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Effect of Big data and Linked Open Data in innovation of IPR management processes (REF: SF18/BAM/DAMIJ)

  • Full or part time
    Dr N Damij
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Intellectual Property Rights (IPR) data can shape (present and future) investment decisions in R&D, protect companies freedom to operate, assess the value and strength of their IP portfolio, assess the return on investment on intellectual property rights, help identify relative strengths and weaknesses in comparison to their competitors and enable companies to understand more about the potential opportunities and explorable empty spaces in new markets or possible future trends. Technology enabled IPR management allows for these goals to be met by combining existing organisational level knowledge with external IPR big data, and by including various actors and setting clear goals.

Big Data is the buzz term at the moment and has been so for a decade now. Companies want to deal with it successfully, analysts analyse it and managers to make informed and effective decisions based on Big Data analytics. Literature (scientific as well as best practices and white papers) provides concrete amount of evidence around the use of Big Data and its advantages/disadvantages (e.g. Ward & Barker, 2013). However, a commonly recognised definition is yet to be published. More importantly, how can companies deal with (IPR) Big data efficiently? McAfee and Brynjolfsson (2012) argue that companies won’t reap the full benefits of a transition to using Big Data unless they are able to manage change effectively. Intellectual property rights data in particular has all key characteristics to be classified as Big Data; the daily created volume of data is exponentially increasing (with over 50 million intellectual property rights (IPR) in force (WIPO, 2016)), velocity is even more imperative and potentially influential for the IPR savvy companies to be first or to gain a competitive advantage via IPR, and variety of IPR related Big Data sources is expanding by the minute. The need to recognise potential IPR Big Data is however, a challenge for the majority of IPR savvy companies.

The project will start with the examination of the innovation of IPR management processes, their evolution from the traditional to current approaches, and continue with investigation of the effect IPR Big Data and IPR Linked Open Data have on IPR management processes. Specifically the aims are fourfold. Firstly, IPR Big Data, and the needs to effectively manage it, demand IPR Big Data-enabling software tools or technology solutions. Secondly, IPR savvy companies cannot utilise IPR Big Data-enabling software tools or technology solutions without addressing all areas of the Five management challenges framework, particularly those linked to the internal factors that need to match the functionalities of the software tools. Thirdly, with a variety of available IPR software tools, some of which with a specific focus (such as recognising IPR legal data, patents text recognition, docking support, etc.) the future of IPR management tools are the hybrid software tools, which incorporate all available IPR databases, analyze them with different data analysis techniques and are adapted for different types of users. Lastly, matching existing IPR Big Data needs with existing IPR Management tools is still challenging as companies on one hand struggle with the identification of potential needs and uses of IPR Big Data-enabled tools and on the other the IPR Big Data software tools themselves are not yet developed to the level that would support that.

Eligibility and How to Apply:
Please note eligibility requirement:
• Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
• Appropriate IELTS score, if required.

For further details of how to apply, entry requirements and the application form, see

Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. SF18/…) will not be considered.

Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University holds an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality and is a member of the Euraxess network, which delivers information and support to professional researchers.

Funding Notes

Please note this is a self-funded project and does not include fees


Recent publications by supervisors relevant to this project

MODIC, Dolores, DAMIJ, Nadja. Towards Intellectual Property Rights Management. Palgrave Pivot, 2017. ISBN 978-3-319-69010-0.
MODIC, Dolores, DAMIJ, Nadja. "Own-it": managing intellectual property processes via the activity table in creative industries. In: LUGMAYR, Artur (ed.), et al. Information systems and management in media and entertainment industries, (International series on computer entertainment and media technology (Online), ISSN 2364-9488). Springer, 2016.
MODIC, Dolores, DAMIJ, Nadja. Optimizing patent exploitation phase processes using the activity table. In: ZHUANG, Xiaodong (ed.), GUARNACCIA, Claudio (ed.). Recent researches in applied informatics: proceedings of the 6th International Conference on Applied Informatics and Computing Theory (AICT'15), Salerno, Italy, June 27-29, 2015. WSEAS Press. 2015.
DAMIJ, Nadja, BOSKOSKI, Pavle, BOHANEC, Marko, MILEVA BOSHKOSKA, Biljana. Ranking of business process simulation tools with DEX/QQ hierarchical decision model. PLOS ONE, 2016.
DAMIJ, Nadja, LEVNAJIC, Zoran, REJEC SKRT, Vesna, SUKLAN, Jana. What motivates us for work?, Intricate web of factors beyond money and prestige. PLOS ONE, 2015, vol. 10, no. 7, pp. 1-13.
DAMIJ, Nadja and DAMIJ, Talib (2014) Process Management: A Multi-disciplinary Guide to Theory, Modeling and Methodology. Springer, ISBN-10:3642366384, ISBN-13:978-3642366383.

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