Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites

Online reviews have become an important source of information for users before making an informed purchase decision. Early reviews of a product tend to have a high impact on the subsequent product sales. In this paper, we take the initiative to study the behavior characteristics of early reviewers through their posted reviews on two real-world large e-commerce platforms, i.e., Amazon and Yelp. In specific, we divide product lifetime into three consecutive stages, namely early, majority and laggards. A user who has posted a review in the early stage is considered as an early reviewer. We quantitatively characterize early reviewers based on their rating behaviors, the helpfulness scores received from others and the correlation of their reviews with product popularity. We have found that (1) an early reviewer tends to assign a higher average rating score; and (2) an early reviewer tends to post more helpful reviews. Our analysis of product reviews also indicates that early reviewers' ratings and their received helpfulness scores are likely to influence product popularity. By viewing review posting process as a multiplayer competition game, we propose a novel margin-based embedding model for early reviewer prediction. Extensive experiments on two different e-commerce datasets have shown that our proposed approach outperforms a number of competitive baselines

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

PROFILING SOCIAL MEDIA TOURISTS USING LITERATURE DURING 2015-2019: CRIMINAL PROFILING METHOD

With the continuous development of mobile commerce and the Internet, social media has deeply penetrated people’s lives and fundamentally changed the way of searching, reading and using travel-related information. With this backdrop, this research studied social media tourists (SMTs) who share or acquire information related to the hospitality and tourism on social media platforms. Based on 271 empirical articles retrieved from major databases and top hospitality and tourism journals in the recent five years from 2015 to 2019, this research developed a profiling framework about SMTs using criminal profiling method. The findings showed the possibility of using the criminal profiling method to analyze SMTs and provided a holistic personal, social-psychological, and behavioral profile of SMTs. Theoretical and practical implications were discussed

Exploring TripAdvisor Online Reviews: The Case of George Eastman Museum

Different studies have looked at visitor related motivations and experiences under different contexts in the tourism industry. The museum-related experiences remain one of the important elements which are sought by visitors to the museums. Therefore, this study purposes to investigate and determine personal context, social context and physical contexts of George Eastman museum visitors. More importantly, the study utilised a content analysis approach in its research. The specific data about visitors to George Eastman House was obtained from the social media website TripAdvisor. The findings of the study were based on three important elements: personal context, social context and physical contexts. In the personal context based on John Falk classifications (experience seeker, explorer, facilitator, prof-hobbyist, and recharger); the experience seeker had the highest number of visitor comments about 76 reviewers, with explorer category being less represented while recharger category had the least number of visitor’s comments. On the aspect of social context, about 12% reviewers indicated the social context where the socio-cultural influence was considered one of the determinants of their museum-visit experience. Furthermore, the physical context was expressed through identified dominant themes that included mansion, the museum exhibits, and George Eastman’s personality. In both the analysis of negative and the positive dimensions of the themes, the mansion theme showed a higher positive response in the while the museum and the exhibits showed relatively a negative dimension in the analysis. Through a word cloud visualization approach memories and learning outcomes were analysed with findings indicating “Memory/impression of visit”; “Takes you back” (their experience of being drawn back into the past or into a personal memory of previous times, etc.); and most importantly, “Learn from the visit”, were the most predominant themes in memorial and learning outcomes. This study provided important insight into visitor experiences, which can be utilised by George Eastman management to understand perceptions of visitors and thus enhance their services. Furthermore, it is recommended that future studies adopt bigger samples as well as diverse contexts besides the museum in order to be able to get an in-depth insight into museum visitor’s experiences

