Accelerating a Technology Commercialization; with a Discussion on the Relation between Technology Transfer Efficiency and Open Innovation

The objective of this article is to present a successful program that built a National Innovation Network based in the University Technology Transfer Offices (TTOs), incubators and science parks.The University Technology Enterprise Network (UTEN), which was launched in March 2007, includes 15 Portuguese Universities and select international partners in a 5-Year program funded by the Portuguese government. The main objective has been to accelerate the development of a sustainable, globally competitive, professional technology transfer and commercialization network within Portugal to increase Portugal’s international competitiveness in university–based science, and technology transfer and commercialization. We argue that all initiatives taken place in the project have gotten UTEN network presently run in the Open Innovation paradigm fostered mostly by the TTOs and their own networks and officers. Science and technology based entrepreneurship was increasingly seen as a key element of Portugal’s ability to grow and prosper (UTEN, 2012). Research universities had worked to foster a range of technology transfer and commercialization activities and offices, together with industrial liaison programs, mostly devoted to fostering entrepreneurial environments, launching technology based start-ups, and bringing ideas from the laboratory to the market. UTEN was created to synergize this growth and stimulate new competencies in international technology transfer and commercialization to facilitate industry access to leading markets worldwide. In other words, UTEN is the living example of an Innovation network - an Open Innovation Network launched to contribute to build the necessary relationships between all actors, giving them the necessary knowledge to play their roles. This working paper shows the actions taken to construct UTEN and improve the Portuguese Innovation Ecosystem. These actions follows the patterns observed in other studies – essentially those ones from Resende et al, 2013; McAdam et al., 2012; Philpott et al., 2011; Todorovic et al., 2011; Rogers, 2002; Rogers et al., 2001; Rogers et al., 2000 and Gibson and Rogers, 1994.We have collected data that shows the success of the program based on the results of the first five years of the project.

The transfer of technology from the organizational viewpoint

The purpose of this research is to develop a model for measuring the efficiency of the TTO incubation process performance to accelerate the commercialization of research results in universities. The method of analyzing the efficiency used in this research is the Data Envelopment Analysis (DEA) method based on Banker, Charnes, and Cooper (BCC), which is output-oriented. The software used in analyzing the efficiency of TTO performance is MaxDEA 8. The output of this research is a mathematical model tool for measuring the efficiency of TTO performance by DEA, which considered 17 parameters and proposed recommendations for TTO performance strategies. The limitation of this research is the object of research in one university that has succeeded in the commercialization of research. This research implies that the performance efficiency measurement model is an alternative predictive way to increase the acceleration of commercialization. The practical implications of this research are that it can evaluate performance or inefficient strategies in the incubation process of higher education research results to the Technology Transfer Office (TTO). This research also provides recommendations on strengthening the TTO function that can be used as a reference for improving performance at universities. This research measures the level of performance evaluation of TTO in the incubation process, which refers to the Death Valley framework. This incubation process is the main process accelerating the commercialization of research results in universities.

Data envelopment analysis (DEA) is a method for measuring efficiency of peer decision-making units (DMUs). Conventional DEA evaluates the performance of each DMU using a set of most favourable weights. As a result, traditional DEA models can be considered methods for the analysis of the best relative efficiency or analysis of the optimistic efficiency. DEA efficient DMUs obtained from conventional DEA models create an efficient production frontier. Traditional DEA can be used to identify units with good performance in the most desirable scenarios. There is a similar approach that evaluates the performance indicators of each DMU using a set of most unfavourable weights. Accordingly, such models can be considered models for analysing the worst relative efficiency or pessimistic efficiency. This approach uses the inefficient production frontier for determining the worst relative efficiency that can be assigned to each DMU. DMUs lying on the inefficient production frontier are referred to as DEA inefficient while those neither on the efficient frontier nor on the inefficient frontier are declared DEA inefficient. It can be argued that both relative efficiencies should be considered simultaneously and any approach with only one of them would be biased. This paper proposed the integration of both efficiencies as an interval so that the overall performance score would belong to this interval. It was shown that efficiency interval provided more information than either of the two efficiencies, which was illustrated using two numerical examples.

Data envelopment analysis (DEA) is a non-parametric technique for measuring the efficiency of decision making units (DMU) with exact input and output terms. So far, a number of DEA models with interval data have been developed. The radial DEA model such as CCR or BCC model with interval data are well known as basic DEA models with interval data. However, these radial DEA models have some defects. In this study, we develop a new approach based upon the slacks-based measurement (SBM) of super-efficiency model for dealing with interval data in DEA. The SBM super-efficiency DEA model can not only discriminate the performance of efficient DMUs from inefficient ones, but can distinguish between the efficient DMUs. In addition, this model can overcome the drawback that the super-efficiency model can be infeasible under variable returns to scale (VRS) assumption. An illustrative example is provided to verify the idea of this paper and show its potential application and validity.

