Effects of problem characteristics on the online helping behavior of shy individuals

The present research examined how problem characteristics might influence the online helping behavior of shy individuals. Three hundred undergraduates were recruited and assessed using the College Students’ Shyness Scale. According to the scores, 68 high-shy students and 68 low-shy students were selected to participate in simulated experiments designed to determine the influence of the shyness level, the presence of a foreseeable reward, the complexity of the problem, and the urgency of the problem influence online helping behavior. The results showed that (a) High-shy individuals tended to exhibit online helping behavior more for low-complexity problems and high-urgency problems than for high-complexity problems and low-urgency problems. (b) Low-shy individuals always exhibited online helping behavior less for high-complexity problems and when no reward was offered than they did for low-complexity problems and when a reward was offered. (c) High-shy individuals showed online helping behavior more than low-shy individuals did for no-reward, high-complexity problems and high-urgency problems. (d) Participants online had higher shyness scores for showing helping behavior than those offline.     

Development of a scale to measure skepticism toward electronic word-of-mouth

In recent years, electronic word-of-mouth (eWOM) has become an influential factor affecting Internet users' perceptions and behaviors (Chatterjee 2001; Lee et al. 2009). However, as more evidence demonstrating the utilization of fake eWOM has been discovered (Forrest and Cao 2010; Malbon 2013), Internet users' trust of eWOM may have been severely undermined, and they may have developed skepticism about this kind of communication in general. Current measurement scales for evaluating Internet users' suspicions/distrust toward eWOM messages are adopted from the marketing discipline and developed for advertising skepticism, which is contextually different from skepticism toward eWOM. The purpose of this study is to create a new measurement scales for eWOM skepticism. Using data from a preliminary survey, new measurement items for eWOM skepticism were established. Then, the new items were validated using a second survey dataset. The reliability and validity of the new scales suggested that the new instrument is suitable for measuring eWOM skepticism. This study contributes to the eWOM literature by highlighting the importance of investigating eWOM situations from the perspective of suspicion and distrust.     

The effects of trust and constructive controversy on student achievement and attitude in online cooperative learning environments

This study investigated the effects of trust and constructive controversy on student achievement and attitude in online cooperative learning environments. Students in one university course were randomly assigned to one of the two treatment groups after they took part in a common initial workshop on general cooperative learning skills. The “trust” and the “constructive controversy” groups received subsequent associated skills training. The overall results indicated that after each group received the treatment during online cooperative group activities, the “trust” groups had significantly higher achievement than the “constructive controversy” groups. In addition, the “trust” groups had significantly more positive attitudes toward online cooperative learning than the “constructive controversy” groups. Specifically, using “trust” building strategies was significantly more effective than using “constructive controversy” strategies for improving the ‘openness and sharing’ and ‘acceptance and support’ components of student attitudes in online cooperative learning environments.     

Value drivers of blockchain technology: A case study of blockchain-enabled online community

There is growing recognition that blockchain technology has significant potential to alter how organizations and people work and communicate. However, theoretical guidance concerning how organizations leverage blockchain technology to enhance value creation for users is still limited. Grounded in the socio-technical perspective and leveraging the rich data obtained from case analyses of blockchain-enabled online communities, this paper develops a theoretical model to identify the core value drivers that blockchain enables for online communities. The core value drivers include: a reputation-value system, data ownership mechanisms, and verification & tracking mechanisms. Our findings suggest that these three value drivers enhance value creation of online communities by motivating participation and protecting contributions.     

基于邻域粗糙集的大规模层次分类在线流特征选择

在分类学习任务中,数据的类标记空间存在层次化结构,特征空间伴随着未知性和演化性.因此,文中提出面向大规模层次分类学习的在线流特征选择框架.定义面向层次化结构数据的邻域粗糙模型,基于特征相关性进行重要特征动态选择.最后,基于特征冗余性进行鉴别冗余动态特征.实验验证文中算法的有效性.     

Mining customer requirements from online reviews: A product improvement perspective

Big data commerce has become an e-commerce trend. Learning how to extract valuable and real time insights from big data to drive smarter and more profitable business decisions is a main task of big data commerce. Using online reviews as an example, manufacturers have come to value how to select helpful online reviews and what can be learned from online reviews for new product development. In this research, we first proposed an automatic filtering model to predict the helpfulness of online reviews from the perspective of the product designer. The KANO method, which is based on the classical conjoint analysis model, is then innovatively applied to analyze online reviews to develop appropriate product improvement strategies. Moreover, an empirical case study using the new method is conducted with the data we acquired from JD.com, one of the largest electronic marketplaces in China. The case study indicates the effectiveness and robustness of the proposed approach. Our research suggests that the combination of big data and classical management models can bring success for big data commerce.     

Customers segmentation in eco-friendly hotels using multi-criteria and machine learning techniques

This study aims to investigate the travellers' choice behaviour towards green hotels through existing online travel reviews on TripAdvisor. Accordingly, a method combining segmentation and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) techniques was developed to segment travellers based on their provided reviews and to prioritize green hotel attributes based on their level of importance in each segment. The data were taken from travellers' online reviews of Malaysian eco-friendly hotels on TripAdvisor. The results showed that the sleep quality was one of the most imporant factors for eco-hotel selection in the majority of segments. The developed method in this study was able to analyse travellers’ reviews and ratings on eco-friendly hotels to identify the future choice behaviour and aid travellers in their decision-making process. The study provides new insights for hotel managers and green policy makers on developing environmental-friendly practices.     

What drives consumer knowledge sharing in online travel communities?: Personal attributes or e-service factors?

Consumer voluntary sharing of e-service knowledge in an online community is of great value to both business and consumers. This study develops and tests a research model integrating two personal attributes (consumer innovativeness and subjective knowledge) and two e-service factors (perceived ease of use and usefulness) in predicting consumer voluntary knowledge sharing in an online community. Data were collected using a web-based survey of 364 airline travelers recruited through an online travel community in China. Partial Least Squares Structural Equation Modeling (PLS-SEM) technique was used to analyze the data. The results reveal that personal factors are stronger drivers of knowledge sharing than e-service factors. This study advances consumer knowledge sharing in online communities, and has implications for enhancing a firm’s e-service strategies.