This study focused on workplace spirituality an intrinsic factor as a determinant of organizational commitment although most recent studies on organizational commitment have focused mostly on the extrinsic rewards such remuneration, training opportunities, promotion and other tangible monetary benefits as predictors of organizational commitment. Employees are viewed as one of the most important assets for most organizations, in particular servicebased organizations like universities because of the benefits of delivering successful performances. It was therefore important to investigate whether workplace spirituality affects employee commitment. This survey study was a form of a cross-section study where both descriptive and correlational research designs were used. The study targeted all the academic staff in the public and private universities in Kenya. Stratified sampling was used where sixteen universities were selected followed by simple random sampling to select both representative department and staff from the selected departments. Data for the study was collected by administering a 25-item questionnaire to a sample of 347 academic. A total of 282 questionnaires were returned and analysis was done with the help of SPSS. Correlation and regression analysis results showed there was a significant positive relationship between workplace spirituality and organizational commitment.

It is the desire of the policy makers in a country is to have access to reliable forecast of inflation rate. This is achievable if an appropriate model with high predictive accuracy is used. In this paper, Seasonal Autoregressive Integrated Moving Average (SARIMA) model is developed to forecast Kenya's inflation rate using quarterly data for the period 1981 to 2013 obtained from KNBS. SARIMA (0,1,0) (0,0,1) 4 was identified as the best model. This was achieved by identifying the model with the least Akaike Information Criterion. The parameters were then estimated through the Maximum Likelihood Estimation method. Diagnostic checks using Jarque-Bera Normality Test indicated that they were normally distributed. ACF and PACF plots for the residuals and squared residuals revealed that they followed a white noise process and were homoskedastic respectively. The predictive ability tests RMSE=0.2871, MAPE=3.9456 and MAE= 0.2369 showed that the model was appropriate for forecasting the inflation rate in Kenya.

Purpose: The purpose of this study was to establish how operational risk management strategies lead to growth of MFI sector in Kenya.Methodology: The study adopted a correlation survey research design. The population of this study was fifty seven (57) MFIs. The sampling frame was the list of MFIs provided in the AMFI website www.amfikenya.com. A sample of thirteen (17) MFIs was selected using the random sampling approach. A questionnaire and an interview schedule were the main data collection tools. Qualitative data was analyzed using content analysis whereas the quantitative data was analysed using Statistical Package for Social Sciences (SPSS) where descriptive and regression analysis were conducted to determine the relationship between enterprise risk management strategies and growth of MFIs.Findings: Findings revealed that the MFI had adequate policies and procedures to manage its operational risks and the MFI had an operations manual. The findings also indicated that the MFIs have adhered to written policies and procedures to manage operational risks in the financial operations area, procurement area, treasury area, and financial management area. Results further indicated that the MFI had effective internal control systems for detecting fraud or other significant operational risks. Finally the study findings indicated that MFI’s internal audit functions ensured effective use of resources, accurate financial reporting, and ample random spot checks of MFI branches, clients, and staff. The regression results indicated that there was a positive relationship between operational risk management strategies and MFI growth.Unique contribution to theory, practice and policy: The study recommends that the MFIs to continue practicing effective operational risk management practices such as internal control framework comprising of policies and procedures. MFIs need to uphold the existence and accessibility of operational manuals. It is suggested that adherence to written policies and procedures is positive strategy and it should be emphasized.  The internal audit functions for effective use of resources and accurate financial reporting needs to be emphasized as it had a positive effect on growth. The MFIs should also benchmark their technology with that of banks to reduce human error, to produce timely and relevant data. It is recommended that implementation of know your client (KYC) requirements should be enhanced as it has an effect on growth.

Financial Time Series Forecasting is an important tool to support both individual and organizational decisions. Periodic phenomena are very popular in econometrics. Many models have been built aiding capture of these periodic trends as a way of enhancing forecasting of future events as well as guiding business and social activities. The nature of real-world systems is characterized by many uncertain fluctuations which makes prediction difficult. In situations when randomness is mixed with periodicity, prediction is even much harder. We therefore constructed an ANN Time Varying Garch model with both linear and non-linear attributes and specific for processes with fixed and random periodicity. To eliminate the need for time series linear component filtering, we incorporated the use of Artificial Neural Networks (ANN) and constructed Time Varying GARCH model on its disturbances. We developed the estimation procedure of the ANN time varying GARCH model parameters using non parametric techniques.

Objectives: The study aimed to determine the tobacco smoking patterns in Kenya. Methods: This research project used the Kenya GATS 2014 data, in which a sample of 5436 total people was interviewed. However since the research focussed on modelling tobacco smoking pattern in Kenya, data from only 4418 people was used for the analysis. Data from 1018 people in the sample was dropped because information about the individuals smoking pattern, age or work status could not be found. Data Analysis: The data was analysed using R-software version 3.0.2, and report presented in form of tables and graphs. Results: This project found out that there is likelihood of a person being a heavy smoker, light smoker or Non-smoker, if the person works in the Government and Non-government /private organization, self-employed or Unemployed. The overall effect of work status was statistically significant with a chi-square value of 129.722 (p-value<0.0001). Conclusion: The results show that a person's working status and their age are good predictors of a specific smoking pattern. From the results we have more people smoking as they grow old.