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Study of the relationship between employees’ commitment, job satisfaction, job safety, job autonomy and employees’ turnover intention in a Construction Industry

Quantitative Research Study Report

Research Paper (postgraduate) 2017 103 Pages

Business economics - Marketing, Corporate Communication, CRM, Market Research, Social Media

Excerpt

Table of contents

1. INTRODUCTION:

2. LITERATURE REVIEW:
2.1 Job Satisfaction:
2.2 Employees’ Commitment:
2.3 Safety at Job:
2.4 Job Autonomy:
2.5 Employees’ Turnover Intention:
2.6 Hypotheses and theoretical framework:

3. METHODOLOGY
3.1 Data Collection Method
3.2 Sampling Method and Size
3.3 Data Collection Instruments
3.4 Questionnaire items
3.5 Measure of Reliability

4. RESULTS
4.1. Correlation
4.2. Descriptive Statistics
4.3. Hypotheses Analytical Significance:
4.4. Linear Regression and Multiple Regression

5. DISCUSSION
5.1 Model / Hypotheses Supported or Not
5.2 Conclusion
5.3 Implications
5.4 Limitation of the study
5.5 Recommendation for future research
5.6 Acknowledgements

6. REFERENCES

APPENDICES:

Appendix-A:
Appendix-B:
Appendix-C:
Appendix-D:
Appendix-E:
Appendix-F:

Appendices:

Appendix-A: Research Survey Questionnaire

Appendix-B: Detail of Variable Items

Appendix-C: Descriptive Statistics

Appendix-D: Correlation Statistics

Appendix-E: Regression Statistics

Appendix-F: SPSS- Step by Step (Lab work)

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1. INTRODUCTION:

Construction companies cannot flourish and fulfil their customer’s needs when their employees are dissatisfied, restricted by too many policies and instructions, not committed to the organization,and are facing significant job hazardousness in their daily assigned tasks. Considerable research has been devoted to develop predictive models of employees’ turnover intention, job satisfaction, employees’ commitment, job autonomy and safety at work place among the past commonly proposed antecedents. Individual studies have generally supported hypothesized linkages among turnover and these variables. Satisfaction, commitment, job autonomy and job safety for instance, have invariably been reported to be negatively related to employees’ turnover intention and positively correlated with one another.

This is not a real life study but purely based on literature review, provided data by the unit convenor (collected in previous research), identified new variable items and assumed responses to newly identified data items. Main purpose of this study is to enable students’ understanding about the quantitative research method and the steps involved in SPSS (Statistical Package for the Social Sciences) program simulation. In addition to seven (7) demographic group variables such as gender, age, income, qualification, position, marital status and employees’ experience; two (2) independent variables i.e. job satisfaction and employees’ commitment and one (1) dependent variable i.e. employees’ turnover intention are already included in the provided EPSS data file. As advised by team leader, team members carried out necessary literature review and identified two (2) new predictor variables namely job safety and job autonomy. Further, to examine increase in reliability for the provided data, team decided to add three (3) more variable items under each existed main variables (larger the sample size better is the prediction about outcomes and variances). Hence, total variable items in our EPSS model is forty five (45) excluding personal demographic variables.

Objective of this is study to analyse the casual relationships between employees’ commitment, job satisfaction, job autonomy and job safety in models of employees’ turnover intention. Therefore, causal model comprising five variables: one dependent (employees’ turnover intention) and four independent/controlled variables (job satisfaction, employees’ commitment, safety at job and job autonomy) is formulated. Other multiple regression for outcome variable employees’ commitment considering job satisfaction, job safety and job autonomy is developed and analysed. This statistical model is tested on a sample of 146 construction industry’s employees (both male and female) over a 6-month period selected from different departments of Multiplex Construction Company (a large size fictional company in Canberra-ACT for the purposes of this research study). The theoretical, empirical, and practical implications of the research findings are discussed as under.

2. LITERATURE REVIEW:

Over period of time and with recent technological boom particular in the construction industry, human resource department’ responsibilities have been increased enormously to take part in the evaluation of employees’ performance and their intent to leave or retain as prediction of company’s turn over. More consideration have been given in the analytical study of employees’ commitment, job satisfaction, job autonomy, job safety against the employees’ intention to turnover. These five (5) main variables are the interrelated concepts studied widely in the literatures of organisational behaviour, human resource management and business management.

The key distinction between satisfaction, commitment, autonomy, safety and turnover intention is that the first two concepts are employees’ attitudes or orientations, while autonomy and safety belong to organizational culture and represent employees ‘adaption and flexibility and the last one i.e. employees’ turnover intention refers to employees’ behavioural responses. Intent to stay or leave in organization refers to employees’ behavioural intentions and has been demonstrated to exert a strong negative influence on actual turn over (Bluedorn, 1982; Iverson, 1992; Muller et al; 1992; Price & Mueller 1986). Both job satisfaction and employees’ commitment contribute uniquely to the turnover process. This independent-effects model follows Porter et al., (1974) suggestion that job satisfaction and organizational commitment, though related, are distinct constructs (Dougherty et al., 1985). It implies no particular causality between the two attitudes, but does not rule out the possibility of reciprocal influences (cf. Farkas & Tetrick, 1989).

Unfortunately, it is not clear whether, or under what conditions, high performers are more or less likely to voluntarily quit. The nature of the relationship between job performance and quitting, if any, has proven to be elusive. Past researchers had found evidence for a positive relationship, a negative relationship, no relationship, and even non-linear relationships between performance and turnover (Jackofsky, Ferris, & Breckenridge, 1986). Few researchers have also argued that the relationship is likely more complex than a direct linear relationship, and that we need to account for potential moderating influences (Trevor, Gerhart, & Boudreau, 1997).

The workers’ positive perceptions of organisational climate influence their perceptions of safety at the workplace (Gyekye S. A. 2005). Regarding the relationship between safety climate and safety perception, safety experts, e.g., Neal et al. 2000 and Silva et al. 2004, who have investigated this relationship have confirmed that “organisational climate predicts safety climate, which in turn is related to safety performance”. Essentially, what these studies have revealed is that the general organisational climate in a work environment imparts significant influence on safety climate, which in turn affects workers’ safety behaviour, and subsequently job satisfaction.

Previous research indicates that employees’ motivation and their work-related outcomes are related to their satisfaction of the basic needs for autonomy, competence, and relatedness (Baard et al. 2004; Deci et al. 2001; Lynch et al. 2005). Principals’ autonomy can be fostered in an autonomy-supportive environment. Autonomy support is a social-contextual factor that has been extensively studied in relation to need satisfaction. It involves taking the other’s perspective, understanding the other’s feelings and encouraging self-initiation (Deci et al. 1994; Deci and Ryan 1985).

