Who leads Research Productivity Change? Guidelines for R&D policy makers

Relying on efficiency analysis we evaluate to what extent policy makers have been able to promote the establishment of consolidated and comprehensive research groups to contribute to the implementation of a successful innovation system for the Spanish food technology sector, oriented to the production of knowledge based on an application model. Using data envelopment analysis techniques and Malmquist productivity indices we find pervasive levels of inefficiency and a typology of different research strategies. Among these, in contrast to what has been assumed, established groups do not play the pre-eminent benchmarking role; rather, partially oriented, specialized and "shooting star" groups are the most common patterns. These results correspond with an infant innovation system, where the fostering of higher levels of efficiency and promotion of the desired research patterns are ongoing.Innovation Policy; Management; Productivity Change; Malmquist Index; Distance Function

A Multiple Criteria Framework to Evaluate Bank Branch Potential Attractiveness

Remarkable progress has occurred over the years in the performance evaluation of bank branches. Even though financial measures are usually considered the most important in assessing branch viability, we posit that insufficient attention has been given to other factors that affect the branches’ potential profitability and attractiveness. Based on the integrated used of cognitive maps and MCDA techniques, we propose a framework that adds value to the way that potential attractiveness criteria to assess bank branches are selected and to the way that the trade-offs between those criteria are obtained. This framework is the result of a process involving several directors from the five largest banks operating in Portugal, and follows a constructivist approach. Our findings suggest that the use of cognitive maps systematically identifies previously omitted criteria that may assess potential attractiveness. The use of MCDA techniques may clarify and add transparency to the way trade-offs are dealt with. Advantages and disadvantages of the proposed framework are also discussed.

Aligning IT Assets to Maximize Healthcare Organizational Performance

This study examines the efficiency impact of healthcare information technology assets on organizational performance. Using an econometric approach with data envelopment analysis, both individual IT assets and IT asset clusters are measured relative to an efficient peer group. The results provide insight to organizational structuring of IT asset portfolios

Measuring efficiency of lean six sigma project implementation using data envelopment analysis at NASA

Purpose: This study aims to review the implementation of the Lean Six Sigma project methodology in the Johnson Space Center (JSC) business environment of National Aeronautics and Space Administration (NASA) with an objective of evaluating performance of individual projects and to develop recommendation for strategies to improve operational efficiencies based on Data Envelopment Analysis (DEA). Design/methodology/approach: In this study, authors propose the Lean Six Sigma project performance evaluation model (LSS-PPEM) based on Data DEA where Critical Success Factors (CSFs) and Total Team Hours serve as inputs while Process Sigma and Cost avoidance are used as outputs. The CSFs are factors that critically affect the performance of LSS at JSC. Six of those are identified by the Black Belts through Analytical Hierarchical Process, and the values of those are decided by project leaders and Green Belts through survey. Eighteen LSS projects are evaluated, and their results are analyzed. Findings and Originality/value: Eventually, four out of the six CSFs are adopted for this study based upon Pearson correlation analysis, and those four include Project execution and follow up of results; Top management’s commitment and participation; The use of data analysis with easily obtainable data; Attention given to both long and short term targets. Using data between the years 2009 and 2011, seven of the eighteen projects are found to be efficient. The benchmark analysis and slack analysis are conducted to provide further recommendation for JSC managers. Three out of those seven efficient projects are most frequently used as an efficient peer. Practical implications: Traditionally, DEA has been considered as a data-driven approach. In this study, authors incorporate the survey-based CSFs into the DEA frame. Since many organizations may have different CSFs, the framework presented in this study can be easily applied to other organizations. Originality/value: This study provides a DEA-based framework and case study of LSS project evaluation in the government sector, which is very unique application to author’s best knowledge. The framework is unique in terms of its input factor selection and quantification procedures.Peer Reviewe

How to create indices for bank branch financial performance measurement using MCDA techniques: an illustrative example

