Examining the Role of Semantic Similarity in Online Restaurant Review Evaluations

Both language and image are critical for the grasp of information embedded in online reviews. While a large quantity of research has focused on the role of textual features and visual features separately, the specific role of similarity between textual and visual information in online review evaluations (e.g., review usefulness and review enjoyment) remains unaddressed. Thus, drawing on dual coding theory, this study attempts to investigate the impacts of textual and visual features on review evaluations by employing the Latent Dirichlet Allocation (LDA) topic modeling and Google Vision API’s web detection techniques in the context of online restaurant review (ORR). Moreover, the moderating role of semantic similarity is examined in the relationships between textual/visual features and ORR evaluations. It is believed that this study could provide implications on information comprehension, draw consumer interest, and provide suggestions for restaurant managers to tune levels of review evaluation in a proper manner

Understanding the Impact of Restaurants’ Initial Online Reputation on Subsequent Online Reputation: Focusing on Source and Message Credibilities

When consumers have no previous experience with products or services, they form trust on them based on credible sources and messages. Thus, online review sites have become crucial word-of-mouth channels where customers search for the credible information. Although considerable literature on online reviews has revealed the role of reviewers and review characteristics in forming consumer behaviors, few studies have examined their impact on business reputation building processes. Therefore, we investigate whether the overall tendency of initial source and message credibilities can moderate the impact of the initial online reputation on the subsequent online reputation. We listed up 1,516 newly opened restaurants located in Manhattan of New York City and collected their reviews posted over the first six months. Expected contributions are also discussed

Collective Classification for Social Media Credibility Estimation

We introduce a novel extension of the iterative classification algorithm to heterogeneous graphs and apply it to estimate credibility in social media. Given a heterogeneous graph of events, users, and websites derived from social media posts, and given prior knowledge of the credibility of a subset of graph nodes, the approach iteratively converges to a set of classifiers that estimate credibility of the remaining nodes. To measure the performance of this approach, we train on a set of manually labeled events extracted from a corpus of Twitter data and calculate the resulting receiver operating characteristic (ROC) curves. We show that collective classification outperforms independent classification approaches, implying that graph dependencies are crucial to estimating credibility in social media

Pengaruh Customer Value terhadap Purchase Decision melalui Beauty Blogger di Youtube (Studi pada Pelanggan Kosmetik Local Brand di Sumatera Selatan)

Tujuan penelitian – This study is aimed to; First, to understand the significant effect of customer value on purchase decision through beauty blogger on Youtube. Second, to understand the influence of beauty blogger on Youtube significantly to purchase decisionDesain/Metodologi/Pendekatan – This study uses 200 respondents as sample. Structural Equation Modelling analysis is used to test the hypotheses.Temuan – The results are based on the regression assessment there is influence of customer value on purchasing decision through beauty blogger. Customer value also directly affects purchasing decision. Only beauty blogger has a significant directly effect on purchasing decision.Keterbatasan penelitian – The limitations of this study focus on viewers of beauty bloggers on youtube with customer value variables and cosmetic purchase decisions as the main variables.Originality/value – The originality of this article examines the effect of customer value, purchasing decisions after watching certain cosmetic brand reviews of beauty bloggers on youtube

An Empirical Examination of Factors Influencing the Intention to Use Physician Rating Websites

Physician rating websites (PRWs) are social media platforms that enable patients to submit ratings and reviews of physicians. While numerous PRWs are available on the Internet and millions of physician reviews are posted on those websites, many people still do not use them when making clinical decisions. This study seeks to understand what factors impact intention to use PRWs. A sample of 109 students was employed. Each subject was randomly assigned to either RateMDs, Vitals, or Brigham and Women’s Hospital’s website. The subjects were asked to choose a primary care doctor based on the reviews posted on the assigned website and complete a survey accordingly. The regression analysis revealed that perceived credibility of reviewers and general use of online reviews influenced intention to use PRWs, whereas perceived integrity of website providers only moderated the relation between perceived credibility of reviewers and intention to use PRWs

Moderating Effects of Time-Related Factors in Predicting the Helpfulness of Online Reviews: a Deep Learning Approach

Given the importance of online reviews, as shown by extensive research, we address the problem of predicting the helpfulness of online product reviews by developing a comprehensive research model guided by the theoretical foundations of signaling and social influence theories. We use review order and time interval to incorporate the moderating effects of the time-related variable on the reviewer’s valuation of products and the related details they provide. Applying deep learning techniques in text processing and model building on a dataset of 239297 reviews, the empirical findings represent strong support of the proposed approach and show its superior performance in predicting review helpfulness compared to current approaches. This research contributes to theory by analyzing online reviews from the points of two well-known information processing theories and contributes to practice by developing a model to sort the newly posted reviews

Revisiting Review Depth in Search for Helpful Online Reviews

This study investigates online review features that constitute review depth and assess their impacts on review helpfulness. It develops a model capturing the moderating effects of heuristic and systematic cues of an online review on the relationship between review length and its helpfulness. In particular, this study examines the moderating effects of price, product type, review readability and the presence of two-sided arguments. For testing the model, a dataset of 568,454 reviews from 256,059 different reviewers on Amazon.com were analyzed. The variables were operationalized using test processing techniques and relationships were empirically tested using regression and machine learning models. The results highlight significant moderating effects of review readability and the presence of two-sided arguments on the relationship between review length and its helpfulness. However, the results did not confirm the moderating effects of price and product type. This article discusses the significant implications for a better understanding of review depth and helpfulness in e-commerce platforms

Segmenting an Online Reviewer Community: Empirical Detection and Comparison of Reviewer Clusters

More people are travelling overseas for health or wellness reasons however, there is limited understanding of the background of those travelling and how information is sourced for decision making. Those travelling for treatment are likely to be unaware of all of the risks. Reliable information sources are scattered and not easy to find. Interviews were conducted with 51 Australians contemplating or who had travelled for stem cell treatment. Information sources people used were identified, and an analysis was undertaken of how this influenced their decision. The data highlight that health travellers are likely to search extensively using a wide range of sources including information on clinics’ websites, Facebook, blogs, friends and family. Interviewees highlight that often decisions are made based on unreliable sources. The implications are that without quality, reliable information health travellers are at risk of suffering adverse outcomes and spending significant funds without any improvement in their condition