Digital channels have been rising to the major shopping paths from the past few years, yet it is interesting to notice that more and more digital retailers advance into offline channel nowadays due to the benefits. For example, the digital retailing giant Amazon opened its first bricks-and-mortar bookstore in 2015. Dell, which distributes its products only in the catalog and digital channels, entered the leading retail stores such as Best Buy and Walmart. The digital retailers’ strategy trend that moves into the real world reveals the unique and powerful capabilities of the offline distribution channel and the importance of launching products in the offline channel. Previous studies in marketing have investigated various complement and substitute impacts of offline channel introduction (i.e., Avery et al. 2012, Wang and Goldfarb 2017). However, most of the literature focus on the pc-based online channel and catalog, there is little research about the impact of offline channel introduction on smartphone-based mobile channel. Although mobile channel is similar to online channel in many ways (i.e., internet access and convenient shopping), it can be distinguished from online channel in terms of search and access, leading to a different interplay with offline channel. This research, therefore, aims to investigate how product launching in offline channel affects purchases in mobile channel versus online channel, and deepen the understanding by exploring the moderating effect of offline store intensity. Besides, we also explore the interaction between two digital channels: online channel and mobile channel. As most of the multi-channel retailers offer products in both online and mobile channels, it is incremental to know the interplay between these two digital channels. Using data from a representative multichannel retailer selling beauty products, we find the following three empirical results. First, product introduction in offline channel has a positive effect on online and mobile sales, and the effect is greater for mobile. Second, as offline store increases, the positive impact of offline product launch weakens on online channel, and even turns to negative once the number of offline stores reaches a threshold. However, the influence on mobile channel stays the same. The results reflect that the complementary interplay between offline and mobile channel is relatively stronger than between online channel. Finally, within the digital channels, online purchases only increase with the growth of sales performance within online channel, whereas mobile purchases are positively affected by both within mobile channel sales and across online channel sales. Our findings contribute critical academic and managerial implications for multichannel retailing.

Recommender systems based on Collaborative Filtering (CF) algorithms have established an extensive means for retailers to suggest personalized item lists that will maximize each consumer’s utility. Nevertheless, in the mobile game industry, which characterized by the intense competition from the avalanche of other game options and fast-changing demands of game users, there has been no marked success with recommender systems. Instead, app stores merely show summaries of general market trends without any individual-level information, fail to suggest personalized lists based on preferences of the future. For modeling dynamics of game usage, we assume that an individual’s preferences on games can be represented as the proportion of each game’s running time, which can be calculated in daily basis by the individual’s usage time for each game apps divided by the individual’s total capacity. Then, we construct a tensor filled with the induced preferences. For the next step, we apply Bayesian probabilistic tensor factorization (BPTF), an extension of Singular Value Decomposition (SVD) to consider dynamic pattern, to restore all the empty entries of the tensor. Each restored component becomes an estimate of each user’s preference on each game at certain period. For empirical analysis, we use mobile log data in app-level for total 1,000 panels over 2 years. Top 100 mobile games in cumulative usage time are treated as focal apps in this study, making the dimension of the tensor by 1000 (users) * 100 (focal games) * 730 (2 years). We compare the model performance by the root-mean-squared error (RMSE) with that of baseline model, the static counterpart in collaborative filtering algorithm (Salakhutdinov, and Mnih, 2008). The results showed that our model (BPTF) defeats the baseline throughout overall user-game pairs, especially outperforming under the conditions that there are severe fluctuations in daily usage pattern and when the life span of newly adopted apps are relatively short. Furthermore, we compose personalized suggestions, which consists of the top-10 highly likable lists according to the predicted usage patterns for each individual, and compare the performance with that of the established general recommender system in app stores. For that matter, our suggestion also outweighed the existing recommender system by the typical performance metrics that commonly used in the mobile game industry.

New product crowdfunding has become an emerging method for start-ups to finance their innovative ideas. Since driving participants’ engagement is crucial to successful fundraising, it is important to study the factors that affect participants’ motivation to back the crowdfunding projects. In this paper, we first examine the impact of goal proximity on backing motivation and then we focus on investigating how group size affects perceived personal impact and resulting motivation to back a project. To test our hypotheses, we collected backing records and creativity ratings of smart wearables projects from a crowdfunding platform in China−Demohour.com. We used daily backers added as our dependent variable to capture potential backers’ motivation to back a project and modeled it using a negative binomial model with project and time fixed effects. Our empirical analyses suggest the following. First, consistent with existing research on the goal gradient effect, the funding ratio of a project is positively related to daily backers added while the positive effect becomes insignificant after reaching 100% funding ratio. Second, group size of backers has a negative effect on potential backers’ motivation. We explain that this is because individuals exert less effort due to decreased perceived personal impact when group size grows (social loafing effect) (Karau & Williams, 1993). Finally, creativity negatively moderates the effect of group size on backing motivation that the negative effect of group size especially calls for attention when the creativity level of the project is high.

