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The development of innovations in all the research and development (R&D) fields is leading to a huge increase of patent data. Therefore, it is reasonable to foresee that, in the next future, Big Data-centered techniques will be compulsory to fully exploit the potential of this kind of data. In this context, network analysis-based approaches are extremely promising. The purpose of this paper is to provide a contribution to this setting. In fact, the authors propose a well-tailored centrality measure for evaluating patents and their citations.

The authors preliminarily introduce a suitable support directed network representing patents and their citations. After this, the authors present the centrality measures, namely, “Naive Patent Degree” and “Refined Patent Degree.’” Then, the authors show why they are well tailored to capture the specificities of the patent scenario and why classical centrality measure fails to fully reach this purpose.

The authors present three possible applications of the measures, namely: the computation of a patent “scope” allowing the evaluation of the width and the strength of the influence of a patent on a given R&D field; the computation of a patent lifecycle; and the detection of the so-called “power patents,” i.e., the most relevant patents, and the investigation of the importance, for a patent, to be cited by a power patent.

None of the approaches proposing the application of centrality measures to patent citation networks consider the main peculiarity of this scenario, i.e., that, if a patent p_i cites a patent p_j, then the value of p_i decreases. So, differently from classical scientific paper citation scenario, in this one performing a citation has a cost for the citing entity. This fact is not considered by all the approaches conceived to investigate paper citations. Nevertheless, this feature represents the core of patent citation scenario. The approach has been explicitly conceived to capture this feature.

I begin with a dispute over a fox hunt, by which to understand the law of tangible property, then develop that metaphor for the major types of intellectual property. I start with domestic U.S. patent law for the sake of concreteness, and generalize to other jurisdictions and types of intellectual property. In the latter parts of the paper I discuss the international implications of intellectual property, including especially the effects of information spillovers. The last part of the paper describes the hazards in analogizing “trade” in intellectual property rights to trade in goods, and particularly in interpreting international patent data. These hazards motivate the search for a structural model specially adapted to the purpose of valuing international intellectual property rights and rules. The goal is to give economists a simple and integrated framework for analyzing intellectual property across time, jurisdiction and regime type, with an eye towards eventually developing other incentive systems that have the advantages of property (such as decentralized decision-making), but fewer of the disadvantages.

This study proposes an approach to solve the fundamental problem in using query-based methods (i.e. searching engines and patent retrieval tools) to screen patents of information and communication technology in construction (ICTC). The fundamental problem is that ICTC incorporates various techniques and thus cannot be simply represented by man-made queries. To investigate this concern, this study develops a binary classifier by utilizing deep learning and NLP techniques to automatically identify whether a patent is relevant to ICTC, thus accurately screening a corpus of ICTC patents.

This study employs NLP techniques to convert the textual data of patents into numerical vectors. Then, a supervised deep learning model is developed to learn the relations between the input vectors and outputs.

The validation results indicate that (1) the proposed approach has a better performance in screening ICTC patents than traditional machine learning methods; (2) besides the United States Patent and Trademark Office (USPTO) that provides structured and well-written patents, the approach could also accurately screen patents form Derwent Innovations Index (DIX), in which patents are written in different genres.

This study contributes a specific collection for ICTC patents, which is not provided by the patent offices.

The proposed approach contributes an alternative manner in gathering a corpus of patents for domains like ICTC that neither exists as a searchable classification in patent offices, nor is accurately represented by man-made queries.

A deep learning model with two layers of neurons is developed to learn the non-linear relations between the input features and outputs providing better performance than traditional machine learning models. This study uses advanced NLP techniques lemmatization and part-of-speech POS to process textual data of ICTC patents. This study contributes specific collection for ICTC patents which is not provided by the patent offices.

In the last two decades, researchers have paid much attention to the role of cultural values on economic and social development. In particular, the crucial role of different aspects of culture on the development of innovation has been stressed in the literature. Consequently, it is vital to understand how social capital, as a core cultural value, affects the innovation process and the innovative performance at the national level. However, to date, the impact of different dimensions of social capital and innovation has not been properly portrayed or explained. Thus, the purpose of this paper is to investigate the influence of four different dimensions of social capital (institutional and interpersonal, associational life and norms) on two of the main functions of national innovation system (NIS) (entrepreneurship and knowledge creation) based on over 50,000 observations in 34 countries.

In this regard, national-level data from the World Values Survey database was employed to quantify social capital. Entrepreneurship is, in turn, assumed to consist of three sub-indexes and 14 indicators based on the Global Entrepreneurship Index. Knowledge creation is also measured through US Patent Office applications. Also, exploratory factor analysis and structural equation modeling approach were used to build the measurement model and investigate the impact that each factor of social capital had on entrepreneurship and knowledge application, respectively. Measurement and structural models were built and their reliability and validity were tested using various fit indices. Research findings suggest the strong positive effect of institutional trust and networking on entrepreneurship. Also, interpersonal trust and networks were shown to have high influence on knowledge development at the national level. Norms appear to have naïve to medium negative effects on both functions.

Research findings suggest the strong positive effect of institutional trust and networking on entrepreneurship. Also, interpersonal trust and networks were shown to have high influence on knowledge development at the national level. Norms appear to have naïve to medium negative effects on both functions.

However, to date, the impact of different dimensions of social capital and innovation has not been properly portrayed or explained.

In this article, the ideas and methods behind the “patent-paper citation” are scrutinised by following the intellectual and technical development of approaches and ideas in early work on patentometrics. The aim is to study how references from patents to papers came to play a crucial role in establishing a link between science and technology.

