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Faced with the mistrust of consumers toward customer reviews due to the multiplication of scandals involving fake reviews, review verification systems offer a solution to reassure Internet users. However, their effectiveness raise...
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Faced with the mistrust of consumers toward customer reviews due to the multiplication of scandals involving fake reviews, review verification systems offer a solution to reassure Internet users. However, their effectiveness raises questions. Based on the Elaboration Likelihood Model and signaling theory, this research examines the moderating effect of review verification on the relationship between review valence and customers' behavioral intentions toward brands. With data from 9,219 online reviews and two survey-based designs, the authors highlight the existence of a "dark side" of verification systems. Results specifically reveal that perceived manipulative intentions of firms lead to weaker perceived credibility of reviews when they include verification badges and, consequently, diminish the effect of review valence on customers' behavioral intentions toward brands.
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Bedeian, Van Fleet, and Hyman offer some data that cast doubt on the qualifications of the reviewers of the flagship journals in our field. Using two alternative measures of authoring success, we found the reviewers of Academy of ...
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Bedeian, Van Fleet, and Hyman offer some data that cast doubt on the qualifications of the reviewers of the flagship journals in our field. Using two alternative measures of authoring success, we found the reviewers of Academy of Management Journal are as a group more accomplished scholars than the authors are as a group. However, we suggest that a more relevant conversation should be about addressing the large gap between the demand for effective reviewers and the supply of individuals who are both successful authors and effective reviewers. We encourage a two-pronged approach involving reducing the demand for reviews on one hand and increasing the supply of effective reviewers on the other hand. We offer several procedural and structural alternatives to initiate a conversation that might help to move us toward some norms to encourage and reward effective reviewing in our field.
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Helpfulness of online reviews is a multi-faceted concept that can be driven by several types of factors. This study was designed to extend existing research on online review helpfulness by looking at not just the quantitative fact...
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Helpfulness of online reviews is a multi-faceted concept that can be driven by several types of factors. This study was designed to extend existing research on online review helpfulness by looking at not just the quantitative factors (such as word count), but also qualitative aspects of reviewers (including reviewer experience, reviewer impact, reviewer cumulative helpfulness). This integrated view uncovers some insights that were not available before. Our findings suggest that word count has a threshold in its effects on review helpfulness. Beyond this threshold, its effect diminishes significantly or becomes near non-existent. Reviewer experience and their impact were not statistically significant predictors of helpfulness, but past helpfulness records tended to predict future helpfulness ratings. Review framing was also a strong predictor of helpfulness. As a result, characteristics of reviewers and review messages have a varying degree of impact on review helpfulness. Theoretical and practical implications are discussed. (C) 2015 Elsevier Ltd. All rights reserved.
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Literature review is part and parcel of scholarly research. Though many literature review guides are available, they remain limited because they do not adequately account for the different types of literature review. Noteworthily,...
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Literature review is part and parcel of scholarly research. Though many literature review guides are available, they remain limited because they do not adequately account for the different types of literature review. Noteworthily, literature reviews can manifest as part of conceptual or empirical studies, or as independent studies, in which the latter may be curated in various ways. Moreover, despite its importance and popularity, literature reviews, particularly as independent studies, continue to attract unfair criticism and remain scarce in service research. To address the aforementioned gaps, this article endeavors to provide an overview and guidelines for writing literature reviews. Specifically, this article explains (1) what a literature review is and is not, (2) why literature reviews are valuable, and (3) how to conduct a literature review, as well as (4) the areas of service research and (5) the innovative ways in which literature reviews can be curated in the future.
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Online reviews provide consumers with valuable information that guides their decisions on a variety of fronts: from entertainment and shopping to medical services. Although the proliferation of online reviews gives insights about ...
