the 40 strictly identical items for the website and the stores. Various qualitative and quantitative surveys examine across several samples of customers of a major French multichannel retailer, including one sample of 1,478 respondents. This study delineates the scope of website and store images and defines reliable scales for evaluating each image dimension of the channels, including those totally missing from the literature. This measurement tool harnesses website and store characteristics that are very operational and easily actionable. This study demonstrates that a website can be described and analyzed along the same lines as a store, and shows the relevance of incorporating connections with other channels, particularly into the scope of website image. © 2014 Published by Elsevier Inc.

Introduction With e-commerce booming, multichannel distribution has become a reality for a growing number of firms across the world, particularly so for brick-and-mortar retailers. Nearly 80% of US retailers operate across several sales channels (Kilcourse & Rowen, 2008). After arranging their channels into silos (Rangaswamy & Van Bruggen, 2005), these retailers are now engaging in multichannel customer management. To enhance their customer value and build loyalty, managers must better evaluate and coordinate their various channels (Neslin et al., 2006). Thus multichannel retailers and researchers need more precise and more complete measurement instruments to compare the channels not only in terms of performance (Gensler, Dekimpe, & Skiera, 2007; Zhang et al., 2010) but also in terms of characteristics perceived by their customers (Hu & Jasper, 2007; Morschett, Swoboda, & Foscht, 2005). Yet prior channel compartmentalization has usually led researchers and managers to analyze and improve the website and the stores disconnectedly (Nicholson, Clarke, & Blakemore, 2002;

☆ The author is deeply grateful to the two anonymous reviewers for their constructive comments and to Bertrand Belvaux (University of Burgundy, France) for his invaluable help throughout this research. ⁎ ISTEC, 12 rue Alexandre Parodi, 75010 Paris, France. E-mail address: [email protected].

Verhagen & Van Dolen, 2009). This channel disconnection does not help understand multichannel behaviors or monitor channels and their “synergistic impact” within one same retailer (Weinberg, Parise, & Guinan, 2007, p. 392). That is, for consumers, the choice of the shopping channel is no longer monolithic but related to the alternative channel (Choudhury & Karahanna, 2008; Van Birgelen, de Jong, & de Ruyter, 2006). Store image determines the customers' perception of the retailer (Morschett et al., 2005). Applied to merchant websites (Spiller & Lohse, 1997), image may have a greater impact online than in-store (Biswas & Biswas, 2004). Yet, most researchers in e-commerce have so far focused on the transaction process, overlooking the retail function of merchant sites. Thus they overestimate the importance of the concept of website quality in online purchase behaviors. But the success of a website is related not only to its quality but also more broadly to its perceived image (Belanger et al., 2006). For this reason, this study, which focuses on the perceived image of channels, builds on Dickinger and Stangl's (2013) work on site quality. Still, existing research does not offer any measurement instrument that might help researchers and managers to compare the perceived image of stores and merchant websites, in a similar way, that is, on strictly identical grounds. These conceptual and methodological gaps pose three main challenges: 1) at a conceptual level, the structural equivalence (basis of measurement invariance) between these two constructs is never actually complied with, which limits the possibility to

http://dx.doi.org/10.1016/j.jbusres.2014.03.016 0148-2963/© 2014 Published by Elsevier Inc.

Please cite this article as: Bèzes, C., Definition and psychometric validation of a measurement index common to website and store images..., Journal of Business Research (2014), http://dx.doi.org/10.1016/j.jbusres.2014.03.016

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accurately compare the perception of the two channels as well as its effects; even in their modeling of the effects of store and website images, Verhagen and Van Dolen (2009) utilize different image constructs for these two channels and do not include classic image dimensions such as price or promotions; 2) at an operational level, nailing down offering, price or discount gaps between these two channels is well-nigh impossible; nor does the absence of identical measurement scales help single out which site and store characteristics must remain completely congruent to smooth out customers' cross-channel path and build the retailer's image (Bèzes, 2013a; Müller, 2008); 3) in a multichannel environment that complicates choices between several alternatives (what channel to buy in?), individuals can more easily compare alternatives with easily alignable characteristics (Chakravarti & Janiszewski, 2003). During an analytical evaluation (Bettman & Kakkar, 1977), they prefer to compare channels attribute-by-attribute (Choice by Processing Attributes) rather than to examine all the attributes of a channel and then all those of another channel (Choice by Processing Channels). Still, the first method of evaluation, which conveniently halves the apparent length of the questionnaire and limits the risk of irritating respondents with annoying repetitive questions, is possible only if all the items describing the two channels are strictly identical. These three reasons most likely explain why some multichannel retail specialists develop identical measurement scales to compare online and offline search and purchase motives (Verhoef, Neslin, & Vroomen, 2007), examine the influence of offline service quality on online service quality (Yang, Lu, & Chau, 2013) or to analyze trust and attitude transfers from store to website (Badrinarayanan, Becerra, Kim, & Madhavaram, 2012). The few measurement scales, even the recent ones, of the perceived image of stores or merchant websites are always built for the restrictive purposes of an empirical study. They never take into consideration as many image dimensions or transpose them as is from one channel to the other. In contrast to previously published measurement instruments, the very comprehensive index developed and validated here, has the advantage of applying to both website and store images. Thus, all types of retailers (brick-and-click, brick-and-mortar, click) can use this index. This research also demonstrates that all the dimensions considered fit into the focal concept of channel image (website or store), which no other previous study has proven from a psychometric perspective. In this respect, this research updates studies on store image and merchant website image. Thus, the purpose of this study is to: 1) conceptually define store image and website image as formative constructs; 2) show that these two constructs have a common component structure that helps compare them; 3) validate these constructs using the procedure of MacKenzie, Podsakoff, and Podsakoff (2011) specific to formative construct validation; 4) verify the nomological validity by modeling the effects of each channel's image on satisfaction and purchase attitude; and 5) cross-validate the measurement index developed (Fig. 1). Step 1 — conceptualization of constructs This first step involves defining conceptually the construct domain in terms of content as well as limitations in relation to closely related concepts (MacKenzie et al., 2011). Store image Store image is one of the earliest concepts used by retail research. Martineau (1958, p. 47) defines this concept as “the way in which the store is defined in the shopper's mind, partly by its functional qualities and partly by an aura of psychological attributes”. Its contribution to consumers' decision processes is undisputable (Grewal, Krishnan, Baker, & Borin, 1998; Joyce & Lambert, 1996); store image influences retailer choice, satisfaction, loyalty and competitive advantage (Hartman

& Spiro, 2005; Steenkamp & Wedel, 1991). Store image helps pinpoint the core characteristics of stores, spanning most variables of the retailing mix actionable by the retailer (Bloemer & de Ruyter, 1998). Consistently equated with a set of attitudes (Doyle & Fenwick, 1974) or an overall attitude (Müller, 2008; Steenkamp & Wedel, 1991), store image is actually a belief. Mazursky and Jacoby (1986, p. 147) define store image as “a cognition and/or affect (or set of cognitions and/or affects), which is (are) inferred, either from a set of ongoing perceptions and/or memory inputs attaching to a phenomenon (…) and which represent(s) what that phenomenon signifies to an individual”. Subjective and reductive (Keaveney & Hunt, 1992), store image is based around a community of perceptions and originally includes a myriad of functional and tangible elements (location, price, products, layout of sales floor) as well as intangible elements (sales people's attitudes, atmospherics, smells, colors, etc.). Lindquist (1974) extends its scope by building in whatever can be associated with the store, including feelings of belonging, warmth and friendship as well as excitement and interest. Each individual perceives and balances these rational and emotional elements (Doyle & Fenwick, 1974; Golden, Albaum, & Zimmer, 1987). However, Reardon, Miller, and Coe (1995) emphasize that the dimensions addressed in store image studies must be actionable by managers (offering, pricing, etc.), even at the risk of dispensing with symbolic or affective attributes over which managers have little control. Nevertheless, some of the initial components of store image tend to become self-contained, such as store atmosphere, for example (Baker, Parasuraman, Grewal, & Voss, 2002; Collins-Dodd & Lindley, 2003). Stimulating the 5 senses, store atmosphere is the “effort to design buying environments to produce specific emotional effects in the buyer that enhance his purchase probability” (Kotler, 1973, p. 50). Through its emotional dimension, atmospherics may determine unplanned purchases made in the store whereas image, through its inherent cognitive factors, may explain store selection and most planned purchases (Donovan, Rossiter, Marcoolyn, & Nesdale, 1994). Similarly, store image differs from the store shopping experience in two major ways: 1) any individual can have an image of an outlet even without having visited that place (Kerin, Jain, & Howard, 1992; Oxenfeldt, 1974); by contrast, in the interactionist perspective, experience can only be lived, being the interaction between an individual and a task in a given situation (Punj & Stewart, 1983); 2) experience is “a subjective episode” (Carù & Cova, 2003, p. 273) when image is more timeless as this concept appeals to a general impression lodged in memory. One can analyze store image holistically (Zimmer & Golden, 1988) or more analytically; the first approach corresponds to category-based processing and the second one to piecemeal-based processing (Keaveney & Hunt, 1992). But even if resorting to unstructured constructs helps grasp the gelstalt of store image (Zimmer & Golden, 1988), Chowdhury, Reardon, and Srivastava (1998) conclude that these two methods of evaluating perception lead to very similar outcomes, with structured scales better at explaining variance. The flurry of studies leads to a proliferation of store image attributes. And yet, their number is unclear and hinges on categorization methods, even more so as few replications exist (Hirschman, Greenberg, & Robertson, 1978). Accordingly, Myers and Alpert (1977) propose to only select those attributes that are both significant and distinctive, to which Kunkel and Berry (1968) or James, Durand, and Dreves (1976) add the criterion of salience. After identifying 15 criteria, Mazursky and Jacoby (1986) single out four degrees of store evaluation, including price information (price level, sales and discounts), product offering (assortment, colors, brands), the physical component of the store (location and layout), and information policy (number of sales people per department and product return policy). Even if those criteria vary depending on the type of commerce, research shows fairly consistent outcomes regarding the characteristics of the perceived image of stores (Hirschman et al., 1978). In fact, an

Please cite this article as: Bèzes, C., Definition and psychometric validation of a measurement index common to website and store images..., Journal of Business Research (2014), http://dx.doi.org/10.1016/j.jbusres.2014.03.016

