Modeling relative efficiency and productivity change using data envelopment analysis and regression models / Zalina Zahid

This thesis developed the panel DEA model to measure the relative efficiency and the productivity change of 17 faculties in Universiti Teknologi MARA (UiTM), Shah Alam, covering the period, 2000-2005. Three types of approaches were used under the panel DEA model, namely, the contemporaneous, the inter-temporal and the productivity approaches. The first two approaches were incorporated under the two-stage approach to measure efficiency and to determine the effects of selected external factors on efficiency performance. To enhance the robustness of the results, the DEA models were constructed based on three alternative data sets which consisted of different input specifications (DEA1, DEA2 and DEA3). In the first stage of the two-stage approach, two different types of DEA models were fitted on the three data sets. First, the original DEA models were used to compute the efficiency scores. Second, the Super Efficiency DEA models were applied to detect the outliers along the frontiers of the original DEA models. However, when a sensitivity test using the Spearman’s Rank Correlation Coefficient was applied, the results showed that the original DEA models were not sensitive to the existence of the outliers along their frontiers. In addition, the original DEA models were also found not to be sensitive to three different input specifications. Therefore, the original DEA models were considered to be robust and appropriate to be used to measure efficiency. Overall, the results showed that the efficiency scores from the three input specifications were consistent and their means of annual efficiency followed the same trend. Using trend analysis, it was shown that UiTM faculties displayed a mixture of patterns in their efficiency performance. A group of 8 faculties exhibited positive trend with non-science faculties consistently performed better than the nonscience faculties. Meanwhile, in the second stage of the two-stage approach, the influences of four selected nondiscretionary factors (age of faculties, percentage of associate professors and above, percentage of part-time students and ratio of non-academic staff to academic staff) on the efficiency of the faculties were determined using two statistical models, namely, OLS Regression and Tobit Regression. Both methods consistently show that the age of faculties has no effect on the efficiency performance across all input specifications. However, the percentage of associate professors and above and the staff ratio were found to be significant under DEA1 and DEA3 specifications. The first variable was negatively related but the second variable was positively related with efficiency. Meanwhile, only the percentage of parttime students was found to be significant and positively correlated with the efficiency scores under all input specifications. In the second approach of the panel DEA model, the DEA based Malmquist Total Factor Productivity (TFP) Index was used to measure the productivity change of the 17 UiTM faculties during the study period. Using DEA1 and DEA2 specifications, it was found that on the average, the 17 UiTM faculties experienced a decrease in their productivity levels over the study period. This was due to the decrease in the technological change but with slight improvement in the technical efficiency

SPATIAL FLOOD VULNERABILITY ASSESSMENT IN PENINSULAR MALAYSIA USING DATA ENVELOPMENT ANALYSIS (DEA)

Flood has caused an enormous negative impact on the environment and the population safety in Malaysia.  Many areas are found to be vulnerable to flood due to heavy rainfall during monsoon seasons. However, not many studies were done to identify how vulnerable the prone areas are affected. This study focused on developing flood vulnerability measurement in Peninsular Malaysian states. Data Envelopment Analysis (DEA) was applied on a set of secondary data consisting of several input and output variables across 11 years from 2004 to 2014. The flood vulnerability index for each dimension was computed based on three aspects of flood vulnerability dimensions, i.e. the Population Vulnerability, the Social Vulnerability and the Biophysical Vulnerability. The result showed that Johor was the most vulnerable state among all the states in Peninsular Malaysia in terms of the Population Vulnerability. Meanwhile, Kelantan was the most vulnerable state in the Social Vulnerability and Kedah was the most vulnerable state in the Biophysical Vulnerability. The assessment of flood vulnerability can provide multi-information that may well contribute to a deeper understanding of flood disaster scenario in Malaysia

Modelling the Efficiency of Paddy Production in Peninsular Malaysia Using Principal Component Analysis and Data Envelopment Analysis (PCA-DEA)