The appreciative democratic voice of DEA: A case of faculty academic performance evaluation

Purpose The purpose of this paper is to assess how technology transfer offices (TTOs) of a public university of the state of São Paulo use patent valuation methods in the process of using developed technology value and transferring technology to industry. Design/methodology/approach This study is an exploratory qualitative investigation based on a case study conducted in a public university in the state of São Paulo. The university has a TTO and an internal structure for technology transfer. In-depth interviews were conducted with the TTO manager about patent valuation and the answers given were analysed. Findings The results on how TTOs use patent valuation methods in the process of assigning value to technology indicate which factors facilitate and which factors hinder the valuation of patents in technologies developed at universities. Research limitations/implications The possible lack of data disclosure due to confidentiality regarding royalties and trading fees makes further comparisons between Brazilian public universities difficult. Therefore, this study recommends that further studies on patent valuation and technology transfer process at private universities, research institutes and public and private companies should be performed. Practical implications In the practice, this study contributes to companies and TTOs by increasing their synergies in licensing negotiations, as well as by reducing the gap of information, between the business parties for assignment and transfer of technologies. With regard to theoretical contribution, this study can cite advances in the methods to measure the financial benefits arising from the valuation of technologies embedded in the patents. Originality/value Owing to the lack of research on the methods of valuation used by TTOs of Brazilian universities, the present study can be useful in serving as a theoretical source for future research and in supporting future TTO negotiations in the process of transferring technologies to productive industry.

In models of data envelopment analysis (DEA), an optimal set of input and output weights is generally assumed to represent the assessed decision making unit (DMU) in the best light in comparison to all the other DMUs. The paper shows that this may not be correct if absolute weight bounds or some other weight restrictions are added to the model. A consequence may be that the model will underestimate the relative efficiency of DMUs. The incorporation of weight restrictions in a maximin DEA model is suggested. This model can be further converted to more operational forms, which are similar to the classical DEA models.

We seek to understand how technology transfer offices (TTOs) help universities translate academic research into commercialised products. Building on the innovation literature that postulates that value capture from inventions depends on the nature of the technology, we theorise that the nature of research that the university pursues likely drives the activities within TTOs. We theoretically extend Pasteur's quadrant to the domain of TTO research and posit that TTOs of universities that belong to the same strategic group in 'Pasteur's cube' are likely to have similar activities. Our research suggests that, on the one hand, future investigations of TTOs need to take into account the heterogeneity in their activities rather than treating TTOs of all universities as homogenous and, on the other hand, universities planning to initiate technology transfer activities should look into the processes of the TTOs of universities that are in the same strategic group.

A generalized model for data envelopment analysis with interval data

Data Envelopment Analysis (DEA) models with weight restrictions (WRs) have proven valuable for benchmarking and target setting. Although the DEA literature has explored the incorporation of managerial preferences and value judgments regarding the relative worth of inputs and outputs, as well as the establishment of targets in benchmarking contexts, little attention has been devoted to target setting under restricted DEA models. Moreover, despite the significant advances offered by minimum distance models for target establishment, limited research has addressed benchmarking improvement plans that integrate expert opinions and prior knowledge. Some studies have examined minimum distance models constrained to the efficient Assurance Region (AR) frontier, primarily by extending the concept of closest targets under WR. In contrast, this paper develops improvement plans that deviate minimally from the closest target projection obtained from the original, unrestricted DEA model—termed the reference target. This reference target is considered an acceptable “peer” since it requires the least effort for a decision making unit (DMU) to reach optimal performance before incorporating WR. To this end, we developed a mixed-integer linear programming (MILP) model under the assumption of Variable Returns to Scale in DEA. The proposed approach is illustrated through an application to benchmarking the tourism performance of localities in Córdoba, Argentina. The results reveal realistic and achievable improvement plans for the analyzed localities, ensuring that both global efforts are managed and expert-imposed restrictions are satisfied.

The main goal of this paper is to develop a new data envelopment analysis (DEA) model to use optimal weights of each decision making unit (DMU) to improve its relative efficiency aligned with other DMUs. In spite of the vast amount of studies in this area and related tools and techniques, current literature deploys the optimal weights of DMUs to calculate the DMUs' relative efficiency and benchmarking for each DMU is less investigated. In order to fill this gap, this paper proposes a model for obtaining benchmark(s) for each DMU. Furthermore, a numerical example is used to illustrate the capability of the proposed model.

‘Mind the gap’

Strategic alliances and partnerships based on the semi-additive production technology in DEA

To transfer and practically implement academic knowledge in wider society is not straightforward, and a range of measures have been used to improve technology transfer and dissemination of research from universities. One example is the technology transfer offices (TTOs), which have been set up at almost every US research university since the Bayh-Dole Act in 1980. Most TTO operations conform to a 'patent agency' model of operation where the focus is on selling patentable inventions to industrial adopters. The success of traditional TTO operations in terms of technology transfer, economic performance, and impact on academic life has, however, been questioned. This article outlines the principles of an alternative model for university technology transfer operations. The 2g model has a wider focus which might lead to increased technology transfer including a wider number of inventions and areas than current TTO practice.