We are living in a world that is increasingly digital and undergoing dramatic changes as a result. In particular for luxury fashion, growing numbers of online customers as well as fast changing business environment, luxury retailers face the challenge of differentiating themselves by offering a better online customer experience (Chen et al. 2021). By doing so, luxury fashion retailers are increasingly deploying chatbots in their service encounters to enhance customer experience (Roy & Naidoo, 2021). Chatbots are powered by Artificial Intelligence (AI) (Hoyer et al. 2020) and are an example of AI robot that can provide human-computer interactions on a retail website (Lee et al. 2017). Intended to enhance the online customer experience, chatbots have the potential to provide a better understanding of the product performance, enable efficient use of customer time, and help build crucial customer relationships (Rese et al. 2020; Wilson-Nash et al. 2020; Xu et al. 2022). Therefore, chatbots’ potential has been highly valued by fashion retail industry and academia (Jiang et al. 2022).

This study investigated how immersive VR store experience generated consumers’ urge to buy via self-imagery and pleasure. It also identified that the processing varied by the level of self-relevance to the VR store. The findings suggest that the impact of VR store experience can be expanded to impulsive/compulsive purchases.

Despite the orientation towards online retailing journey accelerated by the application of new-age technologies in the pandemic context, the role of the physical store still has a central role in luxury shopping in the digital omni-channel perspective. Digital technologies have increased their impact on consumers (Evanschitzky et al., 2020; Klaus & Zaichkowsky, 2020; Kaplan & Haenlein, 2020; Davenport et al, 2020; Huang and Rust, 2021a; Pantano et al, 2022). In today’s digital age, AI is one of the new-age technologies raising growing interest for their potential disruptive impact on marketing and retailing in different sectors (Forbes, 2022).

This study examines the impact of price transparency—specifically the disclosure of cost breakdown—on brand attitudes and purchase intentions. The findings suggest that pricing transparency generally has a positive effect on attitudes and purchase intentions. However, pricing transparency might backfire, and thus reverse the effect, for luxury products originating from a high equity country (e.g., a fashion brand from Italy), but not for luxury and non-luxury products originating from a low equity country (e.g., a fashion brand from China). Luxury retailers in a high equity country should take extra caution before adopting price transparency

Consumer studies on millennials have focused on shopper behavioural differences with their old baby boomer generation. A significant distinction between these two groups have been their relationship and interaction with technology across all facets of life, including shopping. Millennials are generally regarded as early adopters of digital technology and its use in daily activities, hence their reference to digital natives. Compared to the baby boomers, who are late adopters and are called digital immigrants. Africa's millennials constitute at least 30% of Africa's population, making them a key attraction for marketers, yet their interests are often treated as a homogenous segment similar to global millennials from advanced economies.

We are living in a world that is increasingly digital and undergoing dramatic changes as a result. In particular for luxury fashion, growing numbers of online customers as well as fast changing business environment, luxury retailers face the challenge of differentiating themselves by offering a better online customer experience (Chen et al. 2021). By doing so, luxury fashion retailers are increasingly deploying chatbots in their service encounters to enhance customer experience (Roy & Naidoo, 2021). Chatbots are powered by Artificial Intelligence (AI) (Hoyer et al. 2020) and are an example of AI robot that can provide human-computer interactions on a retail website (Lee et al. 2017). Intended to enhance the online customer experience, chatbots have the potential to provide a better understanding of the product performance, enable efficient use of customer time, and help build crucial customer relationships (Rese et al. 2020; Wilson-Nash et al. 2020; Xu et al. 2022). Therefore, chatbots’ potential has been highly valued by fashion retail industry and academia (Jiang et al. 2022).

The main objective of the study is to investigate multichannel consumer choices between supermarkets and convenience stores when consumers buy products in the beverage category. The methodology involves both questionnaire surveys through fieldwork and purchase history data in Japan.

The current study investigates how retailers deal with sustainability issues in different market fields with a specific focus on fashion industry. This work examines the last ten years of the scientific literature on sustainable retailing (SR), through a systematic literature review. 215 papers selected from the EBSCO database are analyzed, in order to develop an overview on the state of the art of research on SR. A comprehensive framework for a holistic definition of SR and for retailers’ practices related to sustainability is outlined. Future research directions on SR are provided.

