This chapter covers the research framework, research hypotheses, research procedure the research method, research sample and data collection, the research instrumentation, and finally the data analysis.
Research Framework
According to the research purposes and literature review, the researcher constructed the framework of the study and this framework is shown in Figure 3.1. It is important to note that the research framework illustrates the relationship between variables and it helps in understanding the research propose.
The study aims to find whether demographic data (age, gender, marital status, organizational tenure, level of education) would make no significant difference on organizational commitment, (2) the positive relationship between emotional intelligence and organizational commitment (3) the positive relationship between emotional intelligence and job satisfaction and (4) the positive and significant relationship between job satisfaction and organizational commitment.
Research Hypotheses
Based on the review of existing literature, the research questions and the framework presented in figure 3.1, the following hypotheses were proposed:
Job Satisfaction
Emotional Intelligence Demographics
Organizational Commitment H1
H2
H4 H3
Figure 3.1 Research Framework
H1 Demographics data would make no significant difference on Organizational Commitment
H2 Emotional Intelligence is positively related to Organizational Commitment
H3 Emotional Intelligence is positively related to Job Satisfaction.
H4 Job Satisfaction is positively and significantly related to Organizational Commitment.
Research Procedure
The procedure for this research is briefly described below:
1. Reviewing relevant literature: Literature review was conducted in order to understand the relationship between the variables in this study. Hence, the literature review supported the research questions proposed in this study.
2. Identifying the research topic: Based on a review of the literature, the research topic was identified.
3. Proposing research questions and hypothesis: Based on the literature review and purposes of this study, the research questions were identified and then research hypothesis were established accordingly.
4. Designing research method and identifying research subjects: The research design was based on a quantitative, non- probability sampling method and hence, this quantitative approach was chosen to answer the research questions in this study. The justifications for adopting this method were provided in the research method section of this study. In line with this, during the course of the development of the topic and purposes of this study, a suitable research sample was identified. This research sample would have satisfied the criteria developed for participation in this research (Please see research sample section for the criteria).
5. Instrument Selection: After discussing the research structure with the advisor and some professors, adequate questionnaires were selected based on previous measurements of the variables. A copy of this questionnaire is found in the appendix section of this study.
6. Conducting the data collection: The sample was collected based on convenient sampling. The reason for choosing this sampling method was discussed in the data collection section of this study.
7. Analyzing the data: The data was collected and coded using Microsoft Excel. It was later analyzed using IBM SPSS version 19. SPSS version 19 was used to do descriptive statistics of the demographic variables as well as the other statistical methods used in this study. Detailed explanations of these statistical methods were provided in the data analysis section of this research.
8. Writing up research findings: Descriptive and inferential statistics was used to report the findings of this study. Hence, the research findings from the hypothesis was reported and conferred in the discussion part of this study.
9. Presenting the final report: Based on the research findings, conclusions, recommendations and limitations were presented.
A summary of the research procedure is provided below:
Figure 3.1 Research procedure
Research Method
A quantitative and survey research method was employed for this study. As Creswell, (2005) states that a quantitative method is used to study research problems that require either a description of trends or an explanation of the relationship among variables and hence, the reasons for choosing this method was based on the nature of the research hypotheses in this study which was based on assumptions about significant differences and the relationships among the variables, thus employing a quantitative method helps in establishing significant
9. Presenting the final report 1. Reviewing relevant literature
2. Identifying the research topic
3. Proposing research questions and hypothesis
4. Designing research method method
5. Selecting the instrument
6. Conducting the data collection
7. Analyzing the data
8. Writing up research findings
differences or causal relationships. In line with this, Cavana, Delahaye, and Sekaran, (2001) suggested that quantitative study was the best design for explaining relationships between variables. In the same vein, since precise measurements are necessary for the validity of the research, quantitative research methods was employed in this study in order to objectively seek precise measurement and analysis of the variables. The design of this study was also cross-sectional hence data was collected from the sample selected at one time point.