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extensive literature review of the last 50 years shows that the store's key attributes have not changed significantly since the pioneering research in the 60s and 70s (Table 1). For example, Hopkins and Alford (2001) come up with 7 non-redundant dimensions (atmosphere, personnel, convenience, merchandise, price, service, self-concept) while Ghosh (1994) selects 8 of them (location, merchandise, store atmosphere, customer service, price, advertising, personal selling and sales incentive programs). Merchant website image Unlike research on store image, few studies have examined website image. In fact, most of them focus on related concepts such as website quality or disparate characteristics that researchers use as antecedents to specific explanatory variables of purchase behavior, often lumping them together under the less accurate term of online beliefs (Kwon & Lennon, 2009; Song & Zahedi, 2005) or website attributes (Verhoef et al., 2007). Initially, websites were mostly evaluated as information systems. However, because the TAM model, based on perceived usefulness and perceived ease of use (Davis, Bagozzi, & Warshaw, 1989), explains the adoption of the Internet better than its usages, this framework emerges as a necessary but inadequate requirement to explain online shopping (Van der Heijden, Verhagen, & Creemers, 2003). The model effectively differentiates between Internet users and non-Internet users but not online shoppers and navigators (Soopramanien & Robertson, 2007). As downloading speed has accelerated, researchers take into greater consideration the subjective perception of websites in terms of content

Steps of procedure

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and usability (e.g., Palmer, 2002). For this reason, the TAM model is often associated with other constructs such as SERVQUAL and transaction costs (Devaraj, Fan, & Kohli, 2002), enjoyment (Childers, Carr, Peck, & Carson, 2001; Loiacono, Watson, & Goodhue, 2007) or trust in website (Pavlou, 2003; Van der Heijden et al., 2003). Approaches based on website quality (Loiacono et al., 2007; Parasuraman, Zeithaml, & Berry, 2005) stem from the same stream of research. In fact, Dickinger and Stangl (2013) incorporate the constructs of the TAM model as well as those of enjoyment and trust into their website quality construct. Still, as mindsets toward online shopping change (Miyazaki & Fernandez, 2001) and the major merchant websites achieve similar ergonomics levels, website quality becomes less relevant for understanding the motivations and impediments of users. Strongly shaped by the benefits of the Internet in terms of transaction, website quality gives primacy to the transaction process (vs. selling) over the retailing mix that makes a retailer's website more attractive than its competitors'. The authors that embrace the concept of website quality seem to suggest that the low search and shop-around costs on the Internet drive consumers to adopt a purely opportunistic behavior that can only be hampered by the internal dysfunctions of the system (for example, for E-S-QUAL, efficiency system availability, fulfillment and privacy). The thinking is that Internet users may only look at the top links on search engines and the prices ranked by price comparison websites. Yet most online shoppers can mention several website names for one same product sought and usually bookmark their favorite websites in their browser, alongside other websites pulled up by search engines. They thus form an opinion (image) of each of those websites over time. This observation leads Rolland and Freeman (2010) to

Methods used

Step 1. Conceptualization of constructs

Literature review: conceptual domain, conceptual theme of the constructs

Step 2. Items generation and content validity assessment

Qualitative studies: definition and test of an evaluation grid of channel image (10 dimensions), 4 case studies, 24 in-depth interviews, validation by experts of 132 items generated.

Step 3. Measurement model specification

Formative vs reflective constructs, existing store and website image scales and index

Step 4. First scale purifications: pretests

First purification of measurement scales (n=80) Return to literature and second purification of measurement scales (n=113)

Step 5. Validation of the measurement index (new sample) - first-order constructs with reflective indicators: scales validity assessment - second-order constructs with formative indicators: index validity assessment

Validation of measurement index (n=1,478) - first-order constructs with reflective indicators: unidimensionality, convergent validity, discriminant validity - second-order constructs with formative indicators: multicollinearity test and external validity; verification of the 10 dimensions belonging to the concept of image (MIMIC), nomological validity, cross-validation on 3 subsamples

Fig. 1. Development and validation process of the measurement index. Adapted from MacKenzie et al. (2011).

Please cite this article as: Bèzes, C., Definition and psychometric validation of a measurement index common to website and store images..., Journal of Business Research (2014), http://dx.doi.org/10.1016/j.jbusres.2014.03.016

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Dimensions

Store image characteristics

Website image characteristics

Attributes

Authors

Attributes

Authors

Accessibility/ convenience

Location; easily accessible; convenience of store location; close to my house; close to variety of stores; ease and duration of travel; convenient to take a public transportation; other conveniences factors (shopping ease, parking, opening hours,…)

Convenience; accessibility

Szymanski and Hise (2000), Currah (2003), Katerattanakul and Siau (2003)

Layout/atmosphere

Layout and architecture; physical facilities; appearance of the store (symbols and colors); attractive display merchandise; physical facilities (elevators, aisle placement and width, number of floors, number of fitting rooms, washrooms, store in trouble, easy to move about in store, easy to locate departments); store atmosphere (feel comfortable in store, feeling of warmth, acceptance, or ease, attractive décor, music in the store, air quality cleanness, lighting, air conditioning); conservativemodem projection of the store; store lifecycle Breadth and depth of assortment; product classes carried; ego-intensive/non-ego intensive mix of merchandise; quality, variety and fashion of merchandise; newest products; many store brands; many well-known brand; search out lines offering the best value; quick special orders; good for specific products; width and replacement turnover rate of the assortment; product availability; minimum out-ofstocks, percentage of stock currently on sale

Kunkel and Berry (1968), Lindquist (1974), Schiffman, Dash, and Dillon (1977), Hansen and Deutscher (1977), Pessemier (1980), Hirschman et al. (1978), Mazursky and Jacoby (1986), Zimmer and Golden (1988), Ghosh (1994), Samli, Kelly, and Hunt (1998), Yoo, Park, and MacInnis (1998), Chowdhury et al. (1998), Koo (2003), Thang and Tan (2003), Wu et al. (2004), Ailawadi and Keller (2004), Mitchell and Harris (2005), Jinfeng and Zhilong (2009) Martineau (1958), Kunkel and Berry (1968), Lindquist (1974), Hansen and Deutscher (1977), Hirschman et al. (1978), Mazursky and Jacoby (1986), Zimmer and Golden (1988), Ghosh (1994), Samli et al. (1998), Joyce and Lambert (1996), Yoo et al. (1998), Chowdhury et al. (1998), Grewal et al. (1998), Baker et al. (2002), Koo (2003), Collins-Dodd and Lindley (2003), Thang and Tan (2003), Ailawadi and Keller (2004), Semeijn, Van Riel, and Ambrosini (2004), Mitchell and Harris (2005), Morschett et al. (2005), Verhagen and Van Dolen (2009), Jinfeng and Zhilong (2009), Kwon and Lennon (2009) Kunkel and Berry (1968), Oxenfeldt (1974), Lindquist (1974), Schiffman et al. (1977), Hansen and Deutscher (1977), Pessemier (1980), Hirschman et al. (1978), Mazursky and Jacoby (1986), Zimmer and Golden (1988), Ghosh (1994), Joyce and Lambert (1996), Samli et al. (1998), Yoo et al. (1998), Chowdhury et al. (1998), Grewal et al. (1998), Baker et al. (2002), Collins-Dodd and Lindley (2003), Thang and Tan (2003), Koo (2003), Ailawadi and Keller (2004), Semeijn et al. (2004), Wu et al. (2004), Mitchell and Harris (2005), Morschett et al. (2005), Hu and Jasper (2007), Verhoef et al. (2007), Verhagen and Van Dolen (2009), Kwon and Lennon (2009) Kunkel and Berry (1968), Lindquist (1974), Hansen and Deutscher (1977), Pessemier (1980), Mazursky and Jacoby (1986), Ghosh (1994), Koo (2003), Thang and Tan (2003), Ailawadi and Keller (2004), Semeijn et al. (2004), Wu et al. (2004), Verhoef et al. (2007) Kunkel and Berry (1968), Lindquist (1974), Schiffman et al. (1977), Hansen and Deutscher (1977), Pessemier (1980), Hirschman et al. (1978), Mazursky and Jacoby (1986), Zimmer and Golden (1988), Ghosh (1994), Joyce and Lambert (1996), Yoo et al. (1998), Samli et al. (1998), Chowdhury et al. (1998), Baker et al. (2002), Koo (2003), Collins-Dodd and Lindley (2003), Thang and Tan (2003), Ailawadi and Keller (2004), Wu et al. (2004), Morschett et al. (2005), Mitchell and Harris (2005), Verhoef et al. (2007), Verhagen and Van Dolen (2009), Jinfeng and Zhilong (2009)

Boring/fun site; well-organized and consistent page-layout with appropriate length; online store navigation efficiency; navigation indices and browsing; site and system facilities; ease of browsing; waiting information; time to get to home pages; product display; ease to find items; number of shopping modes; atmosphere (esthetic appeal, appearance and congeniality)

Spiller and Lohse (1997), Szymanski and Hise (2000), Eroglu et al. (2003), Katerattanakul and Siau (2003), Lim and Dubinsky (2004), Van der Heijden and Verhagen (2004), Chen and Lee (2005), Yun and Good (2007), Verhagen and Van Dolen (2009), Kwon and Lennon (2009)

Catalog size; quality; product variety; newest products; picture of the product; product descriptions; brand selection; product availability

Spiller and Lohse (1997), Szymanski and Hise (2000), Katerattanakul and Siau (2003), Lim and Dubinsky (2004), Chen and Lee (2005), Verhoef et al. (2007), Yun and Good (2007), Verhagen and Van Dolen (2009), Kwon and Lennon (2009)

Sales promotion; appetizers to attract customers; special offers; price discounting; gift certificate; use of banner ads

Spiller and Lohse (1997), Katerattanakul and Siau (2003), Lim and Dubinsky (2004), Chen and Lee (2005), Song and Zahedi (2005), Verhoef et al. (2007) Spiller and Lohse (1997), Katerattanakul and Siau (2003), Lim and Dubinsky (2004), Van der Heijden and Verhagen (2004), Chen and Lee (2005), Song and Zahedi (2005), Verhoef et al. (2007), Yun and Good (2007)

Offering/ merchandise

Promotions

Sales promotions; promotional emphasis; discount on sale merchandise; discount merchandise frequently; coupons; free sample; trading stamps

Price

Price of merchandise; price ranges; lowest everyday price; lowest advertised price; good value for money; fair prices; relative low prices; deal policy; highest prices; too expensive

Product price; reasonable price; value for money; price comparison

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Please cite this article as: Bèzes, C., Definition and psychometric validation of a measurement index common to website and store images..., Journal of Business Research (2014), http://dx.doi.org/10.1016/j.jbusres.2014.03.016

Table 1 Outline of store image and website image characteristics identified by main researchers.

