Entrepreneurial innovation problems associated with the dynamic growth of university spin-outs in China: A capabilities perspective

This research aims to develop a capabilities-based conceptual framework in order to study the stage-specific innovation problems associated with the dynamic growth process of university spin-outs (hereafter referred to as USOs) in China. Based on the existing literature, pilot cases and five critical cases, this study attempts to explore the interconnections between the entrepreneurial innovation problems and the configuration of innovative capabilities (that acquire, mobilise and re-configure the key resources) throughout the lifecycle of a firm in four growth phases. This paper aims to contribute to the literature in a holistic manner by providing a theoretical discussion of USOs' development through adding evidence from a rapid growth emerging economy. To date, studies that have investigated the development of USOs in China recognised the heterogeneity of USOs in terms of capabilities still remain sparse. Addressing this research gap will be of great interest to entrepreneurs, policy makers and venture investors. © Copyright 2010 Inderscience Enterprises Ltd.

Quality practices and problems in free software projects

Free software and open source projects are often perceived to be of high quality. It has been suggested that the high level of quality found in some free software projects is related to the open development model which promotes peer review. While the quality of some free software projects is comparable to, if not better than, that of closed source software, not all free software projects are successful and of high quality. Even mature and successful projects face quality problems; some of these are related to the unique characteristics of free software and open source as a distributed development model led primarily by volunteers. In exploratory interviews performed with free software and open source developers, several common quality practices as well as actual quality problems have been identified. The results of these interviews are presented in this paper in order to take stock of the current status of quality in free software projects and to act as a starting point for the implementation of quality process improvement strategies.

Semi-implicit second order finite volume jet stream computations

Semi-implicit, second order temporal and spatial finite volume computations of the flow in a differentially heated rotating annulus are presented. For the regime considered, three cyclones and anticyclones separated by a relatively fast moving jet of fluid or "jet stream" are predicted. Two second order methods are compared with, first order spatial predictions, and experimental measurements. Velocity vector plots are used to illustrate the predicted flow structure. Computations made using second order central differences are shown to agree best with experimental measurements, and to be stable for integrations over long time periods (> 1000s). No periodic smoothing is required to prevent divergence.

Classifying data quality problems in asset management

© Springer International Publishing Switzerland 2015. Making sound asset management decisions, such as whether to replace or maintain an ageing underground water pipe, are critical to ensure that organisations maximise the performance of their assets. These decisions are only as good as the data that supports them, and hence many asset management organisations are in desperate need to improve the quality of their data. This chapter reviews the key academic research on data quality (DQ) and Information Quality (IQ) (used interchangeably in this chapter) in asset management, combines this with the current DQ problems faced by asset management organisations in various business sectors, and presents a classification of the most important DQ problems that need to be tackled by asset management organisations. In this research, eleven semi structured interviews were carried out with asset management professionals in a range of business sectors in the UK. The problems described in the academic literature were cross checked against the problems found in industry. In order to support asset management professionals in solving these problems, we categorised them into seven different DQ dimensions, used in the academic literature, so that it is clear how these problems fit within the standard frameworks for assessing and improving data quality. Asset management professionals can therefore now use these frameworks to underpin their DQ improvement initiatives while focussing on the most critical DQ problems.