Research Projects

Developing an IP strategy can be a difficult task as there is hardly any established approach that supports this process. In this research, we are developing a novel approach to help organisations to develop their IP strategies.

 

 

The approach is based on the well-established and widely used roadmapping method. The tool aims to facilitate the development of an IP strategy through a structured, yet flexible process.

 

The result is a visible strategy that helps communication and consensus building among stakeholders. The process integrates different levels of strategy in order to increase the prospects for better decision-making in support of developing IP strategies.

 

 

 

This research contributes to a novel IP management tool focusing in particular on the challenges associated with the strategic alignment of IP strategy with overall corporate or business strategy. 

 

The research is kindly supported by the China Scholarship Council (CSC). 

 

Project lead: Tianyi Wang

 

Related publications:

 

Wang, T., F. Tietze and R. Phaal (2018). Intellectual property strategy development through roadmapping. RnD Management Conference. Milan.


Wang, T., F. Tietze, R. Phaal and N. Athanassopoulou (2017). Roadmapping for Strategic Management of Intellectual Property. RnD Management Conference. Leuven, Belgium.

In today’s industry where technologies are more interdependent firms are faced with challenges in managing their IP. Therefore a few questions have been raised: how does IP strategy co-evolve with firms’ innovation ecosystems? What is the mechanism in this process? What are the key factors that influence this process?

This project aims to address these challenges and coin the concept of IP strategy from an external and dynamic view by applying the innovation ecosystem lens. To reach the objective, we investigate the co-evolutionary process of a firm’s IP strategy and innovation ecosystem.

The project employs a process research and inductive qualitative methods. Four companies in the ICT industry have been selected as case companies. By conducting data collection and analysis following the methodology mentioned above, the research expects to deliver the following outcomes:

  1. Theoretical frameworks of IP strategies and ecosystem strategies as well as their co-evolutionary process that describe a technology firm’s behavior in technological competition;
  2. A set of exogenous factors that are key to the co-evolutionary process;
  3. A process model that presents the mechanism of how IP strategies, ecosystem strategies and the identified exogenous factors interact in the co-evolutionary process.

The research project will contribute to both academia and practice. First it employs an external and dynamic view to study IP strategy, which is novel to the IP research field. Particularly it proposes the ecosystem lens and can be further lead to more IP strategy research. Second, it provides managerial implications for IP practitioners or even C-level executives on how to utilise IP strategy to fulfil its ecosystem strategy and orchestrate its innovation ecosystem, in order to compete in the dynamic technology environment.

This project is supported by Cambridge Trust and the China Scholarship Council.

Project lead: Mingjin Guo

In 2015, countries globally adopted the Sustainable Development Goals to protect the planet, end poverty and ensure prosperity for all as part of a new sustainable development agenda. Effective transitions to sustainability require innovations with complex diffusion and adoption processes. The accompanied evolutionary technology development processes involve complex and intertwined IP related issues. The choice of the ‘right’ IP model however depend on a range of yet insufficiently understood contextual factors. Further, the role of IP for effective transitions to sustainability remains insufficiently understood. The project IPACST addresses the above gaps and contributes to the integration of the two fields - IP and sustainability - through frameworks that conceptualize (i) which, (ii) how and under (iii) what conditions IP models accelerate sustainable transitions, in connection with sustainable business models for high impact areas including clean energy and circular economy.

 

Funding support 

 

The project is jointly funded by the Belmont Forum and NORFACE through the Economics and Social Sciences Research Council (ESRC) and the Global Challenge Research Funds (GCRF) as well as the EU Horizon 2020 programme and other national agencies and ministries.

 

Project Lead: Dr Pratheeba Vimalnath

 

Relevant publications / conference presentations

 

Eppinger, E., N. Bocken, C. Dreher, A. Gurtoo, R. H. Chea, S. Karpakal, V. Prifti, F. Tietze, and P. Vimalnath. The Role of Intellectual Property Rights in Sustainable Business Models: Mapping IP Strategies in Circular Economy Business Models. 4th International Conference on New Business Models, Berlin. 1-3 July 2019.

