State control and corporate governance in transition economies:25 years on from 1989

Research Question/Issue - Which forms of state control over corporations have emerged in countries that made a transition from centrally-planned to marked-based economies and what are their implications for corporate governance? We assess the literature on variation and evolution of state control in transition economies, focusing on corporate governance of state-controlled firms. We highlight emerging trends and identify future research avenues. Research Findings/Insights - Based on our analysis of more than 100 articles in leading management, finance, and economics journals since 1989, we demonstrate how research on state control evolved from a polarized approach of public–private equity ownership comparison to studying a variety of constellations of state capitalism. Theoretical/Academic Implications - We identify theoretical perspectives that help us better understand benefits and costs associated with various forms of state control over firms. We encourage future studies to examine how context-specific factors determine the effect of state control on corporate governance. Practitioner/Policy Implications - Investors and policymakers should consider under which conditions investing in state-affiliated firms generates superior returns.

On stopwords, filtering and data sparsity for sentiment analysis of Twitter

Sentiment classification over Twitter is usually affected by the noisy nature (abbreviations, irregular forms) of tweets data. A popular procedure to reduce the noise of textual data is to remove stopwords by using pre-compiled stopword lists or more sophisticated methods for dynamic stopword identification. However, the effectiveness of removing stopwords in the context of Twitter sentiment classification has been debated in the last few years. In this paper we investigate whether removing stopwords helps or hampers the effectiveness of Twitter sentiment classification methods. To this end, we apply six different stopword identification methods to Twitter data from six different datasets and observe how removing stopwords affects two well-known supervised sentiment classification methods. We assess the impact of removing stopwords by observing fluctuations on the level of data sparsity, the size of the classifier's feature space and its classification performance. Our results show that using pre-compiled lists of stopwords negatively impacts the performance of Twitter sentiment classification approaches. On the other hand, the dynamic generation of stopword lists, by removing those infrequent terms appearing only once in the corpus, appears to be the optimal method to maintaining a high classification performance while reducing the data sparsity and substantially shrinking the feature space

Firms’ innovation objectives and knowledge acquisition strategies:a comparative analysis

External partnerships play an important role in firms’ acquisition of the knowledge inputs to innovation. Such partnerships may be interactive – involving exploration and mutual learning by both parties – or non-interactive – involving exploitative activity and learning by only one party. Examples of non-interactive partnerships are copying or imitation. Here, we consider how firms’ innovation objectives influence their choice of interactive and/or non-interactive connections. We conduct a comparative analysis for the economies of Spain and the UK, which have contrasting innovation eco-systems and regulation burdens.