Technology Transfer and Entrepreneurship (Discontinued) - Volume 1, Issue 2, 2014

The pharmaceutical industry heavily relies on external sources to supplement its internal R&D activities and fill its product pipelines. Outsourcing of workpackages, in-licensing of development projects, collaborations with academic research centers or biotech companies and hiring of consultants are classic approaches towards this goal. Here we describe a new open innovation approach, termed “fluctuating open teams (FLOT)” that combines synergistically internal and external talent for a drug discovery project. The approach is compared to other open innovation options and constitutes an ideal way to boost creativity and new ideas for project teams that hit a roadblock.

Physician entrepreneurs, technologists, investors, service providers, patients and other innovation stakeholders have embraced biomedical and health innovation and entrepreneurship in an effort to provide higher value care and lower the discovery and development costs of drugs, devices and digital health products and services. To that end, traditional brick and mortar cluster innovation models are being supplemented with virtual networks that are online, decentralized and community based. This paper reviews the evolution of these models and their future development and presents examples of virtual organizations that support biomedical and health innovation and entrepreneurship, fundraising, and new product development and clinical trial recruitment using crowd sourcing. Community based innovation and biomedical and health online innovation networks have the potential to speed new product development, offer alternative financing platforms for early stage ventures, provide education, information and support to those with particular diseases and help to lower the costs and speed of clinical trials. However, questions remain about their effectiveness in creating value, security, commercial and clinical validity, legal status and sustainability.

It is a growing trend of angel investors to invest more than money into a business opportunity. This ‘active’ approach to angel investing may not only improve the probability of success and increase yields on investments, but it may also save substantial time in the investment process and generate a rich experience that becomes part of the investor’s legacy. This article explores the scope and nature of an angel investor’s participation in the invested business and the beneficial impact that the investor may have upon the business. The article also explores how ‘proactive’ engagement in the investment selection process may generate additional benefits that align with the personal goals of the angel investor.

The creation of startup companies to commercialize university innovations generates revenue for the university and inventor, attracts star researchers, and promotes economic growth. Despite these benefits, the levels of technology licensing and startup companies vary widely across universities. These differences have been attributed to factors such as institutional support for entrepreneurial activity, institutional prestige, proximity to venture capital and related industry, and sources of research funding. All of these factors combine to create an entrepreneurial environment in which inventors either perceive that entrepreneurship is encouraged or discouraged. This paper is a meta case study examining the entrepreneurial environments at twenty universities: ten that have been very successful at generating startup companies and ten that have underperformed. The role of formal policies such as royalty distribution, conflict of interest, and tenure and promotion are explored. In addition the availability of entrepreneurial programming such as education, business plan competitions, and incubators are studied. Together these formal policies and entrepreneurship programming create the ecosystem in which the inventors and entrepreneurs will need to operate in order to find success. Best practices for encouraging technology commercialization through entrepreneurship are identified.

With the changing economic landscape of the 21st century, the role of universities as contributors to the knowledge economy is a topic of increasing national importance. In particular, a relatively new mode of university knowledge transfer – a process commonly known as technology transfer – has increasingly gained attention among the public, policy-makers, and university administrators in recent years. Unlike more mature modes of university knowledge transfer, such as instruction or publication, understanding of the technology transfer process and the appropriate metrics by which to measure technology transfer success are relatively nascent. Current standard and widely-used metrics intended to measure the effectiveness of the technology transfer process often fail to consider many important factors, especially discrepancies in input variables. Lack of critical evaluation may lead to inaccurate conclusions regarding process effectiveness and may even result in the implementation of policies detrimental to universities’ ability to contribute to the knowledge economy. This manuscript identifies a few key factors that play a role in determining the effectiveness of the technology transfer process at a given university and examines a case study involving Colorado State University, where changing some key input factors positively impacted technology transfer outcomes.

While the focus in the global biotechnology industry is often on scientific breakthroughs, emergent industry trends suggest that bio-enterprise success requires essential non-scientific expertise. This includes business, law, government and information systems, with an orientation to cross-disciplinary insights within the life science industry as a whole. This paper documents recent industry research on workforce need, and utilizes the Bio-Enterprise Innovation Expertise Model (BIEM) as a basis for the spectrum of expertise needed in the science-to-product life cycle. As the BIEM model drives the courses taught in the Business of Biotechnology (BoB) Program at the University of San Francisco (USF), and since these courses serve both science and non-science working professionals, the authors posited that student experience might be examined to measure attitudinal changes in relevant expertise areas along with cross-disciplinary insights. If non-science students registered statistically significant increases in confidence levels, the likelihood of entering the bio-enterprise workforce would arguably be increased. One-week BoB immersive study tours were selected for assessment, and adjustments made within the ongoing Gunn-Lorton Attitudinal Surveys (GLAS) Project, which seeks to measure STEM++ avoidance/affinity attitudes. Confidence levels were recorded both pre-and post-study tours in six (6) areas: general business, bio-business, information systems, law, federal government and science. Four (4) study tours reflecting 48 student experiences were evaluated. All non-science, first-time BoB students registered a statistically significant rise in confidence with respect to bio-business, signaling a rise in affinity for the biotechnology industry. All degree programs tested: MBA, JD/MBA, MSIS and PSM/Biotechnology, registered statistically significant confidence increases in every expertise area not central to students’ primary field of study, wherein confidence remained essentially unchanged. Qualitative measures confirmed appreciation for cross-disciplinary expertise. This research demonstrates that academic programs based on the BIEM expertise model can meet emergent industry needs for both non-science and science professionals.

University based technology transfer offices function to identify, protect, and market new technologies all of which require strategic decisions about technology developed at their institutions. Often, limited time and resources are available for pursuing patent protection and identifying commercial partners for licensing. Therefore, determining which technologies have high probabilities for commercial success is necessary. The collection of data regarding the market, referred to as competitive intelligence, helps to make these decisions. In this article we discuss sources for competitive intelligence, provide an evaluation of compiled competitive intelligence platforms from commercial vendors, and discuss approaches for evaluating them.

As careers outside of academia become more accepted and increasingly diverse, the career outlook for PhD students and postdoctoral fellows (postdocs) in science, technology, engineering and mathematics (STEM) continues to improve. However, many academic institutions, failing to adapt to the changing environment, have inadequately prepared their students and postdocs for career placement in non-academic fields. Here we explore the obstacles in career planning, provide approaches to enhance career development, and highlight examples of existing initiatives for change. By utilizing their unique skillset and emphasizing innovation and entrepreneurship, STEM PhD graduates are poised to become the leaders of many different industries and extend the opportunities for future graduates. The goal of this work is to emphasize career opportunities outside of academia, recognize inefficiencies in career planning and ultimately help current and future STEM PhD students and postdocs better prepare for future career success.

China Yuan has been in the deviation of currency value of internal depreciation along with external appreciation since 2002. The paper, basing on exited literature, summarizes the formation mechanism as: the effect of US dollar depreciation, the effect of the export-oriented development model, the effect of sustained rapid economic growth and the effect of interest rates spreads and other factors. The conclusion implies that currency liquidity, wage rising and international inflation can account for the domestic inflation against the foreign exchange rate depreciation. Hence, in order to explore international experience of currency value deviation, we have made a statistic analysis of G20 economies’ currency growing except US and EU. We find it is universe of currency value deviation. Moreover, we have summarized the currency value relationship as consistency, stray and deviation according to the manifestations exchange rate transmission.