PurposeThis study aims at examining the role of the e-store brand personality congruence/incongruence of a multichannel apparel retailer in the formation of consumers' perceived e-store brand fit and e-store patronage intention, based on the concept of image congruence.Design/methodology/approachAn online survey was conducted with a US national sample of 458 female consumers (20–50 years old) who had shopped for clothing online.FindingsResults revealed that e-store brand personality incongruence in three personality dimensions had a negative impact on consumers' e-store patronage intention directly as well as indirectly by reducing the consumers' global perception of the e-store brand fit. Further, the retailer's relevance to the consumer moderated the relationship between the perceived e-store brand fit and e-store patronage intention in that this relationship was significantly greater among consumers with a high (vs low) perceived self-relevance of the retail brand.Practical implicationsThe findings highlight the importance of symbolically integrated cross-channel brand management for multichannel apparel retailers by clearly identifying their brand personality and carefully crafting it into their e-store interface design and e-store visual merchandising to convey the brand personality.Originality/valueThis study expands the application of image congruence to the cross-channel image congruence phenomenon in multichannel retailing environments by examining the e-store brand image congruence employing both direct and indirect approaches.

Introduction and Theoretical FrameworkEasy access and efficiency of the internet and mobile technologies have led consumers to engage in diversified information search behaviors (Shaheen & Lodhi, 2016) characterized by ongoing information search throughout the pre-, in-, and post-purchase stages of consumption (Bugday et al., 2016). Social media play a particularly important role as a major information source for both targeted information search for purchase and ongoing exploratory information search (Moe, 2003). However, no existing literature has offered an empirical investigation or a theoretical discussion that integrates the diverse roles of consumer information search in social media, which is a gap addressed by this study.Consumer segmentation is the process of dividing a population into manageable subgroups according to shared characteristics (Tuten & Solomon, 2015) to offer an overview of the population from a specified perspective (depending on the segmentation base variable used). Social media information search behavior (SMISB) has been rarely employed as a segmentation base variable despite the usefulness of identifying information search patterns for understanding consumer decision-making behavior (Choi & Park, 2006). This study fills this literature gap, by 1) identifying distinctive consumer segments based on their SMISBs and 2) comparing the demographic and psychographic characteristics of identified SMISB-based consumer segments.This study is grounded in Chang and Kwon's (2018) modified consumer decision-making (MCDM) model. Critiquing the over-simplified conceptualization by traditional consumer decision-making models (e.g., Cox et al., 1983) which view information search as just a step that comes between the need/problem recognition and alternative evaluation steps in a linear purchase decision-making process, the MCDM model postulates four information search stages (ISSs) depending on the goal and timing of information search. The four ISSs include the premarket, pre-purchase ISS leading to need/problem recognition (ISS1); the in-market, prepurchase ISS for identifying product attributes and alternatives (ISS2); the in-market, inpurchase ISS to inform purchase decisions (ISS3); and the post-market, post-purchase ISS for post-purchase evaluation (ISS4). This study identified and explained consumer segments based on the consumer's SMISBs, particularly the SMISB frequency and the variety of social media types used for SMISB in each of the four ISSs. The identified consumer segments were then described and compared in terms of psychographic descriptor variables (i.e., innovativeness, impulse buying tendency) and demographic descriptor variables (i.e., gender, age), which have been known to be associated with information search behavior.

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