Roles of Traditional, Online‐Specific, and Cross‐Channel Marketing Instruments in Multi‐ and Omnichannel Fashion Retail Brand Equity

Klink, Angelina; Swoboda, Bernhard Article — Published Version Roles of Traditional, Online‐Specific, and Cross‐Channel Marketing Instruments in Multi‐ and Omnichannel Fashion Retail Brand Equity Journal of Consumer Behaviour Provided in Cooperation with: John Wiley & Sons Suggested Citation: Klink, Angelina; Swoboda, Bernhard (2025) : Roles of Traditional, Online‐Specific, and Cross‐Channel Marketing Instruments in Multi‐ and Omnichannel Fashion Retail Brand Equity, Journal of Consumer Behaviour, ISSN 1479-1838, Wiley, Hoboken, NJ, Vol. 24, Iss. 2, pp. 1017-1035, https://doi.org/10.1002/cb.2453 This Version is available at: https://hdl.handle.net/10419/329821 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Journal of Consumer Behaviour, 2025; 24:1017–1035 https://doi.org/10.1002/cb.2453 1017 Journal of Consumer Behaviour RESEARCH ARTICLE Roles of Traditional, OnlineSpecific, and CrossChannel Marketing Instruments in Multiand Omnichannel Fashion Retail Brand Equity AngelinaKlink | BernhardSwoboda Chair for Marketing and Retailing, University of Trier, Trier, Germany Correspondence: Bernhard Swoboda ([email protected]) Received: 7 May 2024 | Revised: 18 November 2024 | Accepted: 19 December 2024 Keywords: categorization theory| longitudinal modeling| offline and online retail brand equity| reciprocity ABSTRACT Multiand omnichannel retailers use various marketing instruments to position themselves as strong brands. However, we know surprisingly little about how strongly retail brand equity (RBE) benefits from the traditional, onlinespecific, or crosschannel instruments that consumers perceive when switching channels. This study fills this gap by leveraging categorization theory. It contributes to the literature by analyzing the roles of important marketing instruments for consumer loyalty decisions through offline and online RBE via a sample of 379 consumers surveyed at three different points in time as well as sequential mediation and crosslagged structural equation modeling. The findings highlight the distinct importance of the indirect and total effects of the instruments for loyalty through offline and online RBE. Furthermore, offline and online RBE reciprocally affect loyalty decisions to different extents, providing additional insights into the roles of the instruments. These findings have direct implications for managers seeking to understand the role of marketing instruments and the interactive role of offline and online RBE in customer loyalty. 1 | Introduction Multichannel firms integrating offline and online channels and omnichannel firms offering a seamless experience across touchpoints use various marketing instruments (MIs) to increase their positioning as strong brands (e.g., Verhoef, Kannan, and Inman2015; Wichmann etal.2022). Retail brand equity (RBE), that is, consumer associations of a retail firm as a unique, attractive, and strong brand, is a major source of competitive advantage (Keller2010; Zhang, Vorhies, and Zhou2023). Multior omnichannel firms such as H&M(2024) or Adidas(2024) aim for similar offline and online brand positions but emphasize the vital roles of their physical stores. In contrast, Victoria's Secret (2024) and Zara(2022) differentiate offline and online brand environments, for example, by striving for an enjoymentbased brand position offline and an anywhereanytime brand position online. In all cases, firms should know whether MIs have different values for offline and online RBE. We thus analyze important MIs. Traditional MIs such as assortment or price are recognized by shoppers across offline and online sales channels and are said to be losing their importance (e.g., Timoumi, Gangwar, and Mantrala2022; Wichmann etal.2022). Onlinespecific MIs may be accessed differently but are channelspecific, as website ease of navigation or security is perceived online (e.g., Khan and Rahman2016; Toufaily and Pons2017). Crosschannel MIs include channel integration and consistency, linking retailers' channels, and providing experience for consumers across channels (e.g., Gao and Huang2021; Lee etal.2019). We believe that MIs affect consumer loyalty differently through offline and online RBE and that the two types of RBE interact, which may change the role of MIs. Studies have mostly analyzed traditional MIs, followed by onlinespecific and crosschannel MIs, as drivers of RBE (e.g., © 2025 John Wiley & Sons Ltd. 1018 Journal of Consumer Behaviour, 2025 AlHawari2011; Baek etal.2020; Frasquet and Miquel2017; see Table1 and, for details, Supporting Information: Appendix A).1 These authors seldom combine types of MIs or address multiand omnichannel retailing. The effects of both traditional and onlinespecific MIs on RBE have been studied only by Ray etal.(2021) and White, JosephMathews, and Voorhees (2013), who partly show the contradictory strengths of effects, for example, of online (vs. offline) services. Few scholars have linked crosschannel MIs rather generally with brands (e.g., Li etal.2018; Lee etal.2019), while effects through offline and online RBE have not yet been studied. Several studies have analyzed offline versus online retail brands. For example, Allaway etal.(2011) consider the impacts of assortment or price, and Khan and Rahman (2016) explore those of website design, usage, and navigation. Only Kwon and Lennon(2009a) simultaneously examine the effects of traditional and onlinespecific MIs on purchase intention through retailers' offline and online brand image. However, onlinespecific MIs negatively affect consumers' offline brand attitudes. The relative importance of traditional, onlinespecific, and crosschannel MIs for RBE, particularly offline and online RBE, which likely interact reciprocally, has not yet been studied. We observe important research gaps and contradictory insights in the literature and address them by answering the following research questions: How do traditional, onlinespecific, and crosschannel MIs influence offline and online RBE? Which type of MI is most important for consumer loyalty? Are there reciprocal relationships between offline and online RBE, and if so, how do these relationships influence the effects of MIs? We offer two important research contributions. We contribute to our knowledge by analyzing the impacts of MIs on consumer loyalty through offline and online RBE as well as their total effects (the sum of all direct and indirect effects) and thus relative importance for loyalty. We utilize these MIs for several reasons; they are independently perceived by consumers and designed by retailers and are important for engaging consumers and strengthening RBE (Bendoly etal.2005; Timoumi, Gangwar, and Mantrala2022). Importantly, scholars question the role of traditional MIs in modern retail, whereas others call for further examination (e.g., Blut, Teller, and Floh2018; Wichmann etal.2022). We argue that knowledge of the roles of MIs in offline and online RBE is essential, as the respective firm environments, competitors, and shopper associations differ in the two contexts (Swaminathan etal.2020; Troiville, Hair, and Cliquet2019). Moreover, whereas studies refer to different theories, we contribute to the application of categorization theory in our context. Indeed, individuals are known to simplify information processing because there is much salient information in demanding multiand omnichannel contexts. Individuals match information to their subcategory knowledge, such as RBE, and draw inferences that promote decisions (e.g., Alba and Hutchinson1987). For firms, we show which MIs most strongly influence loyalty decisions and identify offline or online paths (also contrasted with overall RBE in stability checks). The respective consumer insights are valuable, as even most omnichannel retailers have not yet completely integrated all sales channels or touchpoints. We further contribute to our knowledge by analyzing the reciprocal effects of online and offline RBE on consumer loyalty. Thus, we explore the feedback relationships between these constructs, as one association likely affects the other, and vice versa (Keller2010). Swoboda and Winters(2021) stress important differences between reciprocally and individually analyze firms' offline and online images, such as underestimated effects. Our study of both RBEs provides further insights into the paths of MIs. Indeed, scholars have called for studies to reveal all relationships (e.g., Iglesias, Markovic, and Rialp 2019; Kwon and Lennon2009a; Swoboda and Winters2021). When making decisions, consumers reciprocally transfer knowledge and draw inferences between the respective subcategories in both directions to different degrees (e.g., Fiske etal.1987). For multiand TABLE 1 | Literature review. Marketing Instruments Traditional Onlinespecific Crosschannel Overall retail brand Baek etal.