Irrespective of the above research and studies, current analytical exercise depends on the adopted data set assumed to fit for this experimental study. EPSS simulation results are also examined within the context of selected data set and drafted hypotheses therefore, conclusions and recommendations of this statistical study might not be fit as prediction of any real life example to apply universally. Academic definitions of variables used under our statistical model are as follows:

2.1 Job Satisfaction:

Job Satisfaction is termed as “the degree of positive emotions an employee has towards job role’’ (Kalleberg, 1977; Locke, 1976; Smith, Kendall, & Hulin, 1969). Research has found that the job satisfaction is the pious determinant of an employee’s commitment and identified the positive influence of satisfaction on commitment (Bluedorn, 1982; Iverson, 1992; Lincoln & Kalleberg, 1985, 1990; Mowday et al., 1982; Mueller et al., 1994). According to Lopopolo (2002), job satisfaction is a person’s attitude towards their own job. Spector (1997) defines job satisfaction as a feeling people have about their job and different job aspects.

2.2 Employees’ Commitment:

Organizational commitment is the degree to which an employee feels loyalty to an organization. (Mueller, Wallace, & Price, 1992; Price, 1997). Commitment takes longer to develop and is more stable than satisfaction, and has received considerable empirical support (e.g. Marsh & Manari, 1977; Mowday, Porter, & Steers, 1982; Price & Mueller, 1986; Williams & Hazer, 1986). Organizational commitment is a positive attitude of the employee, leading also to a psychological connection and identification with the organization. Forming an organizational commitment entails creating and maintaining continuous psychological relations between the employee and the company (Mowday et al., 1979).

2.3 Safety at Job:

Job safety or safety climate is a coherent set of perceptions and expectations that workers have regarding safety in their organisation Griffin M, Neal A. (2000). It is considered as a subset of organisational climate Zohar D. (1980). On contrary, job hazard is a degree to which employees are exposed to harmful working conditions (Iverson, 1994). Both safety climate and job satisfaction negatively affects turnover intention (Todd D. Smith, 2017). The working conditions of an organization plays a key role in employee’s job satisfaction and hence employee turnover.

2.4 Job Autonomy:

Job Autonomy is referred to as a degree to which an individual has influence over his job (Iverson, 1996). The inverse relationship between autonomy and turnover is supported in the literature (Hom & Griffeth, 1995). Job autonomy, or the extent to which a job allows freedom, independence, and discretion to schedule work, make decisions, and choose the methods used to perform tasks (Morgeson & Humphrey, 2006) is deemed an essential tenet in contemporary work design theories (Humphrey, Nahrgang, & Morgeson, 2007).

2.5 Employees’ Turnover Intention:

Turnover intention is a measurement of whether an organization's employees plan to leave their positions or whether that organization plans to remove employees from positions.. Turnover refers to actual movement across the membership boundary of an organization (Price, 1977, 1997). Intent to stay (or leave) refers to an employees' behavioural intentions, and has been demonstrated to exert a strong negative influence on actual turnover (Bluedorn, 1982; Iverson, 1992; Mueller et al., 1992; Price & Mueller, 1981, 1986a). Turnover intention is perceived to be a mindful and deliberate wilfulness to leave the organization.

2.6 Hypotheses and theoretical framework:

Using the above literature review as a basis of our hypothetical framework, team has developed a conceptual framework aiming to explain the relations of employees’ commitment, job satisfaction, job autonomy, job safety and employees’ turnover intention is expressed graphically as under:

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Figure 1: Conceptual Framework with Five (5) Hypotheses

Based on our group discussion, the following hypotheses are devised to be examined:

(H1): Job Autonomy brings Employee’s Commitment

(H2): Higher perception of Job Safety confirms Employees’ Job Satisfaction

(H3): Job Satisfaction and Job Commitment are mutual inclusive (Positive direct relation)

(H4): Employees’ Commitment negatively affects Employees’ Turnover Intention

(H5): Job Satisfaction reduces Employees’ Turnover Intention

(H6): Job Autonomy decreases chances of Employees’ Turnover Intention

(H7): Job Safety has reduction impacts on Employees’ Turnover Intention

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Figure 2: Conceptual Framework of H1 Figure 3: Conceptual Framework of H5

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Figure 4: Conceptual Framework of H3

3. METHODOLOGY

3.1 Data Collection Method

Due to the closed group of selected 146 employees’ from different departments of Multiplex Construction Company, we have used a well-defined paper questionnaire survey for the data collection for our quantitative research and statistical simulation. The questionnaires were handed over to departments’ supervisors to distribute to the selected employees ensuring timely completion and submission of the required information. The instrument is based on ‘closed’ questions, using Likert rating scales (1 to 7) for the variable items mentioned in three questionnaire’ sections A, B and C. Refer to Appendix-A for research survey questionnaire.

3.2 Sampling Method and Size

After obtaining necessary information of all employees’ of Multiplex Const. Co. in February 2017, our research group has identified a reasonable sample size by selecting diversified staff including both males and females w.r.t. their ages, qualifications, positions, income ranges etc. covering all the departments. In April 2017, survey questionnaires were delivered to selected 146 members of Multiplex Co., with a self-addressed envelope to highlight the ‘High Confidential’ nature of the survey responses. After couple of follow-up reminders, a total of 146 sealed questionnaires were submitted to employees’ supervisor which later returned to us in September 2017.

The sample contained respondents with a mean age of 35 years, with 38.4% (56) female respondents and 61.6% (90) male respondents. Analysis from frequency tables showed that three-fourth of the respondents (73%) have been working for Multiples for about 5 years or less and the remaining quarter have been employed for 6 years or more (49 respondents out of 146). About 42% (61) respondents are married. Further demographic information relating to the 146 respondents is provided in Appendix-D “Descriptive Statistics”.

3.3 Data Collection Instruments

The data collection instrument is a simple survey questionnaire contained 45 questions designed to measure and identify socio-demographic characteristics, levels of job satisfaction; employees’ commitment, job safety, job autonomy and their intention to turn over/ leave the Multiplex.

Quantitative Survey: Likert 7-point Scale Questionnair e:

Our research group has developed and well-structured closed questionnaire with a 7-point Likert scale from 1 for highly dissatisfied/ highly disagreed to 7 for highly satisfied/ highly agreed for the identified 45 variables items under 3 sections (A, B & C). The intention is to study the inter-relation between main variables while measuring attitudes relating to employees’ commitment, job safety, job autonomy and employees’ turnover intention. There were no “open” qualitative style questions, to reduce the likelihood of unusable responses.