Although the current paddy production in Peninsular Malaysia shows a satisfying self-sufficiency level, the countrys annual rough paddy production, however, will still have to increase over the next 10 years to sustain up with the local population growth and the growing demand level for this staple food. Thus, immediate action needs to be done to evaluate the efficiency of paddy production, so that the information obtained can help the government to come out with strategies to maximize the outputs with the current existing inputs. The objective of this study is to measure the efficiency of paddy production of 11 states in Peninsular Malaysia using a hybrid of Principal Component Analysis and Data Envelopment Analysis. The variables used as inputs include planted paddy area, number of rice farmers, amount of seeds usage and total budget allocation whereas the variables used as outputs cover paddy output, rice output and rice income. As the input and output variables are highly correlated, this study proposes the combination of Principal Component Analysis (PCA) and Data Envelopment Analysis (DEA) approaches to reduce the data dimensionality problem instead of eliminating the variables with multicollinearity problem from the analysis. PCA is applied separately on both sides of all inputs and all outputs, resulting in one principal component (PC) to represent the input and one PC to represent the output side. Both PCs were found to contribute greater than 70% of the data variation. The results from output-oriented DEA model under variable return to scale (VRS) indicate that Kedah is the most efficient state in producing the paddy output, leaving the rest of states with average efficiency scores ranging from 0.745 to 0.998. Further results show that Kelantan, Terengganu and Pahang were placed at the lowest three states in terms of efficiency levels. There is a need for the government to pay extra attention on these states in bringing off the significant factors that may disrupt the functioning of efficient paddy production

Evaluating the efficiency and productivity of malaysian logistics companies using epsilon-based measure and malmquist index during the Covid-19 pandemic

Purpose: The impact of the Covid-19 outbreak since March 2020 has put Malaysia’s logistics sector in a contrasting reality to other sectors, as during the implementation of the movement control order (MCO), this sector was declared as providing essential service and allowed to operate in order to fulfil customers’ needs. This study aims to assess the efficiency and productivity of the logistics industry in Malaysia before and during the pandemic so that the performance of this industry can be observed. Design/methodology/approach: This study uses secondary data. Yearly records from the annual reports for the period of 2010–2020 were gathered pertaining to 15 Malaysian logistics companies treated as decision making units (DMUs) in this study. The efficiency and productivity of the Malaysian logistics industry during the Covid-19 pandemic have been assessed by using a hybrid DEA model consisting of a combination of epsilon-based measure (EBM) and Malmquist index. Findings: Findings showed that Lingkaran Trans Kota Holdings Berhad was the most efficient and productive logistics company with an average efficiency score of 1 and 12.7% growth in the average productivity index during the study period. In contrast, MISC Berhad obtained the lowest average efficiency score of 0.285. Nevertheless, the average productivity index for MISC Berhad showed an increase by 25.7%. During the early outbreak of Covid-19, Complete Logistics Services Berhad achieved full efficiency and also attained the highest positive growth of 76.2%. Harbour-Link Group Berhad was the least efficient company, scoring an efficiency score of only 0.254 and a decline in productivity growth by 40.8%. Research limitations/implications: The data used in this study may not be sufficient to represent the performance of the entire logistics industry as the pandemic is still not completely over. More useful insights can be obtained if the data can be extended until 2022 to assess the performance of logistics companies after the outbreak of Covid-19 in Malaysia. Many resources that have not been explored in this study and past research may provide an avenue for further research on the performance measurement of logistics companies, particularly in the Malaysian context. Practical implications: This study’s discovery may be used to facilitate the evaluation of resource utilisation and help inefficient logistics companies maximise their efficiency. Also, the findings may be used to help policymakers evaluate the existing policy in order to ensure that logistics companies have sufficient resources to offer reliable and efficient courier services. Originality/value: Although numerous studies have been conducted on the efficiency measurement of logistics companies, so far, scarce research in Malaysia has deployed a quantitative approach to measure the performance of Malaysia’s logistics industry, especially during the Covid-19 pandemic. Therefore, this study fills this gap by assessing the efficiency and productivity of the logistics industry in Malaysia before and during the pandemic of Covid-19Peer Reviewe