(2020); Bruhn, Schoenmueller, and Schafer (2012); Çifci etal. (2016); Datta, Ailawadi, and Van Heerde(2017); Huang and Sarigöllü (2012); Iglesias, Markovic, and Rialp(2019); Omar etal. (2021); Rajavi, Kushwaha, and Steenkamp(2019); Swoboda, Weindel, and Hälsig(2016); Zollo etal. (2020) AlHawari(2011) Frasquet and Miquel(2017)b; Gao and Huang(2021)a; Lee etal.(2019)a; Li etal.(2018)a; SchrammKlein etal.(2011)b Ray etal.(2021); White, JosephMathews, and Voorhees(2013)b— Offline and/ or online retail brand Allaway etal.(2011); Beristain and Zorrilla (2011); Das(2015), GilSaura etal.(2013) Khan and Rahman(2016); Khan etal. (2020); Lin and Lee (2012); Toufaily and Pons(2017)b — Kwon and Lennon(2009a)b— Note: Italic = studies on RBE (further ones on brand trust, image/attitudes, engagement etc.). aOmnichannel studies. bMultichannel studies. 1019 omnichannel retailers, especially those striving for different offline and online brand positioning, the respective insights are valuable because they reveal possible synergies and enhanced conclusions regarding MIs. In summary, this study analyzes the effects of traditional, onlinespecific, and crosschannel MIs on consumer loyalty through offline and online RBE as well as interactions between offline and online RBE. The remainder of this study proceeds as follows. By drawing from theory, the authors develop hypotheses and test them based on consumer evaluations of leading fashion firms in two studies. After the results are presented, implications and directions for future research and practice are provided. 2 | Theory and Hypotheses 2.1 | Conceptual Framework To address our aims, we conceptualize three types of MIs facing consumers shopping at multiand omnichannel retailers as salient information attributes (see Figure1). We choose these MIs for the abovementioned reasons and because they are mostly often studied, albeit mostly separately, and they are most important for consumers shopping in major sales channels at multiand omnichannel retailers (e.g., Hänninen, Kwan, and Mitronen2021; Timoumi, Gangwar, and Mantrala2022). Each MI, including several elements, for example, price or assortment for traditional MIs, is conceptualized, selected in a systematic process on the basis of a review of the brandrelated literature (see Supporting Information: Appendix A) methodologically on a secondorder basis, and grouped without overlap whenever possible. We select the most often studied elements. First, traditional MIs reflect the conventional, most often studied, and important elements (assortment, price, layout, and communication) that multiand omnichannel retailers provide in online and offline sales channels (Blut, Teller, and Floh2018; Datta, Ailawadi, and Van Heerde2017; Swoboda, Weindel, and Hälsig 2016). For example, consumer perceptions of assortment (e.g., quality of all articles offered) and price (e.g., value for money) are of major importance for firm differentiation and consumer attraction and yet are said to lose relevance for multiand omnichannel brand positioning (e.g., Emrich, Paul, and Rudolph2015; Troiville, Hair, and Cliquet2019; Wichmann etal.2022). Assortment, price, layout, and communication exert individual effects but are conceptually integrated in this study (e.g., Swoboda, Weindel, and Hälsig2016). Second, out of many online options that consumers have at multiand omnichannel retailers, we focus on website elements (following Kwon and Lennon 2009a, 2009b; MontoyaWeiss, Voss, and Grewal2003). To avoid overlaps, we refer to these as onlinespecific MIs, even if they are accessible in physical stores as well. The authors differentiate functional elements such as the aesthetics/architecture of a website and ease of navigation (Bressolles, Durrieu, and Deans2015; Toufaily and Pons2017) and relational elements such as specific services/content and security/privacy (AlHawari2011; Toufaily and Pons2017). These elements form the first level of an online shopping experience and thus are particularly designed to compete in online markets (Bolton etal.2022; Khan and Rahman2016), where their importance for consumer loyalty is shown (Toufaily and Pons2017), but not for offline and online RBE (e.g., Bressolles, Durrieu, and Deans2015). Third, we conceptualize crosschannel MIs that represent the degree of channel integration and convey both retailer sales channel synergies and consumer channel switches (e.g., interactive activities across offline and online sales channels; Frasquet and Miquel2017; Gao and Huang2021; Li etal.2018). These are essential for the consumer shopping experience, and firms such as Zara pursue success through their respective integration (Shen etal.2018; Zara2022). We thus integrate perceived online–offline and offline–online integration with channel consistency (e.g., Lee etal.2019) because it is essential for multiand omnichannel strategies (e.g., Timoumi, Gangwar, and Mantrala2022). RBE is defined as brand awareness and strong, unique, and favorable brand associations in consumer memory (Keller1993, 2010; Zhang, Vorhies, and Zhou2023). We follow this theoretical and holistic approach, which is most often used and cited in brand research (RojasLamorena, Del BarrioGarcía, and AlcántaraPilar2022), further develop it for retailers, and conceptually distinguish offline and online RBE for several reasons. First, the ways in which brands behave online and offline differ (Keller2010, 2016). Therefore, it is important for multiand omnichannel firms to differentiate themselves with both RBEs from competing brands that are active in both contexts and to evaluate the two more than overall RBE (e.g., Verhoef, Kannan, and Inman 2015; Wichmann et al. 2022). Second, FIGURE 1 | Conceptual framework. 1020 Journal of Consumer Behaviour, 2025 scholars argue that offline and online stores are too different to be merged into an overall RBE conceptualization without compromising validity (Swaminathan et al. 2020; Troiville, Hair, and Cliquet2019). Third, retailers need to consider offline and online contexts simultaneously, as the major effects of an online brand strategy are contingent on offline activities, and vice versa (Zhang, Vorhies, and Zhou2023). Fourth, customers can perceive RBE channelspecifically through available information (Keller2016), refer to familiar offline or online knowledge to draw inferences, and process information more efficiently for decisionmaking (Epitropaki and Martin2005; Goodstein1993). Differentiation allows us to understand how customer behavior overlaps (Keller 2020). Fifth, as mentioned, leading retailers differentiate offline and online brand environments and positioning. Consumer loyalty is conceptualized as a willingness to recommend and patronize retail firms (Oliver 1999; Sirohi, McLaughlin, and Wittink 1998; Swoboda and Winters 2021). Conative loyalty is a specific focus because it represents a deep longterm commitment to a retail brand, evokes a sense of attachment, and is more readily measured than are objective shopping data (e.g., Gao and Huang2021). Repurchase intention is alternatively tested for stability reasons. 2.2 | Theory The literature in Table 1 adopts different theories; signaling theory assumes stimulus effects on RBE (e.g., Rajavi, Kushwaha, and Steenkamp2019), and social exchange theory (e.g., Lee etal.2019) and shopping goal theory (e.g., Swoboda, Weindel, and Hälsig2016) are also considered. However, reciprocity is hardly explained (for further details, see Supporting Information: Appendix A). We contribute to the field by applying categorization theory, which captures the complexity of consumer cognition in multiand omnichannel settings very well. This theory accounts for cognitive structures in consumer decisions and suggests that individuals are more likely to use a categorical mode of thinking by categorizing information and existing associations if the information environment is demanding (such as in multiand omnichannel shopping; e.g., Epitropaki and Martin2005; Timoumi, Gangwar, and Mantrala2022). This theory fits our study design and provides a rigorous explanation for reciprocal associations. Accordingly, people hierarchically structure knowledge in their memory for efficient information processing (Fiske etal.1987). Individuals categorize environments and objects and compare their perceptions with existing basic categories or lowerlevel category members (subcategories) and associated attributes in memory (Keaveney and Hunt1992; Puligadda, Ross Jr, and Grewal2012). Perceptions of respective unfamiliar salient information attributes evolve into novel basic categories and subcategories; those of familiar information match a basic category or subcategory knowledge (Fiske etal.1987). Therefore, individuals rely on the most familiar and representative attributes and subcategory knowledge to draw the strongest inferences (Alba and Hutchinson1987; Goodstein1993; Loken2006). Moreover, knowledge is transferred among subcategories, and reciprocal inferences are drawn, while the congruence