Using online consumer reviews as a source for demographic recommendations: A case study using online travel reviews

Online consumer reviews play an important role in the decision to purchase services online, mainly due to the rich information source they provide to consumers in terms of evaluating “experience”-type products and services that can be booked using the Internet, with online travel services being a significant example. However, different types of travelers assess each quality indicator differently, depending on the type of travel they engage in, and not necessarily their cultural or age background (e.g. solo travelers, young couples with children etc.). In this study, we present architecture for a demographic recommendation system, based on a user-defined hierarchy of service quality indicator importance, and classification of traveler types. We use an algebraic approach to ascertain preferences from a large dataset that we obtained from the popular travel website Booking.com using a web crawler and compared with the customer-constructed preference matrix. Interestingly, the architecture of the evaluated recommendation system takes into account already defined demand characteristics of the hotels (such as the number of reviews of specific consumer types compared to the total number of reviews) in order to provide an example architecture for a recommendation system based on user-defined preference criteria.     

Effect of linguistic disfluency on consumer satisfaction: Evidence from an online knowledge payment platform

The popularity of online knowledge payment platforms enables online users to disseminate paid knowledge via voice communication. However, such communication provided by users with little professional teaching skills commonly tends to contain linguistic disfluency, which is a potential determinant of consumer satisfaction of paid knowledge products. This study examines how linguistic disfluency inherent in paid knowledge products impacts consumer satisfaction and the moderating effects of two consumer knowledge aspects, namely expertise and familiarity. Based on processing fluency theory, we build a theoretical model to illuminate relationships between consumer satisfaction, linguistic disfluency, and consumer knowledge. Leveraging data from Zhihu Live, a leading online knowledge payment platform, we find that linguistic disfluency is negatively associated with consumer satisfaction; nevertheless, this negative association disappears or turns into a positive effect for consumers with high expertise and low familiarity. Our study offers implications for platforms to accomplish high consumer satisfaction and further improve user retention and revenue.     

Training teacher-researchers through online collective academic supervision: Evidence from a postgraduate teacher education programme

Collective academic supervision (CAS) is a collective model for students' academic supervision to reduce their isolation and as a measure to establish a congenial culture and to develop networks with their peers. Most studies focus on the benefits of online CAS, leaving the pedagogical process and students' learning experiences understudied. This research examines the participation and learning experience of a cohort of Master of Education (MEd) students in online supervision that took place on a Moodle platform. This article reports a case study of Moodle-based CAS in Hong Kong that aims to train postgraduate students into teacher-researchers. A class of MEd students and their supervisors were observed, and their online dialogues were analysed. The bio-ecological student engagement model was used to explain the online supervision process. The results indicated that the students' learning was situated and embodied in the online social processes facilitated by peers' and supervisors' replies. The online interaction behaviours mainly included proposing questions or problems, providing information or solutions, and making comments. The findings have provided an exemplary case regarding the application of the online learning environment in supporting CAS and active research-based learning. The productive online CAS seems to benefit both teacher candidates and their supervisors by promoting the co-construction of the knowledge and skills of educational research, although more evidence is needed.     

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.     

Discovering business intelligence from online product reviews: A rule-induction framework

Online product reviews are a major source of business intelligence (BI) that helps managers and marketers understand customers’ concerns and interests. The large volume of review data makes it difficult to manually analyze customers’ concerns. Automated tools have emerged to facilitate this analysis, however most lack the capability of extracting the relationships between the reviews’ rich expressions and the customer ratings. Managers and marketers often resort to manually read through voluminous reviews to find the relationships. To address these challenges, we propose the development of a new class of BI systems based on rough set theory, inductive rule learning, and information retrieval methods. We developed a new framework for designing BI systems that extract the relationship between the customer ratings and their reviews. Using reviews of different products from Amazon.com, we conducted both qualitative and quantitative experiments to evaluate the performance of a BI system developed based on the framework. The results indicate that the system achieved high accuracy and coverage related to rule quality, and produced interesting and informative rules with high support and confidence values. The findings have important implications for market sentiment analysis and e-commerce reputation management.     

Understanding the intention to follow the advice obtained in an online travel community

Online travel communities are an increasing phenomenon that is motivating great changes in consumer behavior in the travel sector. Travelers prefer to rely on peers’ recommendations and thus visit these communities to look for unbiased information. This work analyzes some of the precursors of the consumer intention to follow the advice obtained in an online travel community. Data show the relevant role of attitude toward the advice, trust in the online community that provides the advice and perceived usefulness of this information in order to determine the consumer intention to follow the advice obtained in the community. As well, trust and usefulness have been found to influence consumer attitude, and usefulness is also directly affected by trust in the community that provides the advice. Finally, a specific personal attribute - namely, consumer susceptibility to interpersonal influence -, moderates the effects of the antecedents of the intention to follow the advice obtained in a travel community. Based on these results, some implications for practice are widely discussed.     

