The Influence Of Market Orientation On The Choice Of A Shipping Firm By Fruit Exporters Of Refrigerated Cargo N Kenya

In the last few years, firms within the shipping business have transitioned from ahomogenous service business towards a heterogeneous market characterized by an increasein number of shipping firms, value offerings, well informed customer, rapid changes incustomer taste and preferences. The motivation of the study was to determine the influenceof market orientation on the choice of a shipping line by fruit exporters of refrigeratedcargo in Kenya. The study population consists of all the 39 fruit exporters of refrigeratedcargo as listed on the Fresh Produce Association of Kenya Website. A cross sectionaldescriptive survey was used. A questionnaire was developed to assist in collecting primarydata, while secondary data were obtained from renowned authors in the field of marketing.Analysis of data was done using descriptive and inferential statistics. The influence ofMarket orientation was measured using a construct developed by Narver and Slater’s(1990), customized for the shipping industry. The results reveal that Market orientationwas depicted by more emphasis on inter functional co-ordination compared to bothcustomer orientation and competitor orientation. Limitations encountered during research.The descriptive cross sectional research design could not measure changes inorganizational culture over time, the study was limited to a single industry put constraintson generalizing of the results. It was recommended that all studies done in future shouldadopt longitudinal research design to evaluate changes in organizational culture and theirinfluence on performance over time. Further, to capture representative view of industry,future studies need to use multiple informant approach

A Study on the Performance Comparison of Three Optimal Alpha-Beta-Gamma Filters and Alpha-Beta-Gamma-Eta Filter for a High Dynamic Target

The Alpha-Beta-Gamma tracking filter is useful for tracking a constant acceleration target with zero lag error in the steady state. It, however, depicts a constant lag error for a maneuvering target. Various algorithms of the Alpha-Beta-Gamma tracking filter exist in literature and each one of them presents its own unique challenges and advantages depending on the design requirement. This study investigates the operation of three Alpha-Beta-Gamma tracking filter design methods which include Benedict-Bordner also known as the Simpson filter, Gray-Murray filter and the fading memory constant acceleration filter. These filters are then compared based on the ability to reduce noise and follow a maneuvering target with minimum lag error, against the jerky model Alpha-Beta-Gamma-Eta. Results obtained from simulations of the input model of the target dynamics under consideration indicate an improvement in performance of the jerky model in comparison with the constant acceleration models

A Study of Optimization of Alpha-Beta-Gamma-Eta Filter for Tracking a High Dynamic Target

The tracking filter plays a key role in accurate estimation and prediction of maneuvering vessel’s position and velocity. Different methods are used for tracking. However, the most commonly used method is the Kalman filter and its modifications. The Alpha-Beta-Gamma filter is one of the special cases of the general solution pro-vided by the Kalman filter. It is a third order filter that computes the smoothed estimates of position, velocity and acceleration for the nth observation, and also predicts the next position and velocity. Although found to track a maneuvering target with a good accuracy than the constant velocity, Alpha-Beta filter, the Alpha-Beta-Gamma filter does not perform impressively under high maneuvers such as when the target is undergoing changing accelerations. This study, therefore, aims to track a highly maneuvering target experiencing jerky motions due to changing accelerations. The Alpha-Beta-Gamma filter is extended to include the fourth state that is, constant jerk to correct the sudden change of acceleration in order to improve the filter’s performance. Results obtained from simulations of the input model of the target dynamics under consideration indicate an improvement in performance of the jerky model, Alpha-Beta-Gamma-Eta, algorithm as compared to the constant acceleration model, Alpha-Beta-Gamma in terms of error reduction and stability of the filter during target maneuver