Although the literature on university–industry links has begun to uncover the reasons for, and types of, collaboration between universities and businesses, it offers relatively little explanation of ways to reduce the barriers in these collaborations. This paper seeks to unpack the nature of the obstacles to collaborations between universities and industry, exploring influence of different mechanisms in lowering barriers related to the orientation of universities and to the transactions involved in working with university partners. Drawing on a large-scale survey and public records, this paper explores the effects of collaboration experience, breadth of interaction, and inter-organizational trust on lowering different types of barriers. The analysis shows that prior experience of collaborative research lowers orientation-related barriers and that greater levels of trust reduce both types of barriers studied. It also indicates that breadth of interaction diminishes the orientation-related, but increases transaction-related barriers. The paper explores the implications of these findings for policies aimed at facilitating university–industry collaboration.

Investigating the factors that diminish the barriers to university-industry collaboration

This book re-examines the rationale for public policy, concluding that the prevailing ‘public knowledge' model is evolving towards a networked or distributed model of knowledge production and use in which public and private institutions play complementary roles. It provides a set of tools and models to assess the impact of the new network model of funding and governance, and argues that governments need to adapt their funding and administrative priorities and procedures to support the emergence and healthy growth of research networks. The book goes on to explain that interdependencies and complementarities in the production and distribution of knowledge require a new and more contextual, flexible and complex approach to government funding, monitoring and assessment.

Links and Impacts: The Influence of Public Research on Industrial R&D

Online streaming feature selection (OSFS) has attracted extensive attention during the past decades. Current approaches commonly assume that the feature space of fixed data instances dynamically increases without any missing data. However, this assumption does not always hold in many real applications. Motivated by this observation, this study aims to implement online feature selection from sparse streaming features, i.e., features flow in one by one with missing data as instance count remains fixed. To do so, this study proposes a latent-factor-analysis-based online sparse-streaming-feature selection algorithm (LOSSA). Its main idea is to apply latent factor analysis to pre-estimate missing data in sparse streaming features before conducting feature selection, thereby addressing the missing data issue effectively and efficiently. Theoretical and empirical studies indicate that LOSSA can significantly improve the quality of OSFS when missing data are encountered in target instances.

A Latent Factor Analysis-Based Approach to Online Sparse Streaming Feature Selection

Review of efficiency ranking methods in data envelopment analysis

The objective of this paper is to estimate comprehensive environmental efficiency measures for Dutch dairy farms. The environmental efficiency scores are based on the nitrogen surplus, phosphate surplus and the total (direct and indirect) energy use of an unbalanced panel of dairy farms. We define environmental efficiency as the ratio of minimum feasible to observed use of multiple environmentally detrimental inputs, conditional on observed levels of output and the conventional inputs. We compare two methods for the calculation of efficiency; namely Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA). This paper reveals the strengths and weaknesses for estimating environmental efficiency of the methods applied. Both SFA and DEA can estimate environmental efficiency scores. The mean technical efficiency scores (output-oriented, SFA 89%, DEA 78%) and the mean comprehensive environmental efficiency scores (SFA 80%, DEA 52%) differ between the two methods. SFA allows hypothesis testing, and the monotonicity hypothesis is rejected for the specification including phosphate surplus. DEA can calculate environmental efficiency scores for all specifications, because regularity is imposed in this method.

Environmental efficiency with multiple environmentally detrimental variables; estimated with SFA and DEA

A flexible cross-efficiency fuzzy data envelopment analysis model for sustainable sourcing

Credit score is a creditworthiness index, which enables the lender (bank and credit card companies) to evaluate its own risk exposure toward a particular potential customer. There are several credit scoring methods available in the literature, but one that is widely used is the FICO method. This method provides a score ranging from 300 to 850 as a fast filter for high-volume complex credit decisions. However, it falls short in the aspect of a decision support system where revised scoring can be achieved to reflect the borrower’s strength and weakness in each scoring dimension, as well as the possible trade-offs made to maintain one’s lending risk. Hence, this study discusses and develops a decision support tool for credit score model based on multi-criteria decision-making principles. In the proposed methodology, criteria weights are generated by fuzzy AHP. Fuzzy linguistic theory is applied in AHP to describe the uncertainties and vagueness arising from human subjectivity in decision making. Finally, drawing from the risk distance function, TOPSIS is used to rank the alternatives based on the least risk exposure. A sensitivity analysis is also demonstrated by the proposed fuzzy AHP-TOPSIS method.

