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With the rapid development of paid online social question and answer (Q&A) communities, monetary social functions have been introduced and have potential benefits for both platforms and users. However, these functions' impact on knowledge contribution remains uncertain. This study proposes a conceptual model based on the stimulus–organism–response framework, according to which monetary and non-monetary social functions can help nurture short-term and long-term relationships among community users, and thereafter improves social identity and knowledge-sharing intentions.

This study selects Zhihu, a famous online social Q&A community in China, and conducts an online survey to collect data from its frequent users. A sample of 286 valid questionnaires was collected to test our research model by using a structural equation modeling method. In addition, a bootstrapping approach is used to test the mediation effect.

Results indicate that monetary social functions help nurture short-term and long-term relationships among community users. However, non-monetary social functions only affect short-term relationships directly. Short-term and long-term relationships both have a positive relationship with social identity and thereafter improve users' knowledge-sharing intentions.

This study focuses on users' knowledge-sharing intentions in Q&A communities from the perspective of social. Specifically, we separated social functions in Q&A platforms into monetary and non-monetary ones and explored their impact on the development of short-term and long-term relationships. Results demonstrate the importance of monetary social functions and explain how monetary and non-monetary social functions affect users' knowledge-sharing intentions in different approaches.

Although studies have demonstrated that knowledge hiding is an important inhibitor of organizational innovation, current research does not clearly address how intragroup relationship conflict influences knowledge hiding. This study aims to identify the underlying mechanism between intra-group relationship conflict and knowledge hiding.

Drawing on affective events theory (AET), the authors propose a theoretical model and empirically test it by applying hierarchical regression analysis and a bootstrapping approach to data from a multi-wave survey of 224 employees in China.

Consistent with AET, the empirical results show that envy mediates perceived intragroup relationship conflict and knowledge hiding. As predicted, trait competitiveness moderates the indirect effect of perceived intragroup relationship conflict on knowledge hiding via envy.

The results support an AET perspective whereby knowledge hiding is shaped by relationship conflict, envy and trait competitiveness. This study introduces the novel proposition that relationship conflict and competitiveness influence envy, and consequently knowledge hiding.

This study aims to assess gender-based differences on farmers’ perception of impacts and vulnerability to climate change and the implementation of adaptation strategies in the Pra River Basin of Ghana, while also providing lessons for other Sub-Saharan nations and regions with similar conditions.

The study used semi-structured interviews and questionnaires to collect data from 344 farmers, 64 participants in focus group discussions and 6 agriculture extension officers (key informants) from 10 districts in the Pra River Basin of Ghana.

Results showed several differences in how climate change is perceived and tackled by male and female genders. In the perception of male farmers, for example, they were found to be more vulnerable to increased temperature, and changes in rainfall and growing season, whereas female farmers on average were considered to be less resilient to floods and droughts for different reasons. Moreover, floods posed higher risks to farming than other climate change impacts. Gender roles had a significant correlation with the type of adaptation strategies practised. Men adopted agrochemicals more often than women, as an adaptation strategy.

Gender-differentiated interventions should be incorporated in the national climate change action plan for sustainable development in a rain-fed agricultural economy such as Ghana. The study recommends several actions to promote gender equity in the assessed region.

This research assessed the gender differentials in climate trends, impact, vulnerability and adaptation based on primary data collected between April and May 2019 and compared the results with climate data in the basin for the period 1991–2014. It is an empirical study focused on primary data analysis obtained in loco by authors, involving approximately 400 participants.

This study aims to propose iStage, i.e. an intelligent hybrid deep learning (DL)-based framework to determine the stage of the disaster to make the right decisions at the right time.

iStage acquires data from the Twitter platform and identifies the social media message as pre, during, post-disaster or irrelevant. To demonstrate the effectiveness of iStage, it is applied on cyclonic and COVID-19 disasters. The considered disaster data sets are cyclone Fani, cyclone Titli, cyclone Amphan, cyclone Nisarga and COVID-19.

The experimental results demonstrate that the iStage outperforms Long Short-Term Memory Network and Convolutional Neural Network models. The proposed approach returns the best possible solution among existing research studies considering different evaluation metrics – accuracy, precision, recall, f-score, the area under receiver operating characteristic curve and the area under precision-recall curve.

iStage is built using the hybrid architecture of DL models. It is effective in decision-making. The research study helps coordinate disaster activities in a more targeted and timely manner.