of subcategories expands these inferences in both directions (Cohen and Basu1987; Mervis and Rosch1981). Category knowledge and inferences are used for judgments in decisions; that is, they affect individual behavior (Epitropaki and Martin2005; Keaveney and Hunt1992). In our context, retailers are the basic category, offline and online RBE are subcategories, and MIs are perceived as salient information attributes by consumers (e.g., GilSaura etal.2013; Toufaily and Pons2017). This theory suggests that consumers perceive MI attributes and match them with online and offline RBE associations. The more representative or familiar the attributes are for consumers, the stronger the matches with the respective RBE. However, MIs match not only online RBE, for example, but also offline RBE. Moreover, consumers draw inferences among RBEs and between such subcategories and the retailer. For example, the reciprocal effects of subcategories are based on perceived RBE congruence, as consumers draw inferences from offline to online RBE, and vice versa (this is also related to schema theory; Epitropaki and Martin2005; Wang, Beatty, and Mothersbaugh2009). Accordingly, consumer category knowledge and inferences are drawn in loyalty decisions (e.g., Loken2006). Next, we derive hypotheses by providing insights into the extent of empirical knowledge for each hypothesis and then by referring to theoretical mechanisms for indirect and total effects of MIs on loyalty through RBE (not assuming direct effects but testing for them) and for RBE's reciprocal effects. 2.3 | Hypotheses on the Effects of MIs Considering traditional MIs, studies often analyze the effects of individual MIs such as assortment or layout on offline or general brand associations (e.g., Rajavi, Kushwaha, and Steenkamp 2019; Swoboda, Weindel, and Hälsig 2016). Only Kwon and Lennon(2009a) show the effects of traditional MIs on purchase intention through consumers' offline and online brands' attitudes but do not theorize as such. We provide novel categorization theorybased reasoning and expect different paths of traditional MIs through offline and online RBE. Traditional MIs are salient information attributes and are categorized by consumers into a matched subcategory, that is, the online or offline RBE of a multiand omnichannel retailer. Consumers refer to their brand associations and draw inferences for loyalty judgments (e.g., Loken2006). However, the attributes can be matched to different subcategories, such as both RBEs, anchored in consumers' memory and enabling specific information processing (Mervis and Rosch 1981; Timoumi, Gangwar, and Mantrala2022). Traditional MIs affect offline RBE and online RBE (Kwon and Lennon2009a), as consumers switch between channels to compare their offers (mostly assortment and prices in offline and online channels; Emrich, Paul, and Rudolph 2015; Haridasan, Fernando, and Balakrishnan2021). The complementary nature of a traditional MI makes it usable for both RBEs (e.g., Melis etal.2015). Since a match with both RBEs is theoretically obvious, we assume inferences drawn from both subcategories in loyalty decisions (e.g., Loken2006). 1021 However, as salient information attributes match more strongly with the most familiar subcategory, we assume different strengths for traditional MIs through the offline and online RBE paths. Owing to the rapid growth of online channels, consumer perceptions can strongly match traditional MIs with online RBE (e.g., Wichmann etal.2022). However, as most multior omnichannel retailers start as former brickandmortar stores, their offline sales channel continues to play a central role for consumers (e.g., Troiville, Hair, and Cliquet2019). A stronger match with offline RBE is likely, and most consumers more easily retrieve such MIs (e.g., GilSaura etal.2013; Keller2010). Matching attributes on the basis of such prior exposure leads to the strongest activation (e.g., Goodstein1993). For example, the perception of a retailer's assortment evokes the associations with which the consumer is most familiar. Consumers rely on this familiar subcategory to draw strong inferences (e.g., Zhang, Vorhies, and Zhou2023). As consumers use the strongest inferences when making decisions, we conclude that traditional MIs have a stronger effect on loyalty via offline (vs. online) RBE (Loken2006). We hypothesize the following: H1. Traditional MIs have a positive indirect effect on loyalty through (a) offline RBE and (b) online RBE, where (c) the indirect effect through offline (vs. online) RBE is stronger. Considering onlinespecific MIs, scholars have studied the effects of individual MIs, for example, website aesthetics or security, through online and overall RBE (e.g., Khan and Rahman2016; Toufaily and Pons2017). Again, only Kwon and Lennon(2009a) reveal the paths of onlinespecific MIs through consumers' offline and online brand attitudes (not RBE), without discussing their relative importance and showing surprising negative paths through offline brand attitudes. We initially provide categorization theorybased reasoning for the different effects of onlinespecific MIs via both RBEs. Onlinespecific MIs are also perceived as salient information attributes, mostly while accessing a retailer's website (e.g., Khan and Rahman2016). Owing to this channel specificity, it seems obvious that shoppers match onlinespecific MIs with online RBE and draw respective inferences (e.g., Chen and Dibb2010). However, onlinespecific MIs can crosswise match offline RBE as well. For example, if a customer faces a choice between two attractive offline retail brands, he or she may also leverage knowledge from a retailer's website when choosing which brand to purchase (Timoumi, Gangwar, and Mantrala2022). We assume that onlinespecific MIs match online and offline RBE when affecting consumer loyalty. Different path strengths of onlinespecific MIs on loyalty through online and offline RBE are theoretically expected. It is conceivable that these MIs are more strongly linked to offline RBE because, in shoppers' mindsets regarding a firm, onlinespecific MIs may be anchored to the offline RBE origin of many retailers (e.g., Ratchford et al. 2022). However, these MIs are salient information attributes from online channels. A stronger channelspecific match based on representativeness is more likely (Toufaily and Pons2017). Without much cognitive effort, shoppers subordinate onlinespecific MIs to the online RBE of a retailer (i.e., draw inferences, Cohen and Basu1987). Websites serve as digital shopping windows, are ubiquitously available, and are used by former brickandmortar firms such as Snipes to strengthen their online brands (e.g., Forbes2021; White, JosephMathews, and Voorhees 2013), because of representativeness and respective onlinespecific inferences in loyalty decisions (Sohn 2017). We assume a stronger indirect effect on loyalty through online (vs. offline) RBE. Therefore, we propose the following: H2. Onlinespecific MIs exert a positive indirect effect on loyalty through (a) offline RBE and (b) online RBE, where (c) the indirect effect through online (vs. offline) RBE is stronger. Scholars have linked crosschannel MIs with the brand identity or engagement of multiand omnichannel firms (e.g., Lee et al. 2019; Li etal. 2018). To our knowledge, important and probable crosschannel MIs' effects through offline and online RBE have not been theorized. The consideration of crosschannel MIs as salient information attributes serves the channellinked evaluation of categorized brand knowledge for inferences in decisions (e.g., Lee etal.2019). The attributes match online and offline RBE when consumers associate them with a particular sales channel. Such channelspecific associations are transferred to draw inferences about loyalty to a retailer (e.g., Gao and Huang2021). For example, when making a purchase at an Adidas online store, a consumer can have the product delivered or pick it up in store, which may match the online brand and positively translate into category knowledge and consumer loyalty (Adidas2024; Frasquet and Miquel2017). In contrast, when making a purchase at an Adidas offline store, consumers can order products online that are out of stock, which may match the offline brand and its path for loyalty. Crosschannel MIs match both types of RBEs (see also Frasquet and Miquel2017 for halo effects). As the strength of a match of crosschannel MIs depends on channelspecific familiarity or relevance, differences in the strength of effects on loyalty through RBEs are likely (e.g., Melis etal.2015; Sohn2017). We can theoretically argue that the match of crosschannel MIs and offline (vs. online) RBE is stronger. This argument can be rationalized by a shopper's stronger familiarity with crosschannel MIs at offline retail stores (Alba and Hutchinson1987). The knowledge transfer from a basiclevel retailer category to an offline RBE subcategory favors this match. In contrast, we can argue that the match