Purpose- The main purpose of this research is to develop and validate the innovation resistance model across Middle Eastern countries. Design/ Methodology/ Approach- The solar panel is used as an example of a disruptive innovation in the Middle East. Data is collected by distributing questionnaires from 810 houshold decision makers from residential areas across three countries in the Middle East: Iran, Saudi Arabia and Jordan. The main method of analysis is Structural Equation Modeling (SEM). Findings- The results show that fatalism and traditionalism are key cultural indicators of innovation resistance in the Middle East. In addition, the prominent role of consumer innovators in reducing resistance to innovation is approved. Research Implications- None of the previous studies have developed an empirical model of innovation resistance using a wide range of forces, i.e. culture, consumer characteristics, attributes of innovation and socio-demographics. Practical Implications- Fast diffusion of innovations can be challenging within fatalistic and traditional societies. Marketers should position solar panels as a continuous innovation that fits well within the context of past experience. In addition consumer innovators as opinion leaders can influence and advise other members of a society to make a purchase decision and should be targeted by marketers.

Applications of Data Envelopment Analysis (DEA) for the assessment of performance of universities have been widely reported in the literature. Often the number of universities under the assessment is relatively small compared to the number of performance measures (inputs and outputs) used in the analysis, which leads to a low discriminating power of DEA models on efficiency scores. The main objective of this thesis is the development of improved DEA models that overcome the above difficulty, using a sample of public universities in Malaysia as an illustrative application. The proposed new approach combines the recently introduced Hybrid returns to scale (HRS) model with the use of additional information about the functioning of universities stated in the form of production trade-offs. The new model developed in this thesis, called Hybrid returns to scale model with trade-offs (HRSTO), is applied to a sample of eighteen universities, which is considered to be a very small sample for the DEA methodology. Our results show that, in contrast with standard DEA models, the new model is perfectly suitable for such samples and discriminates well between good and bad performers. The proposed combined use of HRS model with production trade-offs is a novel methodology that can be used in other applications of DEA. Overall, the thesis makes several contributions of the theory and practice of DEA. First, for the first time, it is shown that the higher education sector satisfies the assumptions and can be modelled using the proposed HRSTO model. Second, also for the first time, it is shown that production trade-offs can be assessed for such applications and the methodology of their assessment has been developed and used in the thesis. Third, it is demonstrated that the HRSTO model significantly improves the discriminating power of analysis compared to standard DEA models, which is particularly important for small data sets. Fourth, it is concluded that the HRS model is further improved if production trade-offs are used. Fifth, by experimenting with different specific values of production trade-offs, it is shown that even the most conservative estimates of trade-offs notably improve the model. Finally, our results contribute to the more general discussion of the performance of universities in Malaysia and identification of the best performers among them.