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Online reviews provide consumers with valuable information that guides their decisions on a variety of fronts: from entertainment and shopping to medical services. Although the proliferation of online reviews gives insights about different aspects of a product, it can also prove a serious drawback: consumers cannot and will not read thousands of reviews before making a purchase decision. This need to extract useful information from large review corpora has spawned considerable prior work, but so far all have drawbacks. Review summarization (generating statistical descriptions of review sets) sacrifices the immediacy and narrative structure of reviews. Likewise, review selection (identifying a subset of 'helpful' or 'important' reviews) leads to redundant or non-representative summaries. In this paper, we fill the gap between existing review-summarization and review-selection methods by selecting a small subset of reviews that together preserve the statistical properties of the entire review corpus. We formalize this task as a combinatorial optimization problem and show that it NP-hard both to solve and approximate. We also design effective algorithms that prove to work well in practice. Our experiments with real review corpora on different types of products demonstrate the utility of our methods, and our user studies indicate that our methods provide a better summary than prior approaches.
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Purpose - Identifying and predicting the most helpful reviews has been a focal interest in the fields including information management, e-commerce and marketing, etc. Though many factors are found correlated to the helpfulness of ...
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Purpose - Identifying and predicting the most helpful reviews has been a focal interest in the fields including information management, e-commerce and marketing, etc. Though many factors are found correlated to the helpfulness of reviews, they may suffer endogeneity problems, as normally the data is observed in the same time window. This paper aims to tackle such a problem by examining the predictive power of different factors on the future increment of review helpfulness. Design/methodology/approach - Adopting a longitudinal data of 443 K empirical business reviews from Yelp.com collected at two different time points, six groups of predictors are extracted from the first snapshot of data to predict the helpfulness increment of old and recent reviews, respectively, between the two snapshots. Findings - It is found that these factors in general are with moderate accuracy predicting the helpfulness increment A different group of features shows quite different predictive power. The reviewer disclosure information is the most significant factor, while the review readability does not significantly improve the accuracy of prediction. Originality/value - Instead of the total number of helpful votes observed in the same time window with the explanatory variables, this paper focuses on the future increment of helpful votes observed in the following time window. With such a two-wave data set, the endogeneity problem can be avoided and the explanatory factors for review helpfulness can, thus, be further tested in the prediction scenario.
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Online review systems try to motivate reviewers to invest effort in writing reviews, as their success crucially depends on the helpfulness of such reviews. Underlying cognitive mechanisms, however, might influence future reviewing...
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Online review systems try to motivate reviewers to invest effort in writing reviews, as their success crucially depends on the helpfulness of such reviews. Underlying cognitive mechanisms, however, might influence future reviewing effort. Accordingly, in this study, we analyze whether existing reviews matter for future textual reviews. From analyzing a dataset from Google Maps covering 40 sights across Europe with over 37,000 reviews, we find that textual reviewing effort, as measured by the propensity to write an optional textual review and (textual) review length, is negatively related to the number of existing reviews. However, and against our expectations, reviewers do not increase textual reviewing effort if there is a large discrepancy between the existing rating valence and their own rating. We validate our findings using additional review data from Yelp. This work provides important implications for online platforms with review systems, as the presentation of review metrics matters for future textual reviewing effort.
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This study proposes and evaluates the effect of "mixed" comparative reviews on review value and compares the results with "separate" comparative and regular reviews. A total of 201 subjects have participated in the experiment cond...
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This study proposes and evaluates the effect of "mixed" comparative reviews on review value and compares the results with "separate" comparative and regular reviews. A total of 201 subjects have participated in the experiment conducted in this study. Results indicate that mixed comparative reviews in text format are perceived as less valuable than separate comparative reviews in text format. However, mixed comparative reviews in tabular format have more review value than those in text format and are perceived as more valuable than regular reviews of one product in either format. Unsurprisingly, the positive reviews of the target product lead to higher product attitude than negative reviews. However, this effect is weak in mixed (vs. separate) comparative reviews.
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Abstract The March 2017 issue of the Health Information and Libraries Journal marks the 10?year anniversary of the inaugural review published in the journal's review series. The review series was conceived to meet the growing appe...
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Abstract The March 2017 issue of the Health Information and Libraries Journal marks the 10?year anniversary of the inaugural review published in the journal's review series. The review series was conceived to meet the growing appetite of health library and information workers to access synthesised evidence to inform their practice; something we'd already been doing to support medics in their practice. This editorial looks back on the 10?years and the inspiration which saw the development of a typology of review types and associated methodologies to address the lack of consistent guidelines on the features a review should incorporate.
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