The structure of Organizational Virtual Social Networks

Organizational virtual social networks (OVSN) reshape social structures due to their ability to strengthen social ties, to change power relations and to enable new forms of cooperation. Research in Information and Communication Technologies (ICT) has led to various approaches that analyze the impact of OVSN on organizations in terms of structure and behavior. Our study aims to analyze important features related to the structure of OVSN. It also aims to strengthen a network approach to analyze organizational phenomena such as working groups and connected individuals, as well as the impact of online networks in organizations. This study was based on the lines of approach described by Oinas-Kukkonen et al. (2010) and on the research carried out by Bobsin & Hoppen (2012) to understand the process of structuring OVSN. Our main results are an OVSN structure consisting of actors and roles, interactions, operating elements and articulating goals. We also analyzed some structural elements of networks which may contribute to the development of a network based approach to study organizational phenomena

Keep them alive! Design and Evaluation of the “Community Fostering Reference Model”

Firms host online communities for commercial purposes, for example in order to integrate customers into ideation for new product development. The success of these firm-hosted online communities depends entirely on the cooperation of a high number of customers that constantly produce valuable knowledge for firms. However, in practice, the majority of successfully implemented communities suffers from stagnation and even a decrease of member activities over time. Literature provides numerous guidelines on how to build and launch these online communities. While these models describe the initial steps of acquiring and activating a community base from scratch very well and explicitly, they neglect continuous member activation and acquistion after a successful launch. Against this background, the authors propose the Community Fostering Reference Model (CoFoRM), which represents a set of general procedures and instruments to continuously foster member activity. In this paper, the authors present the theory-driven design as well as the evaluation of the CoFoRM in a practical use setting. The evaluation results reveal that the CoFoRM represents a valuable instrument in the daily working routine of community managers, since it efficiently helps activating community members especially in the late phases of a community’s LifeCycle

Untangling knowledge creation and knowledge integration in enterprise wikis

A central challenge organizations face is how to build, store, and maintain knowledge over time. Enterprise wikis are community-based knowledge systems situated in an organizational context. These systems have the potential to play an important role in managing knowledge within organizations, but the motivating factors that drive individuals to contribute their knowledge to these systems is not very well understood. We theorize that enterprise wiki initiatives require two separate and distinct types of knowledge-sharing behaviors to succeed: knowledge creation (KC) and knowledge integration (KI). We examine a Wiki initiative at a major German bank to untangle the motivating factors behind KC and KI. Our results suggest KC and KI are indeed two distinct behaviors, reconcile inconsistent findings from past studies on the role of motivational factors for knowledge sharing to establish shared electronic knowledge resources in organizations, and identify factors that can be leveraged to tilt behaviors in favor of KC or KI

Interpretability of machine learning solutions in industrial decision engineering

The broad application of machine learning (ML) methods and algorithms in diverse range of organisational settings led to the adoption of legislation, like European Union’s General Data Protection Regulation, which require firm capabilities to explain algorithmic decisions. Currently in the ML literature there does not seem to be a consensus on the definition of interpretability of a ML solution. Moreover, there is no agreement about the necessary level of interpretability of such solution and on how this level can be determined, measured and achieved. In this article, we provide such definitions based on research as well as our extensive experience of building ML solutions for various organisations across industries. We present CRISP-ML, a detailed step-by-step methodology, that provides guidance on creating the necessary level of interpretability at each stage of the solution building process and is consistent with the best practices of project management in the ML settings. We illustrate the versatility and effortless applicability of CRISP-ML with examples across a variety of industries and types of ML projects