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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.

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 suggest indicators for evaluating Iran national nanotechnology plan at the policy and program levels. Governments are required to evaluate their policies to be accountable to their audience and to improve their policies. The evaluation of nanotechnology policies both in academic and governmental institutions has been mainly confined to measuring the research and development outputs, while explicit models are required for evaluating national nanotechnology policies and programs. In other words, although policies are translated into several activities, it does not mean that the evaluation of activities results in holistic policy evaluation.

The present research uses a qualitative method to explore the process of developing a model for assessing nanotechnology policymaking in Iran. Expert opinions have been collected and analyzed by using local and international surveys to validate different elements of the model.

The model includes the evaluation time-frame, criteria and indicators for policy and program levels. Assessment at program and policy levels, as distinct from activity evaluation, measures the ultimate objectives of a policy intervention, based on which the outcome and impact indicators should be developed. This paper argues that the Iran national nanotechnology policy should be evaluated and revised triennially based on the model in which 46 outcome and impact indicators are included under three general criteria of appropriateness, efficiency and effectiveness. A number of these indicators should be evaluated annually or biannually.

The evaluation of nanotechnology plans has been mainly limited to assessing the research and development outputs that are related to the activity level. The main value of this work is developing outcome and impact indicators that are related to policy and program levels.

This study aims to propose a novel approach based on utility mining to find the associations among wind energy technologies.

The proposed approach uses patent documents and utility mining. Associations among wind energy technologies have been evaluated to show how the proposed approach works in practice.

Determining the relationships between wind energy technologies provide essential information to investors and decision-makers. Therefore, a real-life case study of wind energy technology is performed to show how the proposed approach works in practice. The proposed approach founds technology classes associated with wind energy technology. Furthermore, the strongest associations among technologies are also obtained by the proposed approach. The results of the case study show that the proposed approach can be easily used in practice. The maximum size of itemsets is 18-level itemsets. Y02E and F03D cooperative patent classification (CPC) codes appear on all itemsets. As the technologies of Y02E and F03D are directly correlated, they will be mutually developed in the future. Additionally, the number of patent corresponding to Y02E and F03D CPC codes are 7,494 and 6,577, respectively.

This is the first study that applies the utility mining-based approach to patent documents. Different levels of importance among technologies based on patent citations and the number of repetitions of each technology class are considered in the proposed approach.

Fusion of new technologies and convergence has led to the emergence of new industries. This paper aims to present experiences of the concept of convergence from a patent citation analysis viewpoint. The main objective of the paper is to develop a tool for anticipating the emerging new industry segments.

The patent analysis methods have been used to get an idea of the stage of new industry segment in the intersection of the paper and electronics industries. The authors distinguish self‐citations from external citations, and further divide citations into two groups: within the industry and beyond the industry. Backward citations are used to evaluate the spill‐overs between industries and forward citations are used to evaluate the significance of technology‐based industry transformation.

The recognized trends of the trajectory changes and growing overlaps of technological fields show indications for possible convergence between industries. Differentiating between external and self‐citations within and beyond industry citations helps to provide more comprehensive prospects of a future technology competitive environment.

This study evaluated technology development in converging environments. It would be a scientifically significant contribution if the novel patent analysis methods could be used effectively for creating understanding in advance about the technology development and industry convergences.

The presented patent citation methodology provides new insights into the analysis of industry evolution, technological innovations and business development related to converging industries and technologies.

Technological standardization can promote the development of complex products and systems (CoPS) (e.g. large aircraft, high-speed trains). Many studies argue that a superior network position contributes to improving a firm's ability to innovate; however, few studies have considered the aforementioned line of research as falling under the purview of CoPS standardization. This study focuses on CoPS integrators, which hold the most important enterprise roles in CoPS manufacturing and innovation, to analyze whether the two types of superior positions (i.e. the central position and the brokerage position) help the integrator influence the standardization process in a process moderated by technological complexity.

Considering the outstanding market performance of the railway transportation industry in China, the abundance of cooperative activities in this industry, and data availability, we adopt integrators from this industry as samples for the empirical analysis in this study. A negative binomial regression analysis with random effects was conducted to analyze the acquired panel data.

The results show that the central position has an inverted U-shaped effect on the integrator's ability to influence standardization and that the brokerage position has a positive effect. We also find that the underlying mechanism of the two types of network positions impacts the integrator's ability to influence standardization and that it has two modes: knowledge integration in related domains and knowledge integration in nonrelated domains. Moreover, technological complexity attenuates the effect of the central position and strengthens the effect of the brokerage position on the integrator's ability to influence standardization.

This paper contributes to the theoretical research on CoPS manufacturing and innovation and complements knowledge-based theory. The results could prove enlightening for integrators facing complex technological environments, allowing them to establish a reasonable external knowledge search strategy and choose appropriate cooperation partners according to their knowledge domains.

The purpose of this paper is to highlight the use of big data through patentometric insights for R&D decision-making.

This study assesses the inventive activity through ‘big data’ patents, registered by inventors worldwide, using WIPO Patentscope database. The objective is to use the insights from patentometrics for R&D decision-making. The data from WIPO PatentScope (https://patentscope.wipo.int/search/en/search.jsf) was searched for current patent scenario in area of ‘big data’. The data was further organized and cleaned using the Google ‘OpenRefine’. Data was pre-processed to remove all null values. Cleaned data was analyzed using programming language ‘R’, MS Excel (charts and Pivot tables) and free data visualization tool called ‘Tableau Public’, to get insights for R&D decision-making.

