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The purpose of this study is to analyse and explore the characteristics of patent–paper pairs (PPPs) in the field of fuel cells.

The authors used bibliometric analysis to analyse the bibliometric records of PPPs identified from 20,758 papers and 8,112 utility patents between 1991 and 2010.

The findings show that the percentages of papers and patents constituting PPPs were low, but an increasing trend was identified in the absolute number of PPPs. Researchers affiliated with research institutions were the primary contributors to PPPs. Countries with the most papers and patents had the most PPPs, exploiting the advantage of dual knowledge creation. Similar growth trends were observed in the numbers of patents approved and papers published. Patents in PPPs were typically produced earlier than the papers in PPPs. On average, patents were applied for approximately four years before papers were published, and patents were approved only approximately four months before papers were published.

While the study was limited to the PPPs in the field of fuel cells, PPPs analysis can be applied to numerous fields.

PPPs indicate the coactivity of researchers involved in publishing and patenting. Although this coactivity has been studied, few studies have investigated PPPs. This study helps us better understand the characteristics of papers and patents constituting PPPs, changes in the annual numbers of papers and patents constituting PPPs, delays between papers and patents, as well as individuals, institutions and countries producing numerous PPPs.

The purpose of this study is to evaluate the scientific performance of universities by extending the application of the h‐index from the individual to the institutional level. A ranking of the world's top universities based on their h‐index scores was produced. The geographic distribution of the highly ranked universities by continent and by country was also analysed.

This study uses bibliometric analysis to rank the universities. In order to calculate their h‐index the numbers of papers and citations in each university were gathered from Web of Science, including the Science Citation Index and Social Science Citation Index. Authority control dealing with variations in university names ensured the accuracy of each university's number of published journal papers and the subsequent statistics of their citations.

It was found that a high correlation exists between the h‐index ranking generated in this study and that produced by Shanghai Jiao Tong University. The results confirm the validity of the h‐index in the assessment of research performance at the university level.

The h‐index has been used to evaluate research performance at the institutional level in several recent studies; however these studies evaluated institutions' performance only in certain disciplines or in a single country. This paper measures the research performance of universities all over the world, and the applicability of the h‐index at the institutional level was validated by calculating the correlation between the ranking result of the h‐index and the ranking by the Shanghai Jiao Tong University.

The purpose of this research is to investigate the relationship between journal self‐citation and journal impact factor (JIF)/journal immediacy index (JII).

This research examined research papers in 20 key journals in environmental engineering with a publication year range of 1999 to 2008. The bibliographical information of cited references was obtained from the Science Citation Index Expanded of the Web of Science.

The findings indicated that JIF and JII values changed only slightly regardless of the inclusion or exclusion of self‐citations, suggesting that the influence of self‐citation on journals was insignificant. Consequently there is no need for evaluations to exclude journal self‐citations in journal or researcher evaluations. In addition the findings indicated that JIF and five‐year JIF were highly correlated, suggesting that it would not be necessary to extend the calculation of JIF to five years. Considering the cost in terms of time and effort, the two‐year JIF is sufficient in the discipline of environmental engineering.

This research provides a better understanding of journal self‐citations in journal or researcher evaluation with JIF and JII as indicators.

The purpose of this paper is to examine the characteristics and evolution of the technology‐dependence networks of leading semiconductor companies. By comparing and contrasting technology‐dependence networks in the 6‐, 8‐ and 12‐inch chip eras, this study clarifies the differences among integrated device manufacturers (IDMs) and foundries, and among each company in different eras.

Leading companies were identified by technological crowdedness and technological prestige to avoid massive actors. Strong ties were extracted to avoid too many relationship ties at the company network level. Strong ties represented directional technology relationships among companies whose citation counts and relative citation rates were higher. The technology‐dependence network of leading companies in three chip eras was examined by social network analysis.

Technology dependence among IDMs was the weakest, and their technology dependence upon foundries decreased in the 12‐inch chip era. The highest technology interdependence appeared among foundries and the reduction of their dependence upon IDMs. Technology dependence is expanded primarily by foundries, significant among GlobalFoundries, TSMC, UMC, and VIS.

IDM could invite foundries with technology dependence to form a strategic consortium. That way, the foundries could monitor potential competitors with relationship of technology dependence; in an advanced sense, the foundries could make use of the network to practice commercial maneuvers and create competitive advantage. Scholars may also observe semiconductor manufacturing technology's evolving into the maturity stage of product life cycle by interpreting foundries' highly technology interdependent relationships.

This is the first study to use strong ties in patent citation networks to represent technology‐dependence relationships.

Foundry, Design House, and integrated device manufacturers (IDM) are major characters in the semiconductor industry value chain. The purpose of this paper is to discuss patterns of characters' evolution in technology through patents classified as wafer‐design application patents and wafer‐process patents.

Various patent indicators, such as average patent citation count, and the combination of the average patent citation count and relative patent count share were used to measure the patent activity, patent quality, and the combination of the patent quality and relative patent activity share, respectively. The study period (1979‐2009) was divided into three major technology or wafer size eras, 1979‐1991 for the 6‐ and pre 6‐inch wafer era, 1989‐1999 for the 8‐inch wafer era, and 1997‐2009 for the 12‐inch wafer era.