between these MIs and online (vs. offline) RBE is stronger. One reason for this is that this salient crosschannel information has greater channelspecific relevance for online RBE than for offline RBE, as the former is typically the weaker channel for retailers (Frasquet and Miquel2017). Shoppers at many multiand omnichannel firms mostly use offline channels, both in general and when switching channels (Timoumi, Gangwar, and Mantrala2022). Finally, an easier perception of crosschannel attributes in the online context favors a stronger match. We therefore hypothesize that there are more inferences and stronger effects on loyalty through online RBE than through offline RBE. 1022 Journal of Consumer Behaviour, 2025 We propose the following hypothesis: H3. Crosschannel MIs exert a positive indirect effect on loyalty through (a) offline RBE and (b) online RBE, where (c) the indirect effect through online (vs. offline) RBE is stronger. With respect to the relative importance of traditional, onlinespecific, and crosschannel MIs, no insights exist in the literature. We hypothesize different total effects of MIs on consumer loyalty. Theoretically, all MIs are salient information (Khan and Rahman2016; Lee etal.2019), and their successful categorization enables consumers to draw inferences from different subcategories, which leads to evaluations of and intentions to be loyal to a retailer by consumers (Loken2006). With respect to the strength of these inferences, the perception of the attributes plays a crucial role. The more often MIs are perceived and the more relevantly and strongly they match with the RBEs, the stronger their inferences (Goodstein1993). At least two reasons lead us to argue that consumers draw stronger inferences in loyalty decisions based on traditional (vs. onlinespecific or crosschannel) MIs. Traditional MIs are constantly available to consumers across all channels. They may be unconsciously perceived in all purchasing processes and can therefore be matched with offline and online RBE without a considerable amount of effort (Wichmann etal.2022). In contrast, onlinespecific MIs are perceived when visiting a website. This forces a match with online RBE but a weaker activation of offline RBE (Bolton etal.2022). Crosschannel MIs are perceived primarily when consumers obtain a retailer's website's link offline, and vice versa (Gao and Huang2021). Traditional (vs. other) MIs offer greater information value, serve as orientation aids, and are frequently and consciously referenced (Blut, Teller, and Floh2018). These MIs provide consumers with a benefit, which increases their recognition (comparison shopping; Keller2010; Zhang, Vorhies, and Zhou 2023). Thus, a stronger match with both subcategories occurs. Moreover, crosschannel MIs more strongly match with RBEs in the total effect than do onlinespecific MIs and allow consumers to draw stronger inferences for at least two reasons. First, as consumers perceive crosschannel information attributes, positive synergies can be established between various channels, whereby consumers assign them to offline and online RBE (Frasquet and Miquel2017; Ratchford etal.2022). Second, crosschannel MIs enable omnipresent availability in all channels, whereby shoppers are more likely to perceive and access them than they are onlinespecific MIs (Gao and Huang2021; Haridasan, Fernando, and Balakrishnan2021). Unlike onlinespecific MIs, crosschannel MIs support a seamless experience that is perceived more positively by consumers (e.g., Gao and Huang2021; Zhang, Vorhies, and Zhou 2023). Thus, crosschannel MIs match more strongly with offline and online RBE in loyalty decisions than do onlinespecific MIs. We propose the following hypothesis: H4. The total effect of traditional MIs on loyalty is stronger than those of (a) onlinespecific and (b) crosschannel MIs, and (c) the total effect of crosschannel MIs is stronger than that of onlinespecific MIs. 2.4 | Hypotheses on the Reciprocal Effects of RBE We theorize reciprocity between online and offline RBE to fully understand how both affect loyalty and the hypothesized MI pathways. Empirically, Kwon and Lennon(2009a) assume but do not show or theorize reciprocal links between consumer offline and online brand attitudes. Swoboda and Winters(2021) reveal the reciprocal effects of both multichannel images and their explanatory power. By switching channels, shoppers are likely to transfer knowledge and draw inferences between subcategories (i.e., offline and online RBE; Wang, Beatty, and Mothersbaugh2009). Inferences in both directions are supported by the congruence of characteristics among subcategories (e.g., Epitropaki and Martin 2005). Both arise when the transferred knowledge between offline and online RBE can be compared, and similar characteristics can be identified. This situation can also be explained by the information retrieval that occurs through the activation of brand associations in both directions (e.g., Puligadda, Ross Jr, and Grewal2012). For example, the activation of offline RBE by traditional MIs activates online RBE through cognitive linkages, whereas the activation of online RBE by onlinespecific MIs affects offline RBE. Such inferences, which methodologically represent the sum of all reciprocal (total) effects of offline and online RBE, are drawn by shoppers in loyalty decisions (e.g., Loken2006). We assume that reciprocally linked offline and online RBE affect consumer loyalty differently. We argue that category knowledge transfer differs between subcategories. A weaker (vs. stronger) congruence of subcategories limits the knowledge transfer and strength effect differences of RBEs (e.g., Epitropaki and Martin2005). For example, consumers facing an unappealing online brand are more likely to rely on a familiar offline brand to draw inferences when making loyalty decisions (e.g., Loken2006). We also argue that different effects are caused by different degrees of activation (e.g., Anderson1983). For example, for former brickandmortar consumers, the activation of offline (vs. online) RBE is more likely, as they more frequently access this in shopping decisions (e.g., Frasquet and Miquel2017). We assume that consumers draw the strongest inferences in loyalty decisions from the most knowledgeable and activated subcategory (e.g., Badrinarayanan etal.2012); that is, the reciprocal total effect of offline (vs. online) RBE on loyalty is stronger. Therefore, we propose the following: H5. (a) Offline and online RBE are reciprocally linked, and (b) the reciprocal (total) effect of offline (vs. online) RBE on consumer loyalty is stronger. 3 | Empirical Studies To test the hypotheses and overcome the shortcomings of crosssectional studies (e.g., Zyphur et al. 2020), we conduct 1023 methodologically distinct studies using the same consumer sample over time and refer to these studies as Studies 1 and 2 for clarity. These studies explore the paths and the total effect of MIs (measured at time t1) through RBE (t2) on loyalty (t3) and crosslagged the reciprocal effects of offline and online RBE (to fully understand MI effects). We also perform several stability checks. 3.1 | Study 1 (Sequential Mediation) 3.1.1 | Sample We choose fashion retail in Germany as the study object for various reasons. This sector represents one of the largest sectors in many countries and accounts for 24% and the highest amount of online sales (more than the electronics sector, with 22%, according to the German Retail Association; HDE/IFH2023; Planet Retail2023). It consists of many multiand omnichannel firms, and their systematic selection allows us to avoid individually selected or firmspecific insights. Shoppers have high channel experience, and many retailers are wellknown, which enables a systematic selection of firms and avoids firmspecific results (Timoumi, Gangwar, and Mantrala2022). With approximately 25 firms accounting for 45% of the market share, this sector is not highly concentrated (in contrast, one electronics retailer generates 38% of all industry sales), and RBE is particularly important in this sector. Moreover, leading firms use many MIs to address consumers, who typically shop for fashion items every 40 days (Hult etal.2019). We respect the major requirements of longterm studies and provide several pretests. First, we choose eleven of the 15 topselling fashion firms, as they likely have multior omnichannel structures and are likely those most often frequented by shoppers (Oh, Teo, and Sambamurthy2012). Second, the eight most frequented retailers are selected on the basis of facetoface interviews with 30 graduate students in a pretest. However, two firms that offer not only fashion items but also other items are eliminated. Third, a pretest is used (based on the quota sample according to the age and gender of the population and facetoface interviews, N = 140) to ensure regular experiences at the retailers and to test our measurements. Two more firms with the lowest levels of shopping