Fuzzy-QFD approach based decision support model for licensor selection

The patent marketplace has expanded significantly as an increasing number of patents are being filed. In turn, the demand for patent intermediaries is also expected to grow. One channel of patent flow for patentees to realize revenue is patent out-licensing, which involves high risk and uncertainty and a consuming need for coordination. Most patent licensing studies have focused on licensing modes, patented technology valuation, and price negotiation. However, many patent licensing issues involve a lack of systematic and objective assessment. This study attempts to establish a licensor selection model based on a hybrid model that embeds fuzzy logic within Quality Function Deployment (QFD) for the patent transaction platform. Experts from industries and professional organizations help a patent intermediary establish licensor selection and licensee requirement criteria, and establish weighting measures based on the input criteria and published patent specifications. We found that quality and applicability of patents and qualification and identification of licensors in patent licensing are the main factors for deriving a decision. With this model, licensees can select an optimal licensor according to their requirements. In a case study of a Chinese high-tech company, results reveal that the proposed hybrid fuzzy-QFD approach can successfully deal with complex licensing selection criteria to find an optimal solution and has the potential for handling multiple criteria decision-making problems in the patent marketplace.