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PurposeThis paper seeks to propose a method of discovering uncommercialized research fronts by comparing scientific papers and patents. A comparative study was performed to measure the semantic similarity between academic papers and patents in order to discover research fronts that do not correspond to any patents.Design/methodology/approachThe authors compared structures of citation networks of scientific publications with those of patents by citation analysis and measured the similarity between sets of academic papers and sets of patents by natural language processing. After the documents (papers/patents) in each layer were categorized by a citation‐based method, the authors compared three semantic similarity measurements between a set of academic papers and a set of patents: Jaccard coefficient, cosine similarity of term frequency‐inverse document frequency (tfidf) vector, and cosine similarity of log‐tfidf vector. A case study was performed in solar cells.FindingsAs a result, the cosine similarity of tfidf was found to be the best way of discovering corresponding relationships.Social implicationsThis proposed approach makes it possible to obtain candidates of unexplored research fronts, where academic researches exist but patents do not. This methodology can be immediately applied to support the decision making of R&D investment by both R&D managers in companies and policy makers in government.Originality/valueThis paper enables comparison of scientific outcomes and patents in more detail by citation analysis and natural language processing than previous studies which just count the direct linkage from patents to papers.

PurposeThis paper aims to present an assessment framework which captures the essential characteristics and holistic success factors for disruptive innovation based on the original theory of Christensen, a number of clarifications as reported in the literature and a study of known, successful cases in the literature.Design/methodology/approachThe framework was designed based on the improved understanding of disruptive innovation challenges and on the holistic consideration of innovation as a dynamic process. It consists of structured questions which could be used to guide detailed data collection and analysis needed to answer the key questions which constitute the assessment framework. They are grouped under market positioning, technology and other favourable drivers.FindingsA simple yet comprehensive assessment framework for disruptive innovation has been developed. Two of the known successful cases, namely the steel minimill of Nucor and the 3.5 inch disk drive of Conner/Seagate, were presented in more detail to illustrate the use of this systematic framework in assessing the success potential of these cases of disruptive innovations in either the low‐end or new markets. A third and fairly new example, that of the limited mobile phone system/product of UTStarcom, was then presented to illustrate a case where the framework revealed reasons for potential failure. A fourth example of Google's web‐based office applications then illustrated how the framework might be used to study the disruptive potential of a new product.Originality/valueThis paper enables a more accurate and systematic assessment of disruptive innovation. The framework also has the potential to be further developed into a systematic tool for answering the question of whether the disruptive innovation theory could indeed be used to provide ex ante prediction of the success of a new disruptive innovation.

PurposeAirplane technology is undergoing several exciting developments, particularly in avionics, material composites, and design tool capabilities, and, though there are many studies conducted on subsets of airplane technology, market, and economic parameters, few exist in forecasting new commercial aircraft model introduction. In fact, existing research indicates the difficulty in quantitatively forecasting commercial airplanes due in part to the complexity and quantity of exogenous factors which feed into commercial airplane introduction decisions. This paper seeks to address this gap.Design/methodology/approachThe analysis is based on a literature review, supplemented by a collection of secondary data. The study then focuses on applying three technology forecasting techniques: multiple regression; linear regression; and the Pearl growth curve.FindingsThe results provide a valid model for multiple regression and linear regression on range and composite material percentage for use in commercial airplane forecasting. However, growth curve analysis, comparatively, appears to provide the most intriguing and flexible forecast outlook in alignment with industry dynamics.Research limitations/implicationsResearch implications include a caution for forecasters in support of the difficulty of commercial aircraft forecasting due in part to the quantity of exogenous factors, particularly compared with a related industry, military aircraft. Future work could include: utilizing other forecasting techniques that allow for greater numbers of forecast factors, additional future models, additional range aircraft and/or analyzing the impact that competing transportation modes in mid‐range aircraft could have on long‐range aircraft introduction.Originality/valueThe study provides value in extending a previous descriptive paper on airplane parameters. Additionally, it appears to be one of the first quantitative examples supporting previous research indicating the complexity of forecasting airplane new product introduction, but it overcomes some of this complexity by providing a valid model for forecasting with range and composite material percentage as inputs.