Research Population and Sample
Six banks in The Gambia constituted the target population for this study. A target population is the entire group of people, objects, or events that the researcher wishes to study (Cavana et al., 2001). This population was chosen because like in many developing countries, banks are a major part of the financial sector and they play an important role in facilitating and providing financial resources necessary for economic growth and particularly, The Gambia banking industry contributes about 58.5 % in the Gross Domestic Product (Ghirmay, 2005) and besides we live in an era in which organizations are frequently confronted with the necessity of massive change; committed employees might be an extremely valuable organizational resource in facilitating rapid adaption to changing conditions and hence, an understanding of the factors that predict or relate to organizational commitment therefore becomes more significant.
Specifically, the sample for this study constituted employees from six banks in The Gambia. The criteria for selecting these six banks were: First, these banks must be in operation in The Gambia for at least two years and this is because of the nature of the variables in this study which concerns employees’ satisfaction and commitment and the researcher believes that a bank that has not been in operation for at least two years cannot fully justify employee job satisfaction or organizational commitment. Second, these banks have to be located in the Greater Banjul Area and the reason given is that most of the businesses especially the banking sector in The Gambia is concentrated in this area and hence the need to select the banks participating in this area. Hence, the statistical method used in selecting the sample is already described in the research method section of this study.
A more detailed explanation of how the questionnaires were distributed and the final useable ones are highlighted in the data collection part of this study.
Data Collection
A soft copy of the questionnaire was sent as attachment to a friend who has been working in one of the six banks selected in this study and obviously had access to office accessories specifically an internet and a printer. Thus, two hundred and sixteen questionnaires were printed and distributed equally to the six participating banks. This means that thirty-six questionnaires were given to each of the banks. The research team included my close family members and some friends. It is important to note that procedures were used to
conduct the data collection. First, a contact person was found in each of the six banks selected for this research and hence the contact person was preferably a member of the Human Resources department. These contact persons were phoned to obtain permission from their organization to participate and they were assured that this activity was strictly academic and that any information provided would be treated confidentially and once he/she agreed to participate in the survey, paper-based questionnaires were hand delivered to them. Second, based on the responses from the contact persons and due to the nature of the banking profession, a period of two months was established for data collection. Hence, the paper- based questionnaires was delivered to the contact person in each bank who in turn used convenience sampling strategy to distribute the questionnaires to the participants. It is important to note that convenience sampling method is a non-probability method and this means respondents were chosen in a non-random manner and hence some respondents had no chance of been included in the study. However, the reason for choosing this method was because as Kahl (2000) states when time or cost is an issue, convenience sampling becomes a useful sampling method. Third, once the questionnaires were given out to the participants, the researcher was informed and hence, she communicates with her friend whom the soft copy was sent to earlier and then the friend communicates with the research team and thus a follow- up call or message was sent to the contact person in the respective banks to ensure that the participants completed the questionnaires.
Furthermore, a total of 216 questionnaires were distributed, 210 questionnaires were received, which resulted in a response rate of 97. 22%. However, of the 210 questionnaires received, 10 of them had both invalid and missing data and the researcher decided not to include them in the study and thus, the total of number of usable questionnaires for this study were 200.
Research Instrument
The research instrument of this study was a questionnaire and it comprise of four parts:
organizational commitment, emotional intelligence, job satisfaction and personal information named demographic variables. See the appendix for a full and completed copy of this questionnaire. These measures are described below:
Organizational commitment – Organizational commitment was a dependent variable in this research. The instrument for organizational commitment was adopted from Meyer,
Allen and Smith’s (1993) three dimensions of organizational commitment. These three dimensions were affective, continuance and normative commitment respectively. Six items were in each dimension thus 18 items constituted the total organizational commitment scale that was measured. For example, affective commitment contains items such as “This organization has a great deal of personal meaning to me.” Continuance commitment includes items such as “I feel that I have few little options to consider leaving this organization.”
