随着社会化购物行业的高速发展，直播购物俨然成为企业销售新的增长点，企业如何选择直播购物主播类型成为企业关注的重点。由于直播购物具有社交性、即时性和虚拟性，消费者在直播购物中会更依赖于直播主播这一外部线索去评估产品，制定购物决策。且消费者在进行不同类型产品的购买决策时的价值驱动也有所不同。因此，本研究基于社会影响理论，探讨了直播购物主播类型（企业主播与名人主播）与产品类型（实用品与享乐品）的交互作用对消费者购买意愿和行为的影响。通过情景实验和二手数据发现，名人主播推荐介绍享乐品，促进触发消费者的认同机制，并提高其购买意愿和产品销量。企业主播推荐介绍实用品，促进触发消费者的内化机制，并提高其购买意愿和产品销量。本文拓展了社会化购物中对于直播主播类型的研究范畴，深化了对社会影响理论动力驱动的理解，同时对企业的直播主播选择策略具有一定的指导意义。

We propose a conceptual framework that includes the antecedents and consequences of firms' adopting and integrating robotics into their customer service operations. Drawing insights from literature on customer service, technology marketing, and computer science, our proposed framework elaborates on the concept of the degree of robotics adoption (DRA) as well as the antecedents (employee acceptance of robots and customer acceptance of robots) and multiple sequential consequences (service quality, customer long-term performance, and customer engagement) of DRA. We also discuss how the nature of the firm (Business to Consumer versus Business-to-Business, i.e. B2C vs. B2B), service characteristics (utilitarian vs. hedonic), and brand positioning (low equity vs. high equity) might moderate the relationship between DRA and service quality. Further, we provide actionable guidance for managers to adopt and integrate robotics into their customer service operations.

PurposePrevious studies on social influence and virtual product adoption have mainly taken users' purchase behavior as a dichotomous variable (i.e. purchasing or not). Given the prevalence of competing versions (basic vs upgraded) of a virtual product in online communities, this paper investigated the differences in the effect of social influence on users' adoption of basic and upgraded choices of a virtual product. It also examined how the effect varies with users' social status and user-level network density.Design/methodology/approachA natural experiment was conducted in an online game community. Two competing versions (basic vs upgraded) of a virtual product were provided for in-game purchase while a random set of users selected from 897,765 players received the notification of their friends' adoption information. A competing-risk model was used to test the hypotheses.FindingsSocial influence exerts a stronger positive effect on users' adoption of the upgraded virtual product than of the basic virtual product. Middle-status users have the greatest (least) susceptibility to social influence in adopting the upgraded (basic) virtual product than low- and high-status users. User's network density enhances the effect of social influence on adoption of both virtual products, even more for the upgraded one.Originality/valueThis research contributes to the social influence and product adoption literature by disentangling the different effects of social influence on basic and upgraded versions of a virtual product. It also identifies the boundary conditions that social influence works for each version of the virtual product.

Traditional congestion control protocols may fail to achieve consistently-high performance over a wide range of networking environments as their hardwired policies are optimized over specific network conditions. In this paper, we depart from conventional wisdom and propose GLIDER, a new congestion control protocol that uses deep reinforcement learning to be more versatile and adaptive to dynamic environments. In particular, GLIDER uses a framework based on Deep Q-Network, that a sender keeps adapting its congestion control strategies by continuously interacting with the network environment. In addition, the sender constantly sends data, making it challenging to apply reinforcement learning algorithms that require step-by-step state computation to congestion control. Therefore, we design a Dynamic Bisection Division Algorithm (DBDA) to discretize the packet transmission process into steps to ensure GLIDER'S feasibility on congestion control. We have used an extensive array of experiments on Pantheon to show that GLIDER can adapt well to varying buffer sizes and is resilient to random loss. Moreover, on wide-area inter-data center links, it can achieve 6.4x and 1.4x higher throughput than TCP CUBIC and BBR, respectively, and comparable performance as other learning-based congestion control protocols in the literature.

The product recall is becoming a challenging issue in supply chain management, and both suppliers and manufacturers are motivated to exert recall efforts to improve supply chain performance. Considering a supply chain composed of one supplier and one manufacturer with product recalls and demand uncertainty, we investigate the contract design by integrating the revenue sharing contract with three cost sharing policies, namely, fixed-rate cost sharing (Contract F), linear cost sharing (Contract L), and threshold-based cost sharing (Contract T), to coordinate the production quantity and recall efforts. Results show that cost sharing could improve the product recall probability and supply chain profits, but only Contract T can coordinate the supply chain in both production quantity and recall efforts. Moreover, the cost sharing rate in Contract T first decreases and then increases as the recall cost increases. When the recall cost is small (large), Contract T (Contract F) is optimal for the manufacturer. In contrast to our intuition, we find Contract L could overmotivate supply chain members to exert a recall effort that is greater than the first-best when the cost coefficient of recall efforts is large enough. However, demand uncertainty will mitigate the impact of product recalls on motivating recall efforts.

Mobile edge computing(MEC)is a promising technology for the Internet of Vehicles,especially in terms of application offloading and resource allocation.Most existing offloading schemes are sub-optimal,since these offlo

When powered by artificial intelligence (AI), the effectiveness of advertising generally improves. However, evidence shows that personalized recommendations in charity advertising may have a dark side. The existing literature about the effects of personalized recommendations in advertising is rooted primarily in outcome utility, including outcome benefits and costs. Nevertheless, consumers tend to sacrifice their own interests without expecting anything in return given that they cannot directly monitor and measure the behavior outcome in charitable consumption; this elicits in them a focus on their autonomy and signal utility in responding to charity advertising. Thus, in our article, we focus on the reasons that personalized recommendations have negative effects in charity advertising based on self-determination theory. Through five studies, the results reveal that consumers display lower donation intentions when they receive charity advertising with (versus without) personalized recommendations due to a decrease in perceived autonomy. In addition, this negative effect can be mitigated by servant communication styles and providing consumers with free choices. These conclusions not only enrich the literature on personalized recommendations in advertising, charity advertising, and AI marketing but also provide some guidance for advertisers to enhance the performance of personalized recommendations in charity advertising.

The commuting cost has increased inevitably under the background of industrialization and urbanization, which has vital impacts on daily life. In this paper, we implement an empirical study to explore the impact of commuting time on hedonic consumption and the underline mechanism of perceived stress based on the data of CFPS 2014 (a national social tracking survey project). The Tobit regression combined with the Heckman two-stage model is applied to correct the sample selection bias. The results show that the increase in commuting time will aggrandize residents' tendency and expenditure of hedonic consumption. Moreover, we conduct situational experiments as a supplement to verify the main effect and mediation mechanism to exclude alternative explanations. The mechanism analyses prove that the perceived stress caused by long commuting time is a possible mediator for hedonic consumption. This study enriches the relative studies on social influences of commute, especially proving that this social issue may provide some benefits for business practice.

从计算智能到感知智能再到认知智能技术的演变导致企业正在积极地将不同类型的人工智能整合到产品营销活动之中。随着智能技术能力的快速增强,智能产品对用户消费过程的介入程度越来越深,这将可能会挑战用户心理层面固有的认知和需求从