The key insights included trends (patents with years of publication), top technologies trending the current space, top organizations leading in these technologies and the top inventors who are publishing patents in these technologies through leading organizations were drawn. Details in Section 5 in the paper.

Global patent data is multi-lingual and spreads across a set of multiple databases. Domain experts may be required to assess, identify and extract the relevant information for analysis and visualization of multi-lingual distributed data sets. Government organizations generally have multi-dimensional goals that may be more toward societal benefits. On the other hand, the commercial companies are more focused on profit. Therefore, the performance management process has to be really effective because it is critical for getting value in the government sector.

Insights from patent analytics serve as the important input to R&D managers as well as policymakers to assess the global needs to plan the national orientation according to the global market. This will help further for R&D projects prioritization, planning, budget allocations, human capital planning and other gamut of R&D management and decision-making.

Facilitation for R&D institutions (government as well as private) to formulate the research strategy for the domains or research areas to delve into. R&D decisions will be completely data-driven making them more accurate, reliable, valid and informed. These insights are very relevant for policymakers as well to facilitate the need assessment to determine the National priorities, make improvements in meeting societal country-level challenges during the resource allocation at top and subsequently at all other levels.

Data analytics of global patents in “big data” till 2019 to get insights to facilitate R&D decision-making.

The purpose of the paper is to advance a framework that can analyze the impact of the egocentric alliance network on firm ambidextrous innovation holistically. On this purpose, the framework involves and integrates structural holes (SH), alliance functional diversity (AFD) and alliance partner geographical diversity (APGD) that measure network characteristics from structural, relational and nodal perspectives, respectively.

The authors collected multi-source matching panel data including alliance deals, technical patents and financial information during 2000–2017 of the 106 top high-tech firms in the computer, communications, electronics and biopharmaceutical industries and conducted a three-way interaction model to uncover the complex mechanisms from a contingency perspective.

Empirical results show that SH as structural capital is positive to both exploitative and exploratory innovation. Both AFD as relational capital and APGD as cognitive capital positively moderate the SH-exploitative innovation nexus rather than the SH-exploratory innovation nexus. APGD and AFD co-moderate the relationship between SH and firm ambidextrous innovation in the way that when APGD and AFD are both high, SH has the strongest positive impacts on firm exploitative and exploratory innovation.

This research provides new insights into the benefit-cost paradox of the structural brokerage position (SH) by uncovering relational (AFD) and nodal (APGD) network characteristics that impact the trade-off. Also, it contributes to social capital theory by differentiating three dimensions of network capital (structural, relational and cognitive capital) and integrating them into ambidextrous innovation research. Finally, the findings give firms enlightenment to configure their egocentric alliance network for innovation ambidexterity.

Existing innovation frameworks suggest that manufacturing firms have traditionally developed a complementary model of technological innovations comprising process and product innovations (e.g. Oslo Manual). This article presents digital service innovation as a novel form of technological innovation that is capable of enhancing the performance of firms in certain manufacturing industries.

Drawing on technological innovation and digital servitization fields of research, this study argues that digital service innovation, in manufacturing contexts, complements traditional sources of technological innovation, so increasing the profit margins of firms. This effect is significant in industries characterized by business-to-business contexts, high presence of link channels and long product life spans (e.g. manufacturing and computer-based industries). Predictions are tested on a unique sample of 423 Spanish manufacturing firms using parametric (t-test) and nonparametric (fuzzy-set qualitative comparative analysis, fsQCA) approaches.

The results of this analysis show that a necessary condition so that manufacturing firms can increase profits is the deployment of simultaneous process and product innovations. It also reveals that optimal configuration requires that digital service innovation be undertaken, particularly in machinery and computer-based manufacturing industries. Hence, all three sources of technological innovation are brought together in order to reach the highest levels of company performance. The evidence suggests that technological innovation and digital servitization are closely interrelated in highly innovative manufacturing contexts.

This study's originality and value reside in the fact that it reveals the existence of firms incorporating digital service innovation – a new, technological innovation dimension that challenges existing innovation frameworks – to complement traditional technological innovation sources, namely process and product innovation. Moreover, the study conceptualizes and empirically tests the value-adding role of digital services in firms' technological innovation portfolio.

This paper aims to show the interaction effects between clusters and cluster-specific attributes and the industrial internet of things (IoT) knowledge of a firm on the innovativeness of firms. Cluster theory and the concept of key enabling technologies are linked to test their effect on a firm’s incremental and radical knowledge generation.

Quantitative approach at the firm-level. By combining several data sources (e.g. ORBIS, PATSTAT and German subsidy catalogue) the paper relies on a unique database encompassing 8,347 firms in Germany. Ordinary least squares (OLS)-regression techniques are used for data analysis.

Industrial IoT is an important driver of radical patents, mediated positively by firm size. For incremental knowledge, a substitution effect occurs between a cluster and IoT effects, which is bigger for larger firms and dependent on cluster attributes and firms’ outside connections.

The paper opens up new research paths considering long-term disruptive effects of the industrial IoT compared to short-term effects on the innovativeness of firms within clusters. Additionally, it enables further research enriching the discussion about cluster attributes and how these affect ongoing processes.

Linking cluster theory and policy with Industry 4.0 raises awareness for being considerate in terms of funding and scrutinising one-size-fits-all approaches.

Connecting the concepts of a cluster and advanced manufacturing technologies as a proxy for industrial IoT, specifically focussing on both radical and incremental innovations is a new approach. Especially, taking into account the interaction effects between cluster attributes and the influence of industrial IoT on the innovativeness of firms.