Retail firms have begun to pursue the marketing strategies, which stimulate consumers’ sensibility and lead people to purchase their products. The visible effects of visual merchandising (VM) arouse consumers’ interest and play an effective role in having busy people efficiently choose products. Apparel retail stores such as SPA use the offline store to be the experiential environment of their branding. Consumers’ sensitivity and response toward various visual merchandising strategies needs to be accessed. The purpose of this study is to identify VM consciousness and VM evaluation attribute factors. Relationship of such variables with other variables were accessed. As consequence variables, product satisfaction and unplanned purchase behavior were included in the study. An empirical survey data was collected from men and women of various ages. Results indicated that VM consciousness and VM evaluation attribute factors were not correlated with consumer demographic variables. VM evaluation attributes were factored into appropriateness, attractiveness and functionality dimensions. Clothing involvement and brand orientation significantly influenced product satisfaction and unplanned purchase. The direct and indirect effect (via VM consciousness) were significant. For unplanned purchase, brand orientation only had indirect effect. The influence of VM evaluation attribute factors were significant. Appropriateness had stronger effect on product satisfaction whereas attractiveness had stronger effect on unplanned behavior. Functionality dimension had only indirect effect on product satisfaction but did not show significant direct and indirect effects on unplanned purchase. This study identified the pivotal role of VM consciousness in various shopping and purchasing circumstances in offline retail store of apparel brands.

China emerges to be one of the largest wine importing and wine consumption countries in the world. With the rapid growth of Chinese wine market, it becomes an essential issue to understand the wine importing and distribution practices in Chinese wine market. This study was designated in the context of Australian wine trading to China to explore the characteristics of wine importing channels and wine retailing models in China. A semi-structured interview approach was adopted in this study to fulfil the research purposes. 15 Chinese wine industry practitioners were recruited through the 1st wine tourism trip fair in Ningxia, China, in May 2017. Interviews were transcribed and analysed using NVivo 11. The results revealed the features of 8 wine importing channels from Australia to China, and 4 distribution models in the wine retailing market in China. Regarding the wine importing channels, the 8 main importing channels were (1) retailers selected wines directly from Australian wineries; (2) retailers adopted the Original Equipment Manufacturer (OEM) mode to produce wines; (3) supply chain businesses imported wines through their business networks; (4) wine business companies invested in overseas wineries; (5) companies purchased wines from wine exhibitions and fairs; (6) companies purchased wines from Hong Kong and other neighbouring SAR districts; (7) companies imported wines from other wine business operators; (8) companies smuggled wines by transiting from Southeast Asian countries. In addition, 4 retailing models were identified through the interviews, including (1) complete control over the distribution channels; (2) brand franchising and wholesaling; (3) enterprise subscription; (4) retailing through e-commerce and supermarkets. The findings suggested that OEM mode represented the most popular importing channel in present Chinese wine market, whilst the vendor group purchasing was the most profitable distribution models in the retailing market. Implications for wineries and wine business companies were also provided in the paper.

The channel transformation to omni-channel is currently in progress in the retail industry. For the progress to occur, facilitating meaningful experiences of customers in their customer journeys, capturing such experiences through various channels and touch points, and then analyzing the information acquired as big data are required (Lemon and Verhoef, 2016). With the increase in the number of customer experiences being observed through the internet and mobile communication, the focus is now on engagement. However, there have not yet been many studies conducted to deliberate comprehensively on how the engagements of behavioral aspects captured through various channels and the evaluation indicators of customers, as represented by the RFM or LTV, are related in a broader sense. The purpose of this research is to clarify the relational structure from a comprehensive perspective that are not constrained by monetary amounts. This paper showed results using data from the retailer. This research is divided broadly into two stages. The first stage identifies the engagements of behavioral aspects and the relationship between the respective behaviors, as well as the typification of behavioral patterns. The second stage involves clarifying the relationship between the customer’s evaluation indicators and engagement behaviors. The engagement behaviors are basically correlated with RFM, however authors found that there is no relationship between specific engagement behavior and RFM in the group of low rank customers. On the other hand, using different types of services or shops from the core business strengthens the customer relationship. Finally, the relationship between the types of engagement behaviors and the respective customer evaluation indicators is presented in the conclusion.

