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Service users in Wiltshire have had the opportunity of demonstrating how they could undertake a user‐controlled Best Value review including showing the variety of ways they chose to evaluate the service ‐ in this case Direct Payments. Using the Best Value framework of the four Cs, they compared support services and costings with those in other local authority areas, designed consultation surveys and challenged care managers about their knowledge of the service. Learning from the project has implications for all those involved in social care Best Value reviews and the participation of users.

This study aims to explore how online reviews and users’ social network centrality interact to influence idea popularity in open innovation communities (OICs).

This study used Python to obtain data from the LEGO Innovation Community. In total, 285,849 reviews across 4,475 user designs between March 2019 and March 2021 were extracted to test this study’s hypotheses.

The ordinary least square regression analysis results show that review volume, review valence, review variance and review length all positively influence idea popularity. In addition, users’ in-degree centrality positively interacts with review valence, review variance and review length to influence idea popularity, while their out-degree centrality negatively interacts with such effects.

Drawing on the interactive marketing perspective, this study employs a large sample from the LEGO community and examines user design and idea popularity from a community member’s point of view. Moreover, this study is the first to confirm the role of online reviews and user network centrality in influencing idea popularity in OICs from a social network perspective. Furthermore, by integrating social network analysis and persuasion theories, this study confirms the interaction effects of review characteristics and users’ social network centrality on idea popularity.

This study’s results highlight that users should actively interact and share with reviewers their professional product design knowledge and/or the journey of their design to improve the volume of reviews on their user designs. Moreover, users could also draw more attention from other users by actively responding to heterogeneous reviews. In addition, users should be cautious with the number of people they follow and ensure that they improve their in-degree rather than out-degree centrality in their social networks.

This study integrates social network analysis and persuasion theories to explore the effects of online reviews and users’ centrality on idea popularity in OICs, a vital research issue that has been overlooked.

In present networked society, almost all the entities are present on Google Maps. Google Maps has become the first preference for the users for searching and retrieving information about any place. Google Maps provides an opportunity for a user to post ratings and reviews for an entity, including public libraries about their facilities and services, etc. This study explores how users of public libraries in Delhi (India) use Google Maps to share their opinions on library facilities and services and to assess whether Google Maps have the potential to act as an interactive feedback system between users and the authorities.

The Google Maps platform was used as a data source. To classify reviews into distinct categories, a content analysis was performed. Further Sentiment analysis of reviews was executed to highlight the positive, negative and neutral aspects of selected libraries. Moreover, reviews, endorsements and ratings of library products and services were also checked.

The research reveals that the culture of posting online reviews is increasing day by day and most of the reviews are posted in the English language, with Non Local Guides as the most prolific reviewers. In the reviews, the total number of topics discussed were 685, of which (70.51%) contained positive sentiments, (15.32%) were negative and (14.1%) were neutral reviews. The majority of the negative topics were discussed among the staff category, followed by facilities. Environment and children's areas received positive feedback with five-star ratings and most of them were marked helpful by other users. Users also loved technology, location and collection. The study concluded that Google Maps can be used as a new interactive feedback tool that library authorities can use to connect with users, replacing traditional suggestion boxes and online forms.

The research is limited to a single platform (Google Maps) and the capital city of India (Delhi) and cannot be generalized across platforms and regions. Moreover, such libraries were taken under the scanner of this study which possessed more than 50 reviews.

The study will help to identify the shortcomings of the Delhi public libraries through Google Maps platform and help them to devise effective strategies to improve their management in view of suggestions and complaints.

The purpose of this paper is to investigate the sales impact of different types of online word-of-mouth based on their source (user vs critic) and form (structured vs unstructured).

The paper proposed a model by adopting the heuristic-systematic perspective of information processing and tested it using online movie reviews collected from Rotten Tomatoes. A unique dataset was constructed, which matched critic reviews and user reviews with metadata such as box-office sales and advertisement spending for 90 movies. Sentiment information from the textual contents of both user and critic reviews were text-mined and extracted. Data analyses were used to compare the box-office responsiveness of four types of reviews: user numeric ratings, user text reviews, critic numeric ratings and critic text reviews.

Critic reviews and user reviews influence sales through different forms: while user reviews impact sales through their aggregate numeric ratings, critic reviews exert their impact through textual narratives.

This study provides managerial implications to businesses on how to allocate their resources on different social media-related marketing strategies to maximize the economic value of online user-generated information.

The major contribution of this study is to extend the current understanding of the sales impact of online reviews to their textual aspect, as well as investigate how these textual narratives play different roles when offered by critics and users.

The reviews submitted by users are the foundation of user-generated content (UGC) platforms. However, the rapid growth of users brings the problems of information overload and spotty content, which makes it necessary for UGC platforms to screen out reviews that are really helpful to users. The authors put forward in this paper the factors influencing review helpfulness voting from the perspective of review characteristics and reviewer characteristics.

This study uses 8,953 reviews from 20 movies listed on Douban.com with variables focusing on review characteristics and reviewer characteristics that affect review helpfulness. To verify the six hypotheses proposed in the study, Stata 14 was used to perform tobit regression.

Findings show that review helpfulness is significantly influenced by the length, valence, timeliness and deviation rating of the reviews. The results also underlie that a review submitted by a reviewer who has more followers and experience is more affected by review characteristics.

Previous literature has discussed the factors that affect the helpfulness of reviews; however, the authors have established a new model that explores more comprehensive review characteristics and the moderating effect reviewer characteristics have on helpfulness. In this empirical research, the authors selected a UGC community in China as the research object. The UGC community may encourage users to write more helpful reviews by highlighting the characteristics of users. Users in return can use this to establish his/her image in the community. Future research can explore more variables related to users.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2020-0186.