Dimensions Store image characteristics

Website image characteristics Authors

Attributes

Authors

Reputation

Retailer reputation; store most improved; store wellknown; integrity; reliability; faithful; posttransaction satisfaction; return, exchange, repair, and warranty policy

Reputation of webstore; insurance, reliability; information about security, customer's privacy; warranty policy

Lim and Dubinsky (2004), Van der Heijden and Verhagen (2004), Chen and Lee (2005), Song and Zahedi (2005)

Advice/store personnel

Sales personnel; enough sales help (number of salespersons per department); salesmen expertise; impression of salespeople (available when needed, friendly, helpful, personal attention, good personal advice); negative techniques used during or in relation; quality, quantity, accessibility of information for consumers; ability to compare alternatives

Kunkel and Berry (1968), Lindquist (1974), Schiffman et al. (1977), Hansen and Deutscher (1977), Pessemier (1980), Hirschman et al. (1978), Mazursky and Jacoby (1986), Zimmer and Golden (1988), Joyce and Lambert (1996), Samli et al. (1998), Thang and Tan (2003), Semeijn et al. (2004), Verhagen and Van Dolen (2009), Jinfeng and Zhilong (2009) Martineau (1958), Kunkel and Berry (1968), Oxenfeldt (1974), Lindquist (1974), Schiffman et al. (1977), Hansen and Deutscher (1977), Hirschman et al. (1978), Mazursky and Jacoby (1986), Zimmer and Golden (1988), Ghosh (1994), Joyce and Lambert (1996), Chowdhury et al. (1998), Samli et al. (1998), Yoo et al. (1998), Grewal et al. (1998), Baker et al. (2002), Koo (2003), Collins-Dodd and Lindley (2003), Semeijn et al. (2004), Mitchell and Harris (2005), Hu and Jasper (2007), Verhoef et al. (2007), Jinfeng and Zhilong (2009), Kwon and Lennon (2009)

Spiller and Lohse (1997), Katerattanakul and Siau (2003), Lim and Dubinsky (2004), Van der Heijden and Verhagen (2004), Chen and Lee (2005), Song and Zahedi (2005), Verhoef et al. (2007), Yun and Good (2007)

Services

Service-general; employee service; transaction convenience; presence of self-service; speedy checkout; quick service; assistance when having problems; service quality; layaway available; after sale service (return, repair, refund, easy exchange, delivery service, installation service); credit cards accepted; credit-service; credit policies of the store

Kunkel and Berry (1968), Oxenfeldt (1974), Lindquist (1974), Schiffman et al. (1977), Hansen and Deutscher (1977), Pessemier (1980), Hirschman et al. (1978), Mazursky and Jacoby (1986), Zimmer and Golden (1988), Ghosh (1994), Joyce and Lambert (1996), Samli et al. (1998), Yoo et al. (1998), Chowdhury et al. (1998), Grewal et al. (1998), Baker et al. (2002), Thang and Tan (2003), Semeijn et al. (2004), Morschett et al. (2005), Hu and Jasper (2007), Verhoef et al. (2007)

Connections with the other channels

Comments about catalog

Zimmer and Golden (1988)

Institution

Institutional reputation; history; image strength and clarity; retailer awareness

Hansen and Deutscher (1977), Pessemier (1980), Thang and Tan (2003), Jinfeng and Zhilong (2009)

Quality, quantity, accessibility of information for consumers; detailed description of product features; update rate of information; integrity of information; product rating; sales rank; ability to compare alternatives; personalchoice helper (keyword search, improved search function); expert comments; professional knowledge; product selection assistance; help on product-size selection; feedback section; mechanism to contact the company (phone number; email of sales reps); surfer postings (Customers' review of product/service experience, testimonials, user group, discussion forum); FAQ on product related questions Online store service (willing to help, friendly, very knowledgeable); transaction convenience; interactivity; ease of ordering; order information and tracking; fast checkout; timely delivery; shipping options; transaction support; individual service; product cancelation; assistance when having problems, feedback; multiple payment options; credit and payment policies Information on retail outlet events or specials; coupons or gift certificates redeemable in retail outlets; ability to search the inventory of a retail outlet; ability to make an appointment or reservation for a service in the retail outlet; allow customers to return items purchased online to retail outlets; allow online orders to be picked up at retail outlet; links to other related websites; store location indicator; advertising links to other sites Information about the company; Company background or history

Spiller and Lohse (1997), Katerattanakul and Siau (2003), Lim and Dubinsky (2004), Van der Heijden and Verhagen (2004), Chen and Lee (2005), Song and Zahedi (2005), Verhoef et al. (2007), Yun and Good (2007), Verhagen and Van Dolen (2009), Kwon and Lennon (2009) Steinfield (2004), Song and Zahedi (2005)

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Please cite this article as: Bèzes, C., Definition and psychometric validation of a measurement index common to website and store images..., Journal of Business Research (2014), http://dx.doi.org/10.1016/j.jbusres.2014.03.016

Attributes

Spiller and Lohse (1997), Katerattanakul and Siau (2003), Lim and Dubinsky (2004), Steinfield (2004)

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integrate image dimensions such as offering and price level into their research on website quality. A second stream of research evaluates websites as communication channels by introducing affective dimensions and adapting to this new medium the measurements of the effect of ads disseminated in the traditional media: attitude toward the website (Chen, Clifford, & Wells, 2002), attitude toward the homepage (Singh, Dalal, Mihsra, & Patil, 2005) as well as website personality (Chen & Rodgers, 2006). The third stream, which is now thriving as companies move into online selling, evaluates merchant websites as retail channels, mainly from the perspective of satisfaction (Song & Zahedi, 2005; Szymanski & Hise, 2000), atmosphere (Childers et al., 2001; Eroglu, Machleit, & Davis, 2003) and experiential value (Mathwick, Malhotra, & Rigdon, 2002). Within this stream, few studies resort to image developed for stores by transposing this concept to merchant websites. Spiller and Lohse (1997) are the first to show that merchant websites can be analyzed like stores, which allows them to draw up an initial typology of websites. Katerattanakul and Siau (2003) build on these criteria but test neither the effects nor the perception by consumers or experts. Chen and Lee (2005) assess the significance of those criteria by taking into account the profile of respondents. However, they leave out the dimensions accessibility (or location) and price. Van der Heijden and Verhagen (2004) underscore the positive influence of website image on attitude toward online purchasing. Lim and Dubinsky (2004) show that offering and retailer reliability primarily influence attitude toward online purchasing; on the other hand, due to technical advances, once critical elements such as interactivity and navigation no longer influence attitude toward online purchasing. Finally, some scales, like Van der Heijden and Verhagen's (2004), measure perceived quality more than website image, which make them indiscriminately applicable to any kind of website, merchant or non-merchant. Still, now become retail channels in their own right, merchant websites can no longer be analyzed with the same criteria as those used for brand or institutional websites. Belanger et al. (2006) show in particular that websites should be evaluated differently based on the goal pursued (selling or looking up information) and the motivations of Internet users (buying, exploring, entertainment). Their focus is not so much to try and make cyber-visitors into cyber-shoppers as to attract and retain visitors who have already shopped from competitors. In an increasingly competitive market, the websites ought to differentiate themselves according to marketing criteria more specific to the retailer per se. Hence, managers need to identify the most critical image dimensions, and then leverage them. Website image is mostly measured in terms of conveyed image, only including certain store image dimensions (few or no transpositions of “location-accessibility” or “sales staff” dimensions), and with no intention to use the same items. But the location or, more accurately, the accessibility of a website, is equally relevant for the Internet and the stores (Currah, 2003). As to staff image, Wang and Benbasat (2007) show that Internet users have an anthropomorphic conception of the technological interface; Lohse and Spiller (1999) also find equivalence between store staff image and product information on a website. Perspectives for the creation of an index common to store and website image As MacKenzie et al. (2011) recommend, this extensive literature review brings into focus the great consistency of the concept of image over time as well as through its manifestations (store and website). All the items listed in Table 1 help identify 10 nonredundant and constituent dimensions of store image: 8 of those (accessibility/convenience, layout/atmosphere, offering/merchandise, promotions, price, reputation, advice/store personnel, services) are taken into consideration by nearly every author; the other 2

(institution and connections with other channel) may seem more marginal as only a few authors single them out. Yet, they are particularly interesting to retain in a multichannel environment where perceived risk is reduced by the relationship between the website and the stores (Choi & Lee, 2003) and perception of the institution through the channel, that is, the credibility of the source (Korgaonkar & Karson, 2007). While there has been much more research on store image than on website image, Table 1 demonstrates that the concept of image is based on identical dimensions for both channels, with tailored but very similar items. However, as this research aims to faithfully transpose store image dimensions to the website, the concept of image studied here keeps to the characteristics that are tangible, not specific to one channel, and actionable by the retailer (Reardon et al., 1995). Paradoxically, despite the great consistency of the concept of store image, only research in the last 15 years has produced reliable measurement scales in order to validate particular dimensions. But each of those still presents several limitations: 1) none measures as many dimensions as the current research aims to do; 2) these scales are never transposable as is from the store to the site, except for some scales by Verhoef et al. (2007) who only describe a few dimensions of channel image; 3) mixing together certain dimensions can harm the convergent and discriminant validity of the constructs studied (Verhagen & Van Dolen, 2009 bring together price and merchandise or reputation and service; similarly, Koo, 2003 ties together price and promotions into once large concept called value); 4) none of these studies empirically demonstrates that the dimensions they address are actual components of the broader concept of image, as they fail to observe the specific procedure of formative construct validation. In light of this literature review, no scale, to date, compares, on the same basis, website image and store image. All those reasons require developing a great number of items applicable to the website and store alike to describe each image dimension. Step 2 — item generation and content validity assessment Once the domain of the construct is delineated using the literature review, Step 2 involves generating items that can describe the whole concept (MacKenzie et al., 2011). Several qualitative studies aim to evaluate the content validity of the various constructs, to test for the applicability of the classic store image dimensions to the website, and build up the dimensions common to stores and merchant websites. When building the common evaluation grid for the projected image of channels and after the 24 in-depth interviews, six researchers specialized in multichannel distribution are consulted to evaluate the capacity of items to describe each store and website image dimension and ensure that they are comprehensible to respondents. Defining and testing a common evaluation grid for the projected image of channels In contrast to past transpositions from store image to website image (Spiller & Lohse, 1997), an evaluation grid common to these channels is distinctively built around a larger documentation of the constituent items of store and website images (Table 1). This grid includes ten identical image dimensions for the website and the store, with equivalent items (Table 2). The grid is, however, deliberately limited to the characteristics actionable by retailers, that is, tangible and real factors (Oxenfeldt, 1974; Reardon et al., 1995). The conceptual relevance and operational capacity of this evaluation grid are verified, first through the same panel of 6 researchers (validation of categories and items extracted from the literature) and then on the projected image of the website and stores of 4 multichannel retailers. Basically, this study validates the previously presented evaluation grid by testing for the applicability of each dimension to the store

Please cite this article as: Bèzes, C., Definition and psychometric validation of a measurement index common to website and store images..., Journal of Business Research (2014), http://dx.doi.org/10.1016/j.jbusres.2014.03.016

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Table 2 Evaluation grid common to website and store images (Bèzes, 2013b) Analyzed dimensions

