Secondhand seller reputation in online markets: A text analytics framework

Chen, Runyu; Zheng, Yi; Xu, Wei; Liu, Minghao; Wang, Jiayue

doi:10.1016/j.dss.2018.02.008

Cited by 47 publications

(24 citation statements)

References 53 publications

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“…It shows that the reputation system had a direct effect on buying interest. This finding supports (Chen et al, 2018;Tadelis, 2016;Thompson & Haynes, 2017).…”

Section: The Effect Of Reputation System On Buying Interestsupporting

confidence: 84%

“…The finding shows that the better the reputation, the higher the demand for transactions. Chen et al (2018) stated that there was a significant link between the reputation of the seller and the buying interest, where a high reputation also equals to high interest to buy. However, in research conducted by Tadelis (2016), it was found that the customers had less intention to report the quality of the sellers through the feedback system.…”

Section: Introductionmentioning

confidence: 99%

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Reputation System of C2c E-Commerce, Buying Interest and Trust

Kusuma

Rejeki

Robiyanto

et al. 2020

Business: Theory and Practice

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One form of the development of technology and internet users in Indonesia is the merge of various C2C (Customers to Customers) e-commerce on the marketplace. Bukalapak is one of the examples. This research presents the effects of reputation systems on trust, trust in buying interest, and reputation systems on Bukalapak customers’ buying interests. A total of 120 respondents participated and the data were analyzed using structural equation modeling with Amos 20 software. The results show that the reputation system has a higher direct impact compared to the impacts mediated by trust. Therefore, in an effort to increase the buying interest, the sellers should increase their reputation in the reputation system represented in the list of feedback, review, and the badge level in Bukalapak. To marketplace providers, in this case, Bukalapak, there is still a need to increase the customers’ trust. Although it is significant, however, it has low impacts both directly and indirectly.

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“…It shows that the reputation system had a direct effect on buying interest. This finding supports (Chen et al, 2018;Tadelis, 2016;Thompson & Haynes, 2017).…”

Section: The Effect Of Reputation System On Buying Interestsupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Reputation System of C2c E-Commerce, Buying Interest and Trust

Kusuma

Rejeki

Robiyanto

et al. 2020

Business: Theory and Practice

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show abstract

“…A substantial body of interdisciplinary literature has been developed on reputation star ratings and decisions (Chen, Zheng, Xu, Liu, & Wang, 2018;Zhou, Dresner, & Windle, 2008). Reputation star ratings function as important signals that can influence to varying degrees our cognition of the perceived value of several objects, such as products, services, organizations, and people (George, Dahlander, Graffin, & Sim, 2016).…”

Section: Background -Reputation Star Ratings As Cognitive Signalsmentioning

confidence: 99%

Reputation Star Society: Are star ratings consulted as substitute or complementary information?

Willems

Waldner

Ronquillo

2019

Decision Support Systems

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“…Online reputation is a signal that reflects a doctor's social returns earned from patients [47]. Studies have shown that sellers with a better online reputation in the evaluation feedback system will have a higher performance rating [64]. Reputation is also a reflection of perceived trustworthiness as evaluated by patients [44].…”

Section: Leader Reputation As a Moderatormentioning

confidence: 99%

How to Manage Diversity and Enhance Team Performance: Evidence from Online Doctor Teams in China

Liu

Chen

et al. 2019

IJERPH

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(1) Background: Traditional one-to-one online consultations with doctors often fail to provide timely and accurate treatment plans; consequently, creating cross-hospital and cross-regional teams has become a new pattern for doctors aiming to offer Internet medical services. Because the online doctor team is a new virtual organizational model, it remains to be explained and investigated. (2) Methods: Combining the information processing view and the social categorization view, this study takes the perspective of team diversity and empirically investigates the effect of team diversity on team performance. We consider four kinds of team diversity, including status capital diversity, decision capital diversity, online reputation diversity, and professional knowledge diversity, and we investigate how team composition from the diversity perspective affects online doctor team performance and how leader reputation moderates the effect of team diversity on team performance. We use secondary data from a leading online medical consultation platform in China (Good Doctor), and our research data include 1568 teams with a total of 5481 doctors. (3) Results: The results show that status capital diversity and decision capital diversity negatively affect team performance; diversity in terms of online reputation and professional knowledge positively affect team performance; and leader reputation moderates the impact of status capital diversity and online reputation on team performance. (4) Conclusions: Our study offers management suggestions on how to form a high-performance doctor team and provides advice for the future development of online doctor teams.

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Secondhand seller reputation in online markets: A text analytics framework

Cited by 47 publications

References 53 publications

Reputation System of C2c E-Commerce, Buying Interest and Trust

Reputation System of C2c E-Commerce, Buying Interest and Trust

Reputation Star Society: Are star ratings consulted as substitute or complementary information?

How to Manage Diversity and Enhance Team Performance: Evidence from Online Doctor Teams in China

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