experience are eliminated. Thus, four retailers are included in the survey (e.g., Lazaris etal.2021). These retailers are all omnichannel (e.g., have integrated transactions, product/ price information, etc.; Gao etal.2021; Gao and Huang2021), and we test our hypothesis in this context. This pretest also leads to some item reductions to obtain satisfactory results regarding reliability and validity for the final measurements. We use quota sampling, following the national distribution of age and gender among 500 respondents in a typical midsize city with a similar distribution (see Table2). From an existing panel, 700 individuals who regularly shop offline and online are recruited in the screening phase t0 by email and phone until 500 agree to participate through three survey waves (due to power considerations; Wang and Rhemtulla2021). We inform them of the study focus and ask for sociodemographic information, their internet expertise, and their use of the selected retailers. For each respondent, we randomly select the first or second retailer that he/she knows and has frequently used for offline and online shopping in the past and that matches our pretests for evaluation in all waves (to avoid topofmind selection biases). The main surveys are conducted 4–5 months apart over a period of 10–11 months in 2022–2023 by trained interviewers via standardized questionnaires at individuals' homes (to increase data quality; Bennink, Moors, and Gelissen2013). A minorprice lottery serves as an incentive. Another requirement is for respondents to have shopped online and offline at the selected retailer prior to each wave (to capture current associations). These requirements are fulfilled by 475 respondents in the first wave. In the following waves, 29 and 40 individuals are eliminated. We ensure the stability of the withinperson variance for each person by calculating individual average values of the selfassessment construct of selfefficacy (measured with three items; Hamaker2023; see Supporting Information: Appendix B) and exclude nine respondents. Finally, 18 Mahalanobis distancebased outliers emerge, and the sample consists of 379 respondents. The age group 29–49 is slightly overrepresented compared to our plan. As our data are not normally distributed, robust maximum likelihood estimators are used for hypothesis testing (Gao, Shi, and MaydeuOlivares2020). 3.1.2 | Measurement We use Likerttype scales (from 1 = strongly disagree to 7 = strongly agree) and refer to the literature for the measurements of our constructs (see Table3). MIs are measured at time point t1 and on a secondorder basis via factor analysis for several reasons. Higherorder modeling is an important advance because multiple constructs are conceptualized at a higher level of abstraction; the contribution of each dimension to a higherlevel construct can be assessed and delineated (Koufteros, Babbar, and Kaighobadi2009). Individual dimensions can converge in a higherorder construct, as firstorder dimensions are conceptually and significantly different TABLE 2 | Sample characteristics. Realized quota sample (%; N = 379) Planned quota sample (%; N = 500) Male Female Total Male Female Total Age 15–29 10.3 10.6 20.8 11.7 10.7 22.4 Age 29–49 18.2 18.7 36.9 17.4 16.8 34.2 Age over 50 19.5 22.7 42.2 21.2 22.2 43.4 Total 48.0 52.0 100.0 50.3 49.7 100.0 1024 Journal of Consumer Behaviour, 2025 TABLE 3 | Reliability and validity. Construct MV/std FL KMO ItTC αCR λ Time point one Traditional MI Assortment (acc. to Chowdhury, Reardon, and Srivastava1998; Swoboda, Weindel, and Hälsig2016) [Retailer] have a very good assortment selection. 4.13/1.45 0.874 0.738 0.788 0.879 0.879 0.871 I like and enjoy the variety of branded product at […]. 3.62/1.63 0.791 0.729 0.825 I find everything I need at […]. 3.41/1.58 0.866 0.777 0.828 Price (acc. to Chowdhury, Reardon, and Srivastava1998; Swoboda, Weindel, and Hälsig2016) [Retailer] has reasonable and fair prices. 4.63/1.44 0.907 0.731 0.824 0.896 0.897 0.911 I obtain value for my money at […]. 4.61/1.34 0.910 0.829 0.905 I can buy products for less at […]. 4.35/1.47 0.775 0.736 0.776 Layout (acc. to Chowdhury, Reardon, and Srivastava1998; Swoboda, Weindel, and Hälsig2016) The overall store design of [retailer] generally nice. 3.77/1.59 0.913 0.771 0.885 0.953 0.953 0.917 I really like the presentation of the products at […]. 3.67/1.66 0.954 0.916 0.953 The shopping atmosphere at […] is comfortable for me. 3.54/1.65 0.933 0.900 0.930 Communication (acc to Kelly and Stephenson1967; Swoboda, Weindel, and Hälsig2016) I like the advertising/communication of [retailer]. 3.71/1.52 0.865 0.753 0.811 0.911 0.911 0.888 The advertising/communication of […] is informative. 3.69/1.40 0.914 0.844 0.891 I see the advertising/communication of […] very frequently. 3.75/1.38 0.859 0.806 0.859 Onlinespecific MI Website aesthetic (acc. to Bressolles, Durrieu, and Deans2015; Kwon and Lennon2009a) I like the look and feel of the website of [retailer]. 4.78/1.42 0.956 0.755 0.915 0.951 0.952 0.954 The website of […] is visually appealing. 4.69/1.43 0.960 0.917 0.962 I like the pictures/images used in […] website. 4.74/1.46 0.875 0.866 0.877 Website navigation (acc. to Bressolles, Durrieu, and Deans2015, MontoyaWeiss, Voss, and Grewal2003; Kwon and Lennon2009a) Navigating [retailers] web pages is easy for me. 5.06/1.20 0.933 0.733 0.845 0.900 0.903 0.916 How to order products on […] website is easy to understand. 4.89/1.17 0.871 0.806 0.878 I find what I am looking for easy on […] website. 5.00/1.21 0.796 0.754 0.812 Website service/content (acc. to MontoyaWeiss, Voss, and Grewal2003; Kwon and Lennon2009a) Service provided by [retailer] through the website is convenient. 4.40/1.19 0.761 0.721 0.719 0.887 0.892 0.779 Service provided by […] through the website is reliable. 4.40/1.17 0.932 0.833 0.914 Assistance provided by […] through the website is helpful. 4.32/1.19 0.865 0.791 0.872 Security/privacy (acc. to Bressolles, Durrieu, and Deans2015; MontoyaWeiss, Voss, and Grewal2003; Kwon and Lennon2009b) The [retailer] will not misuse my personal information. 3.70/1.45 0.945 0.500 0.895 0.944 0.945 0.976 I feel save due to […] payment information in online transactions. 3.80/1.49 0.945 0.895 0.916 Crosschannel MI Offline–online integration (acc. to Bendoly etal.2005) If you purchase in the store of [retailer] (Continues) 1031 In contrast, consumers match onlinespecific MIs to online (not offline) RBE, which may not be surprising (Frasquet and Miquel2017). However, this further enhances the only study on offline and online brands (Kwon and Lennon2009a), as we observe nonnegative onlinespecific MIsoffline RBE links and, surprisingly, stronger paths of traditional (vs. other) MIs through online RBE. Notably, this finding is visible in our indirect effect models but not in our total effect models or in those of the overall RBE only and is even complemented by the reciprocity discussed later. We thus may also contribute methodologically to the literature. Finally, we believe that we contribute to the research on crosschannel MIs in two ways. While this research stream has studied only overall RBE (Frasquet and Miquel2017), we show that consumers draw equal inferences through offline and online RBE in loyalty decisions. For the first time, we compare the effects of crosschannel and other MIs and show their stronger (weaker) role than that of onlinespecific (traditional) MIs. All this may be considered in studies that focus only on crosschannel MIs or only onlinespecific MIs. Second, the empirical insights into the total effects of MIs, that is, their general importance, are novel and support our theoretical rationale. Traditional MIs are currently the strongest loyalty lever and are twice as strong as crosschannel MIs. This finding refutes assumptions regarding their possible decreasing importance in modern retail (e.g., Wichmann etal.2022); they may have gained rather than lost through digitalization (e.g., Timoumi, Gangwar, and Mantrala 2022). When drawing inferences through RBEs, consumers value traditional MIs as the most important information attributes in both sales channels. Crosschannel MIs follow, and synergies between sales channels enable a match to both RBEs (e.g., Gao and Huang2021). As mentioned, onlinespecific MIs are nonsignificant in contrast to their shown importance via online RBE. Additionally, our stability test of the overall RBE has further implications. Consumers access one subcategory and draw the respective inferences, which omit the effects and interactions of both RBEs. However, both RBEs may shape consumers' overall RBE or image, which future research may address because we cannot test and thus hypothesize that this due to both RBEs' multicollinearity with overall RBE (Hair etal.2018; S.323; Shrestha2020). Regarding our third research question, the identified reciprocity between offline and online RBE has important implications for their effects on loyalty and the pathways of