The survey consisted of following three sections:

- Section-A contains questions pertaining to Satisfaction, Commitment, Safety and Autonomy;

- Section-B pertaining to Intention to Turnover;

- Section-C relating to Demographic Information.

3.4 Questionnaire items

All questionnaire’s items in this study are sourced from previous research and academic studies those have been modified for suitability for the context of this analytical research. Appropriate permission has been sought to use adapted job safety and job autonomy variable items. Our team further added three more variable items (sub-sets) under existed variables to see the impact on data reliability. List of variable items are given below and details are attached under Appendix-B:

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3.5 Measure of Reliability

The reliability and validity of the items are ascertained to establish if they are reliable and valid for further analysis. According to DeVellis (2003 as cited by Talukder, Quazi & Keating, 2014), reliability values between .70 & .80 are considered respectable, while reliability values between .80 and .90 are considered "very good”. The reliability co-efficient in the form of Cronbach’s alpha (α) for dependent and independent variables under our current study is presented in Table-1 below.

Table-1: Reliability Values of Variable Items

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4. RESULTS

4.1. Correlation

The correlation indicates the strength of the relationship between identified variables Cohen, J. (1988), “Statistical Power Analysis for the Behavioural Sciences” (2nd ed.), New Jersey: Lawrence Erlbaum. Cohen gives the following guidelines for Pearson’s r where the size of the effect is small if r =0.10; the effect is medium if r =0.30 and the effect is large if r =0.50. The current EPSS simulation results are presented in Table-2:

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** Correlation is significant at the 0.01 level (2-tailed). Pearson Correlation for strong relation > 0.30

- Significant positive correlation between Satisfaction and Commitment, r = 0.645, p =<.01

- Major negative correlation between Satisfaction and Turnover Intention, r = -0.529, p =<.01

- Very high positive correlation between Safety and Commitment, r = 0.915, p =<.01

- Large positive correlation between Commitment and Autonomy, r = 0.796, p =<.01

- Significant positive correlation between Safety and Satisfaction, r = 0.520, p =<.01

- Turnover Intention correlates with all other independent/ predictors variables negatively

Lesser r value for two predictors/ independent variables indicate different/ distinct variables otherwise, two variables in fact are behaving like one variable. A very high correlation close to ‘1’ may indicate that compared variables might be measuring very similar concepts. When two predictor variables are perfectly correlated, knowing the value of one variable allows you to exactly predict the value of the other variable e.g. employees’ commitment and job safety. Variables’ multicollinearity check is performed under multiple regression statistics, detailed under Section-5.

4.2. Descriptive Statistics

A total of 146 responses were collected and analysed. The respondents are 56 (38.4%) female and 90 (61.6%) male; ages ranged from 17 to 64 (M=35.01 years, SD=11.763). The average number of years the respondents have been in their current organisations is approximately 5 years (M=4.90 years, SD=5.712). More than half of the respondents (54.1%) have completed at least an undergraduate degree. The majority of employees are (58.2%) are either married or engaged.

The mean job satisfaction score is 4.24 (SD= 0.86, ranges from 2 to 6.11); conveying that the employees are “neither satisfied nor dissatisfied”. The mean job commitment score is 3.94 (SD=1.135, ranged from 1.33 to 6.78); conveying that the employees “neither agree nor disagree” about their attitudes towards employee commitment. The mean turnover intention score is 2.36 (SD=1.129, ranged from 1 to 5); conveying that the staff’ thoughts about their turnover intention are in between “Never” and “Somewhat”. The mean job safety score is 3.83 (SD=1.346, ranged from 1 to 7); conveying that the employees are “neither agreed nor disagreed” with the level of job safety provided to them. The mean job autonomy score is 3.87 (SD=1.293, ranged from 1.22 to 7); conveying that the employees are “neither agreed nor disagreed” with the level of job autonomy embedded in their given role. These numerical details are elaborated in below Tables-3 & 4:

Table-3: Descriptive Statistics

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4.3. Hypotheses Analytical Significance:

Table-4: Linear Regression Model of Hypotheses

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R2 is the coefficient of determination which basically tells us how much variance in our outcome variable explained by predictor variable(s).

T tells us about the predictor statistical significance on the outcome/ dependent variable.

P tells us statistical significance contribution of predictor variables on the dependent variable (p<0.05).

From the tables above strong positive relationship is evident between job autonomy and employees’ commitment (p <0.05) which matches with the studies Baard et al. 2004; Deci et al. 2001; Lynch et al. 2005; Milyavskaya and Koestner 2011. It also demonstrates that job safety positively relates to job satisfaction (p <0.05) which is aligned with the conclusions of Gyekye S. A. 2005. Employees’ commitment and job satisfaction relationship result (p <0.05) is consistent with the studies by (Bluedorn, 1982; Iverson, 1992; Lincoln & Kalleberg, 1985, 1990; Mowday et al., 1982; Mueller et al., 1994; Price & Mueller, 1986a; Wallace, 1995a; Williams & Hazer, 1986). It also demonstrates there is a negative relationship between (job satisfaction & employees) and employee turnover intention (p <0.05), this finding is consistent with the studies by Bluedorn, 1982; Iverson, 1992; Muller et al; 1992; Price & Mueller 1986 as well. Further, there is a negative relationship between job autonomy and employees’ turnover intention (p <0.05) which is consistent with the studies of Hom & Griffeth, 1995 and Anders Dysvik & Bård Kuvaas (2013). Finally, the arithmetic relationship between job safety and employees’ turnover intention is also negative (p <0.05) which is consistent with the finding of Todd D. Smith, 2017. Refer to Section-6 for reference details.

R2 values from the above table indicate that employees’ commitment is 63.4% explained by job autonomy which is a significant influence and they also feel that their perception of job safety impacts of about 27% towards their job satisfaction. Above results also reveal that job satisfaction has significant effect (41.6%) on the employees’ commitment and by providing necessary job autonomy will reduce their intention to leave the company. The other factor impacting on employees’ turnover intention is the job satisfaction which explains the outcome variable by 28%. Refer to Appendix- D for detailed SPSS Correlation Statistics results.

4.4. Linear Regression and Multiple Regression

The overall fit of the model is excellent and analyses determined that all predictors (job safety, job autonomy, job satisfaction and employees’ commitment adequacy negatively affected employees’ intention to leave the Multiplex. Both safety climate and job autonomy also positively influenced job satisfaction and employees’ commitment. All relationship paths are in the hypothesized direction. Refer to Appendix-E for the detailed regression results obtained by EPSS simulation.

