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Innovations in technology and evolution of internet elicited the usage of technology and internet during the shopping process of consumers. Changes in consumer shopping processes opened doors for shifts in consumer buying behavior. As a result of the variations in consumer buying behavior, retailers formed new channel structures to fulfill customer requirements. New channel structures created different retailing formats and enhanced the complexity of retailing processes. As the complexity of retailing processes increased, complexity of consumer shopping behavior increased as well. In this sense, multichannel retailing emerged and expanded all around the world and paved the way for omnichannel retailing. Transformation of multichannel retailing to omnichannel retailing created two different shopping forms as: Showrooming and Webrooming. In this chapter, showrooming and webrooming concepts will be studied and the complementarity dimensions of these concepts will be explained in detail.

This paper aims to examine why retail firms seldom achieve full integration of online and offline channels as prescribed in omni-channel literature. It examines the intermediate process of channel integration from an internal, operational perspective.

This study is composed of two parts. In the first part, the authors interviewed informants from nine firms that were engaged in channel integration. In the second part, the authors conducted case studies with three firms from the cosmetics and skincare industry against the backdrop of the COVID-19 pandemic to find evidence to support or negate the propositions made in the first part.

The first part identified six operational challenges to channel integration. The authors categorized these challenges into two groups: inter-channel communication and inter-channel competition. Inter-channel competition carries more weight at the latter stage of integration. The authors also identified two antecedents that affect the seriousness of these challenges: heterogeneity among channels in business operation and external competitive pressure. In the second part, the authors found that both inter-channel communication and inter-channel competition were improved because of the external competitive pressure exerted by the COVID-19 pandemic. However, the heterogeneity of offline channels against online channels in business operation is a double-edged sword.

The study identifies the changing effects of the challenges of channel integration and their antecedents in the midst of integration. The positive influence of a specific dimension of channel heterogeneity against other channels increases and then decreases along channel integration. The identification of the changing effects lays the foundation for a finer stage model of channel integration.

This study aims to understand why consumers continue to visit physical stores despite the rise in mobile shopping and online channels. Mobile shopping has changed how consumers shop, allowing them to easily switch between channels. However, physical stores continue to remain significant because some consumers still prefer them, challenging the belief that online markets always surpass offline markets. To serve their needs effectively, retailers must understand the motivations and behaviors of shoppers in both channels. Therefore, this study aims to explore why people cross the online channel to offline by examining their dissatisfaction with online shopping, using E-SERVQUAL variables.

This study uses a two-method approach that involves in-depth interviews to develop questions related to E-SERVQUAL variables and a survey to assess respondents’ likelihood of switching from online to offline. Data was collected from 203 participants.

The results indicate that dissatisfaction with the timeliness and condition of online shopping services is a significant factor driving consumers to switch to physical stores. This challenges the notion that online markets always surpass offline markets, emphasizing the continued significance of physical stores in the retail landscape.

This study recognizes the importance and relevance of physical stores in the retail environment while challenging the assumption that online markets always outperform brick-and-mortar markets. In terms of dissatisfaction and satisfaction, it is possible to identify under what circumstances dissatisfied consumers go from online to offline by considering the distribution channel migration phenomenon.

Drawing on open systems theory, this study aims to investigate the direct and moderating effects of information collaboration in the pre-sale stage, transaction management collaboration in the transaction stage and customer service collaboration in the post-sale stage on the linkages of the online–offline store image and the market performance of small sellers.

Data were collected from multiple sources, including self-reported and online objective data from 148 small restaurants that simultaneously sell online and offline, for validating the developed research model. Partial least squares-based structural equation modeling was used for data analysis.

This study illustrates the direct effects of an online store’s image and online–offline collaborations on the market performance of small stores. This study further reveals the boom-bust moderating effects of different collaborations between online–offline images and market performance.

Small stores should be aware of the importance of information congruence and functional integration concerning online–offline collaboration. They should also recognize the paradoxical intervening effects of online–offline collaboration on different channels and arrange appropriate collaboration tactics.

This study presents a significant contribution to the open systems theory by revealing both constructive and destructive properties of the online–offline collaborative system with offline-to-online targeting. Vertically differentiated online–offline collaboration may strengthen one side of the store image but weaken the other side for promoting the market performance of small stores.