MIs to loyalty. First, the results are consistent with our theoretical rationale that online and offline RBE reciprocally affect consumer loyalty, whereas offline RBE does so more strongly. Consumers categorize channelspecific brand associations into a retailer category and draw inferences reciprocally between them in both directions (e.g., Badrinarayanan etal.2012). Consumers rely on the most knowledgeable and activated subcategory for loyalty decisions (e.g., Sohn2017). We contribute to the literature by revealing the links between offline and online RBE, answering the related calls for research, and supporting a few reciprocal studies. Swoboda and Winters(2021) have already shown underor overestimated online or offline image effects when crosssectional studies test them nonreciprocally (in our study, for example, for online RBE, the result is weaker unidirectionally, β1–2 = 0.085 or β2–3 = 0.062, p < 0.05, and stronger reciprocally, β = 0.136, p < 0.001; see Table6). We also initially show that in today's multiand omnichannel retailing, offline RBE is predominant compared to online RBE. Second, the reciprocal insights provide novel research conclusions for the pathways of MIs' impact on loyalty through the RBEs (which cannot be simultaneously tested in one model). A reciprocal online–offline RBE link with a stronger tendency than vice versa emerges (online–offline β1–2 = 0.244, β2–3 = 0.215, p < 0.001; offline–online β1–2 = 0.198, β2–3 = 0.199, p < 0.001; see Table 6). Consumers transfer more category knowledge from online to offline RBE, which we underscore for reciprocally linked RBEs (e.g., Swoboda and Winters2021). This observation does not change the dominant role of traditional MIs, but their path through online (vs. offline) RBE gains importance, as it is the strongest path through online RBE; for crosschannel MIs, the path through online RBE is also reinforced. The greatest implications appear for onlinespecific MIs. Their only significant indirect effect through online RBE is amplified by their respective activations in consumer memory (e.g., Puligadda, Ross Jr, and Grewal 2012); they are strongly matched to online RBE because of their representativeness but have a further influence due to the strong online–offline RBE link (e.g., Toufaily and Pons2017). This is novel in the literature. Studying the crosswise effects and reciprocity of possible mediators is recommended for onlinespecific and further MIs in research on multiand omnichannel firms to fully understand all relationships. 4.2 | Managerial Implications Carefully managed major MIs help retailers retain loyal consumers and find it challenging to do so through major sales channels (e.g., Hult et al. 2019; Kwon and Lennon 2009a). Neglecting important MIs and offline and online RBE may lead to the misinterpretation of market studies, incomplete conclusions, or ineffective strategies for overall RBE (Swaminathan et al. 2020). We believe that the results are relevant for former brickandmortar and pure online players that start opening offline stores (e.g., Ratchford etal.2022). A double strong effect of traditional MIs relative to the following crosssectional MIs through both RBEs indicates retailers' strongest levers from consumers' viewpoint. Currently, multiand omnichannel retailers can, in particular, leverage benefits through offline (vs. online) RBE when competing with online pure players, for example. However, they may face challenges in the future online world because of the demonstrated weaker roles of onlinespecific MIs and online RBE (Hänninen, Kwan, and Mitronen2021). The careful management of consumerbased evaluations of the studied MIs (and of additional MIs and touchpoints) and their roles in offline and online brand positioning is recommended, as this approach seems to be superior to the view of a harmonized brand position across sales channels (due to different environments, competitors, or evaluations; Keller 2020; Zhang, Vorhies, and Zhou2023). However, many retailers must, for example, also overcome frequent silos of consumer information or processes in independently operating online and offline divisions (Swoboda and Winters2021). 1032 Journal of Consumer Behaviour, 2025 Further implications arise from reciprocal effects. The double strong reciprocal effect of offline (vs. offline) RBE on loyalty underscores the role of offline brand positioning today (like banking; CambraFierro etal.2020). Thus, traditional MIs remain the strongest lever, but the totally nonsignificant onlinespecific MIs gain importance owing to their role in online RBE (indirect effects) and the respective strong reciprocity of online to offline RBE. This, however, limits the results of unidirectional studies, which managers should evaluate carefully as a basis for their decisions. Crosschannel MIs such as offline–online or online–offline integration or further unstudied touchpoints need to be orchestrated to steer consumers to channels, manage brand positioning, and control reciprocal RBE mechanisms for the targeted consumer outcome (Frasquet and Miquel2017; Lee etal.2019). Managing complementary offline and online RBE and capturing synergies help build a source of competitive advantage. 5 | Limitations and Directions for Future Research This study has certain limitations that suggest future research directions. We collect specific data over time, but a broader database would allow further implications, for example, for smaller firms, former pure online players, other industries, or occasional shoppers (e.g., Khan and Rahman2016; Troiville, Hair, and Cliquet2019). We select leading firms for which categorization and reciprocity can be analyzed, as the use of fictional firms increases internal validity (e.g., Lee etal.2019). With respect to our measurements, using objective data for loyalty are obvious but difficult to obtain over three time points. We provide strong rationales for our secondorder MI measures, but there is no single correct compositional approach (e.g., Koufteros, Babbar, and Kaighobadi2009). The same applies to measuring MIs, as validated options do not yet exist, but finergrained approaches to further MIs such as social media or offlinespecific MIs are interesting (Swaminathan etal. 2020). Furthermore, less common measurements of RBE also exist (e.g., Keller2016; Zhang, Vorhies, and Zhou2023). Finally, studies may control for consumers' incomes, channel preferences, and other potentially relevant covariates. Our framework is developed using cognitive rationales that fit an ex post study, but it would be interesting to propose alternative hypotheses (e.g., feelings and pleasure; Lee etal.2019). We leverage categorization theory to stimulate further research. Future studies could benefit by measuring categorization theory mechanisms, for example, studying how consumers categorize MIs from different channels, or by using alternative theories to reveal the mechanisms underlying the studied relationships (e.g., Rajavi, Kushwaha, and Steenkamp2019). We select experienced respondents who tend to perceive more MIs; however, testing experience, shopping motivation and search behavior as antecedents (or moderators) of offline and online RBE (effects) would be interesting (e.g., Rajavi, Kushwaha, and Steenkamp2019; Troiville, Hair, and Cliquet2019; Wang and Jia2023). Similarly, studying further consumer groups or the roles of realtime personalization, social media, or artificial intelligence for RBE could be exciting research avenues (e.g., Swaminathan etal.2020; Wichmann etal.2022). Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement Research data are not shared. Endnotes 1 We perform a systematic literature review of 27 leading journals (following Harzing's journal quality list) by referring to papers published since 2009 and those identified through crosscitations. Our keywords are MIs, mix, or individual MIs, such as price, service/content, website, channel integration, RBE, experience, and image. For our Supporting Information: Appendix, click here. References Adidas. 2024. “Adidas Annual Report 2023.” https:// report. adida sgroup. com/ 2023/ en/_ assets/ downl oads/ a nnua lrepor tad ida sar23. pdf. Alba, J. W., and J. W. Hutchinson. 1987. “Dimensions of Consumer Expertise.” Journal of Consumer Research 13, no. 4: 411–454. AlHawari, M. A. 2011. “Do Online Services Contribute to Establishing Brand Equity within the Retail Banking Context?” Journal of Relationship Marketing 10, no. 3: 145–166. Allaway, A. W., P. Huddleston, J. Whipple, and A. E. Ellinger. 2011. “CustomerBased Brand Equity, Equity Drivers, and Customer Loyalty in the Supermarket Industry.” Journal of Product and Brand Management 29, no. 3: 190–204. Anderson, J. R. 1983. “A Spreading Activation Theory of Memory.” Journal of Verbal Learning and Verbal Behavior 22, no. 3: 261–295. Badrinarayanan, V., E. P. Becerra, C.- H. Kim, and S. Madhavaram. 