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As a key marketing tool, online sales promotion has been widely used by online retailers to increase sales of products and brands. Most previous researches on online sales promotion have ignored the effect of consumers' psychological factors and the heterogeneity of product and consumers. The purpose of this study is to examine the role of psychological distance and involvement on consumers' buying behavior in large online promotion activities. The research model was examined using empirical analysis of data obtained from consumer surveys after the Double 11 promotion. Our results indicate that temporal distance has positive impact on purchase decision of high involvement products, while having negative impact on purchase decision of low involvement products. Social distance has negative impact on consumers' purchase decision. Temporal distance is positively associated with consumers' purchase-decision involvement, and then purchase-decision involvement positively impacts consumers' total consumption. Social distance has no impact on consumers' purchase decision involvement. These findings not only advance the understanding of the role of psychological distance and involvement in online sales promotion but also offer implications regarding strategies that online retailers can employ to publish their promotions at different times and encourage consumers more to share promotional information among their friends.

Purpose Different from existing evidence that analyst coverage is shaped by various financial and nonfinancial factors, we fill the gap in the literature by shedding light on how analysts react to value-relevant alternative data, which confirms the governance effect of alternative data disclosure from a new perspective. Design/methodology/approach We utilize the commercial availability of third-party online sales data from WTF after 2018 as an exogenous information shock. In 2018, WTF launched a dataset that included online sales data for nearly 200 listed firms collected from China’s leading third-party e-commerce platforms, such as Taobao, T-mall, JD.com, etc. The launch of this dataset has made previously private online sales data public, greatly reducing such information’s acquisition costs. This shock enables our use of a difference-in-differences (DID) approach. The treatment firms are those included in the online sales dataset WTF launched in 2018, and the others are the control firms. We then compare the changes in firm-specific analyst following for the treatment firms before and after 2018, relative to the control firms. Findings We find that treatment firms attract more analysts following the pre-2018 to post-2018 periods than the control firms. This suggests that the third-party online sales disclosure significantly increased affected firms’ analyst following. The result remains robust after controlling online sales percent, utilizing the propensity score matching (PSM) method, implementing the Heckman two-stage model, conducting falsification tests and examining parallel trend assumptions. Channel tests suggest that efficient information processing, an improved internal information environment, accurate forecast and more social attention are the underlying mechanisms through which the aggregate release of third-party online sales data helps attract analyst following. Moreover, the main result is more pronounced among firms with higher online sales percent, CEO and chairman duality and less individual investor interactions. Originality/value First is the contribution to the literature on alternative data and online sales data. There is extensive evidence on how various alternative data affect different market participant behaviors. Yet, little research focuses on the unique online sales data from China. This paper fills the gap by studying how the aggregate release of online sales data influences analyst following from the perspective of the supply of and demand for analyst services. The second contribution relates to the factors that affect analyst following decisions. There is a wealth of literature providing evidence from firm characteristics, individual analyst attributes and other institutional factors, whereas the research focusing on alternative data is still limited even with the fact that this novel alternative data source is embedded with high information value and outperforms traditional financial information in many ways. This paper underscores that alternative data disclosure can affect analyst following through the benefit-cost tradeoff.

Although relevant literature investigates the economic value of online sales channel (OSC) from the perspective of the stock market, knowledge on this topic remains insufficient or unclear because existing studies are conducted under an extremely turbulent market environment and have not considered different aspects. This study aims to examine the topic by focusing on the market reactions to OSC investment from three aspects (namely, the innovativeness, business model and goods types) in a relatively stable market environment to fill in the research gap. Empirical results, obtained using 69 firm-level OSC announcements from October 2002 to September 2007, show that the stock market reacts positively to OSC investment by firms. Additionally, the stock market reactions to OSC investment mainly depend on two key characteristics, namely investment innovativeness and business model applied.