The study comprises a conceptual history of the “patent paper citation” and its emergence as an important indicator of science and technology interaction. By tracing key references in the field, it analyses the overarching frameworks and ideas, the conceptual “hinterland”, in which the approach of studying patent references emerged.

The analysis explains how interest in patents – not only as legal and economic artefacts but also as scientific documents – became evident in the 1980s. The focus on patent citations was sparked by a need for relevant and objective indicators and by the greater availability of databases and methods. Yet, the development of patentometrics also relied on earlier research, and established theories, on the relation between science and technology.

This is the first attempt at situating patentometrics in a larger societal and scientific context. The paper offers a reflexive and nuanced analysis of the “patent-paper citation” as a theoretical and historical construct, and it calls for a broader and contextualised understanding of patent references, including their social, legal and rhetorical function.

In recent years patents have become a very popular data source for forecasting technological changes. However, since a vast amount of patents are “worthless” (Moore, 2005), there is a need to identify the promising ones. For this purpose, previous approaches have mainly used bibliographic data, thus neglecting the benefits of textual data, such as instant accessibility at patent disclosure. To leverage these benefits, this study aims to develop an approach that uses textual patent data for predicting promising patents.

For the identification of promising patents, the authors propose a novel approach which combines link prediction with textual patent data. Thereby the authors are able to predict the emergence of hitherto unmentioned bigrams. By mapping these future bigrams to recent patents, the authors are able to distinguish between promising and nonpromising patents. To validate this approach, the authors apply the methodology to the case example of camera technology.

The authors identify stochastic gradient descent as a suitable algorithm with both a receiver operating characteristic area under curve score and a positive predictive value of 78%, which outperforms chance by a factor of two. In addition, the authors present promising camera patents for diverse application fields, such as cameras for surgical systems, cameras for rearview vision systems in vehicles or light amplification by stimulated emission of radiation detection and ranging cameras for three-dimensional imaging.

This study contributes in at least three directions to scholarship. First, the authors introduce a novel approach by combining link prediction with textual patent analysis and, in this way, leverage the benefits of both worlds. Second, the authors add to all theories that regard novel technologies as a recombination of existing technologies in presenting word combinations from textual data as a suitable instrument for revealing recombination in patents. And third, the approach can be used by scholars as a complementary or even integrative tool with conventional forecasting methods like the Delphi technique or Scenario planning.

At least three practical implications arise from the study. First, incumbent firms of a technology branch can use this approach as an early-warning system to identify technological change and to identify opportunities related to their company’s technological competence and provide inspiration for new ideas. Second, companies seeking to tap into new markets may also be interested in the approach as managers could anticipate whether their company’s technological competences are in line with upcoming trends. Third, the approach may be used as a supportive tool for various purposes, such as investment decisions or technology life cycle analysis.

The approach introduces textual patent data as suitable means for forecasting activities. As the statistical validation reveals, the promising patents identified by the approach are cited significantly more often than patents with less promising prospects.

The purpose of this paper is to survey and analyse the literature emanating from less developed countries (LDCs) and international agencies and dealing with their perception of the needs of LDCs for scientific and technical information (STI) in relation to social and economic development.

The past few decades have witnessed a phenomenal progress in our understanding of employee mobility as a critical driver and consequence of various outcomes for individuals, organizations, industries, and economies. In the process, researchers have tackled several important issues in conducting empirical research on employee mobility. This chapter provides a critical discussion of the extant literature focusing on five broad areas: identification of mobility, timing of mobility, outcomes of mobility and their operationalization, model identification, and other related issues. In doing so, this article identifies some of the empirical choices and methodologies adopted in prior mobility studies, evaluates those practices, and suggests areas of improvements for the practice. It is hoped that future studies will benefit from this chapter's insight by building on the best practices from the literature while continuously and successfully tackling the issues that have been challenging the researchers on this increasingly important topic of scholarly inquiry.

Guidance Note 1 of the Red Book states that the valuation of an operational entity includes four components: the land and buildings; the trade fixtures and fittings; the trading potential, excluding personal goodwill; and the benefit of any transferable licenses and consents. Accounting changes in recent years have increasingly recognised the importance of intangible assets such as intellectual capital and goodwill. Similarly, recent tax changes demonstrate the government's acceptance of the importance of such items in achieving and maintaining business competitiveness. This paper has two key objectives: first, to analyse the application of the Red Book to trade‐related valuations, paying particular attention to the treatment of goodwill and second, to critically evaluate the accounting treatment of goodwill and in particular the application of Financial Reporting Standard 10. In order to understand the workings of the market, the corporate hotel sector was used as a case study. The key findings of the research are that valuers expressed considerable unease with the apportioning of market value between tangible assets and goodwill, there was no consensus on how (or if) goodwill could be measured reliably. Second, that the valuation methods adopted are, to a degree, naïve. While explicit changes are made to the cash‐flow projections, there is insufficient appreciation of the changing risk profile that might lead to an adjustment to the earnings multiplier. The accounting difficulties and inconsistencies concerning goodwill arise largely because of inadequate valuation methods. Recent tax changes also point to the need for a robust and defendable valuation methodology. Application of one such theoretically sound approach to valuing goodwill (the bridge model) is illustrated in this paper. While the research focused on the corporate hotel sector, the findings have wider implications for other sectors of the market where operational entities are valued with regard to their trading potential.