Trial by market: the Brightstar incubation experiment

Established firms accumulate a significant body of knowledge, expertise and capabilities that are often secondary to their central revenue generating activities. How do they leverage this expertise in non-core technology into future value creation opportunities? In this paper we examine an attempt by the telecommunications firm BT to create value from the accumulated knowledge within its laboratories by setting up an incubator. While conceived by the board as a mechanism for leveraging the value of non-core technology into the workplace, corporate support for the incubator was withdrawn after only three years and prompted the incubator to partner with a venture capital firm, NVP, in the spin-out of ventures. Through analysis of this single case we observe how entering into such a relationship reduces the transaction costs of accessing complementary resources, capabilities and competences, while simultaneously reducing a number of the risks associated with venturing for both parties. Partnering with the venture capitalist allows the established firm to get its intellectual property into the market, for it to be tested by the market and further developed. © 2010 Inderscience Enterprises Ltd.

Improving statistical machine translation by classifying and generalizing inflected verb forms

This paper introduces a rule-based classification of single-word and compound verbs into a statistical machine translation approach. By substituting verb forms by the lemma of their head verb, the data sparseness problem caused by highly-inflected languages can be successfully addressed. On the other hand, the information of seen verb forms can be used to generate new translations for unseen verb forms. Translation results for an English to Spanish task are reported, producing a significant performance improvement.

An ngram-based statistical machine translation decoder

In this paper we describe MARIE, an Ngram-based statistical machine translation decoder. It is implemented using a beam search strategy, with distortion (or reordering) capabilities. The underlying translation model is based on an Ngram approach, extended to introduce reordering at the phrase level. The search graph structure is designed to perform very accurate comparisons, what allows for a high level of pruning, improving the decoder efficiency. We report several techniques for efficiently prune out the search space. The combinatory explosion of the search space derived from the search graph structure is reduced by limiting the number of reorderings a given translation is allowed to perform, and also the maximum distance a word (or a phrase) is allowed to be reordered. We finally report translation accuracy results on three different translation tasks.

Phrase linguistic classification and generalization for improving statistical machine translation

In this paper a method to incorporate linguistic information regarding single-word and compound verbs is proposed, as a first step towards an SMT model based on linguistically-classified phrases. By substituting these verb structures by the base form of the head verb, we achieve a better statistical word alignment performance, and are able to better estimate the translation model and generalize to unseen verb forms during translation. Preliminary experiments for the English - Spanish language pair are performed, and future research lines are detailed. © 2005 Association for Computational Linguistics.

Beyond niche thinking: Market selection in science-based ventures

Matching a new technology to an appropriate market is a major challenge for new technology-based firms (NTBF). Such firms are often advised to target niche-markets where the firms and their technologies can establish themselves relatively free of incumbent competition. However, technologies are diverse in nature and do not benefit from identical strategies. In contrast to many Information and Communication Technology (ICT) innovations which build on an established knowledge base for fairly specific applications, technologies based on emerging science are often generic and so have a number of markets and applications open to them, each carrying considerable technological and market uncertainty. Each of these potential markets is part of a complex and evolving ecosystem from which the venture may have to access significant complementary assets in order to create and sustain commercial value. Based on dataset and case study research on UK advanced material university spin-outs (USO), we find that, contrary to conventional wisdom, the more commercially successful ventures were targeting mainstream markets by working closely with large, established competitors during early development. While niche markets promise protection from incumbent firms, science-based innovations, such as new materials, often require the presence, and participation, of established companies in order to create value. © 2012 IEEE.

Market-pull and technology-push in manufacturing start-ups in emerging industries

Purpose - As traditional manufacturing, previously vital to the UK economy, is increasingly outsourced to lower-cost locations, policy makers seek leadership in emerging industries by encouraging innovative start-up firms to pursue competitive opportunities. Emerging industries can either be those where a technology exists but the corresponding downstream value chain is unclear, or a new technology may subvert the existing value chain to satisfy existing customer needs. Hence, this area shows evidence of both technology-push and market-pull forces. The purpose of this paper is to focus on market-pull and technology-push orientations in manufacturing ventures, specifically examining how and why this orientation shifts during the firm's formative years. Design/methodology/approach - A multiple case study approach of 25 UK start-ups in emerging industries is used to examine this seldom explored area. The authors offer two models of dynamic business-orientation in start-ups and explain the common reasons for shifts in orientation and why these two orientations do not generally co-exist during early firm development. Findings - Separate evolution paths were found for strategic orientation in manufacturing start-ups and separate reasons for them to shift in their early development. Technology-push start-ups often changed to a market-pull orientation because of new partners, new market information or shift in management priorities. In contrast, many of the start-ups beginning with a market-pull orientation shifted to a technology-push orientation because early market experiences necessitated a focus on improving processes in order to increase productivity or meet partner specifications, or meet a demand for complementary products. Originality/value - While a significant body of work exists regarding manufacturing strategy in established firms, little work has been found that investigates how manufacturing strategy emerges in start-up companies, particularly those in emerging industries. © Emerald Group Publishing Limited.

Value creation from the innovation environment: Partnership strategies in university spin-outs

University spin-out (USO) companies play an increasingly important role in generating value from radical, generic technologies, but this translation requires significant resources from other players to reach the market. Seven case studies illuminate how relationships with each type of partner can be leveraged to help the firm create value. We find that most firms in the sample are aware of the importance of corporate partners and actively seek to cultivate these relationships, but may not be taking full advantage of the resources available through nonparent academic institutions and other USOs with similar or complementary technologies. © 2013 The Authors. R&D Management © 2013 Blackwell Publishing Ltd.

Policy as a struggle for meaning: disentangling knowledge translation across international health contexts

Over the last decade, research in medical science has focused on knowledge translation and diffusion of best practices to enable improved health outcomes. However, there has been less attention given to the role of policy in influencing the translation of best practice across different national contexts. This paper argues that the underlying set of public discourses of healthcare policy significantly influences its development with implications for the dissemination of best practices. Our research uses Critical Discourse Analysis to examine the policy discourses surrounding the treatment of stroke across Canada and the U.K. It focuses in specific on how concepts of knowledge translation, user empowerment, and service innovation construct different accounts of the health service in the two countries. These findings provide an important yet overlooked starting point for understanding the role of policy development in knowledge transfer and the translation of science into health practice. © 2011 Operational Research Society. All rights reserved.

N-gram posterior probability confidence measures for statistical machine translation: An empirical study

We report an empirical study of n-gram posterior probability confidence measures for statistical machine translation (SMT). We first describe an efficient and practical algorithm for rapidly computing n-gram posterior probabilities from large translation word lattices. These probabilities are shown to be a good predictor of whether or not the n-gram is found in human reference translations, motivating their use as a confidence measure for SMT. Comprehensive n-gram precision and word coverage measurements are presented for a variety of different language pairs, domains and conditions. We analyze the effect on reference precision of using single or multiple references, and compare the precision of posteriors computed from k-best lists to those computed over the full evidence space of the lattice. We also demonstrate improved confidence by combining multiple lattices in a multi-source translation framework. © 2012 The Author(s).