Dynamic preference elicitation of customer behaviours in e-commerce from online reviews based on expectation confirmation theory

Preference change, also known as preference drift, is one of the factors that online retailers need to consider to accurately collect consumer preferences and make personalised recommendations. Online reviews have been widely used to analyse the preference drift of consumers. However, previous studies on online reviews ignored the psychological perceptions of consumers in terms of satisfaction. This paper aims to develop a method for dynamic preference elicitation from online reviews based on exploring the theory of consumer satisfaction formation. Based on the framework of expectation confirmation theory, we develop formulas for expressing the relations among expectation, perceived performance, confirmation, and satisfaction. We then use the proposed dynamic preference elicitation model to predict the change of consumer overall preference after each review and rank products for consumers’ next purchase. We test the proposed approach with a case study based on a data set from Amazon.com. It is founded that the satisfaction changes in each purchase, and this change will affect the prediction of the next product ranking. The case study is based on one product group, and further research is needed to see if the operation of the proposed method can be extended to other kinds of product

Please, talk about it! When hotel popularity boosts preferences

Many consumers post on-line reviews, affecting the average evaluation of products and services. Yet, little is known about the importance of the number of reviews for consumer decision making. We conducted an on-line experiment (n= 168) to assess the joint impact of the average evaluation, a measure of quality, and the number of reviews, a measure of popularity, on hotel preference. The results show that consumers' preference increases with the number of reviews, independently of the average evaluation being high or low. This is not what one would expect from an informational point of view, and review websites fail to take this pattern into account. This novel result is mediated by demographics: young people, and in particular young males, are less affected by popularity, relying more on quality. We suggest the adoption of appropriate ranking mechanisms to fit consumer preferences. © 2014 Elsevier Ltd

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

Ranking with social cues: Integrating online review scores and popularity information

Online marketplaces, search engines, and databases employ aggregated social information to rank their content for users. Two ranking heuristics commonly implemented to order the available options are the average review score and item popularity-that is, the number of users who have experienced an item. These rules, although easy to implement, only partly reflect actual user preferences, as people may assign values to both average scores and popularity and trade off between the two. How do people integrate these two pieces of social information when making choices? We present two experiments in which we asked participants to choose 200 times among options drawn directly from two widely used online venues: Amazon and IMDb. The only information presented to participants was the average score and the number of reviews, which served as a proxy for popularity. We found that most people are willing to settle for items with somewhat lower average scores if they are more popular. Yet, our study uncovered substantial diversity of preferences among participants, which indicates a sizable potential for personalizing ranking schemes that rely on social information.Comment: 4 pages, 3 figures, ICWS

The Effect of Word of Mouth on Sales: Online Book Reviews

We examine the effect of consumer reviews on relative sales of books on Amazon.com and BarnesandNoble.com. We find that 1) reviews are overwhelmingly positive at both sites, but there are more reviews and longer reviews at Amazon.com, 2) an improvement in a book's reviews leads to an increase in relative sales at that site, and 3) the impact of 1-star reviews is greater than the impact of 5-star reviews. The results suggest that new forms of customer communication on the Internet have an important impact on customer behavior.

Toward a collective intelligence recommender system for education

The development of Information and Communication Technology (ICT), have revolutionized the world and have moved us into the information age, however the access and handling of this large amount of information is causing valuable time losses. Teachers in Higher Education especially use the Internet as a tool to consult materials and content for the development of the subjects. The internet has very broad services, and sometimes it is difficult for users to find the contents in an easy and fast way. This problem is increasing at the time, causing that students spend a lot of time in search information rather than in synthesis, analysis and construction of new knowledge. In this context, several questions have emerged: Is it possible to design learning activities that allow us to value the information search and to encourage collective participation?. What are the conditions that an ICT tool that supports a process of information search has to have to optimize the student's time and learning? This article presents the use and application of a Recommender System (RS) designed on paradigms of Collective Intelligence (CI). The RS designed encourages the collective learning and the authentic participation of the students. The research combines the literature study with the analysis of the ICT tools that have emerged in the field of the CI and RS. Also, Design-Based Research (DBR) was used to compile and summarize collective intelligence approaches and filtering techniques reported in the literature in Higher Education as well as to incrementally improving the tool. Several are the benefits that have been evidenced as a result of the exploratory study carried out. Among them the following stand out: • It improves student motivation, as it helps you discover new content of interest in an easy way. • It saves time in the search and classification of teaching material of interest. • It fosters specialized reading, inspires competence as a means of learning. • It gives the teacher the ability to generate reports of trends and behaviors of their students, real-time assessment of the quality of learning material. The authors consider that the use of ICT tools that combine the paradigms of the CI and RS presented in this work, are a tool that improves the construction of student knowledge and motivates their collective development in cyberspace, in addition, the model of Filltering Contents used supports the design of models and strategies of collective intelligence in Higher Education.Postprint (author's final draft

Understanding Household Preferences For Alternative-Fuel Vehicle Technologies

This report explores consumer preferences among four different alternative-fuel vehicles (AFVs): hybrid electric vehicles (HEVs), compressed natural gas (CNG) vehicles, hydrogen fuel cell (HFC) vehicles, and electric vehicles (EVs). Although researchers have been interested in understanding consumer preferences for AFVs for more than three decades, it is important to update our estimates of the trade-offs people are willing to make between cost, environmental performance, vehicle range, and refuel¬ing convenience. We conducted a nationwide, Internet-based survey to assess consumer preferences for AFVs. Respondents participated in a stated-preference ranking exercise in which they ranked a series of five vehicles (four AFVs and a traditional gasoline-fueled vehicle) that differ primarily in fuel type, price, environmental performance, vehicle range, and refueling conve¬nience. Our findings indicate that, in general, gasoline-fueled vehicles are still preferred over AFVs, however there is a strong interest in AFVs. No AFV type is overwhelmingly preferred, although HEVs seem to have an edge. Using a panel rank-ordered mixed logit model, we assessed the trade-offs people make between key AFV characteristics. We found that, in order to leave a person’s utility unchanged, a $1,000 increase in AFV cost needs to be compensated by either: (1) a$300 savings in driving cost over 12,000 miles; (2) a 17.5 mile increase in vehicle range; or (3) a 7.8-minute decrease in total refueling time (e.g. finding a gas station and refueling)

The impact of regulatory focus and word of mouth valence on search and experience attribute evaluation

Purpose This paper aims to investigate the direct and interactive effects of regulatory focus (promotion versus prevention), attribute type (search versus experience) and word of mouth valence (positive versus negative) on consumption decision for a service and a product. Design/methodology/approach Three empirical studies (two laboratories and a field experiment) using “university” and “mobile phone” as the research setting were used to test the key hypotheses. Findings Promotion (prevention)-focused subjects preferred experience (search) attributes over their counterparts while making consumption decision. This preference was further reinforced for both promotion and prevention-focused people under positive word of mouth. Under negative word of mouth, in comparison to their counterparts, promotion-focused people still retained their preference for experience attributes, whereas prevention-focused subjects reversed their preference and maintained status quo. Research limitations/implications Future research may validate and extend authors’ findings by looking into the underlying process or studying additional word of mouth variables that may moderate the current findings. Practical implications The findings will help managers devise a range of marketing strategies in the areas of advertising and product positioning, especially for products/services that are showcased in terms of experience and search attributes. Originality/value The current research is novel as no prior research has proposed and tested the two-way interaction between regulatory focus and search/experience attributes, or its further moderation by word of mouth valence. </jats:sec