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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.

This study aims to examine the effects of international competitive strategies, i.e. cost leadership and differentiation, on export (market share and strategic) performance. This study further explores the roles of exploitative and exploratory organizational learning in the relationships between international competitive strategies and export performances. To fill research gaps, this study intends to provide guidance on how varied exploitative/exploratory organizational learning and cost leadership/differentiation strategy combinations would affect export performance. The outcomes of this study provide a new match and mis-match conceptualization to extant international competitive strategy and organizational learning literature.

This study selected New Zealand (NZ) exporting as the research setting because exporting plays such a vital role in NZ’s economy and NZ exporting firms have long been highly competitive in international markets (e.g. meat and dairy exporters), with the primary data collected through surveys conducted in 2010 and 2013. This study adopted a three-year lagged performance approach.

Cost leadership strategy has a positive effect on market share performance. This effect is enhanced by exploitative learning but dampened by exploratory learning. Cost leadership also has a positive effect on strategic performance, which is not affected by exploitative and exploratory learning. Differentiation strategy bears no relation to market share and strategic performance, even allowing for exploitative and exploratory learning. Collectively, the contingent role of organizational learning in the international competitive strategies and export performance framework is far more comprehensive than was expected.

This study reveals that a match between cost leadership strategy and exploitative learning may result in a superior market share. The configuration of differentiation strategy and exploitative learning and the integration of cost leadership strategy and exploratory learning are suggested as mis-matches, as these combinations would not lead to any significant and positive market share and strategic performance. Unexpectedly, the co-alliance of differentiation strategy and explorative learning is not suggested as a match, as it does not result in a superior market share and strategic performance. This latter outcome suggests that the differentiation strategy-export performance link may be stimulated by other moderating factors (e.g. business managerial ties).

While choosing an appropriate international competitive strategy, managers may use cost leadership over differentiation strategy to achieve successful export performance in both the market share and strategic perspectives. Export managers focusing on cost leadership strategy may further implement exploitative learning instead of explorative learning, when market share is vital. Meanwhile, they may note that explorative learning may not have a moderating effect on enhancing strategic performance through cost leadership. These points signify that exploitation of existing knowledge may be more effective than exploration of new knowledge for market share expansion when cost leadership strategy is devoted to exporting activities. Differentiation strategy, however, does not influence market share and strategic performance in exporting, even with an alignment of exploitative/exploratory learning. Managers are urged to pay attention to the mis-match of differentiation strategy and organizational learning when market share and strategic performance are the priorities in export performance evaluation.

This study contributes to the organizational learning literature by providing a new match and mis-match conceptualization relating to international competitive strategy and export performance. The new framework provides directions on when firms should use organizational learning to enhance their competitive strategies (a match scenario) and when they should not use it (a mis-match scenario). This study broadens the existing research that has mainly focused on alignment combinations such as organizational learning-internationalization strategy and organizational learning-social network.

Learning through international strategic alliances is usually influenced by dispersed locations and cultural difference between the countries of the two firms. This research highlights the importance of contextual factors on learning through international strategic alliances. Based on an empirical study of 271 alliances, our findings reveal that successful alliance learning not only depends on the partner’s openness to share knowledge but also relies on the firm’s capacity to identify and absorb such knowledge. Institutional differences between the countries from where partner firms originate are considered to hinder the alliance learning by decreasing the firm’s absorptive capacity and by enhancing knowledge ambiguity. However, our research suggests that frequent direct communication and high levels of mutual trust and reciprocal commitment between partner firms positively moderate the noxious effects of institutional differences on the alliance learning process.

The purpose of this paper is to propose an automatic metadata extraction and retrieval system to extract bibliographical information from digital academic documents in portable document formats (PDFs).

The authors use PDFBox to extract text and font size information, a rule-based method to identify titles, and an Hidden Markov Model (HMM) to extract the titles and authors. Finally, the extracted titles and authors (possibly incorrect or incomplete) are sent as query strings to digital libraries (e.g. ACM, IEEE, CiteSeerX, SDOS, and Google Scholar) to retrieve the rest of metadata.

Four experiments are conducted to examine the feasibility of the proposed system. The first experiment compares two different HMM models: multi-state model and one state model (the proposed model). The result shows that one state model can have a comparable performance with multi-state model, but is more suitable to deal with real-world unknown states. The second experiment shows that our proposed model (without the aid of online query) can achieve as good performance as other researcher's model on Cora paper header dataset. In the third experiment the paper examines the performance of our system on a small dataset of 43 real PDF research papers. The result shows that our proposed system (with online query) can perform pretty well on bibliographical data extraction and even outperform the free citation management tool Zotero 3.0. Finally, the paper conducts the fourth experiment with a larger dataset of 103 papers to compare our system with Zotero 4.0. The result shows that our system significantly outperforms Zotero 4.0. The feasibility of the proposed model is thus justified.

For academic implication, the system is unique in two folds: first, the system only uses Cora header set for HMM training, without using other tagged datasets or gazetteers resources, which means the system is light and scalable. Second, the system is workable and can be applied to extracting metadata of real-world PDF files. The extracted bibliographical data can then be imported into citation software such as endnote or refworks to increase researchers’ productivity.

For practical implication, the system can outperform the existing tool, Zotero v4.0. This provides practitioners good chances to develop similar products in real applications; though it might require some knowledge about HMM implementation.

The HMM implementation is not novel. What is innovative is that it actually combines two HMM models. The main model is adapted from Freitag and Mccallum (1999) and the authors add word features of the Nymble HMM (Bikel et al, 1997) to it. The system is workable even without manually tagging the datasets before training the model (the authors just use cora dataset to train and test on real-world PDF papers), as this is significantly different from what other works have done so far. The experimental results have shown sufficient evidence about the feasibility of our proposed method in this aspect.