Time substitution and network effects with an application to nanobiotechnology policy for US universities

Fukuyama, Hirofumi; Weber, William; Xia, Yin

doi:10.1016/j.omega.2015.04.020

Cited by 20 publications

(19 citation statements)

References 35 publications

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“…The selection of variables and parameters is a critical issue in several DDEA works found in the literature (Avkiran, 2015;Fukuyama et al, 2016;Herrera-Restrepo et al, 2016;Pointon and Matthews, 2016). Avkiran (2015) pointed out that the familiar rule-of-thumb has a series of shortcomings when employed with DDEA models.…”

Section: Future Research Directionsmentioning

confidence: 99%

A review of Dynamic Data Envelopment Analysis: state of the art and applications

Mariz

Almeida

Aloise

2017

Int Trans Operational Res

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This article reports the evolution of the literature on Dynamic Data Envelopment Analysis (DDEA) models from 1996 to 2016. Systematic searches in the databases Scopus and Web of Science were performed to outline the state of the art. The results enabled the establishment of DDEA studies as the scope of this article, analyzing the transition elements to represent temporal interdependence. The categorization of these studies enabled the mapping of the evolution of the DDEA literature and identification of the relationships between models. The three most widely adopted studies to conduct DDEA research were classified as structuring models. Mapping elucidated the literature behavior through three phases and showed an increase in publications with applications in recent years. The analysis of applications indicated that most studies address evaluations in the agriculture and farming, banking and energy sectors and consider the facilities as transition elements between analysis periods.

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Section: Future Research Directionsmentioning

confidence: 99%

A review of Dynamic Data Envelopment Analysis: state of the art and applications

Mariz

Almeida

Aloise

2017

Int Trans Operational Res

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“…Weber and Xia () and Fukuyama et al. () review research on the applied/basic research trade‐off. Some research supports the idea that universities gain monopoly power from licensing inventions and that monopoly power favors resources going into industrial applications (Boldrin and Levin, ; Just and Huffman, ; Weber and Xia, ).…”

Section: Related Literaturementioning

confidence: 99%

“…These functions, along with duality theory, have been used to estimate shadow prices for knowledge spillovers between universities (Weber, ), to estimate elasticities of transformation between knowledge outputs of patents, Ph.D. students, and publications (Weber and Yin, ), and to simulate a reallocation of NSF funds given to enhance knowledge outputs at various U.S. institutions of higher education (Fukuyama et al., ). Distance functions can be estimated parametrically (Weber and Xia, ; Weber, ) or using nonparametric DEA (Fukuyama et al., ). We extend these models and use a dynamic network distance function to model the role that knowledge plays in a dynamic network technology.…”

Section: Network Modelsmentioning

confidence: 99%

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Sources and uses of knowledge in a dynamic network technology

Bostian

Daraio

Grosskopf

et al. 2019

Int Trans Operational Res

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We estimate a dynamic network technology where new knowledge in the form of publications in STEM (science, technology, engineering, and mathematics) is an intermediate product. Knowledge is produced in the first stage of production and is used in the second stage of production to produce a final output of real consumption, which equals gross domestic product minus investment spending on physical capital minus research and development expenditures. Knowledge also spills over between producers as it becomes disseminated. The two stages of production are linked between periods as investments in research capital and physical capital enhance future production possibilities. Our model combines several theories of production: dynamic data envelopment analysis (DEA) and two-stage network DEA. Using pooled data on 53 countries during 1999-2012, the model estimates indicate that dynamic efficiency averages about 70%. Countries could increase final consumption by about 25% via greater technical efficiency in production and by another 5% via an optimal intertemporal reallocation of investment spending.

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“…DEA researchers developed various NDEA models for evaluating the efficiency of DMUs Grosskopf (1996, 2000); Lewis and Sexton (2004); Prieto and Zofio (2007); Kao (2009; Tone and Tsutsui (2009); Lozano (2011) ;Du,Chen and Huo (2015)) and other researchers focused on the efficiency performance of DMUs which have internal series (e.g., two-stage or three-stage) structures (Sexton and Lewis (2003); Kao and Hwang (2008) ;Liang, Cook and Zhu (2008); Fukuyama and Weber (2010) ; Cook, Liang and Zhu (2010) ;Fukuyama, Weber and Xia (2015); Halkos, Tzeremes and Kourtzidis (2014) ;Akther, Fukuyama and Weber (2013)). A review of the NDEA models can be found in .…”

Section: Introductionmentioning

confidence: 99%