Most banks have been negatively affected by the recent economic recession, which has forced them to evaluate their operating performance including the financial performance of bank branches. Approaches that have been applied to address the financial performance evaluation of bank branches include: optimization techniques, simulations, stochastic tools, fuzzy logics and decision support systems. Although recent improvements have been made in assessing financial performance, the potential for significant further improvement remains since the recent world economic crisis is adding pressure on business margins. The purpose of this paper is to construct an exemplificative evaluation index for bank branch financial performance by integrating cognitive maps with measuring attractiveness by a categorical based evaluation technique. We aim to apply this methodology constructively to serve as a learning mechanism and introduce transparency in the decision making process. Practical applications, strengths and weaknesses of the proposed evaluation index are also discussed

Who leads research productivity change? Guidelines for R&D policy-makers

We rely on efficiency and productivity analysis based on Malmquist indices to evaluate to what extent policy-makers have been able to promote the creation and consolidation of comprehensive research groups that contribute to the implementation of a successful innovation system. We suggest that this dynamic evaluation offers relevant information to current ex-post policy evaluation methods, helping decision makers to readapt and reorient policies and their associated means, most notably resource allocation (financial schemes), to better respond to the actual needs of promising research groups in their search for excellence (micro-level perspective), and to adapt future policy design to the achievement of medium-long term policy objectives (meso and macro level perspectives). We apply this methodology to the case of the Spanish R&D Food Technology Program finding that a large size and a comprehensive multi-dimensional research output are the key features of the leading groups exhibiting high efficiency and productivity levels. Identifying these groups as benchmark, we conclude that the financial grants allocated by the program, typically aimed at small-sized and partially oriented research group, have no succeeded in reorienting them in time so as to overcome their limitations.Innovation Policy; Management; Malmquist Index.

Knowledge-Based Economy in Developing Countries: Measurements and Impacts

The traditional factors of production, such as land, labour, and capital, have typically determined a nation’s comparative advantage. However, in the context of a global Knowledge-Based Economy (KBE), a nation’s prosperity is now determined by its knowledge assets. This transition to a KBE offers endless advantages and is desirable for all countries. However, developing countries face significant challenges in adopting this new development paradigm, where knowledge is the key driver of economic growth. Yet, to effectively measure the extent to which a country is considered knowledge-based on the international level, a robust framework is needed. Although the burgeoning literature, existing KBE measurement frameworks have limitations and may not accurately reflect the progress and efficiency of the transition to a KBE, especially in developing countries. Consequently, relying on these frameworks can lead to misleading policy directions that hinder the necessary rapid transition in developing countries. This thesis aims to fill the gap in understanding the KBE within developing countries through an extensive analysis. To achieve this, the thesis begins by reviewing the conceptual and theoretical literature on the KBE. It then critically examines existing measurement frameworks and empirical studies related to the KBE, specifically evaluating their suitability for developing countries. In response to the limitations found, a new and more effective measurement framework is proposed. This framework focuses on input-output indicators across four dimensions of the KBE: acquisition, distribution/dissemination, production, and utilization. Notably, it utilizes a non-parametric approach known as Data Envelopment Analysis (DEA), which differs from conventional econometric analysis. The DEA empirical results are then compared with those obtained from other existing KBE measurement frameworks, allowing for a comprehensive assessment of the advantages offered by DEA. Based on the DEA empirical findings, knowledge production is identified as the weakest aspect, despite its utmost importance among the four KBE dimensions. As a result, this thesis places special emphasis on enhancing innovation development in selected developing countries through effective innovation policies tailored to their specific circumstances and utilizing country-specific innovation policy instruments

Aligning IT Assets to Maximize Healthcare Organizational Performance

This study examines the impact of healthcare information technology assets on organizational efficiency. Using an econometric approach with data envelopment analysis, we examine the effect of IT asset clusters on organizational efficiency as measured relative to a peer group of healthcare organizations We observe that different IT asset clusters have varying effects on organizational efficiency based on the size of the organizations. The results of this study have implications for healthcare organizations in planning their investments across various IT asset clusters