OATS: online aggregation with two-level sharing strategy in cloud

Online aggregation (OLA) is an attractive sampling-based technology to response aggregation queries by an approximate estimate to the final result, with the confidence interval becomes tighter over time. It has been built into the MapReduce-based cloud system for big data analytics, which allows users to monitor the query progress, and save money by killing the computation early once sufficient accuracy has been obtained. However, there is a serious limitation that restricts the performance of OLA that is the sharing issue of multiple OLA queries processing. Note that, in the original MapReduce paradigm, each query is processed independently without considering the potential sharing opportunities, leading to two major unnecessary additional execution costs: (1) the large redundant I/O cost, and (2) the replicative statistical computation cost. To eliminate such additional execution cost and improve the overall performance, we present online aggregation with two-level sharing strategy in cloud (OATS) based on MapReduce framework in this paper to effectively support online aggregation for large scale concurrent query processing in skewed data distribution. In the first-level sharing, we propose a sample buffer management mechanism to share the sampling opportunities among multiple OLA queries to reduce redundant I/O cost. While in the second-level sharing, we propose a heuristic algorithm (with a good scalability for large input) for the statistical computation to share partial statistics calculation to decrease the number of final aggregation operations, reducing the statistical computation cost. Based on such two-level sharing strategy, we have implemented OATS in Hadoop and conducted an extensive experiments study on the TPC-H benchmark for skewed data distribution. Our results demonstrate the efficiency and effectiveness of OATS.     

Computer-mediated persuasion in online reviews: Statistical versus narrative evidence

The current study examined the effects of online product reviews on individuals’ credibility perceptions of the reviews and their attitudes about the product targeted in the reviews. Study 1 used a 2 (review type: statistical and narrative) × 2 (review valence: negative and positive) design, and study 2 used a 2 (statistical review valence: positive and negative) × 2 (narrative review valence: positive and negative) design. The findings of study 1 were consistent with those of study 2 and indicated that negative statistical reviews were perceived as more credible than negative narrative reviews, while the credibility of positive statistical reviews did not differ from that of positive narrative reviews. On the other hand, statistical reviews and narrative reviews did not differ in terms of affecting individuals’ attitudes about the product. The current study also found that the vividness and valence of each review type had varying effects on review credibility and attitudes about the product. The implications of these and other findings are discussed in detail in the paper.     

Composable IO: a novel resource sharing platform in personal Clouds

A fundamental goal for Cloud computing is to group resources to accomplish tasks that may require strong computing or communication capability. In this paper we design specific resource sharing technology under which IO peripherals can be shared among Cloud members. In particular, in a personal Cloud that is built up by a number of personal devices, IO peripherals at any device can be applied to support application running at another device. We call this IO sharing composable IO because it is equivalent to composing IOs from different devices for an application. We design composable USB and achieve pro-migration USB access, namely a migrated application running at the targeted host can still access the USB IO peripherals at the source host. This is supplementary to traditional VM migration under which application can only use resources from the device where the application runs. We address reliability issues by keeping a backup VM. In addition, we define a security framework to ensure operating environment security when using composable IO in personal environment. Experimental results show that through composable IO applications in personal Cloud can achieve much better user experience.     

Data analytics and firm performance: An empirical study in an online B2C platform

Data analytics has become an increasingly eye-catching practice in both the academic and the business communities. The importance of data analytics has also prompted growing literature to focus on the design of data analytics. However, the boundary conditions for data analytics in creating value have been largely overlooked in the literature. The objective of this article therefore is to examine the business value of data analytics usage and explore how such value differs in different market conditions. We rely on an online B2C platform as our empirical setting and obtain several important insights. First, both demand-side and supply-side data analytics usage has a positive effect on the performance of merchants. Second, when merchants’ product variety is high, the influence of usage toward demand-side data on performance is strengthened, whereas such impact is weakened for supply-side data analytics. Third, when competitive intensity is high, the performance implication of demand-side data analytics usage is strengthened, whereas such impact is not strengthened for supply-side data analytics. This study contributes by advancing the overall understanding of business value of data analytics.     