In order to test the hypotheses, linear regression analyses have been performed to estimate the causal effects of the independent/ predictor variables on the dependent/outcome variables. Before performing the linear regression analyses, missing data, data validity and reliability are executed to make the analytical model appropriate for statistical simulation. During the correlation statistics simulation, team has further checked correlation strength, predictors’ power between variables (independent and dependent) and found higher Pearson Correlation R >0.7 for relation between job safety, job autonomy and employees’ commitment. This has been further investigated through multicollinearity analyses by using standard multiple regression for the following two cases:

1. Employees’ turnover intention (as outcome variable) and Job safety, autonomy, satisfaction and employees’ commitment (as predictor variables) used in model simultaneously.

2. Employees’ commitment (as outcome variable) and Job safety, autonomy, satisfaction (as predictor variables) used in model simultaneously.

Team has investigate collinearity diagnostic (multicollinearity problem between predictors) for the above two study cases and checked the assumptions made prior analytical analyses and to confirm the correlations which might not picked up just performing correlation analyses only. Tolerance and Variance Inflation Factor (VIF) of case-1 shows that employees’ commitment and job safety has tolerance < 0.10 therefore, it suggests multicollinearity between these two predictor variables when relate simultaneously with outcome variable i.e. turnover intention. Further, VIF value for employees’ commitment (23.2) is well above threshold value of 10 while it is almost equals to 10 for job safety (9.5) which also confirms multicollinearity b/w job safety & employees’ commitment.

Inspection of the normal probability of standardised residuals and the P-P scatterplot of standardised residuals against standardised predicted values indicating the assumptions of normality, linearity, and homoscedasticity of residuals (-3.3 to 3.3) are met with very little deviation indicates zero outliers in this model. Mahalanobis distances produced by multiple regression analyses also did not exceed the critical χ2 for df = 1 (at α = .001) of 19.15 (critical value for four independent variables) for any cases in the data file, suggesting that outliers are of no concern. Hence, model results does not include Case wise Diagnostics Table however, upon our deep investigation; only one case in model with value = 17.27 is found therefore, no unusual data to be removed from this model). P value in Anova table equals to 0.000 <0.05 which indicates a statistical significance of this model predicting outcome. Standardized Coefficients for four predictor variables when measured on the same scale reveals that job satisfaction is the strongest contribution to explaining the outcome i.e. around 52% whereas, job safety only explains the outcome variable about 7.4% and employee’s commitment approximately 14.6%. It is further clear by examining the Sig. value of job safety (0.734) and employees’ commitment (0.669) are well above 0.05 therefore, uniqueness of these two predictors is not evident. Results further show that four predictor variables have about 30% influence on the outcome variable while 70% variance in turnover intention is explained by other things (outside model study) which is pretty respectable results and it can be considered significant for any further future research. Refer to Table-5 below:

The results of case-2 multiple regression analyses indicate that residuals (limit -3.3 to 3.3), tolerance (threshold 0.10), VIF value (limit 10), Mahalanobis distance (threshold 16.27 for three predictors) and Cook’s distance (threshold 1.0) of all predictor variables (job satisfaction, job autonomy and job safety) are well below the threshold values. Model coefficients table confirms the uniqueness of all dependent variables w.r.t outcome variable i.e. employee’s commitment and three dependent variables mutually explain the dependent variable around 96%. Job safety influences about 57% following by job autonomy with 36.5% explanation of outcome variable.

Table-5: Multiple Regression Results for Case-1 Analysis

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Below Table-6 presents the multi-regression analysis results for case-2 to confirm the relationship and explanation percentage of predictors on outcome variable.

Table-6: Multiple Regression Results for Case-2 Analysis

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5. DISCUSSION

5.1 Model / Hypotheses Supported or Not

As mentioned in earlier sections of this study report, the aim of this analytical research is to determine the relationships (positive, negative, no relation) between Job Autonomy & Employees’ Commitment; Job Safety & Job Satisfaction; Job Satisfaction & Employees Commitment; Employees’ Commitment & Turnover Intention; Job Satisfaction & Turnover Intention; Job Autonomy & Turnover Intention and Job Safety & Turnover Intention based on the collected responses from Multiplex company’s staff.

This study results in pragmatic evidence supporting all the original hypotheses that there is a positive linear relationship between job autonomy & employees’ commitment and this applies to positive relations between job safety & job satisfaction and between job satisfaction & employees’ commitment as well. Team members expects these results based on their person experiences which are tested by performing analytical analyses and proved with supportive outcome results.

On contrary, employees’ turn over intention has inverse relation with job satisfaction, employees’ commitment, job autonomy and job safety. Linear regression simulation confirms the originally developed hypothesis about such negative relations. Our all seven (7) hypotheses are in consistent with the previous studies explained in aforementioned Section 4.3. The reason why all the hypothesis are appeared to be supported because team members have thoroughly considered previous similar studies carried out in past in different industries and then draft hypothesis to be tested through SPSS. Additionally, team also performed multiple regression to check the variance of explanation of different variables as predictor variables w.r.t to selected outcome variables.

5.2 Conclusion

Management of Multiplex Construction Co. considers job satisfaction and employees’ commitment are key aspects of their company turn over. Management thinks women are more easily to convince and therefore, most of them are satisfied in their firm and will not leave them in near future. Our communication with management reveals, they also assume that new employees’ are more satisfied than old employees due to recent changes in organization structure and departments’ bifurcation.

From the above discussion, statistical analyses (correlation, linear regression) results conclude that job satisfaction of employees play a vital role in company’s turnover by decreasing their intention to leaving their current jobs. Second important factor is job autonomy followed by employees’ commitment. Also, employees’ commitment relates to perception of their job safety and job autonomy. Employees’ commitment to their current job can be doubled by increasing their perception about their job safety.

Recent analytical results of collected data through survey questionnaire however, do not confirm most of the management’s thoughts and perceptions about their employees like, irrespective of gender both males and females are neither satisfied nor dissatisfied with their current job; and also this applies to perception of safety, autonomy and commitment towards their current jobs at the same levels. T-test of gender compared with employees’ turnover intention as independent variable reveals that female works responses towards leaving either their current job or change current occupation are not very different than their male colleagues. Refer to Appendix-E for T-test results.

Current research survey and quantitative analyses show that both genders rarely think to leave their current job. Results of employees’ turnover intention show that experiences employees are more loyal to their current company while employees with experience lesser than 6 year have comparatively more tendency to leave their current job due to lesser job satisfaction and job autonomy which is also contradiction with the management perception about their old staff. T-test for all variables when compared for marital relation between unmarried and married staff, indicates that married employees are more satisfied, committed and loyal to their current job. Furthermore, under graduated employees (irrespective of their gender) feels almost equal job autonomy but chances to leave their current company is more when compared to graduated employees’ higher qualifications. Higher qualified staffs’ perception of job safety, satisfaction and commitment just are a little more than undergraduate employees.

