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This study aims to predict the impact of utilitarian and hedonic motivations on using enterprise social network (ESN) systems for sharing internal and external knowledge, as well as their effects on employee productivity.

Responses of 115 ESN system users from a wide spectrum of firms have been validated and analysed by means of structural equation modelling using partial least squires (PLS-SEM) method.

The results indicate that using ESN to share internal and external knowledge has a positive significant impact on employee productivity. Surprisingly, empirical analysis reveals that using ESN tends to be significantly influenced by hedonic rather than utilitarian motivations.

The study is an early empirical attempt that examines using ESN for knowledge sharing, emphasizing its upstream motivational influence and downstream business impact. It also offers managers and ESN vendors a frame of reference to maximize the use of ESN in the workplace to boost employees’ productivity within various contexts.

This paper aims to investigate the motivational drivers for knowledge sharing (KS) through the application of mobile social networking (MSN) and their influence on employee productivity in the Malaysian construction industry.

The data was collected by distributing a self-administered questionnaire among employees working in the construction industry. A total of 250 questionnaires were collected and analysed using partial least squares structural equation modeling. This study used a variety of analysis methodologies including correlation analysis, reliability and validity testing, as well as a structural model in evaluating the relationships between KS, motivational factors and influence on productivity.

The empirical results of this study reveal that utilitarian motivation is positively related to internal and external KS. Similarly, internal and external KS have a positive influence on productivity, while internal and external hedonic motivation does not show any significant relationship with productivity.

Considering the paucity of evidence regarding the determinants of KS motivational drives on employee productivity in the Malaysian construction industry, this study enriches the social cognitive theory-based literature on KS.

This study provides some insights to the players in the construction industry in implementing the most appropriate KS promotion strategies that could potentially influence employee productivity. Organisations in the construction industry that place great emphasis on utilitarian motivation including competency, reward and incentives are more likely to have a better influence on KS and, in turn, positively impact employee productivity.

This study meaningfully contributes to enhancing the understanding of the motivational drivers for sharing knowledge on MSN and its influence on productivity. The findings of this study potentially assist construction practitioners in developing a holistic blueprint for managing their KS towards improving the overall productivity of their employees.

The purpose of this paper is to focus on the problem of named entity disambiguation. The paper disambiguates named entities on a very detailed level. To each entity is assigned a concrete identifier of a corresponding Wikipedia article describing the entity.

For such a fine‐grained disambiguation a correct representation of the context is crucial. The authors compare various context representations: bag of words representation, linguistic representation and structured co‐occurrence representation. Models for each representation are described and evaluated. They also investigate the possibilities of multilingual named entity disambiguation.

Based on this evaluation, the structured co‐occurrence representation provides the best disambiguation results. It showed up that this method could be successfully applied also on other languages, not only on English.

Despite its good results the structured co‐occurrence context representation has several limitations. It trades precision for recall, which might not be desirable in some use cases. Also it is not able to disambiguate two different types of entities, which are mentioned under the same name in the same text. These limitations can be overcome by combination with other described methods.

The authors provide a ready‐made web service, which can be directly plugged in existing applications using a REST interface.

The paper proposes a new approach to named entity disambiguation exploiting various context representation models (bag of words, linguistic and structural representation). The authors constructed a comprehensive dataset based on all English Wikipedia articles for named entity disambiguation. They evaluated and compared the individual context representation models on this dataset. They evaluate the support of multiple languages.