The research shopping involves making use of multiple channels for a single shopping incident, such as searching from one channel and buying from another (Neslin et al. 2006, Neslin and Shankar 2009, Verhoef, Neslin and Vroomen 2007). This is an opportunistic behavior on the side of the consumers, and may result in an unfair advantage of the retailers in one channel consumers choose to purchase from. For instance, consumers may browse a product in a brick-and-mortar store, making use of the retail space and sales assistance, and proceed to buy online from another retailer which offers lower price. This is called a “showrooming” behavior (when focusing on the consumer), or channel-free riding (when focusing on the business) (Mehra, Kumar, & Raju 2013, Van Baal & Dach 2005). Offline retailers are wary of this phenomena that may lead to higher costs and lower sales, and attempt to discourage it (Rapp et al. 2015). For instance, Borders and Circuit City, the former US national chains with a substantial offline market presence, went out of business presumably due to the showrooming phenomenon (Gustin, 2012; Passariello, Kapner, & Mesco 2014). However, recent studies show that research shopping across multiple channels within one company can be managed and contributes to firm profitability in the long run (Verhoef, Kannan, & Inman 2015, Zheng et al. 2016). Kumar & Venkatesan (2005) reveal that consumers who uses multiple channels are the ones with greater customer lifetime value and with less churn intention (Blattenberg, Malthouse, & Neslin 2009, Stone, Hobbs, & Khaleeli 2002). Neslin & Shankar (2009) suggest a practical discussion on market strategy in which customers who visited offline stores can be encouraged to repeat-purchase or to foster brand involvement by maintaining contact in another channel (e.g., email newsletter). It is particularly desirable to lure online consumers to an offline site, since they tend to make greater amount of purchases in a brick-and-mortar store (Ansari, Mela, & Neslin 2008). In the age of effortless access to and switching among a plethora of channels by consumers, it is critical for a business to understand and make best of the situation. In this regard, understanding consumers in terms of who are more likely to display research shopping tendency is crucial. Depending on whether a person tends to do research shopping, business should suggest and offer different channels for different purposes (Verhoef, Kannan, & Inman 2015, Zheng et al. 2016). For instance, those with high tendency to research shop can be approached in one channel, and nudged to another for purchasing. Those with low tendency should be directed to the final purchasing channel. With this in mind, we aim to investigate research shopping behaviors and individual covariates of these shoppers using individual-level responses. We conducted an online survey in France during September 2014 in cooperation with an anonymous global marketing research firm. The survey focused mainly on exploring customers’ shopping behavior in the apparel industry, as customers’ research shopping behaviors are salient in this industry because of its experiential attributes (Girard, Silverblatt, & Korgaonkar, 2002; Klein, 1998), while traditional patterns of purchasing using only one channel is also prevalent. After a discussion with the research firm about the French apparel industry and main customers’ demographics, we restricted the respondent pool to those between the ages of 25 and 54 who have abundant experience and an active role in apparel shopping. Responses from a total of 400 participants were used in the analyses. The dataset includes individual-level shopping characteristics, demographic information, and the extent of their research shopping behavior. Specifically, each customer was asked about their apparel shopping history (purchase frequency and expenditures on apparel) over the last three months for both offline and online retailers. In addition, demographic information such as gender, age, and educational background were asked. Using multiple questions, we captured shopper’s shopping traits, such as deal-proneness, quality-consciousness, and the degree of their shopping budget flexibility. To explore the individual characteristics of research shoppers, we modeled the probability of being a research shopper using a logistic regression model. From our modeling results, we suggest two notable findings. First, customers’ qualityconsciousness significantly increases their research shopping behavior, while their deal-proneness exerts little to no influence. We conclude this is due to extensive and systematic search tendency shared by quality-conscious customers, based on previous findings in the literature. That is, quality-consciousness induces customers to search carefully across multiple channels to check on multiple quality dimensions of the options at hand, and possibly discover other similar options that may maximize their satisfaction (Lysonski, Durvasula, & Zotos 1996, Sprotles & Kendall 1986, Wesley, LeHew, & Woodside 2006). However, deal-proneness is not associated with research shopping behavior presumably because the one dimension that these customers value (i.e., price) can be easily searched in one channel—the online channel. The literature supports this finding, since studies show that deal-prone customers tend to shop more online (Close & Kukar-Kinney 2010, Devaraj, Fan, & Kohli 2002, Zhou, Dai, & Zhang 2007). We also find that the association between quality-consciousness and research shopping behavior is more pronounced when the shoppers are flexible with their shopping budget. That is, when shoppers are both quality-conscious and willing and able to consider other options or additional items beside the one they have originally planned before shopping, their research shopping tendency is enhanced. We explain that this is because consumer behaviors and decisions are made upon limited resources (e.g., time, effort, and money), and that customers with flexible resources are more likely to extend and manage their choice set (Maity, Dass, & Malhotra 2014, Malhotra 1982). These consumers do not refrain from including options that are better yet more expensive, as they can afford the superior option that suits their tastes (Becker, 1965; Ghose & Han, 2011), and without the fear of creating regrets due to discovering unaffordable options (Lenton, Fasolo, & Todd 2008). We expect our work on research shopping to provide insights to both researchers and practitioners, as today’s multi-channel environment provides opportunities for businesses to manage their customers strategically over several channels they are present in. Therefore, the research is expected to be a useful reference for understanding multi-channel shoppers for the academics, and a valuable guide to retail firms that aim to not only cope with the multi-channel environment but to become a true omni-channel player.

