Reputation has often been proposed as the central mechanism that creates trust in the sharing economy. However, some sharing platforms that focus primarily on social rather than economically driven exchanges have managed to facilitate exchanges between users without the use of a reputation system. This could indicate that socially driven exchanges are in less need of reputation systems and that having sufficient trust is less problematic. We examine the effect of seller reputation on sales and price as proxies for trust, using a large dataset from a Dutch meal-sharing platform. This platform aims to stimulate social interactions between people via meal sharing. Multilevel regression analyses were used to test the association of reputation with trust. Our main empirical results are that reputation affects both sales and price positively, consistent with the existing reputation literature. We also found evidence of the presence of an information effect, i.e., the influence of reputation on sharing decreases when additional profile information is provided (e.g., a profile photo, a product description). Our results thus confirm the effectiveness of reputation in more socially driven exchanges also. Consequently, platform owners are advised to use reputation on their platform to increase sharing between its users.
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Corporate reputation is becoming increasingly important for firms; social media platforms such as Twitter are used to convey their message. In this paper, corporate reputation will be assessed from a sustainability perspective. Using sentiment analysis, the top 100 brands of the Netherlands were scraped and analyzed. The companies were registered in the sustainable industry classification system (SICS) to perform the analysis on an industry level. A semantic search tool called Open Semantic Desktop Search was used to filter through the data to find keywords related to sustainability and corporate reputation. Findings show that companies that tweet more often about corporate reputation and sustainability receive overall a more positive sentiment from the public.
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This paper presents a Decision Support System (DSS) that helps companies with corporate reputation (CR) estimates of their respective brands by collecting provided feedbacks on their products and services and deriving state-of-the-art key performance indicators. A Sentiment Analysis Engine (SAE) is at the core of the proposed DSS that enables to monitor, estimate, and classify clients’ sentiments in terms of polarity, as expressed in public comments on social media (SM) company channels. The SAE is built on machine learning (ML) text classification models that are cross-source trained and validated with real data streams from a platform like Trustpilot that specializes in user reviews and tested on unseen comments gathered from a collection of public company pages and channels on a social networking platform like Facebook. Such crosssource opinion analysis remains a challenge and is highly relevant in the disciplines of research and engineering in which a sentiment classifier for an unlabeled destination domain is assisted by a tagged source task (Singh and Jaiswal, 2022). The best performance in terms of F1 score was obtained with a multinomial naive Bayes model: 0,87 for validation and 0,74 for testing.
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