This study investigates user behaviours in online innovation communities which are enabled by digital technologies, to obtain an understanding of the relationship between user's social interaction and their innovation contribution. The new type of innovation communities enable firms to crowdsource ideas from their users for developing new products and improving existing ones, and to facilitate the interactions among users. From an empirical study which collects a large-scale, quantitative data set from Microsoft's Idea platform of Business Intelligent products, this paper focuses on the amount and diversity of users' social interaction particularly their commenting behaviours on the platform, and uses the number of posted ideas and the number of implemented ideas to capture users' contribution to the firm's innovation development. The findings indicate that the amount of user interaction is positively related to the number of implemented ideas, but has an inverted U-shaped relationship with idea number. Moreover, diverse user interaction encourages idea posting, but is negatively associated with the number of implemented ideas. The findings should provide managerial guidance to firms on incentivizing and managing user interaction in online communities in order to improve firms' innovation development.

With the growth and prevalence of social media platforms, many companies have been using them to engage with customers and encourage user-generated content about their products and services. In this paper, we analyze user-generated posts from the Facebook business pages of multiple companies across several industries to understand what users post on Facebook business pages and how post valence and content characteristics affect engagement, measured as the number of likes and comments received by a post. Our analysis demonstrates that negative posts are significantly more prevalent than positive posts, and negative posts also tend to attract more likes and more comments than positive posts. Importantly, engagement depends not only on the valence of a post but also on the specific post content. We observe three types of customer complaints respectively related to product and service quality, money issues, and corporate social responsibility issues. We show that social complaints receive more likes, but fewer comments, than quality or money complaints. Our findings reveal the practical challenges of managing Facebook business pages as a new channel of interacting with customers, and they highlight the need to explore effective response strategies to manage customer complaints and other service requests on social media.

Purpose This study aims to conduct a “real-time” investigation with user-generated content on Twitter to reveal industry challenges and business responses to the coronavirus (Covid-19) pandemic. Specifically, using the hospitality industry as an example, the study analyses how Covid-19 has impacted the industry, what are the challenges and how the industry has responded. Design/methodology/approach With 94,340 tweets collected between October 2019 and May 2020 by a programmed Web scraper, unsupervised machine learning approaches such as structural topic modelling are applied. Originality/value This study contributes to the literature on business response during crises providing for the first time a study of using unstructured content on social media for industry-level analysis in the hospitality context.

Cloud federation is an emergent Cloud-computing paradigm that allows services from different Cloud systems to be aggregated in a single pool. To support secure data-sharing in a Cloud federation, anonymisation services that obfuscate sensitive datasets under differential privacy have been recently proposed. However, by outsourcing data protection to the Cloud, data owners lose control over their data, raising privacy concerns. This is even more compelling in multi-query scenarios where maintaining privacy amounts to controlling the allocation of so-called privacy budget. In this paper we propose a blockchain-based approach that enables data owners to control the anonymisation process, and enhances the security of the services. Our approach relies on blockchain to validate the usage of privacy budget and adaptively change its allocation via smart contracts, depending on the privacy requirements provided by data owners. Prototype implementation with the Hyperledger permissioned blockchain validates our approach with respect to privacy guarantee and practicality. Cloud federation builds up interconnectivity and cooperation among already deployed clouds, enabling organisations to achieve various business goals, such as controlled sharing of data, services and optimisation of computing resources usage 1-3. To support secure sharing of federated data, anonymisation services have been proposed as a building component of Federation-as-a-Service (FaaS), 3-4 a recent Cloud federation solution. This component implements differential privacy in order to obfuscate the result of statistical queries towards sensitive datasets, 5 enabling its privacy-preserving sharing. Offering this service in the context of a Cloud federation has benefits-access to multiple data sources and different service providers-but raises significant challenges for privacy management: sensitive datasets

Abstract-Online social networks such as Facebook allow users to control which friend sees what information, but it can be a laborious process for users to specify every receiver for each piece of information they share. Therefore, users usually group their friends into social circles, and select the most appropriate social circle to share particular information with. However, social circles are not formed for setting privacy policies, and even the most appropriate social circle still cannot adapt to the changes of users' privacy requirements influenced by the changes in context. This problem drives the need for better privacy control which can adaptively filter the members in a selected social circle to satisfy users' requirements while maintaining users' social needs. To enable such adaptive sharing, this paper proposes a utility-based trade-off framework that models users' concerns (i.e. potential privacy risks) and incentives of sharing (i.e. potential social benefits), and quantifies users' requirements as a tradeoff between these two types of utilities. By balancing these two metrics, our framework suggests a subset of a selected circle that aims to maximise users' overall utility of sharing. Numerical simulation results compare the outcome of three sharing strategies in randomly changing contexts.