Recently, more and more consumers have changed from shopping in a single channel to multi-channel. Therefore, maintaining a long-term customer relationship becomes an important issue for retailers in this complex shopping circumstance. This study decides to understand how online retailers keep their valuable consumers in current store and even duplicate the original relationship to an extended channel.

It was Macy’s (a department store in the U.S.) which introduced the concept of ‘omnichannel’ in 2010 for the first time, and, at present, representative U.S. retailers have also adopted the approach. In Japan, the effort to interlock real and Internet stores started around the same time. Big retailers have promoted its omnichannel strategies by providing services in which customers can order merchandise on the Internet and receive it in a store. The purpose of this paper is to clarify the characteristics of the Japanese type of omnichannel by comparing it to the U.S. type. Rigby (2011) defines omnichannel as “an integrated sales experience that melds the advantages of physical stores with the information-rich experience of online shopping.” Lazaris & Vrechopoulos (2014) refer to it as “the use of both physical and online channels combined with the delivery of seamless shopping experiences.” Kondo (2015) understands it as “a marketing approach that integrates all (omni) channels and provides consumers with a seamless shopping experience.”

Market orientation has been extensively studied in the last 30 years. Previous studies have mainly focused on manufacturing and in the retail industry market orientation remains rather unexplored. There are only a few studies on market orientation in retailing (e.g. Elg, 2003; Kajalo & Lindblom, 2015; Liu & Davies, 1997). According to Elg (2003) market orientation in retailing differs from manufacturing in several aspects. Most importantly, in retailing individual stores have important roles to implement market orientation. They interact with customers and satisfy customer’s needs in the service encounter. Even if retailer can generate and share market knowledge in organization, the effect of market orientation on performance is weak when store organization does not adopt market oriented behaviour (Liu & Davies, 1997). Therefore, it is important for retailer to control market orientation of a retail store. Most retailers operate as retail chains to increase the scale of business. Retail chain is a multi-unit firm that manages many stores as profit units (Chang & Harrington, 2002). Retail chain includes buying and selling divisions, which specialize in different tasks. Buying division has specialized role and responsibility to search and negotiate with the suppliers, make the merchandising plan, monitor the process of merchandising, and revise the merchandising plan. In a similar manner, selling division has specialized role and responsibility to implement merchandising plan, promote retail services to customers, and manage the stores to differentiate from competitors. Buying division makes standardized merchandising plan for stores to increase scale advantage in buying, inventory management, store delivery, and advertising. Retail chains centralize the decisions of merchandising to buying divisions and formalize the process of merchandising in chain organization. On the other hand, retail chains become market - oriented organization to increase the scale advantage because this advantage depends on the effectiveness of merchandising plan. From the perspective of market orientation, the three behavioral aspects of market orientation – generation, dissemination, and response are performed by buying division and selling division of the retail chain. Buying division needs the market information generated by retail stores as selling division. Buying division makes the merchandising plan under environmental uncertainty. Buying division decreases this uncertainty to analyze the market information from stores. Market information includes not only existing market needs but also potential market needs. Buying division finds potential market needs into the market information and makes an innovative merchandising plan.In the merchandising process, selling division implements market orientation in stores. After the buying division makes merchandising plans to differentiate from competitors, the selling division implements these plans on stores. For example, store manager monitors the process of implementation and revises the action according to merchandising plans. When store managers find problems, they report these problems to the buying division and request to refine merchandising plans. In this way, the buying division takes the planning part of market orientation and the selling division takes the implementation part of market