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The purpose of this paper is to investigate the diffusion patterns of knowledge in inspiring technological innovations and to enable monitoring development trends of technological innovations based on patent data analysis, thus, to manage knowledge wisely to innovate.

The notion of knowledge innovation potential (KIP) is proposed to measure the innovativeness of knowledge by the cumulative number of patents originated from its inspiration. KIP calculating formula is regressed in forms of two specific diffusion models by conducting a series of empirical studies with the patent-based indicators involving forward and backward citation numbers to reveal knowledge managing strategies regarding innovative activities.

Two specific diffusion models for regressing KIP formula are compared by empirical studies with the result indicating the Gompertz model has higher accuracy than the Logistic model to describe the developing curve of technological innovations. Moreover, the analysis of patent-based indicators over diffusion stages also revealed that patents applied at earlier diffusion stages normally has higher forward citation numbers indicating higher innovativeness meanwhile the patents applied at the latter stages usually requiring more knowledge inflows observed by their larger non-patent citation and backward citation amounts.

Although there is a large body of literature concerning knowledge-based technological innovation, there still room for discussing the mechanism of how knowledge diffuses and inspired knowledge. To the best of authors' knowledge, this study is the first attempt to quantitate the innovativeness of knowledge in technological innovation from the knowledge diffusion perspective with findings to support rational knowledge management related to innovation activities.

The purpose of this paper is the development of a patent-based supplier portfolio that can be used to evaluate and select suppliers on account of their technological competencies.

In addition to traditional approaches, the authors develop a supplier portfolio that characterizes suppliers according to the similarity between supplier's and OEM's technological competencies as well as their technological broadness. These variables are measured on the basis of patents, which constitute a valuable source of information in technology-driven industries. Contrary to existing binary measurement approaches, the authors’ portfolio uses semantic analyses to make use of the specific information provided in the patents' texts. The authors test this method in the field of gearings, which is a key driver for the automotive industry.

The authors identify six generic positions, characterizing specific risks for an OEM to become either technologically dependent or dependent on suppliers' production capacities. For each position the authors develop specific management strategies in face of the aforementioned risks. The approach helps OEMs navigate in the competitive landscape based on the most recent and publicly available information medium.

This work explicitly applies the construct of technological competencies to supplier evaluation and selection on the basis of portfolio approaches. Furthermore, the authors improve the use of patents for supplier evaluation in two respects: First, the authors analyze OEMs and upstream suppliers on an organizational level. Second, the authors utilize advanced semantic analysis to generate variables for the measurement of the criteria mentioned above.

There has been a significant rise in the number of patents originating from academic environments. However, current conceptualizations of academic patents provide a largely homogenous approach to define this entrepreneurial form of technology transfer. In this study we develop a novel categorization framework that identifies three subsets of academic patents which are conceptually distinct from each other. By applying the categorization framework on a unique database of Swedish patents we furthermore find support for its usefulness in detecting underlying differences in technology, opportunity, and commercialization characteristics among the three subsets of academic patents.

The purpose of this paper is to explore how enhanced and new technological knowledge of firms affects their performance under varying rates of technological change.

A large‐sample empirical study of US manufacturing firms is used. The main independent variables are measured using firms' patent data. Three hypotheses were developed based on theory and were tested using multivariate regressions. To increase reliability, alternative industry and firm explanators of performance were controlled for.

It was found that performance effects of enhanced technological knowledge increase with increasing rate of technological change. Effects of each knowledge dimension are found to be non‐linear. New technological knowledge has no independent effect on performance but acts jointly with enhanced knowledge in improving performance under moderate to rapid rates of technological change.

The study is narrow in scope being a fine‐grained analysis of a firm's technological competence. It does not take into account the role of marketing and administrative competence.

This is one of the few studies to disaggregate a firm's total stock of technological knowledge into its enhanced and new components.

Defining and validating a map of related technologies is critical for managers, investors and inventors. Because of the increase in the applications of and demand for semiconductor lasers, analyzing the technological position of developers has become increasingly critical. Therefore, the purpose of this study is to adopt the technological position analysis to identify mainstream technologies and developments relevant to semiconductor lasers.

Correspondence analysis and k-means cluster analysis, which are data mining techniques, are used to reveal strategic groups of major competitors in the semiconductor laser market according to their Patent Cooperation Treaty (PCT) patent applications.

The results of this study reveal that PCT patent applications are generally obtained for masers, optical elements, semiconductor devices and methods for measuring and that technology developers have varying technological positions.

Through position analysis, this study identifies the technological focuses of different manufacturers to obtain information that can guide the allocation of research and development resources.

The purpose of this paper is to understand the role of k-networks in knowledge creation, as existing literature argues that network structure does not provide sufficient explanation per se. This paper proposed diversity as an important construct to explain knowledge creation in SMEs’ k-networks.

