Translating university technology via the university–industry route faces an array of challenges. Subsequently, understanding the interrelationships of these challenges hopes to provide a better outlook on the complex nature of the university technology transfer (UTT) process. Such an agenda remains a gap in the domain literature. To advance this oversight, this study intends to identify the UTT challenges and determine their complex contextual relationships. The interpretative structural modeling, together with the MICMAC analysis, was sequentially adopted to derive the overarching structure of the challenges of UTT. A case study in a public university in the Philippines was conducted to carry out these objectives. Findings show that time constraints, knowledge being too theoretical, high costs of managing joint research projects, complex organizational structure, institutional bureaucracy, geographic distance, and lack of national benchmark are driving challenges that influence other challenges in impeding UTT in the representative Philippine university. These findings provide policy insights to key decision-makers and stakeholders on the success of technology transfers.

Current literature merely identifies the driving factors of research productivity in higher education institutions without directly examining their interrelationships that would offer some fundamental insights into the nature of these factors. Thus, this work intends to identify those driving factors and establish their structural relationships to determine those factors with crucial roles in advancing research productivity. Due to the subjectivity of the identified driving factors and the notion that the evaluation of their relationships reflects an expert judgment, an interpretive structural modeling (ISM) approach and the Matrice d’impacts croisés multiplication appliquée á un classment (MICMAC) analysis were adopted. Results show that institutional support, reward system, research funding, mentoring, and electronic information resources are the most crucial factors influencing research productivity. When addressed, these driving factors would motivate other driving factors, contributing to higher research productivity. In particular, these findings encourage higher education institutions to (1) efficiently allocate research funds and design mentoring programs, (2) offer efficient research incentive schemes, (3) develop initiatives that would support promising research proposals beneficial to the institution, and (4) collaborate with external organizations to grant funding for research proposals. These results contribute significantly to the literature as it provides meaningful insights that aid decision-makers in higher education institutions in resource allocation decisions, policy-making, and the design of efficient initiatives for augmenting their innovation potential.

The University technology transfer (UTT) process is hindered by various barriers to achieving a successful translation of innovative technologies from universities to industries and other partners. Identifying these various barriers and understanding their interrelationships would provide a better understanding of the complex nature of the UTT process, which may be considered as inputs to crucial decision-making initiatives. This paper addresses this gap by holistically determining UTT barriers and their intertwined relationships. Using the Delphi method and fuzzy cognitive mapping, a case study in a state university in the Philippines was conducted to carry out this objective. The Delphi process extracts 24 relevant barriers of UTT, out of 46 barriers obtained from a comprehensive review of the extant literature. The results show that misalignment between research and commercialization objectives is the barrier that was influenced most by the other barriers. In contrast, high costs of managing joint research projects in terms of time and money and institutional bureaucracy have the highest out-degree measures or are the barriers that influence other barriers the most. These findings provide guidelines to various stakeholders and decision-makers in understanding the existence of barriers in the formulation of strategies and initiatives for a successful UTT process.

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People with celiac disease depend on gluten-free foods to maintain quality of life. This study aimed to determine the physico-chemical and organoleptic properties of gluten-free composite flour blends for celiac disease patients utilizing different percentage composition of rice flour, potato starch, cassava starch, millet flour and corn flour. The experimental design was laid out in the study with six treatments in three replications using Completely Randomized Design (CRD) under controlled condition. The experimental samples were subjected to different laboratory analyses. Sensory evaluation of the control and gluten-free treatments had revealed that there were no significant mean differences among treatments in all of the sensory characteristics. However, gluten-free flour blend (T2) had higher level of acceptance as compared to other gluten-free flour blend treatments. The composite gluten-free flour blends had zero percent crude gluten. Utilizing available novel/functional ingredients can produce gluten-free composite flour with a comparable sensory characteristics and nutritional quality except protein content with the commercially wheat flours. Hence, it is recommended for the use of gluten-free composite flour blends for food and pharmaceutical uses.

This work offers an integrated methodological framework for decision support in planning the implementation of measures that address the barriers of university technology transfer. The planning problem consists of two parts: (1) identifying the high priority measures, and (2) optimally implementing these measures over a specified planning horizon subject to resource constraints. Treated as a multiple criteria sorting problem under uncertainty, the high priority measures are determined via fuzzy DEMATEL and ANP for evaluating the barriers, and the fuzzy FlowSort for classifying the priority of the various measures. Then, an extended multi-objective extension of the PROMETHEE V is offered to determine the degree of implementation of the high priority measures over multiple periods. Demonstrated in an actual case study with 29 identified measures under 24 previously known barriers, findings reveal six high priority measures, which include designing a sustained partnership, engaging in joint research ventures, establishing partnerships from international financial institutions, streamlining objectives to full support of the technology readiness levels, establishing a holistic system approach towards technology readiness levels, and establishing agreements to have access to the industry laboratory facilities. The implementation plan, represented as a set of Pareto optimal solutions, is obtained through the AUGMECON algorithm for the 𝜀-constrained multiobjective linear programming formulation of the extended PROMETHEE V. A layer of sensitivity analysis was performed to test the robustness of the results to changes in the parameters. Finally, policy insights are provided to key decision-makers for advancing UTT.