This paper aims to investigate the characteristics and differences in sentiment expression in movie review documents from four online opinion genres – blog postings, discussion board threads, user reviews, and critic reviews.

A collection of movie review documents was harvested from the four types of web sources, and a sample of 520 movie reviews were analysed to compare the content and textual characteristics across the four genres. The analysis focused on document and sentence length, part‐of‐speech distribution, vocabulary, aspects of movies discussed, star ratings used and multimedia content in the reviews. The study also identified frequently occurring positive and negative terms in the different genres, as well as the pattern of responses in discussion threads.

Critic reviews and blog postings are longer than user reviews and discussion threads, and contain longer sentences. Critic reviews and blogs contain more nouns and prepositions, whereas discussion board and user reviews have more verbs and adverbs. Critic reviews have the largest vocabulary and also the highest proportion of unique terms not found in the other genres. The most informative sentiment words in each genre are provided in the paper. With regard to content, critic reviews are more comprehensive in coverage, and discuss the movie director much more often than the other genres. User reviews discuss the scene aspects (including action and visual effects) more often than the other genres, while blogs tend to talk about the cast, and discuss the music and sound slightly more often.

The study only analysed movie review documents. Similar content and text analysis studies can be carried out in other domains, such as commercial product reviews, celebrity reviews, company reviews and political opinions to compare the results.

The main contribution of the study is the sentiment content analysis results across genres, which show the similarities and differences in content and textual characteristics in the four online opinion genres. The insights will be useful in designing automatic sentiment summarisation methods for multiple online genres.

The purpose of this research is to examine why and when restaurant consumers use and contribute user-generated reviews. This research is needed to determine the relevance of user-generated restaurant reviews in the current marketplace.

The research methodology is based on a quantitative approach, and focused on current Yelp.com users as its population. Questions focused on the amount of usage, motives for usage, level of trust, users’ tendencies to seek novelty in restaurants and motives for contribution.

Users tend to trust the reviews on Yelp.com and engage in the community aspects of the platform. Yelp.com users also are altruistic in their motivation for contributing reviews to Yelp.com. Yelp.com users who access it tend to act on the information found within the reviews.

Research articles have focused on user-generated reviews in the past; however, few have examined motivations of using and posting restaurant reviews. The value of conducting research comes from being able to understand the importance of user-generated restaurant reviews for customers in a comprehensive manner.

Online ratings and reviews have recently emerged as mechanisms to facilitate accountability and transparency in the provision of goods and services. The purpose of this paper is to examine the nature and outcome of the accountability that online ratings and reviews create in the sharing economy.

The study draws on 30 face-to-face and Skype interviews with Airbnb guests and hosts as well as on secondary materials, including content from Airbnb data analytic reports.

The authors demonstrate that face-saving practices widely condition user ratings and comments. Face saving occurs when individuals attempt to preserve their own identity and the identity of others during a social interaction. At Airbnb, the authors find that reviewers adopt three distinct face-saving strategies: the use of private reviewing channels, the creation of tactful reviews and refraining from reviewing entirely. The authors also find that users are sceptical of rating metrics and public comments and draw upon a wide range of alternative sources, such as private messaging and other publicly available resources, in their decision making.

This paper highlights the overwhelmingly positive character of Airbnb ratings and reviews. It proposes the concept of crowdbased accountability as a limited, partial form of assurance for sharing economy users. Guests and hosts alike prioritise face-saving practices over reviewer responsibilities to provide authentic, reliable accounts to the public. Consequently, reviewers effectively remove the risk of sanctions for those in the network who underperform.

Recommender systems collect information about users and businesses and how they are related. Such relation is given in terms of reviews and votes on reviews. User reviews gather opinions, rating scores and review influence. The latter component is crucial for determining which users are more relevant in a recommender system, that is, the users whose reviews are more popular than the average user’s reviews.

A model of measure of user influence is proposed based on review and social attributes of the user. User influence is also used for determining how influenced has been a business being based on popular reviews.

Results indicate there is a connection between social attributes and user influence. Such results are relevant for marketing, credibility estimation and Sybil detections, among others.

The proposed model allows search parameterization based on the social attribute weights of users, reviews and businesses. Such weights defines the relevance on each attribute, which can be adjusted according to the search needs. Popularity results are then a function of weight preferences on user, reviews and businesses data join.

The purpose of this paper is to present an analysis of Goodreads’ user-generated book reviews from a linguistic perspective for insights into the psychological aspects of reviewers’ perceptions and behaviors. This examination of users’ language and perspectives may shed light on the role and value of user-generated reviews in complementing the traditional representation of resources and facilitating the discoverability of cultural objects.

This study involved a textual analysis of 474,803 unique reviews of Goodreads’ 2015 top-rated books generated by 9,335 Goodreads’ reviewers. In order to better understand the nuances of user-generated reviews, a content analysis was applied to 2,500 reviews of each of the five top-ranked titles in Goodreads’ Fiction Literature genre category.

The analysis of user-generated reviews demonstrates that language is a quite stable and reliable dimension across Goodreads’ users. The high rate of function words utilized, in particular I-words, coupled with positive emotion words, suggests that reviewers tended to convey their opinions in order to influence other individuals’ reading choices, or in Bourdieu’s (1985) terms, influence cultural production. In line with previous studies of user-generated reviews, the prevalence of positive reviews may also imply their unreliable nature. This study supports the importance of transparency regarding inclusion of user-generated reviews in traditional systems of knowledge representation, organization and discovery, such as WorldCat.

This study contributes to better understanding of linguistic characteristics of Goodreads’ reviews, including the role and value of user-generated reviews in complementing traditional representation of resources and facilitating discoverability of cultural objects.