Abstract: A hybrid model based on Markov chain and data interpolation is proposed for evaluating the faculty recruitment policy in higher learning institutions. The question arise is how to evaluate the new recruitment policy in a quantitative projection approach. The data contains about 1033 individuals are collected from the registrar office at one university, the detail of academic staff such age and status ranks are used in the model design. The movement of this academic staff from one state to another categorized by age and status rank is developed using matrix transition diagram of Markov chain. The transition in each state is recomputed using interpolation. The revised transition matrix of Markov chain based on interpolation can be used as an equation solver to calculate mean time estimation for each category of faculties. The hybrid model results are then compared to the classical Markov chain results for both old and new policies by means of mean time estimation. Two scenarios were considered in the study; scenario 1 was based on historical data pattern between year 1999-2014 and scenario 2 was based on the new policies. The results showed the possibility average length of stay by position for both scenarios. The hybrid model shows the projection number of faculties in order to reduce the mean time at each category, if the new policies were to introduced. This study analyses the result of mean time estimation for faculties using classical Markov chain and hybrid Markov chain model. The numerical results showed that the hybrid Markov chain model presented lower mean time estimation than the classical Markov chain model. This approach presents an alternative yet effective way of the use of Markov chain technique in the planning of manpower.
Rahela Rahim, Fadilah Jamaludin, Haslinda Ibrahim and Sahubar Ali Nadhar Khan, 2016. Evaluation of Faculty Employment Policies Using Hybrid Markov Chain Model. The Social Sciences, 11: 2590-2595.