Effects of membership tier on user content generation behaviors: evidence from online reviews

Online shopping websites typically classify customers into different membership tiers in their customer relationship management systems. This study investigates the effects of membership tiers on user content generation behaviors in the context of an electronic commerce marketplace that has a membership tier program and an online review system. Grounded in theories related to status, our study hypothesizes the effects of membership tiers on user content generation behaviors as well as the helpfulness of the content they generated in the context of online reviews. We collected online data from a world-leading shopping website. The results from our empirical analyses indicate that membership tier has a positive effect on review rating and review delay, whereas it has a negative effect on review depth. Additionally, we tested mediation effects of review rating, depth and delay between membership tiers and review helpfulness, and found that membership tier negatively affected review helpfulness indirectly. Interestingly, reviews posted by high-status customers are perceived as more helpful than those of others when we controlled for review characteristics. This study contributes to research on online product reviews and customer relationship management.     

Knowledge sharing in online health communities: A social exchange theory perspective

Online health communities (OHC) are becoming valuable platforms for patients to communicate and find support. These communities are different from general online communities. The knowledge shared in an OHC can be categorized as either general (public) or specific (private), and each category is shared in vastly different ways. Using the social exchange theory, we propose a benefit vs. cost knowledge sharing model for OHCs. The benefits are mainly based on Maslow's hierarchy of needs, and the cost includes cognitive and executional costs. We use this benefit vs. cost model to examine how OHC members share general and specific knowledge. Data were collected from 323 users of two well-known OHCs in China and were analyzed using the structural equation model. The results demonstrate that three factors positively impact the sharing of both general and specific knowledge: a sense of self-worth, members’ perceived social support, and reputation enhancement. Another factor, face concern, has a negative influence on specific knowledge sharing and a positive influence on general knowledge sharing. Executional cost only negatively impacts general knowledge sharing, and cognitive cost only negatively impacts specific knowledge sharing. This study of OHCs reveals that personal benefits promote knowledge sharing and costs prohibit it. These impacts vary between general knowledge and specific knowledge sharing.     

Information diffusion through social networks: The case of an online petition

People regularly use online social networks due to their convenience, efficiency, and significant broadcasting power for sharing information. However, the diffusion of information in online social networks is a complex and dynamic process. In this research, we used a case study to examine the diffusion process of an online petition. The spread of petitions in social networks raises various theoretical and practical questions: What is the diffusion rate? What actions can initiators take to speed up the diffusion rate? How does the behavior of sharing between friends influence the diffusion process? How does the number of signatures change over time? In order to address these questions, we used system dynamics modeling to specify and quantify the core mechanisms of petition diffusion online; based on empirical data, we then estimated the resulting dynamic model. The modeling approach provides potential practical insights for those interested in designing petitions and collecting signatures. Model testing and calibration approaches (including the use of empirical methods such as maximum-likelihood estimation, the Akaike information criterion, and likelihood ratio tests) provide additional potential practices for dynamic modelers. Our analysis provides information on the relative strength of push (i.e., sending announcements) and pull (i.e., sharing by signatories) processes and insights about awareness, interest, sharing, reminders, and forgetting mechanisms. Comparing push and pull processes, we found that diffusion is largely a pull process rather than a push process. Moreover, comparing different scenarios, we found that targeting the right population is a potential driver in spreading information (i.e., getting more signatures), such that small investments in targeting the appropriate people have ‘disproportionate’ effects in increasing the total number of signatures. The model is fully documented for further development and replications.     

Not all posts are treated equal: An empirical investigation of post replying behavior in an online travel community

The post replying behavior in online communities (OCs) has garnered little consideration, even though the feedback behavior represents the central social dynamic of OCs and greatly determines the vibrancy of OCs. To fill this gap, this study aims to identify major sharing post-related variables that explain the heterogeneity in the post replying behavior in knowledge sharing OCs. The research model is validated through a panel dataset assembled from an online travel community. The results reveal that sharing post length and vividness, contributors’ expertise and degree centrality, and members’ social interactions have significant associations with the number of replying posts.     

Exploring the effect of user engagement in online brand communities: Evidence from Twitter

Social media such as forums, blogs and microblogs has been increasingly used for public information sharing and opinions exchange nowadays. It has changed the way how online community interacts and somehow has led to a new trend of engagement for online retailers especially on microblogging websites such as Twitter. In this study, we investigated the impact of online retailers' engagement with the online brand communities on users' perception of brand image and service. Firstly, we analysed the overall sentiment trends of different brands and the patterns of engagement between companies and customers using the collected tweets posted on a popular social media platform, Twitter. Then, we studied how different types of engagements affect customer sentiments. Our analysis shows that engagement has an effect on sentiments that associate with brand image, perception and customer service of the online retailers. Our findings indicate that the level, length, type and attitude of retailers' engagement with social media users have a significant impact on their sentiments. Based on our results, we derived several important managerial and practical implications.     