Store

Website

Accessibility/location

- Proximity to home, workplace, access - Parking facilities - Design, architecture, interior and exterior scenery, displays and colors, prestige - Product layout, aisle width - Disarray, clutter - Store lifecycle - Quality - Range, assortment depth and width - Selection/brands - Newness and distinctive features - Percentage of stock for sale - Sales discount - Bonus - Happening or promotional event - Competitive prices

- Positioning in search engines, Google page rank - Ease of access to website - Website design (arrangement, atmosphere, ease of navigation, loading speed of pages, graphics) - Good clarity of products, ease of use and navigation of website - Clutter, page uniformity - Website lifecycle - Quality - Range, assortment depth and width - Selection/brands - Newness and distinctive features - Warehouse and store inventory - Rebate - Coupons or gift certificates - Deals of the days, private or preferred customer sales - Competitive prices - Price comparison - Safety, integrity, reliability - Returns, exchanges, warranty policy - Individual contact/sales people assistance/phone assistance option, call-back - Rating of products, rank in top sales, product recommendations - Appropriate information on product, picture and detailed description of product - Relevance, updatedness, high reliability and perceived utility of information - Ability to compare alternatives, performance of search engine, FAQ - Limited number of clicks to order page, confidentiality/payment security - Customer service: ease of order cancelation, ease of product return (including in-store for an online purchase), after sale service - Virtual cart, option to book a service in a store - Required travel for purchase, delivery, shipment security, order tracking and product traceability, option to pick up a product ordered online in a store - Ease of credit - Links to other websites - Phone number or email address of stores and telephone sales support - Stores access map - Information about the company - Company's history and record

Layout of physical or virtual website

Merchandise

Promotions

Prices Reputation Information and advice

- Safety, integrity, reliability - Returns, exchanges, warranty policy - Sales assistance, techniques implemented during sale - Expertise - Ability to compare alternatives

Services critical to activity Related services

- Ease and speed of transaction - Customer service: ease of order cancelation, ease of product return, after sale service - Product store-away option - Delivery - Ease of credit - Partner offers

Connections with other channels

Catalog and in-store Internet terminal

Institution

- Information about the company - Company's history and record

and website alike. The obtained results are discussed with the marketing managers of the retailers involved. Two expert coders then tested the grid on the projected image of 94 merchant websites operating in the top ten industries of French B-to-C e-commerce. In order to verify the consistency of the grid's dimensions and items, double-coding was performed on 15 randomly selected websites. Cohen's Kappa coefficient indicates significant agreement between the two evaluations (Bèzes & Belvaux, 2012). This empirical study highlights three core features of the projected image of merchant websites: - one feature centered on the offering mix and choice facilitation (merchandise, activity-based and related services, website layout, information and advice), which is a strong differentiation factor between websites and positively but poorly influences website traffic and the number of pages viewed; - a second feature more internal to the company (reputation, institution, connection with other retail channels), which is another differentiation factor but does not impact website traffic; - a third feature, utilitarian and economic (prices, promotions and website accessibility), which is not a differentiation factor between websites but, instead, is highly explanatory of generated traffic and the number of pages viewed per visitor. In-depth interviews to build up the image dimensions of the website and stores Twenty-four face-to-face qualitative interviews lasting from 45 to 75 min helped enhance the dimensions derived from the literature and validate the initial grid of the image dimensions of the website and the store

in order to build the measurement scales. Buttle (1985) recommends this technique (vs. focus groups) due to the individual subjectivity of image. The convenience sample comprises people of both genders from ages 20 to 70 and representing a wide spectrum of behaviors in terms of channel use, familiarity with the web channel and purchase orientation. The evaluation focuses on the website and stores of two retailers that sell technology products, with each respondent evaluating the channels of the retailer they were most familiar with. The interview follows an exploration of the website and a photographic tour in one of the outlets of the retailer. This phase takes place in front of a connected computer without the interviewer getting involved. The goal is not to analyze the respondent's experience during the visit but simply getting some consistency in the responses to the website and the stores. The actual interview takes place away from the computer to collect the memorized perception (study on image rather than on experience). The interviews and collected feedback consist of a first section centered on the salient perceptions of the website and stores, and a second section, more specific, aiming to gain insights into each dimension of website and store images. Given the already delineated literature review and the past case studies, exploration and validation use content thematic analysis. The collected material is broken down to highlight the significant elements of perceived image, both quantitatively (significance based on occurrence frequency) and qualitatively (newness and value of the element). Specifically, this qualitative research (Bèzes, 2013b) shows that all the classic dimensions of store image apply when analyzing the perceived image as well as the projected image of merchant websites. However, the dimensions “website layout” and especially “information-advice” seem to be the most salient. That is, information in the

Please cite this article as: Bèzes, C., Definition and psychometric validation of a measurement index common to website and store images..., Journal of Business Research (2014), http://dx.doi.org/10.1016/j.jbusres.2014.03.016

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broad sense, and utilitarian information in particular, seems to dominate perceptions not only because most respondents look upon the Internet as an information and comparison tool but also because products on the Internet are not so much equated with real objects as information (like on a 2D advertising brochure). By comparison, the salient perceptions of the physical stores seem to break down more evenly between those that mention product, choice, variety and brands and those that refer to the atmosphere and layout of the shopping floor. After being submitted to the same panel of 6 researchers, the feedback collected helps build the first measurement scales of each dimension of the image of websites and stores. Step 3 — measurement model specification Before data is first collected, MacKenzie et al.'s (2011) recommendation is to specify the measurement model. That is, constructs are rarely formative or reflective by nature as they can usually be conceptualized from a unidimensional or multi-dimensional perspective. MacCallum and Browne (1993) refer to reflective variables as latent variables and formative variables as composite variables. Typically, the indicators that make up a latent reflective variable are viewed as the reflections of this variable (causality goes from construct to items), in combination with the measurement error. Thus, the common variance between its indicators is factored into its evaluation. Conversely, the formative variable results from combining its indicators, and the total variance of its indicators is factored into its measurement (MacKenzie, Podsakoff, & Jarvis, 2005). As a result, addressing formative variables as reflective variables reduces the construct's variance; this is conducive to overestimating the parameters of exogenous variables and underestimating those of endogenous variables (Diamantopoulos, Riefler, & Roth, 2008; MacKenzie et al., 2005). According to Jarvis, MacKenzie, and Podsakoff (2003), this is the case for over a fourth of multiple indicator latent constructs published. Rossiter (2002) mentions in particular the SERVQUAL scale (Parasuraman, Zeithaml, & Berry, 1988), which is actually a composite construct addressed as a reflective variable. Thus, Podsakoff, Shen, and Podsakoff (2006) (in Ruiz, Gremler, Washburn, & Carrion, 2008), encourage social sciences researchers to address complex constructs as variables of higher order that incorporate several dimensions each representing one facet of the socalled concept. In fact, formative models consistently explain more variance than reflective models, which maximizes their predictive capacity (Coltman, Devinney, Midgley, & Venaik, 2008; Finn & Wang, 2014; Podsakoff et al., 2003). The resort to a formative variable causes fewer misspecifications (Hulin, Cudeck, Netemeyer, Dillon, McDonald, & Bearden, 2001), and is a viable alternative to reflective measurements (Diamantopoulos et al., 2008). The previous literature review and qualitative studies confirm that consumers and researchers typically consider store or website image as a multi-dimensional, composite and thus complex construct that pertains to what Rossiter (2002, p. 310) calls “abstract formed object”. Channel image, as conceptualized in this research, is clearly formative and defined by its 10 dimensions, unlike reflective variables. Many of those dimensions are independent from one another, that is, not interchangeable, poorly correlated, and may not have the same antecedents or consequences (MacKenzie et al., 2005). Eliminating dimensions (for example, offering, price or accessibility) would inevitably truncate the image construct. Their aggregation leads to a formative index requiring specific validation to avoid misspecifications. Joyce and Lambert (1996), Grewal et al. (1998), Bloemer and Odekerken-Schroder (2002) or Wu, Petroshius, and Newell (2004) mistakenly validate – through the method suited to reflective constructs alone – store image measurement instruments respectively consisting of 7, 8, 10 or 23 items and describing very diverse image dimensions. For that reason, when replicating the website image construct of Van der Heijden and Verhagen (2004), Chang and Tseng (2013) narrow this scale down to

6 items in order to come up with acceptable reliability and convergence indicators. For the same reason, Aghekyan-Simonian, Forsythe, Kwon, and Chattaraman (2012) only retain 7 items of the 21 from the store image e-tail scale of Yun and Good (2007). The choice of a fully formative first-order index is justified when the items used are distinct but few. Dickinger and Stangl (2013) select this solution to measure website quality from only eight manifest indicators. Conversely, the measurement index developed here aims to evaluate each image dimension more completely and rigorously than using single-item measurements (Chebat, Sirgy, & Grzeskowiak, 2010), so that retailers and researchers can also use each reflective scale separately. In this respect, this theory-driven conception corresponds to the type II construct (Diamantopoulos et al., 2008; Jarvis et al., 2003), which must be addressed as a composite multidimensional construct, with reflective first-order and formative second-order measurement model (Fig. 2). The test of the M3 model in the First-order constructs with reflective indicators: results of the confirmatory factor analysis of website and store images (CFA) section demonstrates that this conception is also the most empirically relevant. Step 4 — first scale purifications: pretests The purpose of this step (EFA) is to verify that all previously generated items effectively aggregate into ten unidimensional, non-redundant and constituent components of the focal concept of image. As the two pretests only aimed to test those items to evaluate their relevance, retain, improve or eliminate them, respondents were not questioned about their demographics. First questionnaire pretest (n = 80 students) The last preparatory step involves developing and pretesting the ten scales of perceived image likely to apply to both website and store. The creation of strictly identical items describing the various image dimensions of the website and stores is made possible by drawing on the existing incomplete literature, intuition, the preparatory research previously mentioned and the opinions of several marketing researchers. The first quantitative pretest uses self-administered questionnaires handed out to a convenience sample of economics and management students. Following Ajzen's (1991) recommendation that the study should be centered on a particular action, conducted with a specific intention, in a given context and time, the questionnaire is based on the scenario of shopping for a digital camera from a French multichannel retailer over the summer vacation. The choice of a digital camera seems relevant for several reasons: 1) this choice focuses respondents' attention on a deliberately limited scope as the retailer selected for this pretest carries many more technology and cultural products across its retail channels; 2) previous qualitative interviews conducted by the researcher show that this type of products may be bought equally in-store and online; 3) finally, this product fits perfectly the consumption aspirations of the student sample involved. The pretested questionnaire includes 132 items in total, 66 items for the 10 store image dimensions and the same items for evaluating the 10 website image dimensions. 80 usable questionnaires are returned, which made this total 8 times higher than the number of items to be tested on each dimension involved. The KMO is still higher than 0.6 and Bartlett's test still significant at 0.00. This item purification process is performed by computing Cronbach alphas and conducting factor analyses. The selection retains all the items whose quality of representation was greater than 0.6 and contributing to the surveyed dimension reaching or exceeding 60% of explained variance. Of the 132 items pretested to measure store and website images, 42 are eliminated. The results of this purification are discussed with the same panel of 6 researchers. Finally, following these prestudies and exchanges, the 10 initial image dimensions are retained for the website and the store. That is,