Based on the numerical analysis of current research, our group recommends to either eliminate job safety variable from the current statistical model due its collinearity/ overlapping with the employees’ commitment or create a composite variable. Team internal discussion highlights that might respondents couldn’t understand the questions related to job safety and therefore, respondents haven’t differentiate between variables of jobs safety and employees commitment. The similarities of respondents’ responses about these two predictor variables are very subjective and therefore, cannot be concluded for the other construction companies and applied universally.

5.3 Implications

The findings from this study will be valuable to Multiplex Company’s administration, human resource department and management as well as other construction companies’ understanding of the phenomenon of management false perception, by better understanding the antecedents leading to the turnover intentions of their current employees with respect to their demographics.

Theoretical Implications

This study contributes to evidence of the relationships between the five variables investigated – the importance of job satisfaction in relation to employee commitment and intent to turnover as well as the impact of job safety and job autonomy on job satisfaction as well as the intent to turnover. Given the relatively narrow scope of variables in comparison to those identified by other similar research, this study provides a solid basis for further study investigating individual variables or the combination of factors contributing to job satisfaction amongst construction staff not only in the Multiplex Construction Co, but potentially any other construction company in Australia or overseas. Identifying the factors that could increase the retention rates of construction staff, which by the findings of this study would indicate a higher job satisfaction, would have a positive effect on the construction industry as a whole, the respective organisations they work for (lower turnover costs), higher clients’ satisfaction contributing to the increasing the quality of life for the staff themselves.

Managerial Implications

Implications for practice that should be considered in terms of improving the job satisfaction of employees at the Multiplex organisation as well as other construction companies across Australia as a result of this study include improving the awareness and knowledge of managers and administrators as to factors that contribute to low jobs satisfaction, low job commitment, as well as providing the starting point for identifying, or exploring other research into the causality of high employee turnover intent. For example; this study showed that almost 60% of respondents have spent only 1-3 years in their current position, thus long term retention strategies should be investigated and other appropriate strategies like trainings and working style should be reviewed.

Though the findings do not indicate high perception of job autonomy and job safety amongst the employees’ of Multiplex Construction Co, the results are significant enough to reveal that job autonomy and job safety is a contributing factor to employee intention to turnover and active steps need to be taken to educate, identify and prevent situations resulting in employees’ lower perception of job safety and job autonomy.

5.4 Limitation of the study

As mentioned in the first Section of this report that this is not a real life study but purely based on literature review, provided data by the unit convenor (collected in previous research), identified new variable items and assumed responses to newly identified data items. Main purpose of this study is to enable students understanding the quantitative research method and the steps involved in SPSS (Statistical Package for the Social Sciences) program simulations.

Objective of this is study to analyse the casual relationships between employees’ commitment, job satisfaction, job autonomy and job safety in models of employees’ turnover intention. Therefore, causal model comprising five variables: one dependent (employees’ turnover intention) and four independent/controlled variables (job satisfaction, employees’ commitment, safety at job and job autonomy) is formulated. This statistical model is tested on a sample of 146 construction industry’s employees (both male and female) over a 6-month period selected from different departments of Multiplex Construction Company (a fictional company for the purposes of this research study).

This study is only conducted with employees at Multiples Construction Company therefore, the findings should be interpreted with caution as participants do not represent all types of construction industries in other states and territories in Australia. This survey is cross-sectional, with the data collected at one point in time from Multiple Construction Company. In future studies, longitudinal research could be designed to examine potential change in satisfaction, commitment, job safety, autonomy, or turnover intention over time. Finally, a greater use of qualitative methods could help enrich understanding of those phenomena.

5.5 Recommendation for future research

This study focused on only five variables from the many as identified in the reviewed literature alone, future study would conceivably produce more significant results by including more variables or focusing on the contributing factors of the variables in a single relationship. As statistical analysis results that selected outcome variable (turnover intention) is explained only 30% by the four selected predictor variables. Additionally, by increasing the sample size and scope beyond the current study to multiple large urban centres and regions in Australia, a more generalized result could be generated to give a more accurate indication of the findings.

As the age of some of the literature reviewed as a part of this study it is clear these issues are ongoing and long term, a long term study using the same variables may provide valuable results as to changes in the relationships among the five variables in relation to extrinsic factors (such as geo-political environment). Other recommendation is to review the research questionnaire about job safety and if needed to be explained to all respondents properly to avoid multicollinearity in respondents’ responses in future researches.

5.6 Acknowledgements

We would like to express our sincere appreciation to the Faculty of Business, Government and Law (BGL) at the University of Canberra (UC) for making this study possible. We would also like to thank Mr. Irfan Khan for his effective contribution to this study.

6. REFERENCES

1. Job Satisfaction, Organizational Commitment, Turnover Intention, and Turnover: Path Analyses Based on Metanalytic Findings. (Robert P. Tett, John P. Meyer, 1993)

2. The Causal Order of Job Satisfaction and Organizational Commitment in Models of Employee Turnover (Douglas B. Currivan University of Massachusetts, Boston, MA, USA)

3. Structural Determinants of Job Satisfaction and Organizational Commitment in Turnover Models (Stefan Gaertner, USA)

4. An event history analysis of Employee Turnover Intention: The Case Of Hospital Employees in Australia (Roderick D. Iverson University of Melbourne, Victoria, Australia)

5. DeCenzo, D.A., Robbins, S.P. and Verhulst, S.L., 2005. Fundamentals of human resource management.

6. Jackson, S.E. and Schuler, R.S., 1995. Understanding human resource management in the context of organizations and their environments. Annual review of psychology, 46(1), pp.237-264

7. Kanwar, Y., Singh, A. and Kodwani, A. (2012). A Study of Job Satisfaction, Organizational Commitment and Turnover Intent among the IT and ITES Sector Employees. Vision: The Journal of Business Perspective, 16(1), pp.27-35

8. Karsh, B., Booske, B. and Sainfort, F. (2005). Job and organizational determinants of nursing home employee commitment, job satisfaction and intent to turnover. Ergonomics, 48(10), pp.1260-1281

9. Meyer, J., Morin, A. and Vandenberghe, C. (2015). Dual commitment to organization and supervisor: A person-centered approach. Journal of Vocational Behavior, 88, pp.56-72

10. Miles, R.E. and Snow, C.C., 1984. Designing strategic human resources systems. Organizational dynamics, 13(1), pp.36-52

11. Noe, R.A., Hollenbeck, J.R., Gerhart, B. and Wright, P.M., 2007. Fundamentals of human resource management. Boston, MA: McGraw-Hill/Irwin

