Review spotlight

Yatani, Koji; Novati, Michael; Trusty, Andrew; Truong, Khai N.

doi:10.1145/1978942.1979167

Cited by 58 publications

(3 citation statements)

References 21 publications

(12 reference statements)

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“…The large number of reviews written by users and the inconsistent writing styles generally require much time and effort to read, which may lead to the blurring of important information. Online review systems with design feature of review tag summaries can enable users to hasten decision making (Yatani et al, 2011). Therefore, the implementation of appropriate designs and policies can improve the quality and effectiveness of online reviews and provide consumers with credible and representative ratings (Askalidis et al, 2017).…”

Section: Online Hospitality Review Systemsmentioning

confidence: 99%

Deconstructing Online Hospitality Review Systems

Xiao

Zhang

et al. 2021

Journal of Organizational and End User Computing

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Online hospitality reviews have an important impact on consumers’ travel and hospitality booking decisions in the Internet age. A well-designed online hospitality review system is crucial to reduce the uncertainty of consumers’ decision making, to grasp the actual needs of consumers, and to improve the quality experience of platforms. In this context, this research conducts an empirical study on the design features of online hospitality review systems based on the Kano model. First, the paper analyzes the design features of online hospitality review systems. Then, the paper proposes an improved method to classify design features on the basis of the Kano questionnaire design and survey data. Finally, the paper quantitatively measures their importance in online hospitality review systems. Results can provide scientific basis for online travel platforms or hospitality operators to optimize the design of online hospitality review systems and to obtain reference value to increase the satisfaction of consumers’ decision making.

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Section: Online Hospitality Review Systemsmentioning

confidence: 99%

Deconstructing Online Hospitality Review Systems

Xiao

Zhang

et al. 2021

Journal of Organizational and End User Computing

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“…Aggregation is another approach for addressing negative feedback. Yatani et al generate a word cloud from review content that surfaces the most popular phrases [41]. The aggregation gives content creators an overview of the feedback before they read the text.…”

Section: Negative Feedback In Crowd Feedback Frameworkmentioning

confidence: 99%

Bitter Sweet or Sweet Bitter?

Bailey

2017

Proceedings of the 2017 ACM SIGCHI Conference on Creativity and Cognition

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Content creators are less receptive to feedback with negative valence, and such feedback is frequently received online. To address this problem, we propose a novel method that orders a set of feedback based on its valence; using the feedback with positive valence to mitigate the effects of the negative valence feedback. To test the method, participants (N=270) wrote a story for children based on a given illustration and then revised their story after receiving a set of feedback. The feedback set was delivered with different valence orders and with different source identity cues. We measured participants' affective states, perceptions of the feedback and its source, revision extents, and story quality. Our main result is that presenting negative feedback last improved content creators' affective states and their perception of the feedback set relative to placing the negative feedback in other positions. This pattern was consistent across all feedback source conditions. The work contributes a simple and novel way to order a set of feedback that improves feedback receptivity.

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“…However, they do not evaluate their system by means of user studies. In recommender systems, interactive visualization is considered more often, albeit not from the fraud detection perspective [63,38].…”

Section: Sensemaking and Visual Analytics Of Seller Reputationmentioning

confidence: 99%

Look Before You Leap

Sänger

Hänsch

Glass

et al. 2016

Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

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Reputation systems in current electronic marketplaces can easily be manipulated by malicious sellers in order to appear more reputable than appropriate. We conducted a controlled experiment with 40 UK and 41 German participants on their ability to detect malicious behavior by means of an eBaylike feedback profile versus a novel interface involving an interactive visualization of reputation data. The results show that participants using the new interface could better detect and understand malicious behavior in three out of four attacks (the overall detection accuracy 77% in the new vs. 56% in the old interface). Moreover, with the new interface, only 7% of the users decided to buy from the malicious seller (the options being to buy from one of the available sellers or to abstain from buying), as opposed to 30% in the old interface condition.

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Review spotlight

Cited by 58 publications

References 21 publications

Deconstructing Online Hospitality Review Systems

Deconstructing Online Hospitality Review Systems

Bitter Sweet or Sweet Bitter?

Look Before You Leap

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