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.

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.

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This study uses statistical change-point analysis to investigate the impact of the COVID-19 pandemic on people's mobility in tourism cities. Based on the collected data sample containing mobility time series of nine tourism cities on three categories of places -Retail and Recreation, Parks and Transit Stations, we find apart from the mobility reduction observed on all place categories, most cities experienced a threephase pattern. Moreover, a time lag between the mobility decrease and introduction of lockdown measures is detected, suggesting that the latter is not the reason for people to reduce movement. Further, the mobility reduction is found less significant on Parks and appeared earlier on Transit Stations. The findings provide useful insights on how tourism, hospitality and travel sectors are affected by crisis events.