Management Research on Reciprocity: A Review of the Literature

Although reciprocity is fundamental to all social orders, management research offers few reviews of the concept’s theoretical origins and current applications. To help bridge this gap, we elucidate the dominant understandings of reciprocity, ask which areas of research emerge from them, and explore how they interconnect. Our bibliometric methodology detects four clusters of management research on reciprocity. Across these clusters, authors subscribe mainly to substantialist ontology, marginalize morally oriented motives consistent with relational ontology, and largely assume that benefit-oriented motives underlie reciprocity. We outline the advantages of a moral-oriented relationalist concept of reciprocity and discuss potential areas for its development in management research.

A Faceted Classification Scheme for Computer-Mediated Discourse

This article describes a classification scheme for computer-mediated discourse that classifies samples in terms of clusters of features, or “facets”. The goal of the scheme is to synthesize and articulate aspects of technical and social context that influence discourse usage in CMC environments. The classification scheme is motivated, presented in detail with support from existing literature, and illustrated through a comparison of two types of weblog (blog) data. In concluding, the advantages and limitations of the scheme are weighed.

How the Entrepreneurial Top Management Team Setup Influences Firm Performance and the Ability to Raise Capital: A Literature Review

This paper reviews research findings on entrepreneurial top management teams within the last 20 years. It concentrates on team-based management factors and their influence on a new venture’s growth and ability to raise capital. This paper integrates recent findings and provides an overview of the current state of research. Moreover, it contributes to the overall topic by proposing five clusters of major team-specific influences, derives determinants of success and failure, and reveals recommendations for further research.

Bitmap Movement Detection: HDR for Dynamic Scenes

Exposure Fusion and other HDR techniques generate well-exposed images from a bracketed image sequence while reproducing a large dynamic range that far exceeds the dynamic range of a single exposure. Common to all these techniques is the problem that the smallest movements in the captured images generate artefacts (ghosting) that dramatically affect the quality of the final images. This limits the use of HDR and Exposure Fusion techniques because common scenes of interest are usually dynamic. We present a method that adapts Exposure Fusion, as well as standard HDR techniques, to allow for dynamic scene without introducing artefacts. Our method detects clusters of moving pixels within a bracketed exposure sequence with simple binary operations. We show that the proposed technique is able to deal with a large amount of movement in the scene and different movement configurations. The result is a ghost-free and highly detailed exposure fused image at a low computational cost.