A fuzzy decision support system for credit scoring

This study focuses on the allocation of R&D resources in R&D active firms. We utilize the input oriented constant (CRS) and variable (VRS) returns to scale data efficiency analysis models to evaluate the efficiency of firms. Scale efficiency and the respective types of returns to scale have been examined by using DEA models with ratio inputs and outputs. We pay attention to the global frontier and the firm's own sector and size frontiers. We highlight the sources of inefficiency and suggestions are proposed to improve efficiencies of R&D resources allocation. The analysis is based on a representative set of (quasi-) permanent R&D active firms in Belgium. We consider R&D related inputs in the year 2009 and include firm performance in terms of turnover and net added value per employee in a four year time span. The paper highlights that on average, R&D active firms suffer from both technical inefficiency and scale size problems since the average of the CRS and the VRS efficiency are low, and also the average of scale efficiency is modest. According to firm size, small-sized firms suffer from scale and technical inefficiency. Medium-sized firms endure scale inefficiency rather than technical inefficiency. Large firms present a higher average scale efficiency and technical efficiency. According to sector of activity, firms in specialized supplier industries tend to outperform other firms in terms of average scale efficiency and average technical efficiency. Firms in science based industries are found to underperform on average in terms of VRS and scale efficiency.

Performance evaluation of R&D active firms

The non-Archimedean epsilon $$\varepsilon $$
 is commonly considered as a lower bound for the dual input weights and output weights in multiplier data envelopment analysis (DEA) models. The amount of $$\varepsilon $$
 can be effectively used to differentiate between strongly and weakly efficient decision making units (DMUs). The problem of weak dominance particularly occurs when the reference set is fully or partially defined in terms of fuzzy numbers. In this paper, we propose a new four-step fuzzy DEA method to re-shape weakly efficient frontiers along with revisiting the efficiency score of DMUs in terms of perturbing the weakly efficient frontier. This approach eliminates the non-zero slacks in fuzzy DEA while keeping the strongly efficient frontiers unaltered. In comparing our proposed algorithm to an existing method in the recent literature we show three important flaws in their approach that our method addresses. Finally, we present a numerical example in banking with a combination of crisp and fuzzy data to illustrate the efficacy and advantages of the proposed approach.

The role of multiplier bounds in fuzzy data envelopment analysis

Dynamic slacks-based data envelopment analysis is applied to measure output performance efficiency of space R&D active private firms. Fractional logit panel analysis is used to identify associations between this firm level R&D output efficiency and space industry level R&D specialisation, presence of anchor firms, and public R&D capacity within geographical clusters. Primary data collected for the entire population of space R&D active firms in Belgium for the period 2011–2015 reveals that an environment specialized in the focus firm's space activities exerts a positive influence on R&D output efficiency. The public space R&D capacity and the presence of space anchor firms within the cluster do not influence R&D output efficiency. Firm age, a mixture of space and non-space R&D, and larger amounts of public funding positively affect R&D output efficiency. These findings should be seen in a context of a space industry dominated by SMEs and in a five year time-span.

Pegah Khoshnevis

Papers

A flexible cross-efficiency fuzzy data envelopment analysis model for sustainable sourcing

A fuzzy decision support system for credit scoring

Performance evaluation of R&D active firms

The role of multiplier bounds in fuzzy data envelopment analysis

Within-cluster determinants of output efficiency of R&D in the space industry