The purpose of this paper is to identify the differences in the perceived media richness of three marketing channels by different consumer generations as well as to indicate the importance of the perceived media richness of each of channel for the intention of online use at pre-purchase stage.

The study is based on a sample of 1,103 consumers in Poland, which research sample included three generations: Baby Boomers, X generation and Y generation. Computer-assisted personal interview method was used with a standardised questionnaire; the research sample was determined using a quota-random method, with the quotas referring to age and gender and the nature of the place of residence (provincial town, town/city other than a provincial one, or a village) – the structure of the sample was maintained at the regional level.

Analysed channels (online, offline, phone) have different perceived media richness. Consumers from different generations can perceive media richness of the same marketing channels in various ways. Perceived media richness of the analysed marketing channels has an impact on the intention to use the channel online.

First, the fact of blurring differences between consumers generation, particularly X and Y generation. As well the fact that each generation has identified an internal diversity. Second limitation refers to the type of marketing channels included in research. Finally, limitation also applies to spatial extent of the study. The above limitations can also be some directions of future research – taking into account some other marketing channels and verifying the conclusions on other consumer groups.

General recommendation refers to the need for treating customers in each marketing channel in the same way – immediate response is one of the aspects of this recommended unified customer service in multichannel environment. Online channel communications with customers should be supported by giving consumers more personalised communication via phone, chat and social media. For older consumers, it is recommended to gradually redirect them to an online channel. Information search is a good start for online customer journey, because this channel at the pre-purchase stage is the most useful and least risky for the consumer unfamiliar with online environment.

Better understanding of the needs of different customers generations and their perception of different marketing channel at the pre-purchase stage.

Identification of intergenerational differences in the perception of media richness of online, phone and offline channels.

This research investigates the novel questions of whether and how specific forms of shopping channels (online vs offline) influence consumers' decision-making. Moreover, this research investigates marketing firms' proper marketing strategies across different shipping channels.

The authors conducted three studies using a large sample (N = 703) recruited from a diverse pool (students and adults) that examined multiple products (camera and car) across different shopping channels (online vs offline). Study 1a (n = 251) and Study 1b (n = 252) examined the effect of an online versus offline channel on consumers' decision-making using a one-factor (shopping channel: online vs offline) between-subjects design. Meanwhile, Study 2 (n = 200) investigated the effective strategies that firms should employ across different shopping channels using a 2 (shopping channel: online vs offline) × 2 (mental simulation: outcome vs process) between-subjects design. Participants in the online condition evaluated the product on a computer screen, whereas participants in the offline condition evaluated the real product assuming a real-world retail store setting.

The three studies supported the predictions that shopping channels (online vs offline) affect consumers' psychological distance and, in turn, affect their decision process. Specifically, results reveal that the online (offline) channel increases (decreases) psychological distance and leads consumers to pay more attention to a product's desirability (feasibility) aspects.

Given that many firms sell the same products through multiple channels, the findings of this research offer insightful theoretical and practical implications.

The purpose of this paper is to provide a framework to help business marketers with online and traditional retail channels (multi‐channel retailers) to find the optimal branding strategy and market structure in order to maximize their profits.

A game‐theoretic model is developed to determine the optimal branding strategy and market structure for the dual‐channel stores of a multi‐channel retailer.

The paper demonstrates that an optimal branding strategy and market structure exists for the dual‐channel stores of a multi‐channel retailer. When a retailer uses multiple channels in parallel to sell its product, the optimal branding strategy is to employ as large as possible brand differentiation between the dual‐channel stores, particularly when the price is less sensitive for consumers and the market base size is larger. Furthermore, it is also found that the optimal market structure is the Stackelberg mode, especially when the product brands between the two channels are less differentiated.

The present study assumed that all information is known to both the online and traditional channels of a multi‐channel retailer. However, information could be incomplete. It is recommended that future research explore the value of product brand differentiation under incomplete information settings.

The paper provides a very useful model framework, branding strategy, and market structure for business managers who are using or planning to use multiple channels to sell their products.

This paper fills a conceptual and practical gap for a structured analysis of the current state of knowledge about multi‐channel branding strategies. The paper provides practical and solid advice and examples demonstrating the application of product brand differentiation strategies for business managers.

Advances in information communication technologies (ICTs) have changed the tourism distribution channels model, as traditional players continue to disappear or change their business model, while new players and channels emerge because of technological developments. Therefore, this study aims to propose a tourism distribution channels model for European island destinations.

