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Predicting Technical Value of Technologies Through Their Knowledge Structure

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Predicting Technical Value of Technologies Through Their Knowledge Structure PREDICTING TECHNICAL VALUE OF TECHNOLOGIES THROUGH THEIR KNOWLEDGE STRUCTURE Praveena Chandra A thesis submitted to fulfil requirements for the degree of Doctor of Philosophy School of Civil Engineering Faculty of Engineering and Information Technologies The University of Sydney Australia February 2019 i Abstract This thesis tests the hypothesis that the characteristics displayed by the knowledge structure of a high technical value invention is different from that of a low technical value invention. The knowledge structure represents the relationship of the invention with all the prior knowledge upon which it is based. This structure crystalizes at the inception of the invention making it ideal for evaluating new inventions. More specifically, this research investigates two characteristics of the knowledge structure: knowledge accumulation and knowledge appropriation. Knowledge accumulation is defined as the collective body of knowledge, know-how and experiences gathered in a sector over time that have contributed to the creation of the invention. A higher degree of accumulated knowledge is more likely to be associated with high technical value inventions. Knowledge appropriation describes absorption of knowledge in the creation of the invention. From a knowledge structure perspective, knowledge absorption is observed by the emergence of edges that connect knowledge elements together. The robustness of this emergent knowledge structure is thus an indicator of the amount of knowledge appropriated by the invention. This research introduces a new metric for the measurement of knowledge accumulation and presents structural robustness as an indicator of knowledge appropriation. Knowledge accumulation and knowledge appropriation are tested as characteristics associated with knowledge structures and are hypothesized to be positively correlated with the technical value of the invention. This research tests the hypotheses by examining the citation networks of patents in four sectors: thin film photovoltaics, inductive vibration energy harvesting, piezoelectric energy harvesting, and carbon nanotubes. In total 152 base inventions and over 4000 patents are investigated. This research shows that knowledge accumulation is a significant predictor of the technical value of an invention. This research also shows that high value inventions show a higher level of knowledge appropriation. The results demonstrate that the characteristics displayed by the knowledge structure are better able to explain the technical value of inventions compared to techniques demonstrated by other studies. i Statement of originality This is to certify that, to the best of my knowledge, the content of this thesis is my own work. This thesis has not been submitted for any degree or other purposes. I certify that the intellectual content of this thesis is the product of my own work and that all the assistance received in preparing this thesis and sources have been acknowledged. Praveena Chandra September 2018 ii Thesis authorship attribution This thesis contains material published or submitted for publication, based on the work presented in the thesis, for which I am the main author. This material is distributed throughout all chapters. Conference Papers v Chandra, P., & Dong, A. (2015). Knowledge accumulation and value of inventions. 2015 Portland International Conference on Management of Engineering and Technology (PICMET), Portland, OR. doi: 10.1109/PICMET.2015.7273056 v Chandra, P., & Dong, A. (2015). Predicting technical viability of inventions. 2015 IEEE International Conference on Engineering, Technology and Innovation/ International Technology Management Conference (ICE/ITMC), Belfast. doi: 10.1109/ICE.2015.7438654 Journal Articles v Chandra, P., & Dong, A. (2018). The relation between knowledge accumulation and technical value in interdisciplinary technologies. Technological Forecasting and Social Change, 128, 235-244. doi: 10.1016/j.techfore.2017.12.006 As supervisor for the candidature upon which this thesis is based, I can confirm that the authorship attribution statements above are correct. Andy Dong September 2018 iii Acknowledgements This thesis is a result of all the incidents in my life, good and bad, that led me to that momentous day when my Google search brought up the name Prof. Andy Dong and his research interests. My belief in “everything happens for a reason” was further strengthened that day! Thank you Andy, for being such a wonderful guide. You have always encouraged me to move outside my comfort zone and think. This has brought results, which I never knew I was capable of. I have always admired your clear analytical thinking and passion for excellence and I hope to attain that myself someday. Your exceptional patience, constant encouragement and words of advice have helped me achieve this important milestone in my life. For this I shall forever be grateful and indebted to you. They say “Guru devo bhava”; a teacher is God for he shapes, guides and inspires his students. You indeed are my Guru! I also owe it to my friends and my family for their part in making this thesis a reality. It’s their confidence in me (and I still fail to understand the reason for their confidence) that gave me the courage to push myself harder. It is my good fortune that I am surrounded by people who emanate positivity. Moreover, if it weren’t for their relentless questions, “when do you finish? Are you not done yet?”, I would still be collecting data. A minor part was also played by a few gallons of coffee, countless packets of paracetamol and one dress size, all of which were sacrificed in the making of this thesis! iv Contents Abstract ..................................................................................................................................................... i Statement of originality .......................................................................................................................... ii Thesis authorship attribution ................................................................................................................ iii Acknowledgements ................................................................................................................................. iv Contents .................................................................................................................................................... v List of tables .......................................................................................................................................... viii List of figures ........................................................................................................................................... x List of abbreviations and acronyms .................................................................................................... xii 1 INTRODUCTION ............................................................................................................................ 1 1.1 Background .................................................................................................................. 1 1.2 Current Challenges ..................................................................................................... 2 1.3 Potential Solution ........................................................................................................ 3 1.4 Contributions ............................................................................................................... 4 1.5 Dissertation Structure ................................................................................................. 5 2 LITERATURE REVIEW ................................................................................................................ 6 2.1 Introduction ................................................................................................................. 6 2.2 Technology Evaluation Techniques ........................................................................... 7 2.2.1 Expert Opinions ...................................................................................................... 8 2.2.2 Delphi Technique ................................................................................................... 8 2.2.3 Technology Readiness Level (TRL) ....................................................................... 9 2.2.4 Technology life cycle (TLC) ................................................................................ 10 2.2.5 Bibliometrics ........................................................................................................ 11 2.2.6 TRIZ ..................................................................................................................... 11 2.3 Patent Based Technology Evaluation ...................................................................... 12 2.3.1 Studies based on single-level relationships .......................................................... 12 2.3.2 Studies based on indirect relationships ................................................................. 18 2.3.3 Complex Networks theory perspective of patent value ........................................ 21 2.4 Summary .................................................................................................................... 25 3 KNOWLEDGE STRUCTURE ..................................................................................................... 27 3.1 Introduction ..............................................................................................................
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