Please cite this article as: Bèzes, C., Definition and psychometric validation of a measurement index common to website and store images..., Journal of Business Research (2014), http://dx.doi.org/10.1016/j.jbusres.2014.03.016

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δ1 δ2 δ3

Component Accessibility

δ4 δ5 δ6

Component Layout

δ7 δ8 δ8

Component Offering

δ9 δ 10 δ 11

Component Promotions

δ 12 δ 13 δ 14

Component Price

δ 15 δ 16 δ 17

Focal construct: website (or store) image

ζ1

Component Reputation

δ 18 δ 19 δ 20

Component Advice/Information

δ 21 δ 22 δ 23 δ 24 δ 25 δ 25

Component Services

Component Connections with the other channels

δ 26 δ 27 δ 28

Component Institution

Fig. 2. Conceptualization of store and website image as formative constructs.

the first studies conducted show that dimensions that may initially appear to be marginal or superfluous (institution, connections with other channels) have a deep impact on the perception of merchant websites. Steinfield (2004) already addresses these two dimensions as characteristics of websites in a click-and-mortar environment. As a return to the literature proved necessary to fine-tune or build up some scales, several items already used by Verhoef et al. (2007) are added in, particularly to better describe the dimensions “offering” and “services”. To make the item descriptions more comprehensible, the term retail channel, confusing for some neophytes, is ruled out and the questions aimed at

measuring the dimensions “connection with the other channels” split into two sections, one regarding the website's links to the stores, the other the connections between the store and the website. Second pretest of the edited questionnaire (n = 133 customers of the retailer surveyed) The following research is conducted on two samples of real customers of a French multichannel retailer, a leader in the B-to-C sale of technology and cultural products (the same retailer as in the first

Please cite this article as: Bèzes, C., Definition and psychometric validation of a measurement index common to website and store images..., Journal of Business Research (2014), http://dx.doi.org/10.1016/j.jbusres.2014.03.016

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pretest). Besides an unwillingness to test the measurement scales on a student population, the sample must be large, sufficiently familiar with the channels surveyed in order to have a solid and accurate image of them, and diverse in terms of usage behavior of the retailer's channels. The selected retailer has sold online since 2000, long enough for a substantial part of its customers to choose this retailer as a purchase channel; its store network is extensive, though with consistent offerings and locations. Since the risk of exclusion is negligible (Duffy, Smith, Terhanian, & Bremer, 2005), the questionnaires are administered online using the customer databases of the partner retailer and under its name. In order to obtain more consistent responses, the type of product and the situational effects unrelated to the characteristics of individuals and stimulus are monitored (Belk, 1974). This precaution simplifies respondents' judgments (Srivastava, 1981). As in the first pretest and for the same reasons, the respondents are placed in a situation where they consider buying a digital camera. Given the size of the questionnaire and although an incentive does not guarantee success at all (Soopramanien & Robertson, 2007), a giveaway contest is staged for the respondents. The goal is to increase the response rate; the digital camera giveaways match the product that is used as a reference throughout the questionnaire, which may encourage respondents to engage in roleplay and enhance their level of involvement. For reasons explained earlier, a choice by processing attribute solution is selected (Bettman & Kakkar, 1977): for each question, the respondents rate on a Likert-type 7-point scale (strongly disagree to strongly agree), their perception of the website, immediately followed by their perception of the store. This order is consistently maintained throughout the questionnaire in order to not confuse the respondents and let them concentrate on their responses. Besides, providing only two options, the website and the store, seems to sharply reduce anchoring risks. A scale for the overall website and store images is also introduced and tested to confirm the validity of the image construct in structural equations. A questionnaire is conducted online on 113 respondents. The size of the sample is still at least 12.5 times greater than the sample of the number of items considered in the analysis of each measurement instrument. Bartlett's test of sphericity is significant at the 0.00 threshold and the KMO index higher than 0.79 for each factor analysis. Despite the small size of the sample processed and due to a more consistent perception of the retailer's channels, the simultaneous factorization of nearly all image dimensions is possible at this stage. Following this test, two modifications are made. The main one aims to better differentiate between the dimensions “service” and “reputation”, which are merged along the same axis in this pretest. Thus, the dimension “service” is narrowed to “customer service” as this sub-dimension encompasses the other tested sub-dimensions (pre-purchase service, service during purchase). The second decision involves enhancing and reinforcing the measurement scale of the “price” dimension, with specific items adapted from Coutelle (2001). Step 5 — validation of the measurement index (n = 1478 customers of the retailer surveyed) The purpose of this step is first to confirm for each channel the reliability, unidimensionality (convergent validity) and non-redundancy (discriminant validity) of each predicted image dimension, that is, of the 10 first-order latent constructs with reflective indicators (MacKenzie et al., 2011). The First-order constructs with reflective indicators: method of psychometric validation of measurement instruments, First-order constructs with reflective indicators: factor structure of store image (EFA), First-order constructs with reflective indicators: factor structure of website image (EFA) and First-order constructs with reflective indicators: results of the confirmatory factor analysis of website and store images (CFA) sections present all the results obtained through the procedure of reflective construct validation.

Secondly, validation of the second-order formative construct involves specific verifications (Second-order constructs with formative indicators: multicollinearity test and external validity and Second-order constructs with formative indicators: discriminant and nomological validity sections) and cross-validation (Second-order constructs with formative indicators: measurement index cross-validation section). The final study involves 1478 customers of the same retailer. The sample consists of 68.6% men; the age average is 47.7 years old (61.2% are 45 and older); their education level is high (78.6% hold a higher education degree); 51.3% visit the retailer's stores at least once a month (81.7% at least once every three months); 88.7% of them visit the retailer' s website at least once a month (50.5% at least once a week); finally, 62% of them have a long online shopping experience (more than 4 years). First-order constructs with reflective indicators: method of psychometric validation of measurement instruments Jöreskog distinguishes between exploratory factor analyses (EFA) and confirmatory factor analyses (CFA): the former focus on latent variables a priori determined by the researcher, the latter on “latent variables that a researcher derives from data analysis” (Bollen, 2002, p. 615). Among possible approaches to confirming measurement scales, structural equation modeling is one of the most commonly used. This method helps test the measurement models, that is, the structure of relations between each latent variable and its indicators. Subsequently, this approach also helps adjust the structural model, that is, to test the structural relations between latent variables as speculated by the model. To verify the reliability of the measurement instruments, composite reliability – which better integrates error terms and is less sensitive to the number of items – is preferred to Cronbach's alpha. To evaluate convergent and discriminant validity, the benchmark criterion Average Variance Extracted is retained (Fornell & Larcker, 1981). Maximum Likelihood is selected as the estimation method. The multivariate or causal analyses imply that the data be normally distributed; all the data are tested for the symmetry and flattening of the distribution. Skewness and Kurtosis coefficients are very satisfactory as the absolute value for each item is lower than the recommended 3 threshold. However, as all variables are not multinormally distributed according to Mardia's test, the bootstrap procedure (Gerbing & Anderson, 1985) is used. The parameters thus computed converge to the actual distribution of the parameter. First of all, the research presents the results of the principal component factor analysis for the final sample, and secondly, for the retained items, the structural equation measurement model and the confirmatory factor analysis. As the literature recommends, EFA are simultaneously performed across the image items of the considered channel in order to test the unidimensionality of the 10 predicted reflective constructs and their psychometric properties. Only the items ultimately retained are mentioned when the CFA is performed. First-order constructs with reflective indicators: factor structure of store image (EFA) Following an Oblimin rotation (the correlation matrix does not allow Varimax rotation), the principal component factor analysis brings out the 10 image dimensions already highlighted in the first two pretests. Those dimensions explain a total 80.6% of the variance of store image (Table 3). Each measurement scale has a good degree of internal consistency and good discriminant validity against the other image dimensions. Nearly all communalities are higher than 0.7, the contributions to the construct are strong (N 0.8) and the internal consistency is satisfactory (N0.8). Items whose elimination would have improved the Cronbach's alpha of an image dimension for one of the channels but degraded

Please cite this article as: Bèzes, C., Definition and psychometric validation of a measurement index common to website and store images..., Journal of Business Research (2014), http://dx.doi.org/10.1016/j.jbusres.2014.03.016

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that of the other channel are maintained (Rossiter, 2002). Only advicerelated indicators, which worked well for both the website and stores, were ultimately retained in the initial dimension “information/advice”, permitting the comparison of homogenous and strictly identical dimensions. First-order constructs with reflective indicators: factor structure of website image (EFA) A principal component factor analysis run on website image highlights the same 10 image dimensions as for the stores, obtained from identical items. They account for 80.2% of the variance of website image (Table 4). The proportion of variance explained by each website image dimension as well as the degrees of internal consistency compare with those observed for stores. Communalities are mostly greater than 0.7, contributions to the construct are usually over 0.8 and internal consistency is satisfactory (N0.8). Although for website and stores alike Cattell's Scree Test permits retaining only a few image dimensions, the ten expected dimensions that all have eigenvalues higher than 1 (Kaiser criterion) are retained in order to account for the richest possible channel image. First-order constructs with reflective indicators: results of the confirmatory factor analysis of website and store images (CFA) Confirmatory factor analysis provides the values of the indicators and goodness-of-fit indexes that are necessary for establishing the convergent and discriminant validity of the measurement scales used. Since the objective is to work out two identical but distinct indexes (one applied to stores, the other to the website), these analyses are first performed on the 10 store image dimensions, then on the 10 website image dimensions. For that reason, Tables 5, 6 and 7 deliberately distinguish between the two manifestations (store image and website image). Testing the convergent and discriminant validity of the measurement scales applied to both manifestations (stores, website) is possible, though fairly irrelevant, and pointlessly detrimental to the clarity of the analysis. By incorporating pairs of mirror variables of strictly identical content and manifest indicators (website offering, store offering, website price, store price, etc.), this method would involve correlating the terms of error of each mirror construct because each of those measurements has structurally the same default value. Four alternative measurement models are tested: M0 (null model), M1 (unidimensional model, all items forced to load on a single factor), M2 (multidimensional model with 10 dimensions uncorrelated) and M3 (multidimensional model with 10 dimensions correlated). The comparison of the chi-squares demonstrates that the M3 model is by far the most efficient in measuring store image (M0: 54,124.03 χ2(780); M1: 34,000.5 χ2(740); M2: 8338.14 χ2(740); M3: 3587.33 χ2(695)) and website image (M0: 52,906.05 χ2(780); M1: 30,734.5 χ2(740); M2: 8381.72 χ2(740); M3: 3187.61 χ2(695)). This result confirms the multidimensionality of the store and website image constructs. Given the complexity of higher-order constructs (Gerbing, Hamilton, & Freeman, 1994), the models' goodness-of-fit is acceptable for most goodness-of-fit indexes retained, for both the stores and the website. Prediction errors of the model are thus very unlikely. Besides, a CFA run on a random draw of 302 individuals shows that the inflation of χ2 by degree of freedom springs from the sample size (2.28 χ2/df for store image and 1.88 χ2/df for website image). Since the goodness-of-fit indexes of the measurement model are acceptable, the construct definition, the item description and, concurrently for the stores and website, the loading (λ), composite reliability (CR) and average variance extracted (AVE) values, are presented in Table 5. λ contributions and Student tests are significant across the measurement scales of store and website image dimensions. Similarly, the internal consistency of measurement