Whereas normative commitment contains items such as “I owe a great deal to my organization”. Meyer et al. (1993) found internal consistency, reliability and validity for the three dimensions. In line with this, the reliability estimate for the three scale scores was found to be significant and hence the Cronbach's alphas for: Affective commitment =0.85, Continuance commitment =0.74 and Normative commitment =0.76. Cronbach’s alpha reliability coefficient (.73) for all 18 items was deemed acceptable. All items were measured on a 5 point Likert-type scale from (1) = “Strongly Disagree” to (5) = “Strongly Agree”.
Emotional Intelligence – Emotional intelligence or EI served as an independent variable in this study. Wong and Law’s (2002) 16 item self-report measure of emotional intelligence was adopted for this study. This measure was consistent with Mayer and Salovey’s (1997) definition of EI as well as Davies et al. (1998) synthesis of the EI literature.
Previous studies have supported the scale’s factor structure, internal consistency, convergent and discriminant validity (Wong & Law, 2002). The scale consists of four dimensions with four items in each dimension. The SEA dimension (Self- Emotion Appraisal) relates to individuals’ ability to understand and express their emotions for example, “I have a good sense of why I have certain feelings most of the time.” The OEA (Others’ Emotion Appraisal) relates to individuals’ ability to perceive and understand the emotions of others for example,
“I always know my friends’ emotions from their behavior.” The ROE (Regulation of emotion) relates to individuals’ ability to regulate their own emotions, for example, “I am able to control my temper and handle difficulties rationally.” The UOE (Use of Emotion) relates to individual’s ability to make use of their own emotions by channeling them toward constructive activities to facilitate performance, sample items include: “I would always encourage myself to try my best”. The 7 point Likert – type response scale ranging from
“Strongly Disagree” to “Strongly Agree” was used to measure all the 16 items. Cronbach's alphas reliability coefficients for SEA (0.86), OEA (0.82), ROE (0.79) and UOE (0.85) were considered acceptable. Cronbach’s alpha reliability coefficient (.88) for all the 16 items was deemed acceptable.
Job Satisfaction – Job satisfaction served both as an independent variable and a dependent variable. It was an independent variable when its relationship with organizational commitment was introduced and a dependent variable when emotional intelligence served as an independent variable and this was specifically shown in the hypotheses. Job satisfaction was measured using the 36- item form of the Job Satisfaction Survey- JSS (Spector, 1985).
The JSS was originally developed for the social service sector but it was shown to be adequate to use in other sectors as well (Roelen, Koopmans, & Groothoff, 2008).The scale consists of nine facets with four items in each facet. These facets are: Pay (items include, “I feel I am being paid a fair amount for the work I do.”); Promotion (items include, “There is really too little chance for promotion on my job.”); Supervision (items include, “My supervisor is quite competent in doing his/ her job.”); Fringe benefits (items include, “I am not satisfied with the benefits I receive.”); Contingent rewards (items include “When I do a good job, I receive the recognition for it that I should receive.”); Operating conditions (items include, “Many of the rules and procedures make doing a good job difficult.”); Co –workers (items include, “I like the people I work with.”); Nature of work (items include, “I sometimes feel my job is meaningless.”) and finally Communication (items include, “Communications seem good within this organization.”). A 5-point Likert type response format was used and this format ranged from (1) = “Strongly Disagree” to (5) = “Strongly Agree”. Van Saane, Sluiter, Verbeek and Frings-Dresen (2003) reported that the JSS was among the job satisfaction measures that met the quality criteria for reliability and validity. This was also evident from the internal consistencies or coefficient alphas drawn from a sample of 3,067 individuals who completed the JSS. The coefficient alphas ranged from 0.60 for co-workers subscale to 0.91 for the total scale (Spector, 1985).
Personal Information – Personal Information or demographic variables provides picture of the sample composition. In this study, it served as an independent variable. Based on the research questions; information on gender, age, marital status, level of education and organizational tenure was included in this study.