12. Swanson, R.A. and Holton, E.F., 2001. Foundations of human resource development. Berrett-Koehler Publishers

13. Tnay, E., Othman, A., Siong, H. and Lim, S. (2013). The Influences of Job Satisfaction and Organizational Commitment on Turnover Intention. Procedia - Social and Behavioural Sciences, pp.201-208

14. Yujin Chang, Nicole Leach, Eric M. Anderman, (2014/2015), The role of perceived autonomy support in principals’ affective organizational commitment and job satisfaction

15. Novi Sad. Dunja Vujicic, Ana Juvicic, (2015), The relationship between job insecurity, job satisfaction and organizational commitment among employees in the tourism sector in Vol. 36(4) 633–652

16. Anders Dysvik and Bard Kuvas, (2013), Perceived job autonomy and turnover intention: The moderating role of perceived supervisor support

17. Seth Ayim Gyekye, (2005) Workers perception of workplace Safety and Job Satisfaction p.p. 291-302

18. Todd D. Smith, (2017), An assessment of safety climate, job satisfaction and turnover intention relationships using a national sample of workers from the USA.

APPENDICES:

Appendix-A: Research Survey Questionnaire

Appendix-B: Detail of Variable Items

Appendix-C: Descriptive Statistics

Appendix-D: Correlation Statistics

Appendix-E: Regression Statistics

Appendix-F: SPSS- Step by Step (Lab work)

Appendix-A:

Research Survey Questionnaire

SURVEY RESEARCH QUESTIONNAIRE

Description:

This research project aims to investigate the employees’ job satisfaction, commitment, job hazards and risks, job autonomy and turnover. The project is being conducted as part of a class project in the unit Research Methods. We are using the project as a class learning tool. Each student in the class has been asked to gather information from several people/may be from their family members/based on self-observation. The data will then be pooled and will be used to conduct.

THANK YOU VERY MUCH FOR YOUR PARTICIPATION–IT IS HIGHLY APPRECIATED

Section A:

The following questions ask you to think about your feelings towards your current job. Please answer each of the following questions by circling the respon se which best describes how you feel about each question.

A- Job Satisfaction:

A1. Overall, how satisfying are you with your pay?

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A2. How satisfied are with your relationships with your co-workers?

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A3. Overall, how satisfied are you with the types of tasks you perform in your job?

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A4. How satisfied are you with your supervisor/manager?

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A5. At my place of work, promotional opportunities are

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A6. At your current position, how satisfied are you with your job security?

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A7. Considering your current works, how satisfied are you with flexible work-life balance (job working hours)?

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A8. How much are you satisfied with your organizational structure of equal opportunities?

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9. How much are you satisfied with the traveling during your working hours?

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B- Employees’ Commitment:

B1 . I feel a strong sense of belonging to my organisation

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B2 . I really feel as if my organisation’s problems are my own

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B3 . I feel emotionally attached to my organisation

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B4 . I have too few options to consider leaving my organisation

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B5 . Too much in my life would be disrupted if I left my organisation now

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B6 . It would be hard to leave my organisation right now, even if I wanted to

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B7. I have gained expertise in my current job leading to my career development

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B8. On long run, I am expecting higher gratuity multiplier which will benefit in my old age on retirement

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B9. My current organization has training program to enhance my knowledge and up to date my capacities

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C- Job Safety:

C1. I have familiarity with the given tasks to me by my supervisor(s)

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C2. All risk are known to me and my company has well defined risk evaluation procedures

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C3. I have complete access to company assets database to know about lessons learned and jobs hazards

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C4. I always feel that I am operating under safe working conditions

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C5. I have received prior personal safety education

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C6. I have concern on my current work load and work pressure

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C7. I have exposure to safety hazards in my routine tasks

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C8. My organization has comprehensive procedures of risks records and relevant polices

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C9. I have well defined risk mitigation processes and method statements

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D- Job Autonomy:

D1. I share ideas and opinions whenever needed without any hesitation

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D2. I have power to make decisions in my assigned tasks to perform my duties

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D3. I have freedom to select my own team prior final approval by my manager

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D4. I feel independent in task breakdown and optioneering to solve the given tasks

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D5. My company structure encourages me to participate in company’s decision making

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D6. I am independent in decision making to manage and complete my assigned tasks

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D7. I have options to transfer in other department for better performance and diversity in my job

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D8. I have been provided more flexible work structure to work remotely to accomplish my tasks

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D9. My organization encouraging culture of creativity and innovation

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Section B:

E- Employees’ Intention to Turnover

The following questions ask you to think about your plans for staying in your current job. Please answer each of the following questions by circling the response which best describes how you feel about each question.

E1. Over the past month, how often have you seriously thought about seeking another job?

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E2. Do you seriously intend to seek another job during the next three months?

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E3. Over the past month, how often have you thought about making a real effort to enter a different occupation?

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E4. How often have you seriously thought about resigning from your job during the past month?

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E5. During the next three months do you seriously intend to resign from your job?

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E6. Do you seriously intend to apply for a job in a different occupation during the next three months?

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E7. Do you feel for job relocation and early termination?

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E8. Do you have plan for gaining higher positions by enhancing qualifications?

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E9. Do you in position to use referral plan for your nearer friends and family member in your current company?

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Section C:

Demographic information:

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THANK YOU VERY MUCH FOR YOUR PARTICIPATION. YOUR RESPONSES ARE VERY MUCH APPRECIATED

Appendix-B:

Detail of Variable Items

A- Job Satisfaction:

A1- Income

A2- Relation with Co-workers

A3- Tasks nature

A4- Supervisor/Manager relation

A5- Promotional opportunities

A6- Job Security

A7- Working hours (work-life balance)

A8- Equal opportunities culture

A9- Traveling

Abbildung in dieser Leseprobe nicht enthalten

B- Employees’ Commitment:

B1- Sense of belonging

B2- Sense of ownership

B3- Emotional attachment with organization

B4- Alternative opportunities to leave organization

B5- Inertia breaking disruptive changes in case job changes

B6- Personal circumstances avoiding leaving current job

B7- Expertise and career development

B8- Increasing gratuity multipliers

B9- Trainings programs

Abbildung in dieser Leseprobe nicht enthalten

C- Job Safety:

C1- Familiarity with the given tasks

C2- Risk identifications and evaluation

C3- Access to organizational assets database

C4- Safe working conditions

C5- Personal safety education

C6- High work load & work pressure

C7- Exposure to safety hazards

C8- Process of risks records and relevant polices

C9- Risk mitigation procedures and method statements

Abbildung in dieser Leseprobe nicht enthalten

D- Job Autonomy:

