crowd content reviews

crowd content reviews

The Impact of Crowd Content Reviews on Online Platforms

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1. Introduction to Crowd Content Reviews

All websites that allow multi-directional communication with consumers conduct some sort of review. The websites aggregate user reviews about products and services, thus presenting a collective judgment to potential consumers. With the widespread availability of crowd-generated data in digital platforms, the number of academic studies has enlarged. The strong evidence of crowd-propelled rating behavior and the impact of reviews on revenues, verified by recent studies, have incited firms to turn to crowd content ratings for decision making. Yet the demands for impacting the information provided by crowd content have not been realized. Offering financial incentives on digital platforms, founding content writing prize competitions and enhancing a reviewer’s penetration in opinion surveys with and without monetary compensation, in addition to rating articles in a different language with and without cash prizes, were different approaches conducted by Koh and Sundar (2007). They concluded that the cash incentive does indeed generate a significant increase in review quality.

The rise of digital platforms and the increase in digital technology has had what appears to be a profound impact on the economy and society at large, as well as having affected specific areas, such as industries and specific sectors of activity. The data generated from the use of digital platforms has become important across various sectors, forming the driving force behind the algorithms used by companies acquiring value from taking out useful information to improving decision and prediction capability. Some types of data from the use of digital platforms are adequate for functional use as ‘crowd-generated data’, particularly data originating in ‘user-generated content’ (online ratings, reviews, feedback, photos, audio, comments, tips, and discussions) and in data generated by ‘crowd tasks’ (badges, localized text, and tags).

2. Benefits and Challenges of Crowd Content Reviews

We propose an extended model to analyze the dynamics of content review participation and quality production, considering that crowd content reviews may be affected by both the informational and strategic aspects within platforms. Platforms differ in their use of signaling mechanisms to provide signals to reviewers about the quality of their reviews before they decide to contribute. Four signaling mechanisms have been identified in recent studies regarding the effects of platforms influencing the quality of reviews: Feedback information encourages professionals to build specific tasks before the review periods. Collusion Management, such as refraining from announcing all contributions at once, is prized. The ratchet effect is an even higher promotion to higher-quality content. Credits awarded for high-quality contributions are the most effective solution. Data from a dataset of consumer-rated reviews are adopted in this study using the reference and authority methods to apply an integrated research framework.

Consumer-generated content is attracting increasing attention from many stakeholders, including researchers, analysts, practitioners, and platform owners. A typical form of consumer-generated content is comments and reviews attached to different types of content products, such as news, videos, articles, and essays on online platforms, such as news-sharing sites and knowledge-sharing sites. Effectively managing and controlling content quality is often of interest to platform owners who are eager to motivate consumers to contribute comments and reviews on content products. Meanwhile, the quality of comments and reviews is of interest to consumers who, given the existence of malicious content (e.g., spam and inaccurate content), are naturally cautious about believing and using the content products.

3. Quality Assurance and Trustworthiness in Crowd Content Reviews

(These authors contributed equally to this work)

Research in crowdsourced perception and interpretation tasks receives sustained attention; thus, clear conventions for quality assurance are available. However, for crowd work where quality is less evident, like creative, structured, text or research activities, opportunities to contribute trustworthiness are missing. Consequently, implementations address only the output, like spelling and completion quality. Adoption of this pool of expertise for crowd content quality would increase the reliability of these activities and confidence in their output, and grow the use of the crowd, as well as its long-term commitment. We reviewed 521 papers addressing these crowd content types, finding only 1 affecting the content in a manner comparable to perception and interpretation quality assurance. Our goal is to understand the attention these four types of crowd content have received, and expose the diversity in crowd content processing that is achieved and still possible. First, we link and distinguish creative, structured, text, and research content processing. Then, we outline opportunities for content processing overlap that can be used if contributions allowing for trustworthiness are welcomed. With this, confidence in the output of various crowd content activities is enforced, and its adoption and long-term commitment shall grow.

Wilco van Dijk*, Chunting Wang, and Sebastiaan Peek School of Business and Economics, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands Data Science Group, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands; LORA Personalized Content Solutions, 1081 HV Amsterdam, The Netherlands; Anniversary Submitted: 4 July 2019; Accepted: 19 February 2020

Quality Assessment and Trustworthiness in Crowd Content Reviews: A Review

4. Incentivizing and Motivating Crowd Contributors

Online platforms are attractive to many firms and service providers as a means to market their goods and services, interact with current and potential customers, and collect and evaluate customer opinions, preferences, and reviews. Users increasingly rely on the information and interactions available on online platforms to inform their decisions and choices about the goods and services they will acquire and consume through the attainment of reviews shared by fellow consumers. Enable experts to scale on demand by deploying their missions and entering the platforms where the tasks are driven. The internet has transformed and delivered the economies of the principles and advances of user-generated ratings and reviews, ratings aggregation, consumer power, product discovery, and opinion walls.

The reviews created and provided by crowd contributors for user-generated content on online platforms are crucial in informing the decision and choice making of end users. This work debates and creates an index to extend the comprehensive framework with a classification of authority, involvement, and reciprocity of crowd content reviews. We contribute to the literature by incorporating two areas which are not thoroughly researched yet—crowdsourcing and internet reviews. The research explores the approaches through which the crowd contributors can be motivated effectively based on their profile characteristics, work control, and reward design. A model evaluation of understanding the relationship between the framework and the considered performers as moderators is conducted based on an empirical study involving new profiles of users sourced from the largest feedback provision platform online.

5. Future Directions and Innovations in Crowd Content Reviews

The management of content quality is an important aspect of platform design with strong implications on traffic, users, and profits. Allowing inexpensive evaluations by large numbers of users permits the use of high-quality reviews contributing to the management of content quality while implementing the evaluations. Future and current implementations of these evaluations on platforms can and should be improved. The most important directions for future research are likely to focus on finding answers to the immediate difficulties explained in Section 3.3. Answers to these questions would indeed permit to effectively reduce the limitations that we have highlighted and to unleash the full potential of such reviews.

Crowd content reviews are a core part of many web platforms and determine the qualitative differentiation of each platform and the success of many applications. Given the relevance of that aspect, we, in this final section, stress future directions and innovations in crowd content reviews. We distinguish two types of future directions. First, we describe several immediate decisions to improve the economic efficiency and effectiveness of the current implementations of crowd content reviews, providing directions for future research. Second, we envision crowd content reviews as the basis for further innovations generating new research challenges both from a methodological point of view but also in terms of data requirements and applications.

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