11

scales is established as all composite reliability indexes are greater than 0.8. Tables 6 and 7 also show that each scale conforms to the convergent and discriminant validity standards recommended by Fornell and Larcker (1981). With respect to the stores and the site, the AVE is consistently higher than 0.5; each construct shares more variance with its measurements than with the other constructs (r2 b AVE); the risks of multicollinearity between variables are negligible (squared correlation coefficient always lower than 0.34 for stores and 0.37 for the site), which makes for an accurate estimation of the relative weight of each dimension in the overall image construct. In accordance with the literature review and the prestudies, measuring each of the ten website and store image dimensions based on the same items is perfectly possible. Retaining some items that are more necessary for one channel or the other allows for the comparison of each image dimension of the stores and website within strictly identical perimeters. This method does not affect in any significant way the goodness-of-fit of the models since very good psychometric qualities are observed across these instruments. The examination of loadings also demonstrates that for each image dimension the measurement scales are consistent across the site and the stores. Each dimension is then scored to yield a first-order factor that is a component of the channel's image (second-order construct). The approach retained is that of Bruhn, Georgi, and Hadwich (2008) who use the non-standardized scores of the latent variables provided by PLS. This method helps obtain accurate ratings for each latent variable (Anderson & Gerbing, 1988). The following steps concern the final validation of the second-order formative construct.

Second-order constructs with formative indicators: multicollinearity test and external validity As theory and the previous analyses suggest, the focal constructs of website and store image can be considered as second-order constructs with formative indicators (Fig. 2). But their multidimensionality and complexity require making further verifications that rule out using classic indicators of internal consistency. That is, seeking a high level of consistency can be detrimental to the relevance of the concept (Jarvis et al., 2003; Rossiter, 2002). Although the validation procedures of those constructs through structural equations have yet to be harmonized in detail (MacKenzie et al., 2011), all specialists (Diamantopoulos et al., 2008: MacKenzie et al., 2011) agree on verifying 4 major points: non-redundancy of indicators, external validity of formative constructs, their discriminant validity, and their nomological validity. After verifying that the retained indicators effectively cut across the entire construct domain, the next step is to eliminate multicollinearity risks between indicators (Diamantopoulos et al., 2008; Jarvis et al., 2003). Although sought by researchers in the case of reflective variables, this interrelatedness, which destabilizes the construct (Bruhn et al., 2008; Roberts & Thatcher, 2009) and clouds the impact of each indicator, is undesirable for formative variables. A simple way to verify the absence of multicollinearity is to use the variance inflation factors (VIF). The strictest researchers set the maximum threshold at 3. Unsurprisingly, given the previously observed r2 between the various image dimensions, the VIF do not exceed 2.05 for store image and 2.25 for website image. A second verification concerns the external validity of the formative constructs. Its purpose is to examine the strength and significance of the relationship between indicators (here the image dimensions) and the formative construct (MacKenzie et al., 2005). One method is to assess a MIMIC (Multiple Indicators and Multiple Causes, model connecting one single dependent latent variable and several independent variables) (Jöreskog & Goldberger, 1975) using the formative variable as an exogenous variable and the same variable measured by two

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Table 3 Results of the factor analysis of store image in the final sample. Items

Communalities

Layout

LAYM3 LAYM2 LAYM1 LAYM5 LAYM4 CHAM2 CHAM4 CHAM3 CHAM1 OFFM5 OFFM4 OFFM6 OFFM3 OFFM1 OFFM2 ACCM4 ACCM3 ACCM1 ACCM2 PRIM3 PRIM4 PRIM5 PRIM6 PRIM2 Variance explained (%) Eigenvalues Cronbach's alpha Bartlett's test KMO index

0.81 0.83 0.77 0.70 0.66 0.93 0.92 0.91 0.74 0.81 0.79 0.80 0.73 0.69 0.75 0.82 0.82 0.82 0.67 0.77 0.77 0.74 0.77 0.76

0.87 0.86 0.86 0.72 0.63

Connections with other channels

Offering

Price

Accessibility

0.96 0.95 0.95 0.86 −0.90 −0.89 −0.87 −0.84 −0.80 −0.79 0.94 0.89 0.86 0.67

34.86 13.94 0.91

11.28 4.51 0.95

6.39 2.56 0.93

5.47 2.19 0.89

Institution

Advice

−0.86 −0.86 −0.84 −0.84 −0.83 5.06 2.02 0.92

0.00 (780 df) 0.93

Items

Communalities

Customer service

SERM3 SERM1 SERM2 PROM3 PROM2 PROM4 PROM1 INSM3 INSM2 INSM1 INFM5 INFM4 INFM6 REPM2 REPM1 REPM3 Variance explained (%) Eigenvalues Cronbach's alpha Bartlett's test KMO index

0.82 0.82 0.82 0.85 0.80 0.80 0.70 0.85 0.84 0.77 0.91 0.89 0.84 0.94 0.93 0.93

0.91 0.90 0.88

Promotions

Reputation

0.92 0.86 0.86 0.77 −0.89 −0.88 −0.86 0.94 0.91 0.87

4.39 1.76 0.89

3.82 1.53 0.90

3.39 1.36 0.88

3.27 1.31 0.93

−0.91 −0.90 −0.89 2.72 1.09 0.97

0.00 (780 df) 0.93

reflective indicators as an endogenous variable (Diamantopoulos & Winklhofer, 2001; MacKenzie et al., 2011): Fig. 3. Accordingly, the MIMIC with the formative construct (Fig. 3) uses the two items that contribute the most to a reflective scale measuring the overall image of each channel: here, IMG2 and IMG3 (Table 8). The various goodness-of-fit indexes are suitable given the large number of dimensions and items grouped into the exogenous construct and the single endogenous variable integrated into the MIMIC (Bagozzi, 2007): for store image (χ2(53) = 975.19; RMSEA = 0.11; SRMR = 0.07; GFI = 0.89; AGFI = 0.84; NFI = 0.89; TLI = 0.87; CFI = 0.89); for website image (χ2 (53) = 1001.08; RMSEA = 0.12; SRMR = 0.06; GFI = 0.88; AGFI = 0.83; NFI = 0.89; TLI = 0.87; CFI = 0.89). The degradation of the RMSEA and CFI compared to the outcomes of the M3 model (First-order constructs with reflective indicators: results of the confirmatory factor analysis of website and store images (CFA) section) is natural for a formative variable (MacKenzie et al., 2005; Wilcox, Howell, & Breivik, 2008).

All retained image dimensions are significant (t N 1.96) for both the stores and the website (Table 9). Regarding the store image construct, the elimination of the dimension “connections with other channels”, which contributes the least to the overall construct, appears to greatly improve the goodness-of-fit of the measurement model (RMSEA = 0.09; GFI = 0.92; AGFI = 0.88; NFI = 0.92; TLI = 0.90; CFI = 0.93); on the other hand, regarding website image, this elimination slightly degrades the goodness-of-fit indexes (RMSEA = 0.12; GFI = 0.86; AGFI = 0.81; NFI = 0.88; TLI = 0.87; CFI = 0.89). In accordance with Rossiter (2002) and given the innovative addressing of the multichannel dimension, this index retains 10 dimensions. Although the site and the stores are two distinct manifestations, a comparison of the loadings shows that the dimensions “layout” and “reputation” dominate the image of both channels; nevertheless, the perception of the site is highly influenced by its accessibility whereas the store's image is more influenced by advice.

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Table 4 Results of the factor analysis of website image in the final sample. Items

Communalities

Layout

LAYS2 LAYS3 LAYS1 LAYS5 LAYS4 PRIS3 PRIS6 PRIS5 PRIS2 PRIS4 OFFS4 OFFS5 OFFS6 OFFS3 OFFS2 OFFS1 CHAS4 CHAS2 CHAS3 CHAS1 SERS3 SERS1 SERS2 Variance explained (%) Eigenvalues Cronbach's alpha Bartlett's test KMO index

0.85 0.84 0.80 0.76 0.71 0.76 0.71 0.73 0.75 0.75 0.83 0.82 0.79 0.73 0.75 0.70 0.80 0.70 0.67 0.54 0.83 0.81 0.80

0.89 0.88 0.86 0.79 0.70

Items INFS5 INFS4 INFS6 PROS3 PROS2 PROS1 PROS4 INSS2 INSS1 INSS3 ACCS4 ACCS3 ACCS2 ACCS1 REPS2 REPS3 REPS1 Variance explained (%) Eigenvalues Cronbach's alpha Bartlett's test KMO index

Price

Offering

Connections with other channels website

Promotions website

0.86 0.85 0.81 0.81 0.81 0.90 0.90 0.86 0.85 0.80 0.78 0.87 0.85 0.80 0.70

37.15 14.86 0.93

9.74 3.90 0.91

6.68 2.67 0.94

4.98 1.99 0.84

0.92 0.88 0.87 4.48 1.79 0.88

Communalities

Advice

Customer service

Institution

Accessibility

Reputation

0.89 0.82 0.84 0.86 0.80 0.76 0.81 0.86 0.81 0.85 0.88 0.89 0.86 0.81 0.95 0.94 0.92

−0.92 −0.89 −0.89

0.00 (780 df) 0.94

−0.92 −0.86 −0.85 −0.82

−0.90 −0.90 −0.89 −0.88 0.93 0.93 0.91 0.83

4.19 1.68 0.91

3.83 1.53 0.92

3.50 1.40 0.90

3.02 1.21 0.94

−0.92 −0.91 −0.90 2.67 1.07 0.97

0.00 (780 df) 0.94

Second-order constructs with formative indicators: discriminant and nomological validity This step is to verify the discriminant and nomological validity by matching the formative construct to at least two theoretically related reflective constructs (Diamantopoulos & Winklhofer, 2001; MacCallum & Browne, 1993; MacKenzie et al., 2011). To perform this match, the relationships already established between the image of each channel, satisfaction and purchase attitude are modeled. Macintosh and Lockshin (1997, p. 489) define store satisfaction as “the customer's overall evaluation of the store experience”; likewise, “e-satisfaction is defined as the contentment of the customer with respect to his or her prior purchasing experience with a given electronic commerce firm” (Anderson & Srinivasan, 2003, p. 125). A great deal of research shows that channel image influences online shopping satisfaction (e.g., Szymanski & Hise, 2000) or store satisfaction (e.g., Bloemer & de Ruyter, 1998; Koo, 2003).