2012. “Transference and Congruence Effects on Purchase Intentions in Online Stores of MultiChannel Retailers: Initial Evidence from the US and South Korea.” Journal of the Academy of Marketing Science 40: 539–557. Baek, E., H. J. Choo, X. Wei, and S.- Y. Yoon. 2020. “Understanding the Virtual Tours of Retail Stores: How Can Store Brand Experience Promote Visit Intentions?” International Journal of Retail & Distribution Management 48, no. 7: 649–666. Bastardoz, N., M. J. Matthews, G. B. Sajons, T. Ransom, T. K. Kelemen, and S. H. Matthews. 2023. “Instrumental Variables Estimation: Assumptions, Pitfalls, and Guidelines.” Leadership Quarterly 34, no. 1: 101673. Bendoly, E., J. D. Blocher, K. M. Bretthauer, S. Krishnan, and M. A. Venkataramanan. 2005. “Online/InStore Integration and Customer Retention.” Journal of Service Research 7, no. 4: 313–327. Bennink, M., G. Moors, and J. Gelissen. 2013. “Exploring Response Differences between FacetoFace and Web Surveys: A Qualitative Comparative Analysis of the Dutch European Values Survey 2008.” Field Methods 25, no. 4: 319–338. Beristain, J. J., and P. Zorrilla. 2011. “The Relationship between Store Image and Store Brand Equity: A Conceptual Framework and Evidence from Hypermarkets.” Journal of Retailing and Consumer Services 18, no. 6: 562–574. Blut, M., C. Teller, and A. Floh. 2018. “Testing Retail MarketingMix Effects on Patronage: A MetaAnalysis.” Journal of Retailing 94, no. 2: 113–135. 1033 Bolton, R. N., A. Gustafsson, C. O. Tarasi, and L. Witell. 2022. “Designing Satisfying Service Encounters: Website Versus Store Touchpoints.” Journal of the Academy of Marketing Science 50: 1–23. Bressolles, G., F. Durrieu, and K. R. Deans. 2015. “An Examination of the Online ServiceProfit Chain.” International Journal of Retail & Distribution Management 43, no. 8: 727–751. Bruhn, M., V. Schoenmueller, and D. B. Schäfer. 2012. “Are Social Media Replacing Traditional Media in Terms of Brand Equity Creation?” Management Research Review 35, no. 9: 770–790. CambraFierro, J., I. MeleroPolo, L. Patrício, and F. J. Sese. 2020. “Channel Habits and the Development of Successful CustomerFirm Relationships in Services.” Journal of Service Research 23, no. 4: 1–20. Chang, C., J. Gardiner, R. Houang, and Y.- L. Yu. 2020. “Comparing Multiple Statistical Software for MultipleIndicator, MultipleCause Modeling: An Application of Gender Disparity in Adult Cognitive Functioning Using Midus Ii Dataset.” BMC Medical Research Methodology 20: 1–14. Chen, J., and S. Dibb. 2010. “Consumer Trust in the Online Retail Context: Exploring the Antecedents and Consequences.” Psychology & Marketing 27, no. 4: 323–346. Chowdhury, J., J. Reardon, and R. Srivastava. 1998. “Alternative Modes of Measuring Store Image: An Empirical Assessment of Structured Versus Unstructured Measures.” Journal of Marketing Theory and Practice 6, no. 2: 72–86. Çifci, S., Y. Ekinci, G. Whyatt, A. Japutra, S. Molinillo, and H. Siala. 2016. “A Cross Validation of ConsumerBased Brand Equity Models: Driving Customer Equity in Retail Brands.” Journal of Business Research 69, no. 9: 3740–3747. Cohen, J. B., and K. Basu. 1987. “Alternative Models of Categorization: Toward a Contingent Processing Framework.” Journal of Consumer Research 13, no. 4: 455–472. Das, G. 2015. “Impact of Store Attributes on ConsumerBased Retailer Equity: An Exploratory Study of Department Retail Stores.” Journal of Fashion Marketing and Management 19, no. 2: 188–204. Datta, H., K. L. Ailawadi, and H. J. Van Heerde. 2017. “How Well Does ConsumerBased Brand Equity Align with SalesBased Brand Equity and MarketingMix Response?” Journal of Marketing 81, no. 3: 1–20. Emrich, O., M. Paul, and T. Rudolph. 2015. “Shopping Benefits of Multichannel Assortment Integration and the Moderating Role of Retailer Type.” Journal of Retailing 91, no. 2: 326–342. Epitropaki, O., and R. Martin. 2005. “From Ideal to Real: A Longitudinal Study of the Role of Implicit Leadership Theories on LeaderMember Exchanges and Employee Outcomes.” Journal of Applied Psychology 90, no. 4: 659–676. Finn, A., and L. Wang. 2014. “Formative vs. Reflective Measures: Facets of Variation.” Journal of Business Research 67, no. 1: 2821–2826. Fiske, S. T., S. L. Neuberg, A. E. Beattie, and S. J. Milberg. 1987. “CategoryBased and AttributeBased Reactions to Others: Some Informational Conditions of Stereotyping and Individuating Processes.” Journal of Experimental Social Psychology 23, no. 5: 399–427. Forbes. 2021. “More Than Just a Retailer.” https:// www. forbes. at/ artik el/ mehralsnureinretai ler. html. Frasquet, M., and M.- J. Miquel. 2017. “Do Channel Integration Efforts PayOff in Terms of Online and Offline Customer Loyalty?” International Journal of Retail & Distribution Management 45, no. 7/8: 859–873. Gao, C., D. Shi, and A. MaydeuOlivares. 2020. “Estimating the Maximum Likelihood Root Mean Square Error of Approximation (RMSEA) with NonNormal Data: A MonteCarlo Study.” Structural Equation Modeling: A Multidisciplinary Journal 27, no. 2: 192–201. Gao, M., and L. Huang. 2021. “Quality of Channel Integration and Customer Loyalty in Omnichannel Retailing: The Mediating Role of Customer Engagement and Relationship Program Receptiveness.” Journal of Retailing and Consumer Services 63: 102688. Gao, W., H. Fan, W. Li, and H. Wang. 2021. “Crafting the Customer Experience in Omnichannel Contexts: The Role of Channel Integration.” Journal of Business Research 126: 12–22. GilSaura, I., M. E. RuizMolina, G. Michel, and A. CorralizaZapata. 2013. “Retail Brand Equity: A Model Based on Its Dimensions and Effects.” International Review of Retail Distribution & Consumer Research 23, no. 2: 111–136. Goodstein, R. C. 1993. “CategoryBased Applications and Extensions in Advertising: Motivating More Extensive ad Processing.” Journal of Consumer Research 20, no. 1: 87–99. H&M. 2024. “H&M Group: Annual and Sustainability Report 2023.” ht tp s: // h m g ro up. com / w p - c ont e nt / uploa ds / 2 0 2 4/ 0 3/ H MGr oup - Annua landSusta inabi lityRepor t2023. pdf. Hair, J. F., W. C. Black, B. J. Babin, and R. E. Anderson. 2018. Multivariate Data Analysis. 8th ed. Boston: Cengage Learning. Hamaker, E. L. 2023. “The WithinBetween Dispute in CrossLagged Panel Research and How to Move Forward.” Psychological Methods 13, no. 1: 1–21. Hänninen, M., S. K. Kwan, and L. Mitronen. 2021. “From the Store to Omnichannel Retail: Looking Back Over Three Decades of Research.” International Review of Retail Distribution & Consumer Research 31, no. 1: 1–35. Haridasan, A. C., A. G. Fernando, and S. Balakrishnan. 2021. “Investigation of Consumers' CrossChannel Switching Intentions: A PushPullMooring Approach.” Journal of Consumer Behaviour 20, no. 5: 1092–1112. HDE/IFH. 2023. Online Monitor. Berlin: Handelsverband Deutschland (HDE). Huang, R., and E. Sarigöllü. 2012. “How Brand Awareness Relates to Market Outcome, Brand Equity, and the Marketing Mix.” Journal of Business Research 65, no. 1: 92–99. Hult, G. T. M., P. N. Sharma, F. V. Morgeson III, and Y. Zhang. 2019. “Antecedents and Consequences of Customer Satisfaction: Do They Differ across Online and Offline Purchases?” Journal of Retailing 95, no. 1: 10–23. Iglesias, O., S. Markovic, and J. Rialp. 2019. “How Does Sensory Brand Experience Influence Brand Equity? Considering the Roles of Customer Satisfaction, Customer Affective Commitment, and Employee Empathy.” Journal of Business Research 96: 343–354. Keaveney, S. M., and K. A. Hunt. 1992. “Conceptualization and Operationalization of Retail Store Image: A Case of Rival MiddleLevel Theories.” Journal of the Academy of Marketing Science 20, no. 2: 165–175. Keller, K. L. 1993. “Conceptualizing, Measuring, and Managing CustomerBased Brand Equity.” Journal of Marketing 57, no. 1: 1–22. Keller, K. L. 2010. “Brand Equity Management in a Multichannel, Multimedia Retail Environment.” Journal of Interactive Marketing 24, no. 2: 58–70. Keller, K. L. 2016. “Reflections on CustomerBased Brand Equity: Perspectives, Progress, and Priorities.” Academy of Marketing Science Review 6: 1–16. Keller, K. L. 2020. “Consumer Research Insights on Brands and Branding: A JCR Curation.” Journal of Consumer Research 46, no. 5: 995–1001. Kelly, R. F., and R. Stephenson. 1967. “The Semantic Differential: An Information Source for Designing Retail Patronage Appeals.” Journal of Marketing 31, no. 4: 43–47. 1034 Journal of Consumer Behaviour, 2025 Khan, I., L. D. Hollebeek, M. Fatma, J. U. Islam, and Z. Rahman. 2020. “Brand Engagement and Experience in Online Services.” Journal of Services Marketing 34, no. 2: 163–175. Khan, I., and Z. Rahman. 2016. “ETail Brand Experience's Influence on EBrand Trust and EBrand Loyalty: The Moderating Role of Gender.” International Journal of Retail & Distribution Management 44, no. 6: 588–606. Koufteros, X., S. Babbar, and M. Kaighobadi. 2009. “A Paradigm for Examining SecondOrder Factor Models Employing Structural Equation Modeling.” International Journal of Production Economics 120, no. 2: 633–652. Kwon, W.- S., and S. J. Lennon. 