The Determinants of IS User Satisfaction and Dissatisfaction: A Text Mining Approach

Too often, in previous marketing, consumer behavior, and IS research, satisfaction and dissatisfaction are treated as two ends of a bipolar continuum. The researchers of this study argue that satisfaction and dissatisfaction are two distinct dimensions and thus have different determinants. Online reviews, as one type of user-generated contents (UGC), can impact consumer purchase decision and IS user adoption decision. Online reviews are also valuable sources for researchers and practitioners to better understand consumers and users. The researchers of this study extract and analyze online user reviews in the App Store. Sentiment analysis is applied to model user satisfaction and dissatisfaction. Significant determinants, as well as their weights are identified. By using the text mining techniques, the current study demonstrates the separability of satisfaction and dissatisfaction and reveals different influencing factors. The research findings can provide insights into extant IS user satisfaction literature

A generalized model via random walks for information filtering

There could exist a simple general mechanism lurking beneath collaborative filtering and interdisciplinary physics approaches which have been successfully applied to online E-commerce platforms. Motivated by this idea, we propose a generalized model employing the dynamics of the random walk in the bipartite networks. Taking into account the degree information, the proposed generalized model could deduce the collaborative filtering, interdisciplinary physics approaches and even the enormous expansion of them. Furthermore, we analyze the generalized model with single and hybrid of degree information on the process of random walk in bipartite networks, and propose a possible strategy by using the hybrid degree information for different popular objects to toward promising precision of the recommendation

Are the “Best Answers†in Online Question-Answering (Q&A) Sites Really Best?

Online question-answering (Q&A) sites are platforms for askers to seek information by posting questions and for answerers to contribute their knowledge by answering questions. Today, Q&A sites’ role is not simply providing answers for only askers but establishing knowledge creating and sharing communities for a broader audience (Anderson et al. 2012). With more and more people relying on online information, Q&A sites have become one of the important online sources for such information. According to the websites’ strategies, the “best answers†selected by askers are prominent on the page. Other online users or information seekers tend to rely on the “best answers†more often than they rely on other answers. Thus, reliable and unbiased judgments by users of the “best answers†are very important. \ \ When dealing with a substantial amount of information, people often rely on heuristics to make judgment (Wu and Cheng 2011). Their judgment processes tend to be influenced by psychological biases, such as anchoring bias and confirmation bias (Hagafors and Brehmer 1983; Tversky and Kahneman 1974). A bias is a systematic deviation from a fact or desired result (Kahneman 2011) and results in a propensity to make decisions when being impacted by some underlying beliefs (Chira et al. 2008). In Q&A sites, when askers make judgment on the “best answers,†they are likely to rely on their heuristics. They may exhibit anchoring bias and confirmation bias in the process. \ \ However, to the authors’ knowledge, a research gap still exists in investigating askers’ biases and their effects on the judgment of the “best answers.†This study develops a research model to investigate anchoring bias and confirmation bias and their impacts on source credibility, information quality, and judgment of the “best answers.†\ \ The objectives of this study are three-fold (1) to identify whether askers exhibit anchoring bias and confirmation bias in Q&A sites; (2) to investigate sources of the two biases; (3) to examine the influences of the two biases on askers’ judgments of the “best answers.†The current study will have both theoretical and practical implications. It will enrich extant IS research on decision makers’ biases and judgment. The findings will help not only online Q&A sites but also other knowledge-related online communities to understand their users’ information seeking behavior and decision making processes

Present and future hotel website marketing activities: Change propensity analysis

International audienc

Present and Future Website Marketing Activities in the US Hotels: Change Propensity Analysis

The objective of this study is to examine and predict website marketing activities of US hotels. An online survey is conducted to collect data from members of three major hotel associations located in the US. Change propensity analysis is utilized to evaluate future trends of website marketing activities. The results demonstrate that current website exploitation by the US hotels is relatively limited, with a focus on information dissemination. It is suggested that a number of transaction and relation-building activities will emerge in the near future. Implications based on the results are discussed

Understanding Chinese Domestic Travelers’ Trip Patterns to Mount Fanjing

The purpose of this study is to identify Chinese domestic travelers’ trip patterns to Mount Fanjing. Data analysis is based on 200 questionnaires completed by travelers to the destination. Consistent with the LCF model, the researchers recognize five trip patterns: the single destination pattern, en-route pattern, base-camp pattern, regional tour pattern, and trip-chaining pattern. In addition, the researchers examine factors that influence Chinese travelers’ decisions on single-destination and multi-destination trips. Time constraints and destination familiarity have significant impacts on Chinese travelers’ destination choice. The research findings have implications for researchers to understand Chinese domestic travelers’ behavior and for local governments to develop their destination marketing strategies

The Impact of Green Experience on Customer Satisfaction: Evidence from TripAdvisor

Purpose The purpose of this paper is to examine guests’ experiences at green hotels and the impact of green experience on customer satisfaction. Design/methodology/approach A total of 727 green reviews (reviews on green experiences) of the top ten green hotels in the USA were downloaded from TripAdvisor for content analysis. Descriptive statistics and ordinal logistic regressions were then used. Findings Guests have both positive and negative experiences at green hotels. “Energy”, “purchasing” and “education and innovation” are the most frequently discussed green practices. Some guests’ green experiences, such as “guest training”, “energy”, “water”, “purchasing” and “education and innovation”, significantly influence their overall satisfaction with hotels. Compared with basic green practices, advanced green practices tend to have greater impacts on customer satisfaction. Research limitations/implications This study provides insight into guests’ green experiences at hotels and their impact on customer satisfaction. More importantly, this study examines the contribution of different types of green practices to customer satisfaction. As the green hotels examined in this study were not randomly selected, the results should be interpreted with caution. Practical implications Different practices impact customer satisfaction in different ways, so hoteliers should refine their green strategies when they implement these green practices. Originality/value Very few studies have examined the relationship between green practices and customer satisfaction. A gap still exists in specifically what types of green practices affect customer satisfaction and whether different levels of green practices have different impacts on customer satisfaction. This study investigates guests’ actual experiences and fills the above research gap