First, by reviewing the literature, this paper proposes a hypothesis that predicts a positive association of content and knowledge creation. Then, focusing on patent co-authorship networks of the biotechnology industry in Victoria, this research used an explanatory multiple case study approach to test the formulated hypothesis.

By introducing new constructs, the results provide more insight on the positive association of knowledge content and knowledge creation. Based on the emergent constructs, rival hypotheses are also developed for further research.

Beyond the role of network structure, which has dominated the knowledge network literature, this research highlights how other factors like knowledge diversity are needed to be consider.

The purpose of this paper is to provide a methodological framework for evaluating the impact of open innovation (OI) adoption on start-up ventures at the knowledge domain level, taking into account the technological specialization and the quality of innovation output.

The framework employs patent data for investigating joint development activities between start-ups and partners, thus defining an openness degree of R&D. The partner typology and the levels of specialization on the knowledge fields affected by each invention are investigated. The methodology is tested on a sample of bio-pharmaceutical start-ups.

On average, the openness level in the start-up phase is higher than the consolidation one. Furthermore, the higher the level of openness during the start-up phase, the higher the propensity to collaborate with scientific organizations. And yet, the occurrence of R&D collaborations is positively correlated to the level of specialization on the knowledge fields involved in joint development activities.

OI strategies are valuable for start-ups, particularly in the bio-pharmaceutical industry, since their adoption improves the quality of innovation output. This is particularly true when R&D collaborations are established with scientific entities.

This work investigates the impact of OI adoption on start-up ventures at the knowledge domain level, by employing objective and standardized data. Hence, the methodology can be considered as a first step in developing a useful tool for managers, researchers and business analysts.

The purpose of this paper is to provide a patent-based framework for investigating the effect of previous and current open innovation (OI) adoption on firms’ knowledge management strategies and type of innovation output.

Patent data are employed for gauging innovation practices, exploitation vs exploration strategies, specialization vs diversification choices and type of innovation. The study is performed on a sample of 1,280 patents granted to 66 top R&D spending bio-pharmaceutical companies. The year of analysis is 2010.

The previous recourse to specific innovation practices influences the current practice selection. R&D collaboration, outsourcing and mergers and acquisitions are employed to pursue exploration. Past purchase of patents increases the likelihood to achieve architectural and radical innovation in current activities.

The work recommends the use of patent data to gauge many key elements for knowledge and innovation management. Results exhort scholars to investigate innovation practices at the knowledge domain level in order to detect specific behaviors.

The study provides a methodology for supporting decision-makers in assessing firms’ OI adoption, also performing the benchmark with competitors and R&D partners. Given the high computational effort required for applying the methodology, the authors are planning to give access to the software specifically developed for this study.

The work contributes to the current debate considering the effect of a combination of innovation practices on knowledge management strategies and type of innovation output, with a particular focus on OI activities. Moreover, the separation between the impact of previous and current innovation practices provides useful insights.

This paper applies and empirically tests a real options approach to conceptualizing the value of patents. Based on a theorem derived by Merton (1973), we propose that greater dispersion in the citations of a firm's patents represents greater flexibility for the firm to exercise the option rights embedded in the patents and thus enhances the option value of the patents. A test of this proposition using a sample of 128 US-based biotechnology firms found corroborative results.

As the university–industry collaboration (UIC) gradually attracts the attention of various national governments, the number of studies on UIC has increased substantially. Past UIC studies have mostly focused on investigating the incentives and the motivation for UIC, forms of UIC and performance output of UIC. However, they have not actively identified the key technologies and technology distribution that are conductive to the commercialization of UIC outcomes. Therefore, this study aims to adopt the licensed UIC patents as the basis for analysis and to construct a patent licensing technology network.

This study focused on licensed patents because past studies have indicated that such patents usually have higher value. Moreover, patent licensing can be seen as the final step for the commercialization of UIC outcomes. Finally, past studies have rarely explored patent examiners’ views on key technologies. However, during the substantive examination of patents, patent examiners often use their background knowledge regarding the technology to include citations to other patented technologies that they consider valuable or indispensable. Therefore, this study focused on investigating the patents recognized and cited by patent examiners and conducted key technology identification.

The results indicated that past key technologies in UIC focused on surveying, medicine, biochemistry and electric digital data processing; these fields are crucial to the commercialization of key UIC technologies. Finally, the USA, Japan, Sweden and Germany play critical roles in the network of global university–industry cooperation and technology licensing.

Patent examiners’ perspectives were adopted to establish a patent licensing technology network, through which the key technologies that could promote UIC patent licensing were mined. This study can also serve as a reference for resource allocation in university research and development and for governments to promote new technologies.