Occasionally, the literature mistakenly equates store or website image with a set of attitudes toward the channel considered (Doyle & Fenwick, 1974; Müller, 2008). Yet, in the theory of reasoned action, store image actually constitutes a core and founding belief of attitude which is defined as “a psychological tendency that is expressed by evaluating a particular entity with some degree or favor or disfavor” (Eagly & Chaiken, 1993, p. 1) and is centered on one particular behavior (here, purchase in the channel considered). In the field of e-commerce, Van der Heijden and Verhagen (2004) or Lim and Dubinsky (2004) demonstrate the positive influence of website image on online purchase attitude. In the multichannel field, Kwon and Lennon (2009) show that the image of each channel influences purchase attitude in the channel considered. The items of the reflective scales selected to verify the nomological validity of the measurement index are identical for both the website and the stores. That is, the scale used for measuring satisfaction with the analyzed channel is that of Macintosh and Lockshin (1997), which

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Table 5 Confirmatory results related to store and site images. Image dimensions

Construct definition and item descriptions

Stores

Website

Offering

Perception of brands, quality and range of products carried by the channel

CR = 0.93 AVE = 0.70

CR = 0.94 AVE = 0.71

t

λ

t

λ

OFF1 OFF2 OFF3 OFF4 OFF5 OFF6

The digital cameras seem to be of premium quality The digital cameras seem to have been well selected I can find all the top brands I can find a wide range of digital cameras I can find the various kinds of digital cameras I can find the digital cameras most suited to my needs

16.42 20.29 21.08 34.62 38.77 32.72

0.75 0.80 0.81 0.88 0.90 0.88

16.94 19.52 22.00 42.50 41.43 32.03

0.76 0.79 0.81 0.91 0.91 0.87

Price

Perception of price competitiveness in the channel

CR = 0.92 AVE = 0.68

PRI2

CR = 0.91 AVE = 0.68

t

λ

t

λ

The prices of the digital cameras are comparatively more competitive than those of competitors A lot of the digital cameras sold here are at very low prices. The prices of the digital cameras are attractive I can find low prices for all the digital cameras on offer All the digital cameras on offer seem cheaper than elsewhere

23.98

0.84

23.00

0.83

22.44 23.01 20.72 21.02

0.83 0.83 0.81 0.82

22.72 21.50 20.90 21.07

0.83 0.82 0.81 0.82

Layout

Perception of the look, clarity, ergonomics, atmosphere and arrangement of the channel

CR = 0.91 AVE = 0.68 t

λ

t

λ

LAY1

26.48

0.85

32.38

0.87

LAY2 LAY3 LAY4 LAY5

The website (store) is orderly, I understand immediately where to go The site (store) looks good I think the products are very easy to locate I like the atmosphere of the site (store) It's easy to navigate the site (walk around the store)

38.54 31.36 16.58 14.45

0.91 0.88 0.76 0.73

43.60 39.72 17.80 21.51

0.91 0.90 0.77 0.81

Accessibility

Perception of ease of access to the channel

CR = 0.90 AVE = 0.69 t

λ

t

λ

ACC1 ACC2 ACC3 ACC4

I have no trouble getting to the site (store) The site (store) is easy to find The site (store) is easy accessible I find it easy to get to the site (store)

26.53 14.28 27.69 23.21

0.86 0.73 0.87 0.84

26.86 39.11 57.15 51.75

0.84 0.90 0.94 0.93

Promotions

Perception of the frequency and visibility of promotions

CR = 0.90 AVE = 0.70 t

λ

t

λ

PRO1

The site (store) regularly has deals on digital cameras (lower prices, discounts, etc.) I can easily find the digital cameras related to the advertised discounts The discounts are prominently displayed The advertised discounts are easy to track down

14.49

0.73

18.99

0.79

22.70

0.83

25.37

0.84

36.65 29.95

0.91 0.88

39.79 31.72

0.91 0.88

Customer service

Perception of customer service as featured in the channel

CR = 0.89 AVE = 0.72 t

λ

t

λ

SER1 SER2

Customer service seems competent and fast It seems to me like I would have no return or exchange problems If there is a mistake, customer service gives me satisfactory solutions

22.88 24.53

0.85 0.86

21.1 21.43

0.83 0.84

22.16

0.84

24.22

0.87

Institution

Perception of the presentation of the retailer as featured in the channel

CR = 0.88 AVE = 0.71

INS1

Visiting the site (store) makes me more familiar with the history of this retailer Visiting the site (store) helps me understand better the mindset of the people working for this retailer Visiting the site (store) has made me more aware of the values promoted by this retailer

PRI3 PRI4 PRI5 PRI6

PRO2 PRO3 PRO4

SER3

INS2 INS3

CR = 0.93 AVE = 0.73

CR = 0.95 AVE = 0.81

CR = 0.92 AVE = 0.74

CR = 0.88 AVE = 0.72

CR = 0.90 AVE = 0.75

t

λ

t

λ

14.85

0.75

19.05

0.80

26.66

0.88

31.09

0.90

28.45

0.90

29.27

0.89

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Table 5 (continued) Connections with the other channels

CHA1 CHA 2 CHA 3 CHA 4

Advice

Perception of the connections between the channel analyzed and the other retail channels

CR = 0.95 AVE = 0.84 t

λ

On the site (in the store), I can find out if the products are available in the stores (on the site) On the site (in the store), I can find out about the discounts offered in the stores (on the site) On the site (in the store), I am aware of the events staged in the stores (on the site) On the site (in the store), I know what the store sells (what the site sells)

19.61

0.78

8.26

0.59

81.17

0.95

23.90

0.87

77.66

0.95

12.55

0.71

87.93

0.96

24.68

0.88

Perception of the advice given in the channel

CR = 0.93 AVE = 0.82

λ

t

CR = 0.91 AVE = 0.77

t

λ

t

λ

0.92 0.94 0.85

23.60 38.57 27.19

0.84 0.93 0.86

INF4 INF5 INF6

I get good advice I can get any advice I want I can easily get advice

41.59 49.46 27.72

Reputation

Perception of the retailer' reputation as conveyed by the channel

CR = 0.97 AVE = 0.91

REP1

When I see the site (stores), this retailer really seems trustworthy When I see the site (stores), this retailer seems honest When I see the site (stores), this retailer seems reliable

REP2 REP3

CR = 0.85 AVE = 0.59

CR = 0.97 AVE = 0.91

t

λ

t

λ

69.14

0.94

64.32

0.94

99.77 91.13

0.97 0.96

97.82 85.82

0.97 0.96

In the structural model “website”, website image also has a strong influence on satisfaction (0.72; p b 0.00) and on purchase attitude on the website (0.50; p b 0.00). The goodness-of-fit is equally acceptable here: 1005.33 χ 2(102); RMSEA = 0.08, SRMR = 0.05; GFI = 0.91; AGFI = 0.88; NFI = 0.94; TLI = 0.93; CFI = 0.94. These results corroborate the nomological validity of the website image construct. These various modeling processes help verify across the sample the nomological validity of the measurement index common to website and store images. The goodness-of-fit of the tested models can still be improved by working on more homogenous groups of shoppers (Calder, Phillips, & Tybout, 1981) and/or linking the formative construct to more endogenous and theoretically related variables (Bagozzi, 2007). Eliminating the dimension “connections with the other channels” might also greatly improve the goodness-of-fit of the structural model “store” (601.64 χ2(88); RMSEA = 0.06; CFI = 0.97); however, this deletion would change neither the meaning nor the intensity of relationships. On the other hand, the goodness-of-fit indexes poorly improve when the dimension “connections with the other channels” is eliminated from the website construct image (884.02 χ 2(88) ; RMSEA = 0.08; CFI = 0.95). As in the MIMIC performed to validate the measurement index, this result confirms the significance of the multichannel dimension in the definition of website image.

is seamlessly transposable across channels and has identical psychometric performance for both channels. The scale used for measuring purchase attitude is adapted from the scale of Jarvenpaa, Tractinsky, and Vitale (2000). The scales have good psychometric qualities (Table 10). Firstly, the intercorrelations between the measurement model variables are examined. All of them are lower or very close to the 0.71 acceptance threshold set by MacKenzie et al. (2005). The discriminant validity of the two measurement indexes is verified. Secondly (Fig. 4), two structural models (one connecting store image, satisfaction, and purchase attitude in-store, the other website image, satisfaction and purchase attitude on the website) verify the nomological validity, that is, “the ability of a new measure to perform as expected in a network of known causal relations and well-established measures” (Loiacono et al., 2007, p. 68). In the “store” structural model, store image has a strong influence on satisfaction (0.73; p b 0.00) and on purchase attitude in-store (0.56; p b 0.00). Given the sample size as well as the number of dimensions factored into the definition of the store image construct, the model's goodness-of-fit is acceptable: 950.81 χ2(102); RMSEA = 0.08; SRMR = 0.05; GFI = 0.92; AGFI = 0.89; NFI = 0.94; TLI = 0.94; CFI = 0.95. These results corroborate the nomological validity of the store image construct.

Table 6 Convergent and discriminant validity for store image. Store image

1

2

3

4

5

6

7

8

9

10

AVE

0.70

0.68

0.68

0.69

0.70

0.72

0.71

0.84

0.82

0.91

1. r2 Offering 2. r2 Price 3. r2 Layout 4. r2 Accessibility 5. r2 Promotions 6. r2 Customer service 7. r2 Institution 8. r2 Connections other channels 9. r2 Advice 10. r2 Reputation

1 0.08 0.27 0.14 0.23 0.15 0.07 0.01 0.22 0.26

1 0.14 0.06 0.21 0.13 0.21 0.19 0.17 0.11

1 0.25 0.25 0.22 0.19 0.05 0.34 0.31

1 0.11 0.12 0.07 0.01 0.11 0.17

1 0.12 0.13 0.08 0.20 0.14

1 0.18 0.05 0.26 0.33

1 0.18 0.19 0.18

1 0.04 0.02

1 0.25

1

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Table 7 Convergent and discriminant validity for website image. Website image

1

2

3

4

5

6

7

8

9

10

AVE

0.71

0.68

0.73

0.81

0.74

0.72

0.75

0.59

0.77

0.91

1. r2 Offering 2. r2 Price 3. r2 Layout 4. r2 Accessibility 5. r2 Promotions 6. r2 Customer service 7. r2 Institution 8. r2 Connections other channels 9. r2 Advice 10. r2 Reputation

1 0.09 0.23 0.22 0.32 0.10 0.07 0.05 0.17 0.24

1 0.18 0.05 0.19 0.17 0.26 0.17 0.24 0.14

1 0.34 0.28 0.19 0.17 0.14 0.31 0.37

1 0.18 0.12 0.05 0.05 0.09 0.32

1 0.14 0.13 0.13 0.20 0.18

1 0.16 0.14 0.18 0.25

1 0.20 0.20 0.16

1 0.15 0.11

1 0.18

1

Beyond this nomological validation of measurement indexes, the comparison of the structural parameters between image, satisfaction and attitude toward purchasing in the channel shows that at a conceptual level, channel image cannot be considered as an attitude; in fact, image has less influence on attitude than on satisfaction. At an operational level, the merchant site and the stores can be considered as absolutely comparable channels of distribution. Not only do their respective images influence attitude and satisfaction in a very similar way but also the image dimensions that influence those two variables the most are nearly identical for the site and the stores. Backward linear regressions performed additionally (tables available upon request) show that three same image dimensions determine purchase attitude. For the stores, these include reputation, offering and then advice: for the site, they include advice, reputation and then offering. These results corroborate Lim and Dubinsky's (2004). Similarly, satisfaction is always dominated by the dimensions “reputation” and “layout”, along with a third dimension: “accessibility” for the site and “advice” for the stores. These dimensions also dominate the image of both channels (Secondorder constructs with formative indicators: multicollinearity test and external validity section).