 

Vimalnath, P., F. Tietze, E. Eppinger and J. Sternkopf. Open, semi open or closed? Towards an intellectual property strategy framework. 19th European Academy of Management (EURAM) Conference, Lisbon (Portugal). 26-28 June 2019.

 

Tietze, F., J. Sternkopf, E. Eppinger and P. Vimalnath. 2017. IP Strategies and Sustainable Technologies: When Do Open IP Strategies Increase Sustainable Impact?, DRUID17, New York, USA, June 12-14, 2017.

 

Tietze, F., J. Sternkopf, E. Eppinger and P. Vimalnath. 2017. IP strategies for sustainability - When does Open IP Increase Sustainable Impact?. 1st Annual International Conference of the IEEE Technology and Engineering Management Society (TEMSCON 2017), California, USA. June 8-10, 2017.

 

Sternkopf, J., Tietze, F., Eppinger, E., & Vimalnath, P. Open IP strategies for enabling sustainability transitions. CTM Working Paper series, No. 10, 2016.

This research project focuses on the recent phenomenon of Open IP strategies and approaches. Examples are the 2014 patent pledge of Tesla Motors following Elon Musk's announcement that everyone can use Tesla’s patenThis research project focuses on the phenomenon of so-called patent pledges. Over the past decade, firms have increasingly announced the free or reasonable availability of active patents to the public or a large group of parties. Tesla Motors, Toyota, and Google are only a few prominent examples for firms who apply such strategies. 

 

To better understand this seemingly contradictory approach to intellectual property, this project aims to answer the following three research questions:

(1) What are the characteristics of patent pledges?

(2) What are the motives of firms to adopt patent pledges?

(3) How do patent pledges impact the technology diffusion of emerging technologies?

 

The individual research questions and their methodology are briefly summarised below.

 

(1) What are the characteristics of patent pledges?

We analyse 60 patent pledges using qualitative coding to develop two taxonomies: the patent pledge taxonomy and the patent licensing taxonomy. While the patent pledge taxonomy distinguishes between eight types of pledges, the patent licensing taxonomy (see figure below) gives an overview of common licensing-approaches such as patent pools and cross-licensing.

 

Figure 1: IP Licensing Framework (Source: Ehrnsperger, University of Cambridge, 2018)

 

(2) What are the motives of firms to adopt patent pledges?

We conduct about 30 semi-structured interviews with IP-experts from a variety of industries to uncover rationales behind patent pledges, half of which work in an organisation that applies patent pledges. Furthermore, we gain insights about the motives from the 60 patent pledges collected for research question 1. 

 

(3) How do patent pledges impact the technology diffusion of emerging technologies?

We use agent-based modeling to simulate technology adoption processes in the context of patent pledges. Specifically, we model the adoption-decision of technology-users while also incorporating their social network. Factors that influence the decision will be derived from the literature and statistical analysis of survey data. After conducting experiments with the model parameters, we will be able to measure the impact of patent pledges on technology diffusion in different technology life-cycle stages. Ultimately, this will benefit organisations in their decision whether to apply patent pledges to increase adoption rates.

 

This project is funded by the Engineering and Physical Sciences Research Council (EPSRC), respectively UKRI and the Research and Development Management Association (RADMA).

 

Project lead: Jonas Ehrnsperger

 

 

Related publications

 

Ehrnsperger, J. F. and F. Tietze (2019). "Patent pledges, open IP, or patent pools? Developing taxonomies in the thicket of terminologies." Plos One 14(8): e0221411.

 

Ehrnsperger, J. and F. Tietze (2019). The patent pledge taxonomy. IEEE Temscon. Atlanta, US.

Our transition towards a more sustainable and resource-efficient bioeconomy has been urged by both researchers and policy makers. This project aims to transform our understanding of the impact of opening up IP models with bioeconomy related technologies.

 

The first part of the project focuses on exploring existing open IP models, their characteristics and firms’ willingness to adopt them. With the results, case studies are built to assess the impact of firms adopting these open IP models. After that, investigation will be conducted with regard to mechanisms and conditions of how open IP models can be best used to impact our transition towards a bioeconomy. Among all the bioeconomy related technologies, this project currently focuses on synthetic biology, particularly targeting on synthetic engineering of plant and microbial systems.

