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Based on the perspectives of social capital, image motivation and motivation affordances, this paper explores the direct and moderation effects of different kinds of motivations (i.e. relationship-based motivation, community-based motivation and individual-based motivation) on users' continuous knowledge contributions in social question and answer (Q&A) communities.

The authors collect the panel data of 10,193 users from a popular social Q&A community in China. Then, a negative binomial regression model is adopted to analyze the collected data.

The paper demonstrates that social learning, peer recognition and knowledge seeking positively affect users' continuous contribution behaviors. However, the results also show that social exposure has the opposite effect. In addition, self-presentation is found to moderate the influence of social factors on users' continuous use behaviors, while the moderation effect of motivation affordances has no significance.

First, this study develops a comprehensive motivation framework that helps gain deeper insights into the underlying mechanism of knowledge contribution in social Q&A communities. Second, this study conducts panel data analysis to capture the impacts of motivations over time, rather than intentions at a fixed time point. Third, the findings can help operators of social Q&A communities to optimize community norms and incentive mechanisms.

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.

The purpose of this paper is to study the ablative behavior of the silicone resin-coated carbon fabric (coated fabric) that will swell significantly during ablation.

The ablation experiments of three coated fabrics were conducted by quartz lamp radiant. Based on the experimental analysis, a numerical model was proposed for the coated fabrics to study the ablative process in term of the energy balance, mass conservation and thermal decomposition equations.

Results showed that the average relative errors between the simulated temperatures and experimental values of back surfaces of coated fabric 1, 2 and 3 were 10.01, 7.53 and 7.32%, respectively. The average density of silicone resin of coated fabric 1 was reduced by 47.96%, and the closer the distance from the heated surface was, the more the density decreased. The thermal conductivity and specific heat capacity of silicone resin of coated fabric 1 increased with time. Before 50 s, each decomposition rate curve showed an inflection point, at which the silicone resin decomposed most intensely.

Based on experimental observations, the ablative behavior of the material with fixed expansion layer was simulated. In the further research, the moving expansion layer could be considered.

This paper provides the theoretical basis to evaluate the effectiveness of thermal protection materials that will swell during ablation.

In crises such as the coronavirus disease 2019 (COVID-19) pandemic, governments need to act in time to lead citizens toward rational reactions and disclose information effectively to the public. This paper aims to understand the content published by the government and identify how citizen engagement relates to content type and emotional valence.

The grounded theory approach was adopted and nine types of content posted by the government were observed. The data were obtained from “People's Daily”, an official Sina Weibo account representing the voice of the Chinese government, from January 3 to June 22 in 2020.

The analysis shows that information related to emotional support and social mobilization were the most reposted, while those mentioning immoral and illegal incidents were the most liked and commented. Also, it was found that positive posts tend to attract more likes, yet with fewer reposts than neutral posts.

The authors adopted thematic analysis and focused on the impact of post content and valence on user participation behavior. This study expands the existing literature. The government can improve crises management capability by learning about citizen engagement behaviors on social media.

The purpose of this paper is to investigate the effects of structural parameters of fabric on thermal insulation properties of the coated fabric.

The authors established a numerical model for the ablation of silicone resin-coated fabric under high heat flow, and the simulation results have been validated by quartz lamp ablation experiment. The model was used to investigate the effects of structural parameters of glass fiber fabrics on the heat transfer process of the coated fabric.

The numerical values were in agreement with the experimental values. The thermal insulation of the coated glass fiber fabric was better than coated carbon fabric. Thermal insulation performance of the coated glass fiber fabrics was in order plain < 2/1 twill < 3/3 twill < 5/3 stain fabric. Increasing the warp density, from 100 to 180 ends/10 cm, the temperature of the back surface of the coated glass fiber fabric was reduced from 601°C to 553°C. Thermal insulation performance dramatically increased as yarn fineness went from 129 to 280 tex, and the temperature difference was 63°C.

In the ablation process, to simplify the calculation, the combustion reaction of silicone resin was ignored, which can be added in the future research.

This paper provides the ablation model of the silicon-coated fabric based on the 3D geometry model to explore the influence of the structural parameters of coated glass fiber fabric on its thermal protection performance.

The integration of e-commerce platforms and artificial intelligence (AI) into the marketing channel ecosystem is challenging the explanatory capacity of traditional channel power theories, indicating a significant yet unaddressed research gap concerning the impact of these digital entities and AI on channel power exercise dynamics. This study adopts a contingency perspective to critically revisit how e-commerce platforms exercise channel power and the ensuing effects on channel conflicts. The purpose of this study is to extend the boundaries of traditional channel power theories, enhancing their relevance in today’s digital marketplace.

Building on channel power theories, the authors developed a framework tested with survey data collected from 262 sellers. This framework incorporates three key contingent variables: inter-platform competition, AI capabilities and platform value co-creation. Regression analysis was used to perform the analyses.