Essays on value creation in online marketplaces

This dissertation consists of three essays that study the transformative impact of new information technologies under three specific contexts using both empirical and theoretical approaches. Chapter 2 examines the online review system, which is the new type of information technology that replaces the traditional word-of-mouth communication. Particularly, we study the practice of the platform owner that uses monetary incentives to attract reviewers. The research problem is important as firms, which seek to strengthen their online review platforms, have considered various forms of incentives, including extrinsic rewards, to encourage users to write reviews. We encountered a natural experiment design where one review platform suddenly started offering monetary incentives for writing reviews. Along with data from Amazon.com and using the difference-in-differences approach, we compare the quantity and quality of reviews before and after rewards were introduced in the treated platform. We find that reviews are significantly more positive but the quality decreases. Taking advantage of the panel data, we also evaluate the effect of rewards on existing reviewers. We find that their level of participation after monetary incentives decreases, but not their quality of participation. Lastly, even though the platform enjoys an increase in the number of new reviewers, disproportionately more reviews appear to be written for highly rated products. In Chapter 3, we investigate the economic implications of the new online communication system that has become increasing popular in recent years. This system allows consumers to ask and answer questions regarding the products that are available on the platform. It typically co-exists with the standard online review system where consumers share their own experience of the products. Although several websites adopt this Q&A system or even replace the standard review system with it, the economic implications of such a Q&A system have not been studied in the previous literature. We collected the data from two online shopping platforms and employed the difference-in-differences approach to empirically examine the effect of question & answer elements, which exist only on one platform, on product sales. Interestingly, we find that, controlling for everything else, question elements negatively affect product sales while answer elements, particularly the depth of the answers, have a positive impact on sales. However, as we focus on the initial sales, it turns out that the number of questions and the fraction of questions that have at least one answer positively influence the sales. We also find that there is an interaction between Q&A elements and review elements, in that an increase in the number of questions seems to be positively correlated with an increase in the number of reviews in the following period. Meanwhile, an increase in the number of answers appears to reduce the average review length in the subsequent period. Our findings suggest that incorporating the question & answer system could be a potential approach to drive sales. However, it is crucially important for managers to develop appropriate policies to gather necessary answers to questions asked on the platform in order to capitalize on such a system. In Chapter 4, we provide an analysis of a two-sided platform, which becomes a dominant framework adopted by new Information Technology platforms such as Uber and Airbnb. We develop a game-theoretic model featuring a platform owner who acts as an intermediary that services two types of users to examine the influence of incentive policies the platform owner enforces. Specifically, our main interest is to study the implication of the incentive policy on user behavior and welfare metrics. We find that although the seller welfare always increases with the amount of incentives given by the platform, an adjustment of the incentive allocation policy can also yield similar results in many scenarios. In addition, there exists a case where the platform can increase both the seller welfare and its own welfare without increasing the amount of incentives

Understanding, Analyzing and Predicting Online User Behavior

abstract: Due to the growing popularity of the Internet and smart mobile devices, massive data has been produced every day, particularly, more and more users’ online behavior and activities have been digitalized. Making a better usage of the massive data and a better understanding of the user behavior become at the very heart of industrial firms as well as the academia. However, due to the large size and unstructured format of user behavioral data, as well as the heterogeneous nature of individuals, it leveled up the difficulty to identify the SPECIFIC behavior that researchers are looking at, HOW to distinguish, and WHAT is resulting from the behavior. The difference in user behavior comes from different causes; in my dissertation, I am studying three circumstances of behavior that potentially bring in turbulent or detrimental effects, from precursory culture to preparatory strategy and delusory fraudulence. Meanwhile, I have access to the versatile toolkit of analysis: econometrics, quasi-experiment, together with machine learning techniques such as text mining, sentiment analysis, and predictive analytics etc. This study creatively leverages the power of the combined methodologies, and apply it beyond individual level data and network data. This dissertation makes a first step to discover user behavior in the newly boosting contexts. My study conceptualize theoretically and test empirically the effect of cultural values on rating and I find that an individualist cultural background are more likely to lead to deviation and more expression in review behaviors. I also find evidence of strategic behavior that users tend to leverage the reporting to increase the likelihood to maximize the benefits. Moreover, it proposes the features that moderate the preparation behavior. Finally, it introduces a unified and scalable framework for delusory behavior detection that meets the current needs to fully utilize multiple data sources.Dissertation/ThesisDoctoral Dissertation Business Administration 201