 

 

 

 

The project also aims to provide insight into i) the impact of changing business strategy ii) the impact of aligning the company’s IP strategy to the change in the overall business strategy iii) implications for the ability of businesses to adopt new synthetic biology tools and technologies and iv) identifying IP models that will play a role in successful business models for implementation of a sustainable bioeconomy.

 

This project is supported by UKRI, respectively the Economic and Social Research Council (ESRC) and the Cambridge Trust, and is in collaboration with the Department of Plant Sciences and the OpenPlant Synthetic Biology Research Centre. It is also aligned with the Synthetic Biology Strategic Research Initiative of University of Cambridge.

 

 

Project lead: Aocheng Tang

 

 

Related Publications

 

Tang, A., Tietze, F., & Molloy, J. (2019) Openness in Intellectual Properties of Synthetic Biology Start-ups. IEEE Technology and Engineering Management Society Conference, Atlanta, USA.

 

Tang, A., Tietze, F., & Molloy, J. (2018) Openness and Closedness of Company’s IP Strategies: an exploratory study on synthetic biology start-ups in the UK. R&D Management Conference, Milan, Italy.

Navigating Competitive Dynamics with Intellectual Property Strategies in Service Business Ecosystems in the Context of Servitizing Manufacturers 

Next to building offshore factories in developing economies and aiming for cost leadership to sustain competitive advantage, manufacturing companies have also chosen to develop services and solutions complementing their products, so-called integrated product-service offerings. In a seminal paper published in 1988, Vandermerwe and Rada observed this movement and were the first to denote this phenomenon as the servitization of business. When applying the lens of the more recent business ecosystem approach, it becomes apparent, however, that a servitizing manufacturing firm may develop capabilities during this transition process that have previously been performed by other actors downstream in the value chain, thus creating competitive tensions with the incumbent firms.

In this research project we focus on the competitive challenges exerted by servitizing manufacturers on a particular group of actors in a business ecosystem whose competitive position is most obviously affected by servitization. We propose to name this group of business ecosystem actors Incumbent Service Providers (ISP) and define them as firms that provide advanced services even before manufacturing firms embark on a servitization transformation journey. Ultimately, this research project has the following goals:

  • To develop an understanding of the effects of servitization on ISP’s competitive position in a service business ecosystem; and
  • To apply this knowledge for the identification of a bespoke portfolio of Intellectual Property (IP) and respective strategies enabling ISP to sustain their competitive advantage in a service business ecosystem affected by the servitization of manufacturing.

 

This will be accomplished by first investigating the challenge mechanisms that servitizing manufacturers exert on a services business ecosystem (initial results from our research are shown in the figure below) and subsequently by identifying IP management strategies utilized by service providers.

The primary focus of this project will initially be the commercial aircraft maintenance, repair and overhaul industry sector, but we aim for an analytic generalization to other industry sectors.

 

Project lead: Alexander Mörchel

 

Publications

 

Moerchel, A. and Tietze, F. (2019). How Servitizing Manufacturers Impact Ecosystem Dynamics – The Case of Incumbent Service Providers in Commercial Aircraft Maintenance, Repair and Overhaul. In: Proceedings of the Spring Servitization Conference. Birmingham: The Advanced Services Group, pp. 291-293.

This international and collaborative project focuses on IP rights and their management with emphasis on digital property, such as data and data driven businesses and industries.

The project is funded by Vinnova, the Swedish agency for innovation system research and  involves colleagues from Chalmers University of Technology (e.g. Ove Granstrand and Marcus Holgersson), the University of Gothenburg (e.g. Marueen McKelvey) and Uppsala University (e.g. Bengt Domeij).

 

The aim of this research project is to increase our managerial, economic, legal and technological (MELT) knowledge about:

 

  • the theoretical and empirical relations between the 5 key factors: R&D, intellectual assets (IAs), innovations, growth and societal value creation 
  • how digital technologies interact with these relations.

 

We are particularly involved work packages focusing on patent information analysis and the role and design of IP rights for data based industries. The project runs for two years starting in February 2018.

 

Further details are available here. 

