The influence of organization learning, organization commitment and internal marketing on patient satisfaction: A case at Poliklinik Eksekutif USM / Sara Aiffa Mohamad A‟Idil

Introduction: The learning organization, organization's commitment and internal marketing are important elements to achieve patient satisfaction. In order to satisfy patients, Poliklinik Eksekutif USM‟s staffs must continually advance their competence through ongoing learning, they must feel committed to their work task so it will hold an emotional attached to their job and a good internal marketing. These factors could complement the successfulness of Poliklinik Eksekutif USM services which turn to patient satisfaction. Objective: The objective of the study was to investigate the extent to which the learning organization, organization‟s commitment and internal marketing of Poliklinik Eksekutif USM influence their patient satisfaction. Methodology: Two sets of questionnaire were developed to examine all the variables studied. The first set was distributed to 50 Poliklinik Eksekutif USM‟s staffs which asked about dependent variables (learning organization, organization commitment and internal marketing). The second sets of questionnaire were distributed to 50 patients. The questionnaire asked about their satisfaction toward Poliklinik Eksekutif USM services. Only 86 questionnaires were returned. SPSS analysis was used to evaluate the dependent variables toward patient satisfaction. Result: The finding indicated that learning organization and organization‟s commitment were statistically significant elements which contributed to patient satisfaction. Whereas, internal marketing was not a contributing factor for patient satisfaction. Conclusion: The Poliklinik Eksekutif USM‟s should emphasize more on learning organization and the organization commitment because it influenced patient satisfaction. Patient satisfaction can be improved if the staffs understand about learning organization and organization commitment, hence it can foster Poliklinik Eksekutif USM service

EXTENSIVE DESCRIPTIVE LITERATURE REVIEW REVEALING THE IMPACT OF BANKING SECRECY ON MONEY LAUNDERING: PROMINENCE ON THE LEBANESE BANKING SECTOR

Worldwide financial institutions face complexity in balancing between business ethical standards on one hand, and the act of competitiveness on the other hand. Thus, while selecting their customers they seek customers that lead them to the optimum position of competitiveness in the market, taking into consideration the act of financial investigation to avoid money launderer customers. Money launderer customers seek to deal with financial institutions to proceed with operating their financial crimes. Therefore, this paper spots the light on “money laundering” alongside with the impact of banking secrecy on money laundering with prominence on the Lebanese banking sector. Thus, in this paper, researchers present an extensive descriptive literature review that discuss in depth prior fundamental studies published since 1970 when banking secrecy was introduced until year 2018. Research limitations and future recommendations are provided at the end of this paper

DESCRIPTIVE LITERATURE REVIEW TO CLASSIFY AND ANALYZE GROWTH OF CHINESE FIRMS FROM 1990’s UNTIL 2018

This paper presents an understanding of the economic situation of the Chinese firms and their growth model, using a descriptive research model with an extensive literature review of factors argued to be generating growth and persistent high-growth in China. The selection of reviewed and embedded papers included several quantitative, as well as qualitative papers for analysis from the 1990s until 2018. Thus, the descriptive literature review was used as the methodology by which researchers present the history of the firm’s development and discuss the work of others using scientific arguments and comments. The examination has revealed that numerous factors discussed in this paper affect growth. However, there is no specific structural characteristics or performance of promising persistent-high-growth. Finally, there is a paucity of existing literature focusing on sustainable high-growth dynamics. Studies do not delve into details on this subject; accordingly, it is suggested that the lack of review articles has been hindering the progress of this area, thus, more investigations are requested from future researchers with attention to the use of a plurality of methods

Estimasi Panjang Antrian Pada Simpang Bersinyal Dengan Menggunakan Artificial Neural Network

Signalized intersections are nodes in the transportation network where vehicles in different directions meet and are critical points for congestion. Vehicle queue length is one of the performance parameters of a signalized intersection. Long queues of vehicles are at high risk of accidents involving many vehicles. Feedback signal control (actuated signal control) can be used to improve intersection performance. One of the variables that can be used as a feedback input is the length of the vehicle queue. Traffic in Indonesia is mixed traffic where various types of vehicles use the same road lanes and with low lane discipline. This causes the traffic system to become complex and to be stochastic and non-linear. Queue length modeling using a static linear algorithm is unable to capture the phenomenon of this complex traffic system. Therefore, this study aims to build a queue length model based on machine learning, that is, using an artificial neural network (ANN). This model studies traffic systems with historical data so that through the training process it can model queue lengths with a good degree of accuracy. An estimation model was built and applied to a section of the Muara Rapak signalized intersections, Balikpapan. Data on queue length for 10 days, 2 hours/day, obtained using CCTV and direct field surveys. The results of the model test show that ANN has a good level of accuracy with MAE, RMSE and MAPE of 3.8 m, 4.9 m and 6%, respectively

The Forecastability of Underlying Building Electricity Demand from Time Series Data

Forecasting building energy consumption has become a promising solution in Building Energy Management Systems for energy saving and optimization. Furthermore, it can play an important role in the efficient management of the operation of a smart grid. Different data-driven approaches to forecast the future energy demand of buildings at different scale, and over various time horizons, can be found in the scientific literature, including extensive Machine Learning and Deep Learning approaches. However, the identification of the most accurate forecaster model which can be utilized to predict the energy demand of such a building is still challenging.In this paper, the design and implementation of a data-driven approach to predict how forecastable the future energy demand of a building is, without first utilizing a data-driven forecasting model, is presented. The investigation utilizes a historical electricity consumption time series data set with a half-hour interval that has been collected from a group of residential buildings located in the City of London, United Kingdo

An improvised similarity measure for generalized fuzzy numbers

Similarity measure between two fuzzy sets is an important tool for comparing various characteristics of the fuzzy sets. It is a preferred approach as compared to distance methods as the defuzzification process in obtaining the distance between fuzzy sets will incur loss of information. Many similarity measures have been introduced but most of them are not capable to discriminate certain type of fuzzy numbers. In this paper, an improvised similarity measure for generalized fuzzy numbers that incorporate several essential features is proposed. The features under consideration are geometric mean averaging, Hausdorff distance, distance between elements, distance between center of gravity and the Jaccard index. The new similarity measure is validated using some benchmark sample sets. The proposed similarity measure is found to be consistent with other existing methods with an advantage of able to solve some discriminant problems that other methods cannot. Analysis of the advantages of the improvised similarity measure is presented and discussed. The proposed similarity measure can be incorporated in decision making procedure with fuzzy environment for ranking purposes

Women Volunteerism for Protecting Watershed Ecosystem in Langat Basin

Forested watershed provides abundant ecosystem services for water users including catchment area protection, flood regulation, provisioning of clean drinking water and climate regulation. However, the value of ecosystem services only partially takes into account by policy makers and indirectly cause overexploitation of natural resources. This study observes the economic role of women in protecting watershed ecosystem by calculating willingness to pay (WTP). Langat Basin is an imperative watershed that supplying fresh water source and other necessities to approximately 1.2 million people within the catchment area. Langat Basin has getting the effect of hasty growth through industrialization and land development actions. Women within Langat Basin has been selected through face to face interview for measure willingness to pay (WTP) towards ecosystem protection. Single Bounded Dichotomous Choice technique in Contingent Valuation Method (CVM) is engaged to calculate WTP of women. Willingness to pay of women in upstream is RM184.28, followed by women in middle stream is RM168.60 and lastly WTP of women in downstream is RM190.16. The willingness to pay women shows that women has potential to be part of ecosystem protection and signal to policy makers to include women in decision making process especially for environmental perspective