With the development of artificial intelligence technology, virtual live streamers emerge in the field of live streaming e-commerce. Previous studies have identified how human live streamers affect online sales performance. However, little is known about the virtual live streamer. Can virtual live streamers play a role in e-commerce as well as their human counterparts? The purpose of this study is to examine the influence of virtual live streamers on online sales performance from the perspective of source credibility. The perceived credibility of virtual live streamers can modify viewer behavior in the virtual live streaming room. The relevant data is thus generated and recorded by the e-commerce platform. We collected data from 300 virtual live streaming rooms in the e-commerce platform known as Taobao.com. Using the behavior data, a multiple regression model was built to empirically study the relationship between the characteristics of virtual live streamers and online sales performance. The results showed that virtual live streamers’ characteristics of trustworthiness, attractiveness, and expertise had a positive effect on online sales performance, whereas the characteristic of interactivity had an adverse effect. This study provides insight into the virtual live streamer. Virtual live streamers are still unable to interact with viewers in a human live streamer manner. Marketing managers should improve virtual live streamers to meet viewers’ hedonic shopping motivations in the future. Plain language summary With the development of artificial intelligence technology, virtual live streamers emerge in the field of live streaming e-commerce. With the advantage of real-time interaction, human live streamers have been proven to be effective in improving online sales performance. Can virtual live streamers play a role in e-commerce as well as their human counterparts? The purpose of this study is to examine the influence of virtual live streamers on online sales performance from the perspective of source credibility. We measure virtual live streamers’ credibility from the dimensions of trustworthiness, attractiveness, expertise, and interactivity. Viewers participate in live streaming, receive product information from the virtual live streamer, interact with the virtual live streamer, and make a purchase decision ultimately. Throughout this process, the viewers can perceive the credibility of virtual live streamers and modify their behavior in the virtual live streaming room. The results of this study showed that virtual live streamers’ characteristics of trustworthiness, attractiveness, and expertise had a positive effect on online sales performance, whereas the characteristics of interactivity had an adverse effect. This study helps managers understand virtual live streamers better and design a suitable virtual live streamer. The main drawback of virtual live streamers is that they are still unable to interact with viewers in a human live streamer manner due to insufficient support from artificial intelligence technology. Improvement of interactivity to meet viewers’ hedonic shopping motivations is the main direction for the development of virtual live streamers.

Online sales forecasting has become an essential aspect of effective business planning in the digital era. The widespread adoption of digital transformation has enabled companies to collect substantial datasets related to consumer behavior, market trends, and sales drivers. This study attempts to uncover patterns and predict sales growth by utilizing product images and their associated filenames as input. To achieve this, we use EDA combined with LSTM and Gated Recurrent Unit (GRU), which excel in processing sequential data. However, the performance of these networks is significantly affected by the quality of data and the preprocessing methods applied. This study highlights the importance of Exploratory Data Analysis (EDA) and Ensemble Methods in enhancing the efficacy of RNNs for online sales forecasting. EDA plays a crucial role in identifying significant patterns such as trends, seasonality, and autocorrelation while addressing data irregularities such as missing values and outliers. These findings show that integrating EDA substantially improves the performance metrics of RNN, as indicated by the reduction in loss and mean absolute error (MAE) values across training epochs (e.g. loss: 0.0720, MAE: 0.1918 at epoch 15). These results indicate that EDA improves the accuracy, stability, and efficiency of the model, allowing RNN to provide more reliable sales predictions while minimizing the risk of overfitting.

Largely motivated by the industrial practice in which a platform giant will encroach online retailing by introducing private label (PL) products, this paper aims to investigate the optimal introduction decision for a platform and identify the best sales format, between the reselling format and the agency format, for a manufacturer in an e-commerce platform supply chain. In response to these two sales formats, this paper characterizes and proposes three different PL product introduction strategies, including No Introduction, Partial Introduction, and Full Introduction. By developing a game-theoretic framework and applying the Karush–Kuhn–Tucker optimality, this paper examines the optimal PL product introduction decision and the best sales format. With analytical studies and numerical experiments, several significant implications are derived in this paper. For example, it is first found that the consumer preference for the platform and the quality of the PL products are two key factors influencing the platform’s PL product introduction, with associated effects differing notably. Secondly, improving the PL products’ quality does not necessarily lead to an increased profit for the platform. It will also not lead to a loss in profit for the manufacturer. Lastly, the best sales formats for the manufacturer are significantly influenced by the PL products’ introduction strategy chosen by the platform.

The growing adoption of social web technologies such as social software (SSW) in online configuration environments has enabled the possibility of supporting configurator users in interacting digitally with real people while they are shopping for customizedproducts. Previous research has identified that online sales configurators (OSCs) are currently connected to SSW with different modalities to provide configurator users with a variety of options to digitally interact with real people. Enriching the configuration environment with social-interaction tools has engendered the phenomenon of social-product customization. Recent studies considered the social product-customization by investigating the impact that community feedback and social comparisons has on configurator user. However,theOSCs users’ need to interact with different referentsduring theirconfiguration process, and whether the SSW-OSCs connections respond to this needare still unsearched. To address this gap, the present study explores (a) whether users experience the need to interact with different referents while shopping via OSCs and (b) which interaction modalities users are looking for. By considering 943 configuration experiences from 189 users of 378 OSCs for various consumer goods, the present study finds that the need for social interaction by OSC users is highly relevant. Moreover, OSC users perceive the need to interact with different referents during different stages of the configuration process, and, depending on the referent with whom they wish to interact, they are interested in different interaction modalities in terms of how and where those interactions take place. These findings imply that mass customizers may leverage their customers’ need to interact with real people while shopping online via OSCs in order to better engage their actual and potential customers.