The integration of TQM and technology/R&D management in determining quality and innovation performance

This paper presents an empirical study which examines the co-alignment between Total Quality Management (TQM) and technology/research and development (R&D) management in predicting organizational performance in terms of quality and innovation. This study improves our understanding of the relationship between TQM and innovation based on the following two major issues. First, this study contributes to the understanding of the co-alignment between TQM and technology management along with R&D management by bridging the gap between the two areas which are often addressed in a separate fashion. Second, this study also examines the impact of the integration between TQM and technology/R&D on quality and innovation performance which have been considered as the primary sources of a competitive advantage. The empirical data was drawn from 194 Australian organizations and analyzed using the Structural Equation Modeling (SEM) technique. The findings indicate that TQM shows a strong predictive power against quality performance but no significant relationship against innovation performance. On the other hand, technology and R&D management shows a significant relationship with quality performance but at a lower level than that of TQM, and shows much stronger relationship with innovation performance. In addition, there is strong and positive correlation between TQM and technology/R&D management. The major implication of this study is that technology/R&D management is an appropriate resource to be used in harmony with TQM to enhance organizational performance, particularly innovation.     

Exploring the efficiency of Mexican universities: Integrating Data Envelopment Analysis and Multidimensional Scaling

National policy initiatives require the expenditure of large amounts of resources over several years. It is common for these initiatives to generate large amounts of data that are needed in order to assess their success. Educational policies are an obvious example. Here we concentrate on Mexico׳s “Educational Modernisation Programme” and try to see how this plan has affected efficiency in teaching and research at Mexico׳s universities. We use a combined approach that includes traditional ratios together with Data Envelopment Analysis models. This mixture allows us to assess changes in efficiency at each individual university and explore if these changes are related to teaching, to research, or to both. Using official statistics for 55 universities over a six year period (2007–2012), we have generated 12 ratios and estimated 21 DEA models under different definitions of efficiency. In order to make the results of the analysis accessible to the non-specialist we use models that visualise the main characteristics of the data, in particular scaling models of multivariate statistical analysis. Scaling models highlight the important aspects of the information contained in the data. Because the data is three-way (variables, universities, and years) we have chosen the Individual Differences Scaling model of Carroll and Chang. We complete the paper with a discussion of efficiency evolution in three universities.     

The performance evaluation of regional R&D investments in China: An application of DEA based on the first official China economic census data

R&D (Research and Development) activities represent the basic core of corporate science and technology activities, and play a crucial role in enhancing the ability of companies to achieve rapid and sustainable growth. In recent years, the total R&D investments in China have increased significantly and the proportion of the industrial investments in R&D activities relative to national R&D investments has increased rapidly. In order to investigate the effectiveness of these R&D investments, we utilize Data Envelopment Analysis (DEA) models to evaluate the relative efficiencies of 30 regional R&D investments using the First Official China Economic Census Data in 2004. Our investigation and study indicate the following: (1) Only six provinces are global technical efficient and the performance of regional R&D investments in China needs to improve dramatically. (2) Increasing returns to scale have not yet occurred in any province. Constant returns to scale have prevailed in most provinces in the Western region, and decreasing returns to scale have prevailed in most provinces in the Eastern and Central regions. (3) There are no direct relationships between global technical efficiency and the amount of R&D investment. The Western region has the highest average radial efficiency score, followed by the Eastern region, and then by the Central region. (4) The Eastern region has advantages in local technical efficiency, the Western region has advantages in scale efficiency, while the Central region has neither technical efficiency advantages nor scale efficiency advantages. Suggestions are proposed to improve efficiencies of regional R&D investments.