Online social interactions are known to be useful to improve business performances. As l ocal business retailers have limited resources in marketing, they can benefit by using onli ne social interactions for their business performances. In the same line of purpose, the ret ailers also exploit an online platform, such as discount coupon sites: they sell online coup ons for their offline products and services in the platform. Notably, the online platform ca n play an important role in generating online social interactions as well as final sales arou nd the retailing brands. It also provides a distinctive setting for consumers in that they pur chase products and services online only to consume their use offline. Given that consume rs are motivated by different purposes, their online social interactions may differ in the di sparity of purchasing online and consuming offline. Previous studies have witnessed the r elationship between social interactions and sales, but the relationship between environme ntal influences and social interactions remains unexplored. In this paper, we focus on the influences of online and offline environments where consumers are situated with the online platform on generating online social interactions as well as final sales. To this end, we look into two types of social interactions, i.e., product discussion and social referral, and two distinctive environmental influences, i.e., the influences from the same product page and from the local retail revenue where the focal business is located. Using data on online social interactions and offline retail revenues around a major coupon site in South Korea, our empirical analysis demonstrates interesting findings. The two types of social interactions and final sales respond in different way to the environmental influences. To be specific, in the online purchase context, the absolute influence lowers the generation of product discussion and sales while promoting social referrals. In the offline consumption context, however, the proportionate influence plays a role in driving these three outcomes. Our findings suggest that local business retailers should deploy their online platform strategies by concerning online and offline environments, in accordance with the specific marketing objectives regarding social interactions and sales.

This paper examines the effects of the mergers and acquisitions (M&A) announcement through social media on the consumer perception of the luxury brand consumption. A M&A is becoming more wide spread in the luxury market. Yet, the academic research examining the M&A in the luxury brand context has been sparse albeit the growing interests. Moreover, previous research has not paid attention to the effect of social media as a vehicle to communicate the M&A deal with consumers although social media is increasingly used by luxury brands in their brand communication these days. We aim to fill the gap in the luxury brand literature by examining how a horizontal M&A announcement delivered through social media would affect the brand loyalty derived from the luxury consumption values. Specifically, our research focuses on the four distinctive luxury brand values, which are symbolic, experiential, economic and quality values as well as the perceived sustainability of the M&A deal. We examine how a M&A announcement would affect these five values which in turn influence the brand loyalty, as well as examining the differential effect of social media and non-social media as a brand communication vehicle. In addition, we examine how the vertically differentiated luxury brand perceptions (i.e. different luxury tiers) between acquiring and acquired brands influence the consumption values and brand loyalty. Using a scenario-based online survey, our results reveal several interesting insights on the luxury brand M&A. First, our results show that use of social media as a communication vehicle has differential effects on how the M&A announcement influences consumption values and brand loyalty, comparing with the non-social media communication vehicle. Second, we find that a M&A announcement via social media has a positive impact on the consumer values. Third, the symbolic and experiential values have a positive influence on the brand loyalty, regardless of the luxury tier difference between brands. Fourth, our results show that the perceived sustainability has a positive impact on the brand loyalty as long as the M&A was completed between brands at different tiers. Fourth, the perceived quality has a positive impact on the brand loyalty only if the brand is acquired by a less prestigious brand. Lastly, economic value has a positive impact on the brand loyalty only if the acquiring brand is of more luxurious. In sum, our paper provides useful insights to both academics as well as practitioners in the luxury brand M&A context.

From the first inspiration of a desire until a product reaches hands, consumers go through complex and multi-faceted interactions with brands, involving myriads of channels and media. As such, it is important for marketers to understand this shopping “journey” since it is where brands can influence and persuade consumers most effectively and efficiently. In this study, we conduct an exploratory study to understand the patterns and correlates of pre-purchase behaviors and the relationship to actual purchases (made both online and offline) by analyzing survey data obtained in Korea and China. In particular, we examine three key drivers for pre-purchase behaviors: product category, market maturity, and consumer tendencies. To address category-level differences, we compare and contrast grocery and electronics. As for the market maturity, we study Korea and China in which e-commerce is at different stages of development. Finally, consumer tendencies are measured by general shopping tendencies, online activity patterns, and risk perception for e-commerce. Our empirical analyses provide the following findings. First, general shopping tendencies impact the pre-purchase behavior in grocery buying, but not electronics, for both Korea and China. Second, online activity patterns (entertainment and content-creating activities) have an impact on the shopping journey in both categories. Third, risk perceptions for ecommerce only affect pre-purchase behaviors of Chinese consumers. That is, in a developing market at an earlier stage of e-commerce development, consumers perceive online transactions more risky and uncertain, leading to greater pre-purchase behaviors. Lastly, we investigate the link from the information search to the final purchase stages. Consumers buying grocery present a positive and significant relationship between prepurchase and final purchase behaviors whereas electronics present no such relationship across the stages of the consumer shopping journey.