Big data is increasingly available in all areas of manufacturing and operations. Increased data availability presents an opportunity for better decision making, to introduce the next generation of innovative and disruptive technologies. While intellectual property (IP) data is abundantly available, for many firms still remains a problem on how they can fully use this source of technical information. Firms struggle to decide how to better analyse IP data, to support and complement strategic decision making processes in the stages of technology and innovation development projects. In addition, while machine learning algorithms have widely been applied in other fields to analyse large amounts of data, they hardly been applied in the IP domain.

 

 

We aim to complement technology strategic decision making with IP analytics, which in turn improves the human judgement at the technology development process. We follow a system design approach, where we design, develop and test a series of IP Decision Support Tools (DST), which make use of deep learning algorithms, to analyse patent data and classify a technology project, which is underpinned by a technology patent, as successful or not, to go through the innovation management funnel. From the literature, a successful patent can be defined as one with a large number of forward citations, one with consecutive renewal periods and one which has been litigated in court and won. The methodology is applied to a number of case studies to test its suitability within the technology development process. After refinement, we explore a number of alternatives to expand the model such as the addition of more data sources or its application at different stages of the innovation process. This methodology also improves the data quality, and the quality and validity of patents that are granted, as it benchmarks a potential application before the patent application stage.

 

Project lead: Leonidas Aristodemou

 

Related Publications

 

  • Aristodemou, L., & Tietze, F., 2018. The state-of-the-art on Intellectual Property Analytics (IPA): A literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data. World Patent Information, 55 37-51. https://doi.org/10.1016/j.wpi.2018.07.002
  • Aristodemou, L., & Tietze, F., 2018. Citations as a measure of technological impact: a review of forward citation-based measures. World Patent Information, 53 39-44. https://doi.org/10.1016/j.wpi.2018.05.001Conference

 

Conference papers

 

  • Aristodemou L., & Tietze F., 2019. Early Stage Identification of Valuable Technologies: a Deep Learning approach, In Topic: Intellectual Property and New Research Methods, European Policy for Intellectual Property (EPIP) Conference 2019, ETH Zurich & EPFL, Zurich, Switzerland
  • Jeong Y., Aristodemou L., & Tietze F., 2019. Exploring disruptive innovation opportunity using patent analysis and deep learning, In Track 1: Artificial Intelligence and Data Science, R&D Management Conference 2019, L' École Polytechnique & HEC Paris, Paris, France
  • Silva, R., Koshiyama, A., & Aristodemou, L., 2019 Linking Research Entities to Industrial Sectors: a hybrid methodology applied to Brazil’s nanotechnology sector, In Data for Policy 2019: Digital Trust and Personal Data Conference, University College London, London, United Kingdom
  • Aristodemou, L., Tietze, F., & Brintrup A., 2018. Early Stage Technology Strategic Decision Making: a machine learning approach using Intellectual Property Analytics, In Track 18: Big Data Analytics for R&D Management, R&D Management Conference 2018, Politecnico di Milano, Milan, Italy
  • Aristodemou, L., Tietze, F., Athanassopoulou, N., & Minshall, T., 2017. Exploring the Future of Patent Analytics: A Technology Roadmapping Approach. In Theme MC-4: Intellectual Property Management in Innovation, R&D Management Conference 2017, KU Leuven, Leuven, Belgium

 

Working papers

 

  • Aristodemou, L., Tietze, F., Brintrup, A., & Deeble, S., 2019. Intellectual Property Analytics Decisions Support Tool (IPDST) for Early Stage Technology Decision Making. Centre for Technology Management (CTM) Working Paper Series, January 2019 (1), pp.1-7. https://doi.org/10.17863/CAM.35544
  • Aristodemou, L., Tietze, F., O'Leary, E., & Shaw, M., 2019. A Literature Review on Technology Development Process (TDP) Models. Centre for Technology Management (CTM) Working Paper Series, January 2019 (6), pp.1-32, Cambridge, UK. https://doi.org/10.17863/CAM.35692
  • Aristodemou, L., & Tietze, F., 2019. Technology Strategic Decision Making (SDM): an overview of decision theories, processes and methods. Centre for Technology Management (CTM) Working Paper Series, January 2019 (5), pp.1-22, Cambridge, UK. https://doi.org/10.17863/CAM.35691
  • Aristodemou, L., Tietze, F., Athanassopoulou, N., & Minshall, T., 2017. Exploring the Future of Patent Analytics: A Technology Roadmapping Approach, Centre for Technology Management (CTM) Working Paper Series, November 2017 (5), pp.1-10, Cambridge, UK. Available at: http://doi.org/10.17863/CAM.13967
  • Aristodemou, L. & Tietze, F., 2017. A literature review on the state-of-the-art on intellectual property analytics, Centre for Technology Management (CTM) Working Paper Series, November 2017 (2), pp.1-15, Cambridge, UK. Available at:http://doi.org/10.17863/CAM.13928