D1- Ideas sharing and opinion making

D2- Decision making in assigned task

D3- Freedom of team selection

D4- Independence of task breakdown and optioneering

D5- Participation in company’s decision making

D6- Independent decision making and managing Task

D7- Options for departmental transfer

D8- Choice of remote working

D9- Encouragement of creativity and innovation

Abbildung in dieser Leseprobe nicht enthalten

E- Intention to Turnover:

E1- Seeking alternative job in past month

E2- Plan to apply for new job opportunity in next three months

E3- Efforts to adapt another occupation within current employment

E4- Alternative opportunities to leave organization

E5- Plan to resign in next three months

E6- Plan to resign and adapt a different occupation

E7- Fear of relocation and early job termination

E8- Plan for higher positions by enhancing qualifications

E9- Referral plan for friends and family members

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Appendix-C:

Descriptive Statistics

A- DESCRIPTIVE ANALYSIS OF GROUPING VARIABLES

Statistics

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a. Multiple modes exist. The smallest value is shown

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Descriptive Statistics

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1 = Females, 2 = Males

Gender

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Age

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Position

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Qualification

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Marital Status

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Salary

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B- DESCRIPTIVE ANALYSIS OF DEPENDENT AND INDEPENDENT VARIABLES

Descriptive Statistics

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Statistics

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Job_Safety

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Job_Autonomy

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Job_Satisfaction

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Empl_Commitment

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Empl_Turnover

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Reliability Analysis for Main Variables (Dependents and Independents)

Job Satisfaction

Reliability Statistics

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Reliability Statistics

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Reliability Statistics

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Reliability Statistics

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Reliability Statistics

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Appendix-D:

Correlation Statistics

- (H1): Job Autonomy brings Employee’s Commitment

- (H2): Higher perception of Job Safety confirms Employees’ Job Satisfaction

- (H3): Job Satisfaction and Job Commitment are mutual inclusive (Positive direct relation)

- (H4): Employees’ Commitment negatively affects Employees’ Turnover Intention

- (H5): Job Satisfaction reduces Employees’ Turnover Intention

- (H6): Job Autonomy decreases chances of Employees’ Turnover Intention

- (H7): Job Safety has reduction impacts on Employees’ Turnover Intention

Abbildung in dieser Leseprobe nicht enthalten

H1: Job Autonomy → Employees’ Commitment

Correlations

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**. Correlation is significant at the 0.01 level (2-tailed).

H2: Job Safety → Job Satisfaction

Correlations

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**. Correlation is significant at the 0.01 level (2-tailed).

H3: Job Satisfaction → Employees Commitment

Correlations

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**. Correlation is significant at the 0.01 level (2-tailed).

H4: Employees ’ Commitment → Turnover Intention

Correlations

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**. Correlation is significant at the 0.01 level (2-tailed).

H5: Job Satisfaction → Turnover Intention

Correlations

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**. Correlation is significant at the 0.01 level (2-tailed).

H6: Job Autonomy → Turnover Intention

Correlations

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**. Correlation is significant at the 0.01 level (2-tailed).

H7: Job Safety → Turnover Intention

Correlations

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**. Correlation is significant at the 0.01 level (2-tailed).

Appendix-E:

Regression Statistics

Abbildung in dieser Leseprobe nicht enthalten

H1 Regression: Job Autonomy → Employees’ Commitment

Variables Entered/Removeda

Abbildung in dieser Leseprobe nicht enthalten

a. Dependent Variable: Empl_Commitment

b. All requested variables entered.

Model Summaryb

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a. Predictors: (Constant), Job_Autonomy

b. Dependent Variable: Empl_Commitment

ANOVAa

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a. Dependent Variable: Empl_Commitment

b. Predictors: (Constant), Job_Autonomy

Coefficientsa

Abbildung in dieser Leseprobe nicht enthalten

a. Dependent Variable: Empl_Commitment

H2 Regression: Job Safety → Job Satisfaction

Variables Entered/Removeda

Abbildung in dieser Leseprobe nicht enthalten

a. Dependent Variable: Job_Satisfaction

b. All requested variables entered.

Model Summaryb

Abbildung in dieser Leseprobe nicht enthalten

a. Predictors: (Constant), Job_Safety

b. Dependent Variable: Job_Satisfaction

ANOVAa

Abbildung in dieser Leseprobe nicht enthalten

a. Dependent Variable: Job_Satisfaction

b. Predictors: (Constant), Job_Safety

Coefficientsa

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a. Dependent Variable: Job_Satisfaction

H3 Regression: Job Satisfaction → Employees Commitment

Variables Entered/Removeda

Abbildung in dieser Leseprobe nicht enthalten

a. Dependent Variable: Empl_Commitment

b. All requested variables entered.

Model Summaryb

Abbildung in dieser Leseprobe nicht enthalten

a. Predictors: (Constant), Job_Satisfaction

b. Dependent Variable: Empl_Commitment

ANOVAa

Abbildung in dieser Leseprobe nicht enthalten

a. Dependent Variable: Empl_Commitment

b. Predictors: (Constant), Job_Satisfaction

Coefficientsa

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a. Dependent Variable: Empl_Commitment

H4 Regression: Employees’ Commitment → Turnover Intention

Variables Entered/Removeda

Abbildung in dieser Leseprobe nicht enthalten

a. Dependent Variable: Empl_Turnover

b. All requested variables entered.

Model Summaryb

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a. Predictors: (Constant), Empl_Commitment

b. Dependent Variable: Empl_Turnover

ANOVAa

Abbildung in dieser Leseprobe nicht enthalten

a. Dependent Variable: Empl_Turnover

b. Predictors: (Constant), Empl_Commitment

Coefficientsa

Abbildung in dieser Leseprobe nicht enthalten

a. Dependent Variable: Empl_Turnover

H5 Regression: Job Satisfaction→ Turnover Intention

Variables Entered/Removeda

Abbildung in dieser Leseprobe nicht enthalten

a. Dependent Variable: Empl_Turnover

b. All requested variables entered.

Model Summary

Abbildung in dieser Leseprobe nicht enthalten

a. Predictors: (Constant), Job_Satisfaction

ANOVAa

Abbildung in dieser Leseprobe nicht enthalten

a. Dependent Variable: Empl_Turnover

b. Predictors: (Constant), Job_Satisfaction

Coefficientsa

Abbildung in dieser Leseprobe nicht enthalten

a. Dependent Variable: Empl_Turnover

H6 Regression: Job Autonomy→ Turnover Intention

Variables Entered/Removeda

Abbildung in dieser Leseprobe nicht enthalten

a. Dependent Variable: Empl_Turnover

b. All requested variables entered.