Using an exploratory approach, interviews with 34 tourism stakeholders were conducted at ITB Berlin and WTM London in March 2014 and March/November 2016, and analyzed using thematic analysis.

The findings revealed that a number of changes have taken place within the distribution channels market over the past six years. The disappearance of incoming travel agents has increased, while new forms of online communication and distribution have appeared. In particular, social media, online review sites and mobile channels play an increasingly important role for hoteliers.

ICTs change the online landscape for tourist distribution in island destinations, and practitioners should make use of new online channels and be aware of disappearing tourism players to remain competitive.

First, this paper provides indications for the increased disintermediation in regard to incoming travel agents within the Cretan hospitality and tourism industry. Second, it investigates the issue of tourism distribution channels using a broad range of key tourism and hospitality players to provide a tourism distribution channels model for future reference. Finally, this study offers implications for the development of distribution strategies for tourism businesses and hoteliers in Crete.

Hotel managers are being challenged by the increasing multitude of distribution and sales channels. Online travel agencies (OTAs) in particular generate a great deal of uncertainty: Which are the best ones? Which ones offer the best conditions? How many channels are optimal for my hotel? How can I evaluate costs versus benefits? These and other questions concerning the optimal online distribution channel strategy have produced different reactions in practice. The aim of this paper is to challenge the need for an over-optimization of channel strategy by proposing that the consumer, at the end, deals with a network of information presented on one networked environment, including the Web. Hence, the network effect of the numerous online platforms is what drives consumer choice and, finally, bookings.

A series of multiple regressions with representative samples of hotels in Switzerland from the years 2009, 2010, 2011 and 2012 was performed to estimate the importance of the number of platforms against other independent variables. Additionally, further multiple regressions with samples from the years 2011 and 2012 using the most important platforms (first-tier channels) shows again that the number of platforms is more important.

The analyses show that the estimated number of online bookings by the respondents in the hotels is a result of the number of channels, not the type of channel. This is particularly true for non-categorized establishments and one- and two-star hotels. The analyses do not confirm the billboard effect, according to which particular platforms (first-tier channels) increase the probability of bookings. Thus, the survival strategy is to maximize share of shelf and to build on interdependencies and network effects.

The study looks only at online bookings. Additional research into the connection between online and offline channels, particularly from the viewpoint of the consumer, will provide further insights. The study looks at the booking volume per channel, not the monetary sales volume or the profit. A study that quantifies not only the volume of bookings but also the total profit or the contribution to profit per channel could quantify the benefits of the multi-channel strategy.

The multiple online channel strategy seems to be the more effective approach to maximizing bookings online, regardless of the platforms chosen. Results of the study challenge the current opinion among practitioners that the multitude of distribution channels forces them to choose among single online channels and, therefore, drives the search for criteria to assess these channels or even to disregard them. The consistent results across 2009-2012 show that even in the turbulent phase of the advent of OTAs in the travel industry, hotels can adopt a winning strategy. Finally, the results suggest that the intermediation of online distribution of hotel beds has approached the condition of perfect competition, causing the OTA business model to be cannibalized.

This study examines the dynamic relationships of visit behavior in the multiple channels [personal computer (PC) and mobile channels] on online store sales performance.

The empirical data were from an online store for the period between August 14, 2015 and May 15, 2016. The data consisted of consumer visit behavior and online store sales performance. Vector autoregression with an exogenous variables model was adopted to investigate the dynamic relationships.

The empirical results show significant relationships between visit behavior metrics (number of visitors, average number of visits per visitor and average length of each visit) in the two channels and online store sales performance. The number of visitors through the PC and mobile channels strongly and positively affects online store sales performance both in the short term and in the longer term. Moreover, the number of visitors in the PC channel has the strongest influence on sales performance metrics, followed by the number of visitors and the average number of visits in the mobile channel. The PC channel's visit behavior metrics explain a larger proportion of the sales performance variance than that in the mobile channel.

The previous literature on consumer behavior in multichannel marketing mainly focuses on channel selection or migration, and examines the different factors affecting channel choice behavior. Little is known about the impacts of visit behavior in the multiple channels. This study adopts the heuristic-systematic information processing theory to unveil the impacts of visit behavior metrics in the PC and mobile channels on online store sales performance.