Reports

  • Aristodemou, L. & Tietze, F., 2017. Exploring the Future of Patent Analytics. Centre for Technology Management (CTM) Insights Report, ISBN: 978-1-902546-84-1, Institute for Manufacturing, University of Cambridge, Cambridge, UK. Available at:https://www.ifm.eng.cam.ac.uk/insights/innovation-and-ip-management/expl... the-future-of- patent-analytics/
  • Aristodemou, L., 2015. Analysing Patent Influence and Patent Importance Across the Industrial Boundary of 3D Printing, Masters Thesis, Institute for Manufacturing, University of Cambridge

Online articles

This project aims explore and predict potential disruptive innovation opportunity. The disruptive innovation opportunity means potential and promising technology and innovation to penetrate and reshape mainstream as well as niche market. In other words, we find answer the questions – first, which technology will penetrate new market? Second, how is this disruptive technology implemented as business?

 

 

The main technique to solve these questions are machine learning and deep learning. Deep learning is a technique for learning and inference based on big data and it can solve hard problem in practice without information loss. Especially, it provides ‘right’ insights through iterative learning with high performance improvement. Machine learning and deep learning support are well-suited to deal with various type of data – both structured and unstructured data as coming of big data age.

 

This project serves as an ex-ante forecasting while considering existing technological paradigm because we will use both unsupervised learning and deep learning such as semi-supervised learning. It enables to early detection and forecasting by accumulating newly generated data not evaluating and classifying after implementing technology.

 

Project team: Dr Yujin Jeong, Leonidas Aristodemou, Frank Tietze

 

Related publications

 

Yujin Jeong, Leonidas Aristodemou, Frank Tietze (2019). Exploring disruptive innovation opportunity using deep learning. The R&D Management 2019 Conference, Paris, France.

Scholars have long emphasized the importance of cross-functional teams for the purpose of successful innovation. Some argue that a cross-functional team is essential for product success but recent findings and also mention the inconsistency of research findings. The conflict potential in cross-functional interactions are present due to the problem of heterogenic goals, activities and knowledge.


The concept of organisational citizenship behaviour is grounded on the premise that individual, citizenship-like behaviour supports the effective functioning of the organization. OCB is, despite its appearance in the 80’s, still an emerging stream of research with multiple adaptations and conceptualisations. We aim to develop a novel measurement model based on the OCB theory with a distinct departmental directionality in order to measure behavioural disequilibrium in cross-functional activities.

 

Project lead: Cihat Cengiz

Developing innovations for the digital economy, such as IoT devices and connected mobility solutions is likely to require OEMs to combine IP from multiple sources (licensors) who exploit their IP to as many as possible licensees (other OEMs). Those involved in the provision of what we call distributed multi-IP solutions (d-mIPs) find themselves entangled in a complex many-to-many network or ‘licensing web’ having to operate payments based on licensing contracts under a variety of terms and conditions.

 

 

 

In the digital economy and ‘pro-licensing era’ efficiently operating licensing payments to/from multiple licensors/licensees becomes increasingly mission-critical. Unfortunately, the current semi-manual processes are inherent of information asymmetries, uncertainties, trust problems and transaction costs, hence must be considered as inefficient. This project explores the challenges licensees and licensors face when operating licensing payments for d-mIPs.

