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Enterprise social networks (ESN) are increasingly used by companies to reinforce collaboration and knowledge sharing. While prior research has investigated ESN use practices, little is known about potential user roles emerging on these platforms. Against this backdrop, this paper develops an ESN knowledge actor role framework.

The framework is constructed based on a systematic review of literature covering knowledge worker role typologies, user roles identified in public online social spaces as well as findings from ESN research.

The ESN knowledge actor role framework distinguishes eight contributing and two reading roles. It describes the associated participation behaviours and establishes metrics to identify the roles.

Providing a notion of knowledge management-related roles in ESN, the framework enables a better understanding of knowledge processes and the involved actors. Moreover, the paper advances the field of ESN data analytics by designing a set of ESN metrics to characterise user behaviour.

Understanding ESN user roles, in particular regarding their knowledge contributions to the platform, can improve knowledge transparency in companies. The framework may usefully support the identification and management of critical knowledge resources and support decision-making in the areas of human resources management and knowledge management.

Providing a platform for knowledge-intensive interactions as well as record of user activities, ESN are well suited to observe and identify knowledge actor roles emerging in this context.

Continually evolving market structures characterized by high competition and simultaneous coopetition raise the need for the awareness of strategic actions of industry stakeholders and therefore the field of competitive intelligence (CI). Hence, this paper aims to provide a case-based method to enrich and automate the entire CI cycle using open-source data to analyze the industry environment.

The research method is based on design science research and accompanied by a three-year continuous in-depth case study in the automotive supply industry using unstructured, qualitative data to examine the activities of 25 industry stakeholders.

This paper provides a new holistic method for gaining valuable insights for decision-makers presented through a multiple-layer perspective of the industry development including structural transformation and strategic alignments in functional and cross-sectional areas of the stakeholders.

The development of a holistic approach using open-source data combines the knowledge-based view and industry economics and allows easy transferability to any other industry.

The proposed method shows an increase in knowledge for managers to support daily work as well as strategic decision-making. Furthermore, it is proven that even unexperienced CI analysts are empowered to deliver high-quality results.

The paper contributes to the literature and practice of CI by using an approach, which uses unstructured, qualitative data to enrich the entire CI cycle in a business-to-business environment. The case description confirms performance and time improvements of the method and shows the potential of the created insights.

Increasingly, dynamic market environments lead to growing complexity in manufacturing and pose a severe threat for the competitiveness of manufacturing companies. Systematic guidance to manage this complexity, especially in the context of Industry 4.0 and the therewith rising trends such as digitalization and data-driven production optimization, is lacking. To address this deficit a case-based reasoning (CBR) system for providing knowledge about managing complexity in Industry 4.0 is presented.

First, the explicit knowledge representation for managing complexity in IT-based manufacturing is introduced. Second, the CBR process step to retrieve knowledge from an artificially composed case base with in total 70 cases of data-based complexity management in the context of Industry 4.0 is set out. Third, knowledge transfer alongside several maturity levels of information technology capabilities of manufacturing systems for reuse in new problem scenarios is introduced.

The paper comprises the conceptual approach for designing a CBR system to support data-based complexity management in manufacturing systems. Furthermore, the appropriateness of the CBR system to provide applicable knowledge for reducing and managing complexity in corporate practice is shown.

The presented research results are evaluated in the course of an embedded single case study and may therefore lack generalizability. Future research to test and enhance the appropriateness of the developed CBR system will strengthen the research contribution.

The paper provides a novel approach to systematically support knowledge transfer for data-based complexity management by transferring the well-known and established methodology of CBR to the rising application domain of manufacturing systems in the context of Industry 4.0.

The paper's aim is to mine and analyze opinion formation on the basis of consumer dialogs in online forums.

The study identifies opinions, communication relationships, and dialog acts of forum users using different text mining methods. Utilizing this data, social networks can be derived and analyzed to detect influential users and opinion tendencies. The approach is applied to sample online forums discussing the iPhone.

Combining text mining and social network analysis enables the study of opinion formation and yields encouraging results. Out of the four methods employed for text mining, support vector machines performed best.

The data set applied here is fairly small. More threads on different products will be considered in future work to improve validation.

The approach represents a valuable instrument for online market research. It enables companies to recognize opportunities and risks and to initiate appropriate marketing actions.

This work is one of the first studies that combine communication content, relationships and dialog acts for analyzing opinion formation.