This study finds that intense inter-platform competition mitigates the (positive) negative relationship between platform channel power and the exercise of (non-) coercive power. Moreover, a platform’s AI capabilities and value co-creation activities diminish the potential for channel conflicts induced by the exercise of coercive power. AI capabilities can also strengthen the negative relationship between the exercise of non-coercive power and channel conflicts.

This study contributes to the advancement of traditional channel power theories by integrating contemporary digital elements like AI and platform dynamics. This study provides theoretical and practical insights on navigating channel power in modern marketing environments, offering strategic guidelines for optimizing channel relationships.

The aim of this study is to determine the impact of different dimensions of consumer engagement in social media on firm performance in social media.

Beyond likes and comments, combined with the interactive behavior of consumer engagement with firms on social media, we proposed a set of metrics for consumer engagement on social media, namely personal engagement, user-interactive engagement, and fan-interactive engagement. We used the fixed-effect model and validated the impact of consumer engagement in different dimensions on firm performance.

The result shows that personal engagement and user-interactive engagement significantly correlate with firm performance; however, the same effect was not observed in fan-interactive engagement.

Prior researches on consumer engagement in social media are mainly about consumer engagement in firm-initiated social media page. In this study, we consider the consumer engagement behavior in firm-initiated and consumer-initiated social media pages, then validate the impact of different dimensions of consumer engagement on firm performance.

Using Chinese A-share listed firms between 2007 and 2020 with 21,380 observations, we aim to examine the impact of cross-ownership on firms’ innovation output and explore the underlying mechanisms.

To test the influence of cross-ownership on firms’ innovation output, this paper constructs an ordinary least square regression model. The explained variables are firms’ innovation output, including the total number of patent applications (Apply) and the number of invention patent applications (Apply_I). Considering the long period of patent R&D, we take the value of the explained variables in the following year for regression. Cross-ownership (Cross) is the explanatory variable; Control is the control variable; and ε is the regression residual term.

We find that cross-ownership significantly promotes corporate innovation output, indicating that cross-owners play an important role in “collaborative governance.” This finding remains unchanged after conducting a series of robustness tests. We also find that cross-ownership contributes to innovation output mainly through two plausible channels: the relaxation of financing constraints and reducing both types of agency costs. Further analysis shows that cross-ownership has a more pronounced influence on innovation output in those firms with higher equity restriction ratios and facing more competitive markets. Moreover, cross-ownership has a profound impact on firms’ innovation quality and innovation efficiency, thereby increasing firm value.

This study provides important policy implications. First, cross-owners should actively play their resource and supervision advantages to improve firms’ long-term development capability through the “collaborative governance” effect. Second, listed companies in China should be fully aware of the value of the cross-ownership and use the cross-ownership as a bridge to strengthen the cooperative relationship with firms in the same portfolio. Meanwhile, they need to pay attention to cross-ownership’s “collaborative governance” effect to provide an impetus for the healthy development of enterprises. Finally, government regulators should maintain appropriate supervision of the cross-ownership linkage in the market.

Our findings show that cross-ownership significantly contributes to firms’ innovation output, indicating that cross-owners play the role of “collaborative governance.” While paying attention to the collusion effect of the cross-ownership, they shall not ignore its governance effect, for example, the promotion effect on the innovation level. Government regulators should appropriately supervise the cross-ownership linkage, which is conducive to maintaining the market order and driving the healthy development of the capital market.

In the context of carbon peaking and neutrality, effectively controlling agricultural carbon emissions has gained academic attention. As an essential form of agricultural service scale management, this study investigates whether and how trusteeship affects the carbon emission behavior in planting production.

The authors established a theoretical framework to analyze the impact of agricultural production trusteeship on carbon emissions from planting. China's provincial panel data in the 2012–2021 period were selected to test the impact, mechanisms and heterogeneity of agricultural production trusteeship on carbon emissions from planting using the bidirectional fixed effect model and the panel correction standard error regression model.

The findings indicate that agricultural production trusteeship significantly inhibits carbon emissions from planting, especially in the dimensions of fertilizer input, pesticide application, agricultural film use and mechanical fuel. Agricultural production trusteeship primarily affects the intensity of these carbon emissions through contiguous farmland management and planting structure adjustment. Further examinations revealed that the influence of agricultural production trusteeship on carbon emissions from planting was heterogeneous with respect to geographical location, proportion of non-farming income and scale of agricultural production.

This study is the first to systematically evaluate the impact of agricultural production trusteeship on carbon emissions from planting.

A new direct blind equalization algorithm is presented in this paper. It relies on the auto‐correlation matrix of the channel, and adopts its eigenvector decomposition for designing an equalizer, which exploits the correlation characteristics of the channel. The proposed equalization is insensitive to the inaccuracy of the channel order estimation, and its data sequence length required for estimating the channel's correlation matrix is shorter than that of the other eigenvector decomposition methods that assumed that the channel order was known or had been estimated. Simulation results show that this algorithm is capable of acquiring a good performance under lower SNR in a communication system employing 16‐QAM modulation.