An Empirical Study of Guarantee in Service E-Commerce

Service e-Commerce (SeC) is emerging as a booming form of e-commerce where various services are contracted, managed, sold, and even delivered via the Internet. However, the uncertainty of service quality due to information asymmetry has been a major challenge to the development of SeC. Some SeC platforms tried to promote service business by lowering buyer’s perceived risk through the service guarantee mechanism. However, the mechanism seems not very successful to lift the low participation rate. This study investigated the effects of service guarantee on service e-marketplace by examining the case of zhubajie.com, a well-known service e-marketplace in China. A total of 30,406 providers (including 406 service-guarantee and 30,000 non-service-guarantee providers) were collected and analyzed. The analyses found that there are different modes for low-reputation and high-reputation service providers to participate in the service guarantee. In addition, results also show that service guarantee only improves business performance for the service providers with high reputation. For low-reputation service providers, the service guarantee mechanism does not have significant effects. Implications and suggestions were made to guide future practice and research in similar contexts

UNDERSTANDING THE ROLE OF COMMITMENTS IN EXPLAINING CROWDFUNDING INVESTING WILLINGNESS: ANTECEDENTS AND CONSEQUENCES

Crowdfunding is a new financing channel for small- and medium-sized enterprises and start-ups to raise funds for innovation projects online. Despite its rapid development, few empirical research has been performed to identify individuals’ motivations to continuously invest in crowdfunding. The high practical significance and lack of research indicate the importance of the present study. This study aims to apply Meyer & Allen’s three-component model of commitment to construct a research model, incorporating context-specific antecedents. The results of our survey of 186 actual funders of the crowdfunding platforms in China indicated that affective and calculative commitment are the main drivers of funders’ continuous investments in crowdfunding. Calculative commitment was proved to have a positive influence on affective commitment. Further, perceived self-worth and trust performed well as antecedents of both affective and calculative commitment, though trust played a negative role in the latter, which differed from the three other paths. And also, perceived critical mass was significantly associated with calculative commitment. The results of this research provided theoretical implications for future research and practical implications for the success of crowdfunding platforms

ANTECEDENTS OF PROJECT IMPLEMENTATION SUCCESS IN CROWDFUNDING

Crowdfunding is emerging as a booming financing channel for entrepreneurs to raise money for their projects. However, crowdfunding project implementation has been a major challenge which does not gain enough attention in the current literature. This paper developed a research model to investigate the antecedents of crowdfunding project success measured in three dimensions, i.e., award (product) delivery timeliness, the extent to which the award meets the specifications (meeting specifications), and sponsor overall satisfaction. We conducted a survey in Demohour, one of the famous crowdfunding platforms in China, to test the proposed model. The findings suggested that compared with delivery timeliness, meeting specifications is of more importance to increase overall satisfaction. The results also revealed that crowdfunding project difficulty significantly reduces the implementation success. In addition, team experience plays an important role in improving project success. Project planning is also a critical predictor for delivery timeliness. Both of theoretical research and crowdfunding industrial practice can draw some enlightenment from this study

Not only Online Review but also its Helpfulness is Manipulated: Evidence from Peer to Peer Lending Forum

Online reviews have become proposed as useful information for consumers to make decision. Meanwhile, review manipulation will weaken the credibility of online reviews. Except manipulating the review text and rating, we propose that review helpfulness, an important signal for consumer to filter the reviews, could also be manipulated. This study thus explores the existence of review helpfulness manipulation and the relationship between firm quality and review manipulation. Based on a dataset from a review forum in www.wdzj.com which is the leading and largest portal of peer to peer lending industry in China, we get the following interesting results. First, due to the manipulation of review helpfulness, a manipulated positive review is more likely to receive higher helpfulness, while a manipulated negative is more likely to get lower helpfulness. Second, a manipulated review tends to be lower quality in terms of readability and word count, which are found as positive predictors for review helpfulness. Third, high quality firms tend to manipulate more positive reviews, and at the same time high quality firms will receive more negative manipulated reviews. This study extends current understanding about online review manipulation, thereby providing theoretical and practice implications

The Value of Backers’ Word-of-Mouth in Screening Crowdfunding Projects: An Empirical Investigation