Offering

Price

Layout

Accessibility

Promotions

Customer service

IMG2 Website (or store) image IMG3

Institution

Connection with other channels

Second-order constructs with formative indicators: measurement index cross-validation Even if the absence of any modification when developing scales makes cross-validation less necessary (MacKenzie et al., 2011), the final step of this research is to verify the measurement invariance of the newly developed index. This verification can be performed by running multi-group analyses on subsamples. Analysis of the retailer's databases shows that the sample used for developing first-order and second-order constructs consists of three widely different subsamples in terms of actual purchase behavior. That is, even if they all visit the website and the stores regularly, 1015 customers have in the last two years bought from the retailer's website and stores, 152 customers have bought from the retailer's stores but not from its website, and 311 customers have only bought from its website. To this day, only Diamantopoulos and Papadopoulos (2010) have empirically used type II formative construct cross-validation (Fig. 2). Those authors test the measurement invariance on the MIMIC of Fig. 3. After checking over the measurement invariance of the reflective indicators used in the MIMIC, the structural invariance of the MIMIC is verified by comparing fit between the M1 model, which leaves unconstrained all parameters except for those corresponding to the two reflective items (IMG2 and IMG3) and a M2 model where only factor scores are constrained (except for the terms of error of the two reflective indicators IMG2 and IMG3 which remain constrained). The Chisquare test demonstrates configural invariance between the three subsamples (non-rejected null hypothesis) for both store (no significant difference between the models M1 and M2: Chi Square test = 15.98 for 20 df) and website images (no significant difference between the models M1 and M2: Chi Square test = 25.37 for 20 df). However, measurement gaps become significant when both factor scores and terms of error are constrained (M3): store (significant difference between the models M1 and M3, full metric invariance rejected: Chi Square test = 81.5 for 41 df); website (significant difference between the models M1 and M3, full metric invariance rejected: Chi Square test = 68.48 for 41 df). As configural invariance is more necessary than full metric invariance (Diamantopoulos & Papadopoulos, 2010; Steenkamp & Baumgartner, 1998), the study successfully cross-validates the measurement indexes developed in this research. Thus the robustness of the measurement index is demonstrated for each channel.

Discussion and implications

Advice

Reputation

Fig. 3. MIMIC of website or store image.

In an environment that increasingly encourages retailers and their customers to compare the image of the various retail channels available (Chiang & Dholakia, 2003; Sinha & Banerjee, 2004), this research aims to propose and test a measurement instrument common to website and store images. By carefully observing the rigorous validation process of formative constructs using structural equations, the study results in a particularly comprehensive measurement index that culls 10 channel

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Table 8 Confirmatory results for store and website reflective images. Overall website and store images

IMG1 IMG2 IMG3

Overall, the website (the store) looks good Overall, the website (the store) is pleasant Overall, the website (the store) is attractive

image dimensions (offering, price, layout, accessibility, promotions, customer service, advice, reputation, institution, connections with other channels) from the 40 strictly identical items for the website and the stores. The number of tests run and the large size of the sample of real customers underpinning the study make for good external validity.

Research implications Developing any new instrument model is useful for research as long as this work helps better describe a phenomenon by conforming to all psychometric criteria. As a result, researchers save considerable time and energy to focus on the crux of their research (Loiacono et al., 2007). Three conceptual or methodological contributions deserve mention here. The first one relates to delineating the scope of website and store images. That is, unlike previous research on site or store image, this research does not merely define new measurement scales but verifies that they effectively fall into the concept of channel image in their aggregated form. At this level, the still uncommon addressing of formative image constructs using structural equations helps avert past misspecifications. The second contribution relates to defining reliable and valid scales for evaluating each image dimension of the channels, including those underrepresented or totally missing from the literature. They include reputation, accessibility or connection with the other retail channels. Not only is the retailer' reputation conveyed by the channel one of the most important dimensions of site and store images but it is also one of the main antecedents of satisfaction and purchase attitude in both channels. Similarly, accessibility highly influences website image and online satisfaction; this result confirms that merchant sites need excellent ranking. The connection with the other retail channels is particularly appropriate for evaluating website perception in a multichannel environment. In fact, website image seems to be much more dependent on its connection with the other retail channels than store image is. However, developing a measurement scale that merges the sub-

Table 9 Results of MIMIC between formative image variable and reflective image variable. Channel image

Accessibility Connections with other channels Advice Layout Institution Offering Price Promotions Reputation Customer service Variance explained (R2)

Store image

Website image

t

λ

t

λ

7.09 3.33 10.98 18.46 7.52 9.51 6.07 8.09 14.57 9.11 67.9%

0.54 0.32 0.67 0.78 0.56 0.63 0.49 0.58 0.74 0.62

9.85 5.86 9.15 21.13 6.84 8.53 6.87 9.16 15.28 7.46 71.8%

0.64 0.48 0.62 0.82 0.53 0.59 0.53 0.62 0.75 0.55

Stores

Website

CR = 0.94 AVE = 0.84

CR = 0.94 AVE = 0.84

t

λ

t

λ

30.42 53.66 40.04

0.87 0.96 0.91

32.49 52.13 37.70

0.88 0.96 0.90

dimensions “advice” and “information” for the store and the website could be an avenue to explore in the future. The third benefit of this research is certainly the index's capacity to evaluate merchant website and store images in the exact same way. As the literature review and the prestudies suggested, measuring the image of these two channels from the same dimensions and manifest indicators is absolutely possible. In fact, the influence of their image dimensions is very similar. Retaining some items that are more necessary for one or the other helps compare each of the store and website image dimensions within identical scopes. This process does not affect in any significant way the models' goodness-of-fit as all the scales used have excellent psychometric qualities. For these three reasons, the definition and validation of the index proposed here is a first that arguably fills a gap in the existing research. Finally, even if the number of methodological articles on formative construct validation is on the rise, this research ranks among the few studies that have implemented those methods. Retail implications This measurement index enables retailers to better control and upgrade that image. This tool harnesses website and store characteristics that are very operational and easily actionable as they correspond to the classic variables of the retailing mix (Reardon et al., 1995). Implementing this measurement instrument allows retailers to get a very clear and comprehensive picture of the features of their website or stores that should be enhanced first and foremost to attract and retain customers in the channel. This valuable and precise information can be obtained without increasing the time and cost of data collection through the Choice by Processing Attributes method. This study also demonstrates that a website can be described and analyzed along the same lines as a store, thus enabling retailers to mesh together their channels and better handle their multichannel management. Depending on the multichannel strategy pursued by the retailer, the contribution of this index can be two-fold: this index helps calculating more accurately the price or offering gaps conducive to guiding customers to the stores or site, and identify the site and store image features that must remain congruent in order to smooth out customers' cross-channel path and build consistency in the retailer's image. Also, this index helps analyze competitive positioning by comparing a retailer's channel image against the website and store images of its competitors. In fact, a logical continuation of this research would involve translating this index to the catalog for comparing its image with website and store images. The 10 dimensions addressed here can apply to the catalog, with a few adaptations. Limitations The main limitation of this study relates to the systematic nature of the analytical method as criticized by Zimmer and Golden (1988). All the expected dimensions must be evaluated in the provided

Please cite this article as: Bèzes, C., Definition and psychometric validation of a measurement index common to website and store images..., Journal of Business Research (2014), http://dx.doi.org/10.1016/j.jbusres.2014.03.016

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C. Bèzes / Journal of Business Research xxx (2014) xxx–xxx

Table 10 Confirmatory results for channel satisfaction and purchase attitude. Channel satisfaction

SAT1

Satisfaction with the website and stores

When I consider my experience at this store (this website), I am very dissatisfied (1) vs very satisfied (7) In general when I think of this store (this website), I am very dissatisfied (1) vs very satisfied (7) When I come out of this store (this website), I am usually very dissatisfied (1) vs very satisfied (7)

SAT2 SAT3

Purchase attitude

AA1

AA3

measurement index and none can be removed or added by the respondents. Besides, this analytical approach, according to Keaveney and Hunt (1992), would tend to suggest that store image is always considered by customers as a totally new experience. Yet these authors contend that customers immediately examine how the store has common characteristics with the store category or the prototype, which presumably activates pre-established patterns as image and pattern can “simplify and structure complex, abstract information” (Keaveney & Hunt, 1992, p. 170). Finally, another limitation relates to the country and the retailer in which the index is tested and defined. Replicating the index across many retailers from other countries than France could help verify the consistency of the index.

Offering

Price

Layout

SAT1

Accessibility

SAT3

Website (or store) image Purchase attitude on the website (instore)

Institution

Connection with other channels

SAT2

Website (or store) satisfaction

Promotions

Customer service

Website CR = 0.94 AVE = 0.84

t

λ

t

λ

42.89

0.92

45.07

0.93

57.48

0.97

57.60

0.97

28.61

0.86

28.56

0.86

CR = 0.94 AVE = 0.85

I like the idea of buying a digital camera on this website (in this store) Using this website (this store) to buy a digital camera would be a good idea The idea of using this website (this store) to buy a digital camera is appealing

AA2

Stores CR = 0.94 AVE = 0.84

AA1

AA2

AA3

Advice

Reputation

Fig. 4. Structural model used to verify the measurement index nomological validity.

CR = 0.95 AVE = 0.86

t

λ

t

λ

37.35

0.89

44.26

0.91

56.80

0.95

58.74

0.95

45.78

0.92

45.68

0.92

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Please cite this article as: Bèzes, C., Definition and psychometric validation of a measurement index common to website and store images..., Journal of Business Research (2014), http://dx.doi.org/10.1016/j.jbusres.2014.03.016