EOSentiMiner: an opinion-aware system based on emotion ontology for sentiment analysis of Chinese online reviews

With the growing availability and popularity of online reviews, consumers' opinions towards certain products or services are generated and spread over the Internet; sentiment analysis thus arises in response to the requirement of opinion seekers. Most prior studies are concerned with statistics-based methods for sentiment classification. These methods, however, suffer from weak comprehension of text-based messages at semantic level, thus resulting in low accuracy. We propose an ontology-based opinion-aware framework – EOSentiMiner – to conduct sentiment analysis for Chinese online reviews from a semantic perspective. The emotion space model is employed to express emotions of reviews in the EOSentiMiner, where sentiment words are classified into two types: emotional words and evaluation words. Furthermore, the former contains eight emotional classes, and the latter is divided into two opinion evaluation classes. An emotion ontology model is then built based on HowNet to express emotion in a fuzzy way. Based on emotion ontology, we evaluate some factors possibly affecting sentiment classification including features of products (services), emotion polarity and intensity, degree words, negative words, rhetoric and punctuation. Finally, sentiment calculation based on emotion ontology is proposed from sentence level to document level. We conduct experiments by using the data from online reviews of cellphone and wedding photography. The result shows the EOSentiMiner outperforms baseline methods in term of accuracy. We also find that emotion expression forms and connection relationship vary across different domains of review corpora.     

The motivations,enablers and barriers for voluntary participation in an online crowdsourcing platform

This paper examines the phenomena of online crowdsourcing from the perspectives of both volunteers and the campaign coordinator of Tomnod – an online mapping project that uses crowdsourcing to identify objects and places in satellite images. A mixed-methods approach was used to study the enablers and barriers to participation, taking into consideration the whole spectrum of volunteers. The results show broad diversity in online volunteers, both in their demographics and the factors affecting their voluntary participation. The majority are older than 50 years and many – particularly the most active volunteers – have disabilities or long term health problems. The personal circumstances of participants are highlighted as a major factor affecting involvement in campaigns. Like many other platforms, altruism is a key motivator, yet many participants are more interested in the quality of their data and the impact it has on the ground. For many participants of online crowdsourcing campaigns, their involvement is strongly linked to the level of contact they have with campaign coordinators, both in the design of the platform and in providing feedback on the impact of their contributions.     

Social value creation through digital activism in an online health community

The study explores how online health communities produce social value by uniting individuals under a common purpose, to advance healthcare in post‐conflict states. We selected MedicineAfrica – a digital platform known for creating social value by providing medical education in regions with under‐resourced healthcare systems – and drew on multiple data collection methods. We found that it is through a unique form of digital health activism that social value is created in this context. Drawing on a sociological understanding of digital health activism, we make the following contributions: First, we identify three types of non‐economic, social value: cognitive, professional and epistemic. Second, we indicate that social value creation is enabled by three emergent forms of digital health activism (ie, philanthropic, moral and reciprocal activity). Third, we elicit three enabling mechanisms explaining how these forms of activism are technically and socially afforded through the platform's connective capacity and emerging collective practices in tandem with its members' growing commitment. Our article contributes to the growing IS literature on digital activism by offering a framework that elucidates how digital health activism relates to social value creation. The article provides practical implications as to how platforms can enable sustainable online (health) communities.     

Error detection through mouse movement in an online adaptive learning environment

While response time and accuracy indicate overall performance, their value in uncovering cognitive processes, underlying learning, is limited. A promising online measure, designed to track decision-making, is computer mouse tracking, where mouse attraction towards different locations may reflect the consideration of alternative response options. Using a speedy arithmetic multiple-choice game in an online adaptive learning environment, we examined whether mouse movements could reflect arithmetic difficulties when error rates are low. Results showed that mouse movements towards alternative responses in correctly answered questions mapped onto the frequency of errors made in this online learning system. This mapping was stronger for the younger children, as well as for easy arithmetic problems. On an individual level, users showed more mouse movement towards their previously made response errors than towards other alternative options. This opens the possibility of adapting feedback and instruction on an individual basis through mouse tracking.