Reward-based crowdfunding is an emerging financing channel for entrepreneurs to raise money for their innovative projects. How to screen the crowdfunding projects is critical for crowdfunding platform, project founder, and potential backers. This study aims to investigate whether backers’ word-of-mouth (WOM) is a valuable input to generate collective intelligence for project screening. Specially, we answer three questions. First, is backers’ WOM an effective signal for implementation performance of crowdfunding projects? Second, how do the WOM help screen projects during the fund-raising process? Third, which kind of comments (positive or negative) is more effective in screening crowdfunding projects? Research hypotheses were developed based on theories of collective intelligence and WOM communication. Using a cross section dataset and a panel dataset, we get the following findings. First, backers’ negative WOM can effectively predict project implementation performance, however positive WOM does not have that prediction power. The prediction power of positive and negative WOM differs significantly. One possible reason is that negative WOM does contain more information of project quality. Second, project with more accumulative negative WOM tend to attract fewer subsequent backers. However, accumulative positive WOM is not helpful for attracting more potential backers. We conclude that negative WOM is useful for project screening project, because it is a signal of project quality, and meanwhile it could prevent backers make subsequent investments

UNDERSTANDING INVESTMENT INTENTION TOWARDS P2P LENDING: AN EMPIRICAL STUDY

P2P lending is an innovation of micro-financial operation pattern, which is mainly used to meet the petty loan and investment demands of small and micro businesses and individuals. Given the rapid development of P2P market, there is a pressing need to understand lenders’ initial investment intentions in P2P platform. Although there are some studies exploring the factors explaining P2P lenders’ investment intentions, none of research has been reported from the perspective of the platform. This study extended technology acceptance model with perceived risk and initial trust as a theoretical framework to examine the roles of individual factors and platform factors in determining P2P lenders’ initial investment intentions. This study suggests that risk appetite, trust propensity, perceived ease of use, perceived security assurance, perceived privacy protection, perceived reputation, third-party certification, perceived risk and initial trust together provide a strong explanation for initial investment intention in P2P lending. The finding of this research provided a theoretical foundation for future academic studies as well as practical guidance for rapid development of P2P platform

The Antecedents and Consequences of Crowdfunding Investors’ Citizenship Behaviors – an Empirical Research on Motivations and Stickiness

This study investigates the antecedents (internal and external motivations) and consequences (stickiness intentions) of crowdfunding investors’ citizenship behavior. In addition, this study examines the moderating effects of investors’ perceived project novelty on the relationships between motivations and citizenship behavior. Based on a sample of 226 crowdfunding investors, results indicate that internal and external motivations significantly influence investors’ citizenship behavior, which further affect investors’ stickiness intentions. Furthermore, results show that investors’ perceived project novelty moderates the relationships between internal/ external motivation and citizenship behavior

Learning Transferable Self-attentive Representations for Action Recognition in Untrimmed Videos with Weak Supervision

Action recognition in videos has attracted a lot of attention in the past decade. In order to learn robust models, previous methods usually assume videos are trimmed as short sequences and require ground-truth annotations of each video frame/sequence, which is quite costly and time-consuming. In this paper, given only video-level annotations, we propose a novel weakly supervised framework to simultaneously locate action frames as well as recognize actions in untrimmed videos. Our proposed framework consists of two major components. First, for action frame localization, we take advantage of the self-attention mechanism to weight each frame, such that the influence of background frames can be effectively eliminated. Second, considering that there are trimmed videos publicly available and also they contain useful information to leverage, we present an additional module to transfer the knowledge from trimmed videos for improving the classification performance in untrimmed ones. Extensive experiments are conducted on two benchmark datasets (i.e., THUMOS14 and ActivityNet1.3), and experimental results clearly corroborate the efficacy of our method

Real-Time Parallel Trajectory Optimization with Spatiotemporal Safety Constraints for Autonomous Driving in Congested Traffic

Multi-modal behaviors exhibited by surrounding vehicles (SVs) can typically lead to traffic congestion and reduce the travel efficiency of autonomous vehicles (AVs) in dense traffic. This paper proposes a real-time parallel trajectory optimization method for the AV to achieve high travel efficiency in dynamic and congested environments. A spatiotemporal safety module is developed to facilitate the safe interaction between the AV and SVs in the presence of trajectory prediction errors resulting from the multi-modal behaviors of the SVs. By leveraging multiple shooting and constraint transcription, we transform the trajectory optimization problem into a nonlinear programming problem, which allows for the use of optimization solvers and parallel computing techniques to generate multiple feasible trajectories in parallel. Subsequently, these spatiotemporal trajectories are fed into a multi-objective evaluation module considering both safety and efficiency objectives, such that the optimal feasible trajectory corresponding to the optimal target lane can be selected. The proposed framework is validated through simulations in a dense and congested driving scenario with multiple uncertain SVs. The results demonstrate that our method enables the AV to safely navigate through a dense and congested traffic scenario while achieving high travel efficiency and task accuracy in real time.Comment: 8 pages, 7 figures, accepted for publication in the 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023