Foundry has gradually become the technology transferor rather than purely the manufacturing capacity provider. Foundry's impact on the technology level has risen steeply on both the wafer‐process technology fields and the wafer‐design application technology fields. As a result, IDM, traditionally considered the primary technology contributor in the semiconductor value chain for the past 30 years, will continue to be challenged in the semiconductor industry.

Some hypotheses are clarified to provide managerial implications for the semiconductor industry. Owing to Foundry's rise in technology activity and quality, IDM/Design House should not merely view it as one of their capacity providers but should also pursue a technology alliance with it.

The paper clarifies the traditional hypotheses of the characters of technology in the semiconductor value chain.

Academic rankings are facing various issues, including the use of data sources that are not publicly verifiable, subjective parameters, a narrow focus on research productivity and regional biases and so forth. This research work is intended to enhance creditability of the ranking process by using the objective indicators based on publicly verifiable data sources.

The proposed ranking methodology – OpenRank – drives the objective indicators from two well-known publicly verifiable data repositories: the ArnetMiner and DBpedia.

The resultant academic ranking reflects common tendencies of the international academic rankings published by the Shanghai Ranking Consultancy (SRC), Quacquarelli Symonds (QS) and Times Higher Education (THE). Evaluation of the proposed methodology advocates its effectiveness and quick reproducibility with low cost of data collection.

Implementation of the OpenRank methodology faced the issue of availability of the quality data. In future, accuracy of the academic rankings can be improved further by employing more relevant public data sources like the Microsoft Academic Graph, millions of graduate's profiles available in the LinkedIn repositories and the bibliographic data maintained by Association for Computing Machinery and Scopus and so forth.

The suggested use of open data sources would offer new dimensions to evaluate academic performance of the higher education institutions (HEIs) and having comprehensive understanding of the catalyst factors in the higher education.

The research work highlighted the need of a purposely built, publicly verifiable electronic data source for performance evaluation of the global HEIs. Availability of such a global database would help in better academic planning, monitoring and analysis. Definitely, more transparent, reliable and less controversial academic rankings can be generated by employing the aspired data source.

We suggested a satisfying solution for improvement of the HEIs' ranking process by making the following contributions: (1) enhancing creditability of the ranking results by merely employing the objective performance indicators extracted from the publicly verifiable data sources, (2) developing an academic ranking methodology based on the objective indicators using two well-known data repositories, the DBpedia and ArnetMiner and (3) demonstrating effectiveness of the proposed ranking methodology on the real data sources.

The innovation process demands an interaction between environment agents, knowledge generators and policies of incentive for innovation and not only development by companies. Universities have gradually become the core of the knowledge production system and, therefore, their role regarding innovation has become more important and diversified. This study is aimed at identifying the mechanisms of university–industry (U–I) collaboration, as well as the operationalization steps of the U–I collaboration process.

This study is aimed at identifying, based on a systematic literature review, the mechanisms of university–industry (U–I) collaboration, as well as the operationalization steps of the U–I collaboration process.

The analysis of the 72 selected articles enabled identifying 15 mechanisms of U–I collaboration, proposing a new classification for such mechanisms and developing a framework presenting the operationalization steps of the interaction process.

In this paper, the authors screened nearly 1,500 papers and analyzed in detail 86 papers addressing U–I collaboration, mechanisms of U–I collaboration and operationalization steps of the U–I collaboration process. This paper provides a new classification for such mechanisms and developing a framework presenting the operationalization steps of the interaction process. This research contributes to both theory and practice by highlighting managerial aspects and stimulating academic research on such timely topic.

With the development of Web2.0 and publishing digitalization, traditional libraries and evaluation citation system can no longer indicate academic paper influence validly. Therefore, it is necessary to construct smart library and find the evaluation effect of Internet metrics-Usage.

This study puts forward four indexes of scholars’ evaluation based on Usage (total Usage (U), average Usage rate (U/N), h_u-index and p_u-index), which refer to citation indexes, takes the 35 high-output scholars in the field of library and information science in the WoS database as examples, analyzes performance of different scholars evaluation indexes based on Usage and compares the differences and correlations between “citation indicators” and “usage indicators.”

This study results show that p_u-index is the strongest index to evaluate scholars. Second, there is a high correlation and strong mechanism based on time dependence and interactions between Usage and citation. Third, compared to “citation indicators”, the “usage indicators” has a larger numerical value and wider measurement range, which can break the time limitation of citation, and scientifically evaluate young scholars and newly published paper by scholars.

This paper proposes the p_u-index – a relatively superior mathematical model for Usage and provides reference for the scholars’ evaluation policy of the smart library. This model can not only provide fair evaluation conditions for young scientists but also shorten the evaluation effect of the time lag of cited indicators. In addition, the “usage indicators” in this paper are new scientific evaluation indicators generated in the network environment. Applying it to the academic evaluation system will make the research papers widely accepted by the public and will also encourage scientists to follow the development of the Internet age and pursue research with equal emphasis on quantity and quality.