Data Analysis
The responses collected from the 200 questionnaires were coded using Microsoft Excel 2007 and these data was later transported to IBM SPSS version 19 for analysis. It is important to note that SPSS is a statistical programme meant for the social sciences and it is
used for statistical analysis. Hence, this statistical package allows the researcher to test for the different hypotheses of the study. Because of the nature of the hypotheses, composite scores were aggregated for emotional intelligence, job satisfaction and organizational commitment.
These scores were used to represent the total score for each of these variables and this helped the researcher to test for the proposed hypotheses. Specifically, this study uses different statistical techniques to analyze the data. Descriptive statistics was used to understand the general trends in the categorical variables. Inferential statistics using one-way Anova, Pearson-moment correlation and simple linear regression was used to answer the research questions and thus, the following describes the statistical analysis that was carried out using SPSS to answer the research questions.
Descriptive Statistics
Descriptive statistics portray the characteristics of the sample. Specifically, in this study, descriptive statistics was used to describe the frequencies and percentages of the categorical variables and hence the following demographic variables were reported for each participant: gender, age, marital status, level of education and organizational tenure.
One - Way ANOVA
In this study, hypothesis one was concerned about whether there was no significant difference between the demographic data on organizational commitment and thus one-way analysis of variance was applied to test the first hypothesis. In statistics, one – way Analysis of variance is a statistical technique used to compare the means of two or more groups.
Though T- test could have been used to test the means of some of the demographic data in this study with two groups for example, gender and marital status but it is important to note that this study opted to use One-way Anova because if you are comparing two groups means, one –way Anova will give the same results as the t test for independent samples. Besides the T-test is limited to comparing only the means of two groups, one –way Anova can compare two and more than two group means. Similarly, some of the demographic data had more than two groups and hence, T-tests cannot be used because multiple T-tests would cause a cumulative increase in Type 1 Error. This means the more T-tests done; the more error there would be in the estimates derived from the test.
Although, the purpose of one- way Anova is to test for significant differences between group means, it is important to note that one-way Anova does not specifically indicate which pair of groups exhibits statistical differences. Hence, if there was a significant difference then
a Post- Hoc Scheffé Test was further used in the first hypothesis to determine which specific group means among the demographic data was significantly different with regards to organizational commitment.
Pearson Product – Moment Correlation Analysis
The Pearson product- moment correlation analysis is used to describe the strength and direction of a relationship between two quantitative or numerical variables. A correlation coefficient (r) is usually used during a correlational study. Its numerical value ranges from -1.0 to + 1.0 coefficient values. It is important to note that if the coefficient is greater than 0, this indicates a positive relationship; if it is less than 0 then it indicates a negative relationship whereas if it equals to 0, it indicates non-existence of any relationship. In line with this, a negative relationship indicates that high values on one variable are associated with low values on the other variable while positive correlation indicates that high values on one variable are associated with high values of the other variable. Hence, in a study of relationships, two variables are said to be correlated if change in one variable is accompanied by change in the other. It could either in the same or reverse direction. The p value tells you whether the relationships or correlation between the variables are statistically significant and thus in this study, p < 0.05 was the cut-off point under which the p value was set for the finding to be considered statistically significant. Hence, this Pearson product- moment correlation was used to examine the relationships between the variables in hypotheses 2 – 4.
Simple Linear Regression
Simple linear regression examines the relationship between a single independent predictor variable and a single dependent outcome variable. In other words, it is a statistical method in which there is one outcome or dependent variable and one predictor or
independent variable. In a simple linear regression, it is important to determine the coefficient of determination (R2). The R2 is the amount of variation in the response variable that is
explained by the predictor variable and if the constant or an intercept is included in the analysis, the R2 is the square root of the sample correlation (Pearson’s correlation) coefficient between the outcome and the predictor value. It is however important to note that the Pearson
explained by the predictor variable and if the constant or an intercept is included in the analysis, the R2 is the square root of the sample correlation (Pearson’s correlation) coefficient between the outcome and the predictor value. It is however important to note that the Pearson