Towards a process framework for assessing the potential value of technologies

Current technology valuation literature predominantly focuses on explaining the merits and implications of specific tools, but little research is available that takes a contextual process perspective. The aim of this paper is to further develop an integrative process framework that supports the structuring of the valuation process and the more systematic choice of valuation techniques for new technologies. The paper starts by reviewing key concepts and issues that surround the assessment of technology investments and the evidence of what companies use. Many factors need to be brought into the appraisal process, reflecting technological and market conditions. While there is usually a desire to reduce the assessment to a financial value, it is also widely appreciated that there is long term strategic value in securing a technological lead, which is difficult, or even inappropriate, to assess in purely financial terms. The multiple factors involved in the evaluation activity are identified with respect to the changing nature of the appraisal process as the technology matures and the implications for associated tools. The result of the literature review is a process framework which provides a conceptual basis for integrating valuation techniques. This framework is then populated with the results of industrial case studies on technology valuation to allow conclusions on its applicability to be drawn. © 2011 IEEE.

The process of making the business case for technology: A sales and marketing perspective for technologists

Technological investment is a key driver of innovation and the evaluation of technology potential is becoming increasingly important in this context. There is a range of approaches and tools for developing an understanding of the value of technology. However the process of communicating this potential to possible customers is not well documented in terms of theory and practice and falls outside the skill set of many technologists. This paper seeks to integrate the concepts of marketing and consultative selling into making business cases for new technologies. It describes an exploratory study which results in an outline process activity model for technologists wishing to build an effective business case for securing investment internally or when selling a technology externally. Following a review of literature, we suggest that there is potential to learn from market research and consultative sales techniques, and propose a five step process. The work has been industrially validated and forms a novel foundation for further development. © 2012 Elsevier Inc.

Mapping experience in organizations: A learning process for strategic technology planning

Today's fast-paced, dynamic environments mean that for organizations to keep "ahead of the game", engineering managers need to maximize current opportunities and avoid repeating past mistakes. This article describes the development study of a collaborative strategic management tool - the Experience Scan to capture past experience and apply learning from this to present and future situations. Experience Scan workshops were held in a number of different technology organizations, developing and refining the tool until its format stabilized. From participants' feedback, the workshop-based tool was judged to be a useful and efficient mechanism for communication and knowledge management, contributing to organizational learning.

Bayesian inference and learning in Gaussian process state-space models with Particle MCMC

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning (i.e. state estimation and system identification) in nonlinear nonparametric state-space models. We place a Gaussian process prior over the state transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. To enable efficient inference, we marginalize over the transition dynamics function and, instead, infer directly the joint smoothing distribution using specially tailored Particle Markov Chain Monte Carlo samplers. Once a sample from the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. Our approach preserves the full nonparametric expressivity of the model and can make use of sparse Gaussian processes to greatly reduce computational complexity.

Growth of nonpolar a-plane GaN on nano-patterned r-plane sapphire substrates

Sapphire substrates were nano-patterned by inductive coupled plasma etching process. Nonpolar a-plane GaN films were grown on planar and nano-patterned r-plane sapphire substrates by metal organic chemical vapor deposition. The anisotropic characteristic and the crystalline quality of the a-plane GaN films were studied through XRD rocking curves. The cross section and surface morphologies of the a-plane GaN films were studied using SEM and AFM measurements, respectively. The crystal quality and surface flatness of the nonpolar a-plane GaN were greatly improved through the usage of the nano-patterned r-plane sapphire substrates. (C) 2008 Elsevier B.V. All rights reserved.