Creative Thinking and Modelling for the Decision Support in Water Management

This paper reviews the state of art in knowledge and preferences elicitation techniques. The purpose of the study was to evaluate various cognitive mapping techniques in order to conclude with the identification of the optimal technique for the NetSyMod methodology. Network Analysis – Creative System Modelling (NetSyMod) methodology has been designed for the improvement of decision support systems (DSS) with respect to the environmental problems. In the paper the difference is made between experts and stakeholders knowledge and preference elicitation methods. The suggested technique is very similar to the Nominal Group Techniques (NGT) with the external representation of the analysed problem by means of the Hodgson Hexagons. The evolving methodology is undergoing tests within several EU-funded projects such as: ITAES, IISIM, NostrumDSS.Creative modelling, Cognitive mapping, Preference elicitation techniques, Decision support

Who leads research productivity growth? Guidelines for R&D policy-makers

[EN] This paper evaluates to what extent policy-makers have been able to promote the creation and consolidation of comprehensive research groups that contribute to the implementation of a successful innovation system. Malmquist productivity indices are applied in the case of the Spanish Food Technology Program, finding that a large size and a comprehensive multi-dimensional research output are the key features of the leading groups exhibiting high efficiency and productivity levels. While identifying these groups as benchmarks, we conclude that the financial grants allocated by the program, typically aimed at small-sized and partially oriented research groups, have not succeeded in reorienting them in time so as to overcome their limitations. We suggest that this methodology offers relevant conclusions to policy evaluation methods, helping policy-makers to readapt and reorient policies and their associated means, most notably resource allocation (financial schemes), to better respond to the actual needs of research groups in their search for excellence (micro-level perspective), and to adapt future policy design to the achievement of medium-long term policy objectives (meso and macro-level).Jiménez Saez, F.; Zabala Iturriagagoitia, JM.; Zofio, JL. (2013). Who leads research productivity growth? Guidelines for R&D policy-makers. Scientometrics. 94(1):273-303. doi:10.1007/s11192-012-0763-0S273303941Abbring, J. H., & Heckman, J. J. (2008). Dynamic policy analysis. In L. Mátyás & P. Sevestre (Eds.), The econometrics of panel data (3rd ed., pp. 795–863). Heidelberg: Springer.Acosta Ballesteros, J., & Modrego Rico, A. (2001). Public financing of cooperative R&D projects in Spain: the concerted projects under the national R&D plan. Research Policy, 30, 625–641.Arbel, A. (1981). Policy evaluation in the dynamic input–output model. International Journal of Systems Science, 12, 255–260.Arnold, E. (2004). Evaluation research and innovation policy: A systems world needs systems evaluations. Research Evaluation, 13, 3–17.Arrow, J. K. (1962). Economic welfare and the allocation of resources for inventions. In R. Nelson (Ed.), The rate and direction of inventive activity: Economic and social factor (pp. 609–625). Princeton: Princeton University Press and NBER.Autio, E. (1997). New, technology-based firms in innovation networks symplectic and generative impacts. Research Policy, 26, 263–281.Balk, B. (2001). Scale efficiency and productivity change. Journal of Productivity Analysis, 15, 153–183.Balzat, M., & Hanusch, H. (2004). Recent trends in the research on national innovation systems. Journal of Evolutionary Economics, 14, 197–210.Berg, S. A., Førsund, F. R., & Jansen, E. S. (1992). Malmquist indices of productivity growth during the deregulation of Norwegian banking. Scandinavian Journal of Economics, 94, S211–S228.Bergek, A., Carlsson, B., Lindmark, S., Rickne, A., & Jacobsson, S. (2008). Analyzing the functional dynamics of technological innovation systems: A scheme of analysis. Research Policy, 37, 407–429.Bonaccorsi, A., & Daraio, C. (2005). Exploring size and agglomeration effects on public