2009a. “Reciprocal Effects between Multichannel Retailers' Offline and Online Brand Images.” Journal of Retailing 85, no. 3: 376–390. Kwon, W.- S., and S. J. Lennon. 2009b. “What Induces Online Loyalty? Online Versus Offline Brand Images.” Journal of Business Research 62, no. 5: 557–564. Lazaris, C., P. Sarantopoulos, A. Vrechopoulos, and G. Doukidis. 2021. “Effects of Increased Omnichannel Integration on Customer Satisfaction and Loyalty Intentions.” International Journal of Electronic Commerce 25, no. 4: 440–468. Lee, Z. W. Y., T. K. H. Chan, A. Y.- L. Chong, and D. R. Thadani. 2019. “Customer Engagement through Omnichannel Retailing: The Effects of Channel Integration Quality.” Industrial Marketing Management 77: 90–101. Li, Y., H. Liu, E. T. K. Lim, J. M. Goh, F. Yang, and M. K. O. Lee. 2018. “Customer's Reaction to CrossChannel Integration in Omnichannel Retailing: The Mediating Roles of Retailer Uncertainty, Identity Attractiveness, and Switching Costs.” Decision Support Systems 109: 50–60. Lin, M.- Q., and B. C. Lee. 2012. “The Influence of Website Environment on Brand Loyalty: Brand Trust and Brand Affect as Mediators.” International Journal of Electronic Business Management 10, no. 4: 308–321. Loken, B. 2006. “Consumer Psychology: Categorization, Inferences, Affect, and Persuasion.” Annual Review of Psychology 57: 453–485. Loureiro, S. M., L. Cavallero, and F. J. Miranda. 2018. “Fashion Brands on Retail Websites: Customer Performance Expectancy and EWordofMouth.” Journal of Retailing and Consumer Services 41: 131–141. Melis, K., K. Campo, E. Breugelmans, and L. Lamey. 2015. “The Impact of the MultiChannel Retail Mix on Online Store Choice: Does Online Experience Matter?” Journal of Retailing 91, no. 2: 272–288. Mervis, C. B., and E. Rosch. 1981. “Categorization of Natural Objects.” Annual Review of Psychology 32, no. 1: 89–115. Miller, B. K., and M. J. Simmering. 2023. “Attitude toward the Color Blue: An Ideal Marker Variable.” Organizational Research Methods 26, no. 3: 409–440. MontoyaWeiss, M. M., G. B. Voss, and D. Grewal. 2003. “Determinants of Online Channel Use and Overall Satisfaction with a Relational, Multichannel Service Provider.” Journal of the Academy of Marketing Science 31, no. 4: 448–458. Muthén, B. 2024. “Mplus: A Brief Overview of Its Unique Analysis Capabilities.” In Robust STATISTICAL Methods and the Credibility Movement of Psychological Science, edited by J. E. Edlund and A. L. Nichols. Cambridge: Cambridge University Press. (in press). Oh, L.- B., H.- H. Teo, and V. Sambamurthy. 2012. “The Effects of Retail Channel Integration through the Use of Information Technologies on Firm Performance.” Journal of Operations Management 30, no. 5: 368–381. Oliver, R. L. 1999. “Whence Consumer Loyalty?” Journal of Marketing 63, no. 4: 33–44. Omar, N. A., A. S. Kassim, S. Shah Alam, and Z. Zainol. 2021. “Perceived Retailer Innovativeness and Brand Equity: Mediation of Consumer Engagement.” The Service Industries Journal 41, no. 56: 355–381. Planet Retail. 2023. “Planet Retail Data.” http:// plane treta il. net. Puligadda, S., W. T. Ross Jr., and R. Grewal. 2012. “Individual Differences in Brand Schematicity.” Journal of Marketing Research 49, no. 1: 115–130. Rajavi, K., T. Kushwaha, and J.- B. E. Steenkamp. 2019. “In Brands We Trust? A Multicategory, Multicountry Investigation of Sensitivity of Consumers' Trust in Brands to MarketingMix Activities.” Journal of Consumer Research 46, no. 4: 651–670. Ratchford, B., G. Soysal, A. Zentner, and D. K. Gauri. 2022. “Online and Offline Retailing: What We Know and Directions for Future Research.” Journal of Retailing 98, no. 1: 152–177. Ray, A., P. K. Bala, S. Chakraborty, and S. A. Dasgupta. 2021. “Exploring the Impact of Different Factors on Brand Equity and Intention to Take up Online Courses from ELearning Platforms.” Journal of Retailing and Consumer Services 59: 102351. RojasLamorena, Á. J., S. Del BarrioGarcía, and J. M. AlcántaraPilar. 2022. “A Review of Three Decades of Academic Research on Brand Equity: A Bibliometric Approach Using CoWord Analysis and Bibliographic Coupling.” Journal of Business Research 139: 1067–1083. SchrammKlein, H., G. Wagner, S. Steinmann, and D. Morschett. 2011. “CrossChannel Integration—Is It Valued by Customers?” International Review of Retail Distribution & Consumer Research 21, no. 5: 501–511. Shen, X.- L., Y.- J. Li, Y. Sun, and N. Wang. 2018. “Channel Integration Quality, Perceived Fluency and Omnichannel Service Usage: The Moderating Roles of Internal and External Usage Experience.” Decision Support Systems 109: 61–73. Shrestha, N. 2020. “Detecting Multicollinearity in Regression Analysis.” American Journal of Applied Mathematics and Statistics 8, no. 2: 39–42. Sirohi, N., E. W. McLaughlin, and D. R. Wittink. 1998. “A Model of Consumer Perceptions and Store Loyalty Intentions for a Supermarket Retailer.” Journal of Retailing 74, no. 2: 223–245. Sohn, S. 2017. “Consumer Processing of Mobile Online Stores: Sources and Effects of Processing Fluency.” Journal of Retailing and Consumer Services 36: 137–147. Swaminathan, V., A. Sorescu, J.- B. E. M. Steenkamp, T. C. G. O'Guinn, and B. Schmitt. 2020. “Branding in a Hyperconnected World: Refocusing Theories and Rethinking Boundaries.” Journal of Marketing 84, no. 2: 24–46. Swoboda, B., J. Weindel, and F. Hälsig. 2016. “Predictors and Effects of Retail Brand Equity—A CrossSectoral Analysis.” Journal of Retailing and Consumer Services 31: 265–276. Swoboda, B., and A. Winters. 2021. “Reciprocity within Major Retail Purchase Channels and Their Effects on Overall, Offline and Online Loyalty.” Journal of Business Research 125: 279–294. Timoumi, A., M. Gangwar, and M. K. Mantrala. 2022. “CrossChannel Effects of Omnichannel Retail Marketing Strategies: A Review of Extant DataDriven Research.” Journal of Retailing 98, no. 1: 133–151. Toufaily, E., and F. Pons. 2017. “Impact of Customers' Assessment of Website Attributes on ERelationship in the Securities Brokerage Industry: A Multichannel Perspective.” Journal of Retailing and Consumer Services 34: 58–69. Troiville, J., J. F. Hair, and G. Cliquet. 2019. “Definition, Conceptualization and Measurement of ConsumerBased Retailer Brand Equity.” Journal of Retailing and Consumer Services 50: 73–84. Van de Schoot, R., P. Lugtig, and J. Hox. 2012. “A Checklist for Testing Measurement Invariance.” European Journal of Developmental Psychology 9, no. 4: 486–492. 1035 Verhoef, P. C., P. K. Kannan, and J. J. Inman. 2015. “From MultiChannel Retailing to OmniChannel Retailing: Introduction to the Special Issue on MultiChannel Retailing.” Journal of Retailing 91, no. 2: 174–181. Verhoef, P. C., F. Langerak, and B. Donkers. 2007. “Understanding Brand and Dealer Retention in the New Car Market: The Moderating Role of Brand Tier.” Journal of Retailing 83, no. 1: 97–113. Victoria's Secret. 2024. “Victoria's Secret Annual Report 2023.” https:// www. victo riass ecret andco. com/ stati cfiles/ 48070 697c3504fb1b792896b3 6efc77f. Wang, J., and X. Jia. 2023. “Click It, and Increase Hedonic Consumption Ratio: How Does Online Shopping Improve the LongTerm Subjective WellBeing of Consumers?” Journal of Consumer Behaviour 22, no. 2: 235–252. Wang, S., S. E. Beatty, and D. L. Mothersbaugh. 2009. “Congruity's Role in Website Attitude Formation.” Journal of Business Research 62, no. 6: 609–615. Wang, Y. A., and M. Rhemtulla. 2021. “Power Analysis for Parameter Estimation in Structural Equation Modeling: A Discussion and Tutorial.” Advances in Methods and Practices in Psychological Science 4, no. 1: 1–17. White, R., S. JosephMathews, and C. M. Voorhees. 2013. “The Effects of Service on Multichannel Retailers' Brand Equity.” Journal of Services Marketing 27, no. 4: 259–270. Wichmann, J. R. K., A. Uppal, A. Sharma, and M. G. Dekimpe. 2022. “A Global Perspective on the Marketing Mix across Time and Space.” International Journal of Research in Marketing 39, no. 2: 502–521. Williams, L. J., and A. K. McGonagle. 2016. “Four Research Designs and a Comprehensive Analysis Strategy for Investigating Common Method Variance with SelfReport Measures Using Latent Variables.” Journal of Business and Psychology 31: 339–359. Zara. 2022. “Zara Annual Report 2021.” https:// static. indit ex. com/ annual_ report_ 2021/ en/ docum ents/ annual_ report_ 2021. pdf. Zhang, C., D. W. Vorhies, and W. Zhou. 2023. “An Integrated Model of Retail Brand Equity: The Role of Consumer Shopping Experience and Shopping Value.” Journal of Brand Management 30: 1–16. Zollo, L., R. Filieri, R. Rialti, and S. Yoon. 2020. “Unpacking the Relationship Between Social Media Marketing and Brand Equity: The Mediating Role of Consumers’ Benefits and Experience.” Journal of Business Research 117: 256–267. Zyphur, M. J., P. D. Allison, L. Tay, etal. 2020. “From Data to Causes I: Building a General CrossLagged Panel Model (GCLM).” Organizational Research Methods 23, no. 4: 651–687. Supporting Information Additional supporting information can be found online in the Supporting Information section.