SOCIAL PRESENCE, TRUST, AND SOCIAL COMMERCE PURCHASE INTENTION: AN EMPIRICAL RESEARCH

Lacking the presence of human and social elements is claimed one major weakness that is hindering the growth of e-commerce. The emergence of social commerce (SC) might help ameliorate this situation. Social commerce is a new evolution of e-commerce that combines the commercial and social activities by deploying social technologies into e-commerce sites. Social commerce reintroduces the social aspect of shopping to e-commerce, increasing the degree of social presences in online environment. Drawing upon the social presence theory, this study theorizes the nature of social aspect in online SC marketplace by proposing a set of three social presence variables. These variables are then hypothesized to have positive impacts on trusting beliefs which in turn result in online purchase behaviors. The research model is examined via data collected from a typical ecommerce site in China. Our findings suggest that social presence factors grounded in social technologies contribute significantly to the building of the trustworthy online exchanging relationships. In doing so, this paper confirms the positive role of social aspect in shaping online purchase behaviors, providing a theoretical evidence for the fusion of social and commercial activities. Finally, this paper introduces a new perspective of e-commerce and calls more attention to this new phenomenon

Automatic Generation of Matching Function by Genetic Programming for Effective Information Retrieval

With the advent of the Internet, online resources are increasingly available. Many users choose popular search engines to perform an online search to satisfy their information need. However, these search engines tend to turn up many non-relevant documents, which make their retrieval precision very low. How to find appropriate ranking metrics to retrieve more relevant documents and fewer non-relevant documents for users remains a big challenge to the information retrieval community. In this paper, we propose a new framework that combines the merits of genetic programming and relevance feedback techniques to automatically generate and refine the matching functions used for document ranking. This approach overcomes the shortcoming of traditional ranking algorithms using a fixed ranking strategy. It also gives some new ideas and hints for information retrieval professionals

A Knowledge Adoption Model Based Framework for Finding Helpful User-Generated Contents in Online Communities

Many online communities allow their members to provide information helpfulness judgments that can be used to guide other users to useful contents quickly. However, it is a serious challenge to solicit enough user participation in providing feedbacks in online communities. Existing studies on assessing the helpfulness of user-generated contents are mainly based on heuristics and lack of a unifying theoretical framework. In this article we propose a text classification framework for finding helpful user-generated contents in online knowledge-sharing communities. The objective of our framework is to help a knowledge seeker find helpful information that can be potentially adopted. The framework is built on the Knowledge Adoption Model that considers both content-based argument quality and information source credibility. We identify 6 argument quality dimensions and 3 source credibility dimensions based on information quality and psychological theories. Using data extracted from a popular online community, our empirical evaluations show that all the dimensions improve the performance over a traditional text classification technique that considers word-based lexical features only

Microscopic Characteristics and Modelling of Pedestrian Inflow Process with Inactive Persons

Inflow and outflow processes are common phenomena in daily life. Many types of research have been conducted to study the features of the outflow process, especially in scenarios with a single room or a straight corridor. A few scholars have paid attention to the movement characteristics of pedestrian inflow. Further explorations are still under great demand. In this contribution, a set of pre-conducted experiments are used to analyze the characteristics of the pedestrian inflow process with inactive persons. In these experiments, inactive persons were required to randomly cease within the room, leading to intensive detour behavior of pedestrians. The characteristics are carefully investigated using gradient analysis and curl analysis. To mimic the aforementioned inflow process, static global field is constructed to heuristically navigate a social force based microscopic model. The proposed model can reproduce the self-organized phenomena in the experiments. Our work can help understand the field feature of the pedestrian inflow process with inactive persons. High chaos level areas can be marked out providing practical information for managers

Personalization of Search Engine Services for Effective Retrieval and Knowledge Management

The Internet and corporate intranets provide far more information than anybody can absorb. People use search engines to find the information they require. However, these systems tend to use only one fixed term weighting strategy regardless of the context to which it applies, posing serious performance problems when characteristics of different users, queries, and text collections are taken into consideration. In this paper, we argue that the term weighting strategy should be context specific, that is, different term weighting strategies should be applied to different contexts, and we propose a new systematic approach that can automatically generate term weighting strategies for different contexts based on genetic programming (GP). The new proposed framework was tested on TREC data and the results are very promising

Searching for Authoritative Documents in Knowledge-Base Communities

Knowledge-based communities are popular Web-based tools that allow members to share and seek knowledge globally. However, research on how to search effectively within such knowledge repositories is scant. In this paper we study the problem of finding authoritative documents for user queries within a knowledge-based community. Unlike prior research on the ranking function design which considers only content or hyperlink information, we leverage the social network information embedded in the rich social media, in addition to content, to design novel ranking strategies. Using the Knowledge Adoption Model as the guiding theoretical framework, we design features that gauge the two major factors affecting users’ knowledge adoption decisions: argument quality (AQ) and source credibility (SC). We design two ranking strategies that blend these two sources of evidence with the content-based relevance judgment. A preliminary study using a real world knowledge-based community showed that both AQ and SC features improved search effectiveness

Firm Actions Toward Data Breach Incidents and Firm Equity Value: An Empirical Study

Managing information resources including protecting the privacy of customer data plays a critical role in most firms. Data breach incidents may be extremely costly for firms. In the face of a data breach event, some firms are reluctant to disclose information to the public. Firm may be concerned with the potential drop in the market value following the revelation of a data breach. This paper examines the impact of data breach incidents to the firm’s market value/equity value, and explores the possibility that certain firm behaviors may reduce the cost of the incidents. We use regression analysis to identify the factors that affect cumulative abnormal stock return (CAR). Our results indicate that when data breach happens, firms not only should notify customers or the public timely, but also try to control the amount of information disclosed. These findings should provide corporate executives with guidance on managing public disclosure of data breach incidents

The effect of mandatory regulation on corporate social responsibility reporting quality: evidence from China

Corporate Social Responsibility (CSR) disclosure has attracted attention from regulatory bodies and academics over the past few decades. Due to the unreliability resulted from CSR voluntary disclosure, an increasing number of researchers are calling for more government regulation on CSR disclosure. Based on 1830 standalone CSR reports disclosed by the Chinese-listed firms during 2009-2012, we examine the effect of mandatory regulation on CSR reporting quality. We further hypothesize and test for the moderating effect of firm size and other characteristics on the link between government regulation on CSR reporting quality. Our results suggest that government mandatory regulation leads to an overall improvement in CSR reporting quality. We also find that this positive effect is greater when firms are larger and have better financial performance, but less when firms are controlled by government. Our study provides a direct answer to the recent calling for mandatory disclosure on CSR reports, and helps to understand why recent studies of social disclosure regulation suggest that government interventions do not seem to resolve the problems that are generally attributed to voluntary disclosures. Our findings should be of interest to the academics, regulators, and investors

Quantifying Learning and Competition among Crowdfunding Projects: Metrics and a Predictive Model

The performance of a crowdfunding project is highly situational-dependent. In this study, we quantify the interactions between crowdfunding projects in order to understand how these interactions can help predict the performance of crowdfunding campaigns. Specifically, we utilize Natural Language Processing (NLP) techniques to create a semi-automated system to label the associated product for each crowdfunding campaign. We also propose three sets of metrics to measure how crowdfunding projects learn from and compete with each other. Finally, we propose a machine learning model and demonstrate that the proposed metrics and the proposed model outperform other combinations when predicting the performance of crowdfunding projects