 

We are interested in automated licensing payment systems (ALPS) based on distributed ledger technologies and smart contracts for automating trustworthy licensing payments that can substantially reduce currently existing challenges. Those systems not only contributes to enabling the digital economy, but have further potential to enable new business models. Automated Licensing Payment Systems (ALPS) are defined as platform technologies that enable the automated calculation, execution and verification of accurate payments from licensees to licensors. ALPS seek to lower costs for administering payments, while increasing accuracy, reducing the trust problem and eliminating the need for audits, overall enabling more efficient licensing and novel business models as well as contributing to fairer value distribution to inventors.

 

The project is kindly supported by Research England via the Pitch-In project. 

 

  Related publications

 

Fletcher, S. and F. Tietze (forthcoming). Automating licensing payments for connected devices – A techno-economic analysis of DLT based systems. Blockchain: a managerial perspective for industry. F. Urmetzer, Springer.


Tietze, F. and O. Granstrand (2019). Enabling the digital economy - distributed ledger technologies for automating IP licensing payments. Managing Innovation in a Global and Digital World - Meeting Societal Challenges and Enhancing Competitiveness. R. Tiwari and S. Buse, Springer Gabler.

 

 

Project lead: Dr Frank Tietze

 

The European Union (EU) has been promoting Open Innovation collaboration models as part of its digital single market and innovation policies. Open Innovation enables inter alia faster innovation cycles. 

 

The POINT project (Project on Open Innovation and Intellectual Property) has been comissioned by EASME (EASME/2019/OP/OO17) to provide an empirical basis for existing collaboration models based on Open Innovation and the extent to which intellectual property (IP), including patents, utility models, designs, trade secrets and know-how, facilitates or hinders Open Innovation.

 

 

Description 

 

POINT consists of 8 work items, over four main tasks: Planning, Data collection, Analysis and Policy Recommendations. Commencing in May 2020 POINT aims to engage with SMEs, HEIs and PROs across Europe in order to collect evidence through interviews.

 

Organisations will be selected based on pre-determined criteria, and from within six value chains from: 

  1. Microelectronics
  2. Internet of Things (IoT)
  3. Connected, clean and autonomous vehicles
  4. Food and FMCG
  5. Smart health
  6. Cybersecurity

If your company is an SME active in any of the above value chains and based anywhere in Europe, please consider sharing your contact details if you like to participate in our project. Essentially that requires a short interview. Use this link:

 

https://forms.office.com/Pages/ResponsePage.aspx?id=RQSlSfq9eUut41R7TzmG6Wc43czMjclPnvsnMGJ1KK1UNkJEUzJQOEtHREs1NjBSNzI3TkVQOEQ5WS4u

 

 

Objectives 

  • to identify and describe categories of Open Innovation (‘OI’) processes 
  • to identify and describe the typical IP issues/arrangements/implications occurring in OI processes 
  • to identify ways in which IP should be managed to facilitate the implementation of certain OI processes as well as their potential impact on the profitability of individual stakeholders involved 
  • to detect possible problems relating to the use of IP in an Open Innovation context, and identify ways to overcome them 
  • to analyse the participation of SMEs, Higher education institutes (HEIs) performing research and other public research organisations (PROs) in Open Innovation collaborations and identify the opportunities and challenges facing SMEs, in particular with regard to their IP strategy

 

Outcomes

 

POINT’s timeline also includes two workshop events that will seek to bring together experts from Open Innovation, Intellectual Property and Policy development. With an interim workshop in December 2020, and final workshop in April 2021, POINT aims to establish the driving factors and uses of OI collaborations and determine which IP factors promote or hamper the entry of the resulting product/services on the market. 

 

About EASME 

The EASME - Executive Agency for SMEs (https://ec.europa.eu/easme/en/section/about-easme) 

 

EASME’s vision: 'We aim to help create a more competitive and resource-efficient European economy based on knowledge and innovation' 

 

Contact information of POINT core team: 

 

Graham Bell (project lead) - IfM ECS Industrial Associate

gb633@cam.ac.uk 

 

Dr Frank Tietze - Head, Innovation and IP Management (IIPM) Lab 

frank.Tietze@eng.cam.ac.uk  

 

Dr Pratheeba Vimalnath - Research Associate, Innovation and IP Management (IIPM) Lab 

pv302@cam.ac.uk 

 

Dr Diana Khripko - IfM ECS

dk530@cam.ac.uk