research productivity. Scientometrics, 63(1), 87–120.Buisseret, T. J., Cameron, H., & Georghiou, L. (1995). What difference does it make? Additionality in the public support of R&D in large firms. International Journal of Technology Management, 10, 587–600.Bustelo, M. (2006). The potential role of standards and guidelines in the development of an evaluation culture in Spain. Evaluation, 12, 437–453.Chavas, J. P., & Cox, T. M. (1999). A generalized distance function and the analysis of production efficiency. Southern Economic Journal, 66, 295–318.CICYT. (1987). Programa Nacional de Tecnología de los Alimentos. Madrid: Ministerio de Educación y Ciencia.CICYT (1988). Plan Nacional de Investigación Científica y Desarrollo Tecnológico 1988–1991. Ministerio de Educación y Ciencia, Secretaría de Estado de Universidades e Investigación, Madrid.Cooper, W. W., Seiford, L. M., & Tone, K. (2000). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-software. Boston: Kluwer Academic Publishers.David, P., Mowery, D., & Steinmueller, W. E. (1994). Analyzing the economic payoffs from basic research. In D. Mowery (Ed.), Science and technology policy in interdependent economies (pp. 57–78). Boston: Kluwer Academic Publishers.Dopfer, K., Foster, J., & Potts, J. (2004). Micro-meso-macro. Journal of Evolutionary Economics, 14, 263–279.Edquist, C., & Hommen, L. (2008). Comparing national systems of innovation in Asia and Europe: Theory and comparative framework. In C. Edquist & L. Hommen (Eds.), Small country innovation systems: Globalisation, change and policy in Asia and Europe (pp. 1–28). Cheltenham: Edward Elgar.Färe, R., Grosskopf, S., Norris, M., & Zhang, Z. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. American Economic Review, 84, 66–83.Farrell, M. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, Series A, General, 120(3), 253–281.Førsund, F. R. (1993). Productivity growth in Norwegian ferries. In H. O. Fried, C. A. K. Lovell, & S. S. Schmidt (Eds.), The measurement of productive efficiency: Techniques and applications (pp. 352–373). New York: Oxford University Press.Førsund, F. R. (1997). The Malmquist productivity index, TFP and scale. University of Oslo, Oslo: Working Paper, Department of Economics and Business Administration.Freeman, C. (1987). Technology policy and economic performance: Lessons from Japan. London: Printer Publishers.García-Martínez, M., & Briz, J. (2000). Innovation in the Spanish food & drink industry. International Food and Agribusiness Management Review, 3, 155–176.Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., & Trow, M. (1994). The new production of knowledge: The dynamics of science and research in contemporary societies. London: Sage Publications.Grammatikopoulos, V., Kousteiios, A., Tsigilis, N., & Theodorakis, Y. (2004). Applying dynamic evaluation approach in education. Studies in Educational Evaluation, 30, 255–263.Grifell-Tatjé, E., & Lovell, C. A. K. (1999). A generalized Malmquist productivity index. Top, 7(1), 81–101.Grimpe, C., & Sofka, W. (2007). Search patterns and absorptive capacity: A comparison of low- and high-technology firms from thirteen European countries. Discussion paper no. 07-062. Centre for European Economic Research (ZEW), Mannheim, Germany.Guan, J., & Wang, J. (2004). Evaluation and interpretation of knowledge production efficiency. Scientometrics, 59(1), 131–155.Hekkert, M. P., Suurs, R. A. A., Negro, S. O., Kuhlmann, S., & Smits, R. E. H. M. (2007). Functions of innovation systems: A new approach for analysing technological change. Technological Forecasting and Social Change, 74, 413–432.Jiménez-Sáez, F. (2005). Una Evaluación del Programa Nacional de Tecnología de Alimentos: análisis de la articulación fomentada sobre el Sistema Alimentario de Innovación en España. PhD dissertation, Servicio de Publicaciones de la Universidad Politécnica de Valencia, Valencia.Jiménez-Sáez, F., Zabala-Iturriagagoitia, J. M., Zofío, J. L., & Castro-Martínez, E. (2011). Evaluating research efficiency within National R&D Programmes. Research Policy, 40, 230–241.Kao, C. (2008). Efficiency analysis of university departments: An empirical study. OMEGA, 36, 653–664.Kuhlmann, S. (2003). Evaluation of research and innovation policies: A discussion of trends with examples from Germany. International Journal of Technology Management, 26, 131–149.Laitinen, E. K. (2002). A dynamic performance measurement system: Evidence from small Finnish technology companies. Scandinavian Journal of Management, 18, 65–99.Laranja, M., Uyarra, E., & Flanagan, K. (2008). Policies for science, technology and innovation: Translating rationales into regional policies in a multi-level setting. Research Policy, 37(5), 823–835.Lee, T.