Browsing by Author "Al Shaer, Jamal"

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    Job satisfaction and organizational climate in the UAE work context
    (British University in Dubai, 2009-12) Al Shaer, Jamal
    Job satisfaction within the organisational context plays an important role in determining the level of motivation attained by the workers and thus their overall input towards attainment of organisational long and short term goals. Increase in business activity within the UAE places pressure on organisations to ensure that they adopt effective human resource management practices so that they can optimise their human resource ability and deal with rising levels of competition. This obvious change in the UAE job environment has led to an increase in emphasis on effective human resource management especially in ensuring employees’ job satisfaction as a means of maintaining high levels of motivation and increased input. The aim of this study is to test the degree to which employees’ job satisfaction in the UAE is influenced by their job environment. It is notable that the UAE is one of the nations that have a diversified workforce that provides a complex mix of factors that have to be considered in determining the right approach to effective human resource management and ensuring high levels of job satisfaction. A review of the existing literature reveals that organisational justice, decision making, psychological contract, organisational climate and performance-reward relationship are variables that could be influential on the levels of job satisfaction attained by employees in UAE. These variables are central to the five research hypotheses that are developed and tested in the study. A survey is used to facilitate the quantitative research approach adopted in the study. An existing questionnaire survey developed by Dr. Abubakr Suliman is used to test the hypotheses. It is noteworthy that the questionnaire has standard questions developed from the Likert five-point scale. Use of the questionnaire aids quantifying employees’ perception which also can all be treated asqualitative variables consequently the standardised test instrument used in this study facilitates effective assessment principally of the quantitative aspects of the research problem. Five big organisations in Dubai were involved in the data collection. A random sample of eighty four respondents from the five organisations is considered for analysis. The use of a random sample not only ensures statistical viability of the data but also minimises the effects of random errors on the results by ensuring that they are normalised. Various statistical tests are carried out with the aid of SPSS version 14 resulting in various results relating to the hypotheses and reliability of the results. Cronbach Alpha reliability tests and Pearson and Spearman Rank correlation tests are used in the study to determine the consistency and therefore reliability of the results and test the developed hypotheses respectively. The results show that the data retrieved from the survey is of high levels of reliability since over half of the observed results can be attributed to the interaction between the test variables. Moreover, the results lead to the rejection of all test hypotheses resulting in the conclusion that employees’ job satisfaction within the UAE is significantly affected by the organisational climate, decision making environment, psychological contract, organisational justice and performance-reward relationship. Therefore, organisations in the UAE have to be cautious of these variables when developing their practical strategies. Measures aimed at attaining high levels of employees’ satisfaction should apply holistic approaches to ensure that these variables are all addressed. Moreover, there is an urgent need for organisations to instigate research to determine the effect of national and organisational culture on their employees. This must also be facilitated by increased research to model the nature of interaction between the variables that affect employees' satisfaction which may require the use of complex regression models.