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1 – 3 of 3Jiangnan Qiu, Wenjing Gu, Zhongming Ma, Yue You, Chengjie Cai and Meihui Zhang
In the extant research on online knowledge communities (OKCs), little attention has been paid to the influence of membership fluidity on the coevolution of the social and…
Abstract
Purpose
In the extant research on online knowledge communities (OKCs), little attention has been paid to the influence of membership fluidity on the coevolution of the social and knowledge systems. This article aims to fill this gap.
Design/methodology/approach
Based on the attraction-selection-attrition (ASA) framework, this paper constructs a simulation model to study the coevolution of these two systems under different levels of membership fluidity.
Findings
By analyzing the evolution of these systems with the vector autoregression (VAR) method, we find that social and knowledge systems become more orderly as the coevolution progresses. Furthermore, in communities with low membership fluidity, the microlevel of the social system (i.e. users) drives the coevolution, whereas in communities with high membership fluidity, the microlevel of the knowledge system (i.e. users' views) drives the coevolution.
Originality/value
This paper extends the application of the ASA framework and enriches the literature on membership fluidity of online communities and the literature on driving factors for coevolution of the social and knowledge systems in OKCs. On a practical level, our work suggests that community administrators should adopt different strategies for different membership fluidity to efficiently promote the coevolution of the social and knowledge systems in OKCs.
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Sampson Asiamah, Kingsely Opoku Appiah and Ebenezer Agyemang Badu
The purpose of this paper is to examine whether board characteristics moderate the relationship between capital adequacy regulation and bank risk-taking of universal banks in…
Abstract
Purpose
The purpose of this paper is to examine whether board characteristics moderate the relationship between capital adequacy regulation and bank risk-taking of universal banks in Sub-Saharan Africa (SSA).
Design/methodology/approach
The paper uses 700 bank-year observations of universal banks in SSA between 2009 and 2019. The paper further uses the two-step generalized method of moments as the baseline estimator.
Findings
The paper finds that capital adequacy regulation is positively related to overall bank and liquidity risks. Nonetheless, capital adequacy regulation increases credit risk in the sampled banks. The paper further reports that board characteristics individually and significantly moderate the relationship between capital adequacy regulation and risk-taking.
Practical implications
The findings have implications for regulators of universal banks that board characteristics matter for capital adequacy regulation to impact risk-taking behavior.
Originality/value
The paper extends the existing literature on the effect of board characteristics on the capital adequacy regulations and risk-taking behavior nexus of universal banks.
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Elena Fedorova and Polina Iasakova
This paper aims to investigate the impact of climate change news on the dynamics of US stock indices.
Abstract
Purpose
This paper aims to investigate the impact of climate change news on the dynamics of US stock indices.
Design/methodology/approach
The empirical basis of the study was 3,209 news articles. Sentiment analysis was performed by a pre-trained bidirectional FinBERT neural network. Thematic modeling is based on the neural network, BERTopic.
Findings
The results show that news sentiment can influence the dynamics of stock indices. In addition, five main news topics (finance and politics natural disasters and consequences industrial sector and Innovations activism and culture coronavirus pandemic) were identified, which showed a significant impact on the financial market.
Originality/value
First, we extend the theoretical concepts. This study applies signaling theory and overreaction theory to the US stock market in the context of climate change. Second, in addition to the news sentiment, the impact of major news topics on US stock market returns is examined. Third, we examine the impact of sentimental and thematic news variables on US stock market indicators of economic sectors. Previous works reveal the impact of climate change news on specific sectors of the economy. This paper includes stock indices of the economic sectors most related to the topic of climate change. Fourth, the research methodology consists of modern algorithms. An advanced textual analysis method for sentiment classification is applied: a pre-trained bidirectional FinBERT neural network. Modern thematic modeling is carried out using a model based on the neural network, BERTopic. The most extensive topics are “finance and politics of climate change” and “natural disasters and consequences.”
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