-L., & von Tunzelman, N. (2005). A dynamic analytic approach to national innovation systems: The IC industry in Taiwan. Research Policy, 34, 425–440.Lipsey, R., & Carlaw, K. (1998). A structuralist assessment of technology policies: Taking Schumpeter seriously on policy. Ottawa: Industry Canada Research Publications Program.Lipsey, R., Carlaw, K., & Bekar, C. (2005). Economic transformations: General purpose technologies and long term economic growth. Oxford: Oxford University Press.Lundvall, B. Å. (1992). National systems of innovation: Toward a theory of innovation and interactive learning. London: Printer Publishers.Lundvall, B. Å., Johnson, B., Andersen, E. S., & Dalum, B. (2002). National systems of production, innovation and competence building. Research Policy, 31, 213–231.Markard, J., & Truffer, B. (2008). Actor-oriented analysis of innovation systems: Exploring micro-meso level linkages in the case of stationary fuel cells. Technology Analysis & Strategic Management, 20, 443–464.Metcalfe, J. S. (2002). Equilibrium and evolutionary foundations of competition and technology policy: New perspectives on the division of labour and the innovation process. CRIC Working Papers series, University of Manchester.Miettinen, R. (1999). The riddle of things. Activity theory and actor network theory as approaches of studying innovations. Mind, Culture and Activity, 6, 170–195.Molas-Gallart, J., & Davies, A. (2006). Toward theory-led evaluation: The experience of European science, technology, and innovation policies. American Journal of Evaluation, 27, 64–82.Mytelka, L. K., & Smith, K. (2002). Policy learning and innovation theory: An interactive and co-evolving process. Research Policy, 31, 1467–1479.Olazarán, M., Lavía, C., & Otero, B. (2004). ¿Hacia una segunda transición en la ciencia? Política científica y grupos de investigación. Revista Española de Sociología, 4, 143–172.Potts, J. (2007). The innovation system & economic evolution. Productivity commission submission, public support for science & innovation, productivity commission, Camberra.Ray, S., & Desli, E. (1997). Productivity growth, technical progress, and efficiency change in industrialized countries: Comment. American Economic Review, 87(5), 1033–1039.Rip, A., & Nederhof, A. J. (1986). Between dirigism and laissez-faire: Effects of implementing the science policy priority for biotechnology in the Netherlands. Research Policy, 15, 253–268.Schmidt, E. K., Graversen, E. K., & Langberg, K. (2003). Innovation and dynamics in public research environments in Denmark: A research-policy perspective. Science and Public Policy, 30, 107–116.Schmoch, U., & Schubert, T. (2009). Sustainability of incentives for excellent research—The German case. Scientometrics, 81(1), 195–218.Shephard, R. (1970). Theory of cost and production functions. New Jersey: Princeton University Press.Simar, L., & Wilson, P. W. (1998). Productivity growth in industrialized countries. Discussion paper 9810, Universite Catholique de Louvain, Belgium.Van Raan, A. F. J. (2000). R&D evaluation at the beginning of the new century. Research Evaluation, 8, 81–86.Zofio, J. L. (2007). Malmquist productivity index decompositions: A unifying framework. Applied Economics, 39, 2371–2387.Zofio, J. L., & Lovell, C. A. K. (1998). Yet another Malmquist productivity index decomposition. Working paper, Department of Economics, University of Georgia, Athens, GA 30602, USA.Zofio, J. L., & Lovell, C. A. K. (2001). Graph efficiency and productivity measures: An application to US agriculture. Applied Economics, 33(10), 1433–1442.Zofio, J. L., & Prieto, A. M. (2006). Return to dollar, generalized distance function and the Fisher productivity index. Spanish Economic Review, 8, 113–138