PurposeThis paper aims to forecast the future of data storage technologies, using the case of two major technologies driving the data storage world; hard disk drive (HDD) or conventional longitudinal recording and flash memory.Design/methodology/approachFour principal tools of forecasting technology are applied to present the coming future of data storage technologies. These tools consist of bibliometric trend analysis, patent trend analysis, technology cycle time (TCT), and growth curve. Numbers of publications each year and cumulative patents are used to analyze the future of these competitive technologies. The median age of the patents is applied to find the technology lifecycle of both technologies. Finally, areal density of HDD is plotted on the growth curve to forecast the saturation point of researching on conventional longitudinal recording.FindingsThe results produced from utilizing these tools indicate that these two technologies are continuously under development. It is found that the growth of improving areal density of conventional recording will be nearing zero by year 2013 and the maximum growth rate of development was in year 2006.Research limitations/implicationsThe forecasting is based on principal technologies of conventional longitudinal recording of HDD. Also, NOR and NAND are considered as two main technologies dominating flash memory. Other emerging data storage technologies such as holographic data storage, phase‐change memory (PCM) are not included in this paper. Likewise, the growth curve of flash memory is not analyzed as part of this paper.Originality/valueThis paper forecasts the future of data storage technologies. Integration of scientific indicators and growth curves is demonstrating a powerful tool for forecasting technology futures.

Series Foreword -- Preface -- Contents -- Part I: A Dynamic Capabilities Theory-Based Innovation Diffusion Model for Spread of Health Information Technology in the USA -- Chapter 1: Introduction to the Adoption of Health Information Technologies -- 1.1 The Healthcare Crisis in the United States -- 1.2 Government Efforts and HIT Meaningful-Use Initiative -- 1.2.1 State of Diffusion Research: General and Health IT -- References -- Chapter 2: Background Literature on the Adoption of Health Information Technologies -- 2.1 Overview of the Healthcare Delivery System -- 2.2 A Methodological Note -- 2.3 The Critical Stakeholders and Actors -- 2.3.1 Care Providers -- 2.3.1.1 Physicians, Nurses, and Medical Assistants -- 2.3.1.2 The Hospital or Clinic -- 2.3.2 Government -- 2.3.3 Patients and Their Family and Care Givers -- 2.3.4 Payers -- 2.3.5 HIT/Innovation Suppliers -- 2.3.5.1 HIT Vendors -- 2.3.5.2 Regional Health Information Organizations -- 2.4 Attributes of the Stakeholders -- 2.5 Important Factors Effecting Diffusion and Adoption for HIT -- 2.5.1 Barriers and Influences -- 2.5.2 Tools, Methods, and Theories -- 2.5.3 Policy Making -- 2.5.4 Hospital Characteristics and the Ecosystem -- 2.5.5 Adopter Attitudes, Perceptions, and Characteristics -- 2.5.6 Strategic Management and Competitive Advantage -- 2.5.7 Innovation Champions and Their Aids -- 2.5.8 Workflow and Knowledge Management -- 2.5.9 Timing and Sustainability -- 2.5.10 Modeling and Forecasting -- 2.5.11 Infusion -- 2.5.12 Social Structure and Communication Channels -- 2.6 The Need for Multiple Perspectives in Research -- 2.7 Linstone's Multiple Perspectives Method -- 2.8 The "4 + 1 View" Model for Software Architectures -- 2.9 Categorization of Important Factors in HIT Adoption Using Multi-perspectives -- References -- Chapter 3: Methods and Models

PurposeThis paper aims to research literature to describe the business processes used when planning IT infrastructure refreshes.Design/methodology/approachThe paper uses analytical hierarchical process (AHP) and pairwise comparisons to model and quantify the decision process for IT infrastructure refreshes.FindingsThe research found that most companies keep their refresh processes private and very little academic research is available on this topic. While supportability, manageability, compatibility, cost, and scalability are important factors to large organizations, performance and availability of the systems are important for smaller organizations.Originality/valueAHP was not ever used to evaluate the refresh planning. The paper demonstrates that it would be a very useful tool.