The Nexus of Corporate Entrepreneurship and Radical Innovation

This paper explores the linkage between the entrepreneurial orientation of established firms and the development of radical innovation. Through five case studies in firms involved in radical innovation, three propositions are developed, suggesting that proactiveness, risk‐taking and autonomy stimulate the development of radical innovation, whereas competitive aggressiveness does not necessarily do so, as radical innovations are directed towards the creation of entirely new arenas of business, where existing competitors are not present.     

A new method of characterization for stratified thermal energy stores

A new method for characterization of stratified thermal energy stores (TES) that integrates both the first law and the second law concerns is presented here. The first law concern is incorporated into a quantity called energy response factor and the second law concern into an entropy generation ratio. A product of these two quantities is at the heart of the TES efficiency definitions. This approach removes the overemphasis of the existing methods either on the first or the second law of thermodynamics which often biases the characterization results. The information about the evolution of the temperature field of the system in time is the prerequisite of the new method. It may be obtained from experiments or from suitable numerical simulations. The current method can be easily integrated into computational fluid dynamic (CFD) simulations and thus facilitate CFD-based design analysis. As an example of such CFD-integrated analysis, a large-scale hot water seasonal heat store is numerically studied to identify the effects of aspect ratio, containment shape, internal structures, and containment size on their efficiency. The results suggest the effectiveness of the new method in deriving useful design insights.     

Valuation of online social networks taking into account users’ interconnectedness

Online social networks have been gaining increasing economic importance in light of the rising number of their users. Numerous recent acquisitions priced at enormous amounts have illustrated this development and revealed the need for adequate business valuation models. The value of an online social network is largely determined by the value of its users, the relationships between these users, and the resulting network effects. Therefore, the interconnectedness of a user within the network has to be considered explicitly to get a reasonable estimate for its economic value. Established standard business valuation models, however, do not account for these aspects sufficiently. Thus, we propose an economic model for the valuation of online social networks, which takes into account the users’ interconnectedness within the network. Furthermore, we analyze different centrality measures, which can be used to quantify users’ interconnectedness in online social networks and propose a measure which is based on the PageRank-algorithm. Finally, the practical application of the model is illustrated by an example of the European online social network XING.com.     

The optimal level of CRM IT investments

In light of the growing relevance of customer-oriented business strategies IT investments in the field of Customer Relationship Management have increased considerably. However, firms often could not realize sufficient returns on these IT investments. One major reason for this failure seems to be the lack of appropriate approaches to determine the economic impact of such investments ex ante. Therefore, we develop an economic model to determine the optimal level of Customer Relationship Management IT investments. Using this approach, firms can evaluate, to what extent investments in Customer Relationship Management IT are reasonable. One major result is that in most cases the “all or nothing strategy” pursued by many firms does not lead to the optimal level of investments. To illustrate the practical utility and applicability of the approach, we provide a real world example of a German financial services provider.     

Interactive survival analysis with the OCDM system: From development to application

Medical data mining is currently actively pursued in computer science and statistical research but not in medical practice. The reasons therefore lie in the difficulties of handling and statistically analyzing medical data. We have developed a system that allows practitioners in the field to interactively analyze their data without assistance of statisticians or data mining experts. In the course of this paper we will introduce data mining of medical data and show how this can be achieved for survival data. We will demonstrate how to solve common problems of interactive survival analysis by presenting the Online Clinical Data Mining (OCDM) system. Thereby the main focus is on similarity based queries, a new method to select similar cases based on their covariables and the influence of these on their survival.     

Cytomechanics of axonal development

Mechanical tension is a robust regulator of axonal development of cultured neurons. We review work from our laboratory, using calibrated glass needles to measure or apply tension to chick sensory neurons, chick forebrain neurons, and rat PC12 cells. We survey direct evidence for two different regimes of tension effects on neurons, a fluid-like growth regime, and a nongrowth, elastic regime. Above a minimum tension threshold, we observe growth effects of tension regulating four phases of axonal development: 1. Initiation of process outgrowth from the cell body; 2. Growth cone-mediated elongation of the axon; 3. Elongation of the axon after synaptogenesis, which normally accommodates the skeletal growth of vertebrates; and 4. Axonal elimination by retraction. Significantly, the quantitative relationship between the force and the growth response is surprisingly similar to the simple relationship characteristic of Newtonian fluid mechanical elements: elongation rate is directly proportional to tension (above the threshold), and this robust linear relationship extends from physiological growth rates to far-above-physiological rates. Thus, tension apparently integrates the complex biochemistry of axonal elongation, including cytoskeletal and membrane dynamics, to produce a simple "force input/growth output" relationship. In addition to this fluid-like growth response, peripheral neurons show elastic behaviors at low tensions (below the threshold tension for growth), as do most cell types. Thus, neurites could exert small static forces without diminution for long periods. In addition, axons of peripheral neurons can actively generate modest tensions, presumably similar to muscle contraction, at tensions near zero. The elastic and force-generating capability of neural axons has recently been proposed to play a major role in the morphogenesis of the brain.     

Information-based adaptive fast-forward for visual surveillance

Automated video analysis lacks reliability when searching for unknown events in video data. The practical approach is to watch all the recorded video data, if applicable in fast-forward mode. In this paper we present a method to adapt the playback velocity of the video to the temporal information density, so that the users can explore the video under controlled cognitive load. The proposed approach can cope with static changes and is robust to video noise. First, we formulate temporal information as symmetrized Rényi divergence, deriving this measure from signal coding theory. Further, we discuss the animated visualization of accelerated video sequences and propose a physiologically motivated blending approach to cope with arbitrary playback velocities. Finally, we compare the proposed method with the current approaches in this field by experiments and a qualitative user study, and show its advantages over motion-based measures.     

Efficient Vector Quantization Using the WTA-Rule with Activity Equalization

We propose a new algorithm for vector quantization, the Activity Equalization Vector quantization (AEV). It is based on the winner takes all rule with an additional supervision of the average node activities over a training interval and a subsequent re-positioning of those nodes with low average activities. The re-positioning is aimed to both an exploration of the data space and a better approximation of already discovered data clusters by an equalization of the node activities. We introduce a learning scheme for AEV which requires as previous knowledge about the data only their bounding box. Using an example of Martinetz et al. [1], AEV is compared with the Neural Gas, Frequency Sensitive Competitive Learning (FSCL) and other standard algorithms. It turns out to converge much faster and requires less computational effort.