orientation. To control market orientation in chain organization, retail chain coordinate buying division and selling division by organizational structure - centralization and formalization (Lechner & Kreutzer, 2010). Organizational structure has effect on market orientation. First, formalization has opposite effect on market orientation (Jaworski & Kohli, 1993). According to Ouchi (1978) formalization reduces the ambiguity of goals and makes clear the criteria of performance evaluation in organization among organizational members. When formalization motivates organizational member to be market oriented, formalization facilitates market intelligence generation and sharing of market intelligence with organizational members. On the other hand, formalization limits the behavior of organizational members (von Krog, 1998). López et al. (2006) suggest that the rules and procedures set by formalization give the pattern to organizational communication. As results, formalization reduces the chances for organization members to communicate market intelligence and interact with each other because organizational member put priority on formalized communication channel. Second, centralization has negative effect on market orientation. According to Pelham and Wilson (1996) decentralization increases organizational commitment to satisfy customer needs and motivates market orientation. Souitaris (2001) and Ouchi (2006) assert that centralization reduces the degree of information sharing among organizational members. Therefore, centralization has negative effect on market orientation. Organizational structure has indirect effect on innovation orientation of store thought market orientation. There are two streams about the relationship between market orientation and innovation orientation (Grinstein 2008). One stream suggests that market orientation is negatively related to innovation. Another stream suggests that market orientation is positive related to innovation. In this study, we argue according to recent research that market orientation is likely to enhance. To test the conceptual model that incorporates these concepts (Figure 1), a survey was conducted among Japanese retailers. The sample (N=191) consists of store managers (71), vice-store managers (22), and floor managers (98) of a Japanese retail chain. The scales used in the study were adapted from previous research (Table 1). Concerning common method bias, we conducted Harman’s one-factor test and applied confirmatory factor analysis (CFA) testing of a model with all of the items loading on a common method factor. Comparing this model with a measurement model containing seven latent variables revealed a significant deterioration in chi-square (χ2 = 378.446; p < .01). This finding suggests that common method bias is not a serious threat in the study. This data was analyzed by following a two-step structural equation modeling approach. First, a CFA was carried out to assess the reliability and validity of theconstruct measures included in the study. In order to evaluate the reliability of the latent variables, composite reliability for all latent variables was calculated. We assess scale reliability using average variance extracted (AVE) and composite reliability (CR). The CR of each scale exceeds 0.80. The AVE of each scale exceeds 0.50. Discriminant validity was evaluated by Fornell and Larcker (1981). We found that the square root of the average variance extracted is greater than all of the corresponding correlations. These findings indicate that reliability and validity of the construct measures was adequate. Second, a structural equation model analysis was done to test the hypothesis. As seen in Figure 2 the SEM model exhibits good overall fit of the model. The results of the model provide several interesting contributions. First, the study shows that centralization has a statistically significant negative impact on formalization in retail chain. Second, the study demonstrates how centralization and formalization are linked to innovation orientation through three dimensions of market orientation. Third, the study demonstrates to retail managers the importance of organizational design and how good market orientation can benefit retailers in their increasingly innovation orientation. For retail chain, centralization and formalization of decision making about merchandising are important for gaining scale advantage. But centralization has negative effect on market orientation. Retail chain has trade – off between scale advantage and market orientation in practice. Overall, our framework demonstrates the effects of organizational structure on market orientation and innovation orientation in retail chain. Thus, our framework shows the direct and indirect impacts that organizational structure has on innovation orientation.