Model Summaryb

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a. Predictors: (Constant), Job_Autonomy

b. Dependent Variable: Empl_Turnover

ANOVAa

Abbildung in dieser Leseprobe nicht enthalten

a. Dependent Variable: Empl_Turnover

b. Predictors: (Constant), Job_Autonomy

Coefficientsa

Abbildung in dieser Leseprobe nicht enthalten

a. Dependent Variable: Empl_Turnover

Abbildung in dieser Leseprobe nicht enthaltenAbbildung in dieser Leseprobe nicht enthalten

H7 Regression: Job Safety → Turnover Intention

Variables Entered/Removeda

a. Dependent Variable: Empl_Turnover

b. All requested variables entered.

Model Summaryb

a. Predictors: (Constant), Job_Safety

b. Dependent Variable: Empl_Turnover

ANOVAa

Abbildung in dieser Leseprobe nicht enthalten

a. Dependent Variable: Empl_Turnover

b. Predictors: (Constant), Job_Safety

Coefficientsa

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a. Dependent Variable: Empl_Turnover

Abbildung in dieser Leseprobe nicht enthaltenAbbildung in dieser Leseprobe nicht enthalten

MULTI REGRESSION TO CHECK MULTICOLLINEARITY

Case-1:

Employees’ turnover intention (as outcome variable) and Job safety, autonomy, satisfaction and employees’ commitment (as predictor variables) used in model simultaneously.

Variables Entered/Removeda

Abbildung in dieser Leseprobe nicht enthalten

a. Dependent Variable: Empl_Turnover

b. All requested variables entered.

Model Summaryb

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a. Predictors: (Constant), Empl_Commitment, Job_Satisfaction, Job_Autonomy, Job_Safety

b. Dependent Variable: Empl_Turnover

ANOVAa

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a. Dependent Variable: Empl_Turnover

b. Predictors: (Constant), Empl_Commitment, Job_Satisfaction, Job_Autonomy, Job_Safety

Coefficientsa

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a. Dependent Variable: Empl_Turnover

Residuals Statisticsa

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a. Dependent Variable: Empl_Turnover

Case-2:

Employees’ commitment (as outcome variable) and Job safety, autonomy, satisfaction (as predictor variables) used in model simultaneously.

Variables Entered/Removeda

Abbildung in dieser Leseprobe nicht enthalten

a. Dependent Variable: Empl_Commitment

b. All requested variables entered.

Model Summaryb

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a. Predictors: (Constant), Job_Satisfaction, Job_Autonomy, Job_Safety

b. Dependent Variable: Empl_Commitment

ANOVAa

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a. Dependent Variable: Empl_Commitment

b. Predictors: (Constant), Job_Satisfaction, Job_Autonomy, Job_Safety

Coefficientsa

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Residuals Statisticsa

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a. Dependent Variable: Empl_Commitment

T-Test Results

Group Statistics

Independent Samples Test

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Group Statistics

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Independent Samples Test

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Group Statistics

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Independent Samples Test

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Group Statistics

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Independent Samples Test

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Appendix-F:

SPSS- Step by Step (Lab work)

SPSS Program Steps of Variables Data Entry and analyse

STEP-1: Variables Identification and Entries in Variable View:

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STEP-2: Define Labels, Write Appropriate values for various Levels of each Item in Variable View and Select correct Measures

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STEP-3: Continue defining and copying the Values against each Variables’ Item as per Available data

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STEP-4: Look at the Data View of Defined Variables and Entries of Selected Data

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STEP-5: Checking for Missing data and Entries

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STEP-6: Click on Transform Tab and Select Replace Missing Values (Drag variables to right sides of screen and press OK)

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STEP-7: SPSS will analyse all the data and fill the missing values with the most appropriate means of each variable entries

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STEP-8: Look at the Data View and new entries against each variables with missing data

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STEP-9: Adjust Decimals to Zero for all new entries in Variable View, appeared after Missing Values Analysis

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STEP-10: Complete the Decimals adjustment for Variables Items

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STEP-11: Perform Frequency Analysis for each Variables Items by Selecting Descriptive Statistics under Analyse Tab and Click on Frequencies

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STEP-12: Perform Frequency Analysis for each Variables Items (Starting from Nominal Measures Items) by dragging into Variable view and Select Statistics

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STEP-13: Select Means of Variables Items and drag to Variable view for Frequencies Analyses instead of Original Entries (to avoid blank entries)

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STEP-14: Select Mean, Median, Mode for the selected Variables Items

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STEP-15: Select Dispersion and Distribution analysis items from the given choices

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STEP-16: Select Charts and turn on the ‘Show Normal Curve on Histogram’,Click Continue and OK to perform frequencies analysis

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STEP-17: View the results under Output Viewer Screen and make comparison between various Variables Items

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TEP-18: Look at the Graphical presentation of each Variables Item to compare the histograms

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STEP-19: Perform Reliability Analysis of each Variables Items to check the validity of data (Select Scale under Analyse Tab and Click on Reliability Analysis)

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STEP-20: Select All the Measure Scales Items under Common Variable, Drag for Statistics, Select Item and Click on Continue and OK buttons for Analysis

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STEP-21: Look at the results on Output Views Screen, if Value of Reliability Alpha is ≥ 0.75, Data is valid otherwise continue to next Step

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STEP-22: Select again the Common Variables Items for Reliability Analysis, Click on Statistics and Select ‘Scale if Item Deleted’ then Press Continue & OK

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STEP-23: Look at the Analysis’ Result and watch for the highest value of ‘Alpha if Item Deleted’

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STEP-24: Go back to the Reliability Analysis Tab and delete the item with the highest value of ‘Alpha if Item Deleted’ then press OK to continue analysis

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STEP-25: Look at the Analysis’ Result. If Reliability Value ≤ 0.75 Stop here Otherwise, watch for the highest value of ‘Alpha if Item Deleted’ to Continue

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STEP-26: Continue performing Reliability Analyses for all Variables Items Set until, Alpha value ≥0.75 achieved

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STEP-27: Continue performing Reliability Analyses for all Variables Items Set until, Alpha value ≥0.75 achieved

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STEP-28: Perform Mean of Validated Variables Items (excluding deleted items to achieve Alpha value ≥0.75), Select Transform Tab, Click on Compute Variables

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STEP-29: Select ‘All’ from Function Group and Double Click on ‘Mean’ from Special Variables then Select Validated Variables separated by Comma, Give a Name to Target Variable. Click on OK to perform this analysis. Look at the Result Screen and confirm all Alpha Values are now greater than 0.75

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STEP-30: Perform Step-29 for all Variables (Select common variable Items and give a unique name to identify and differentiate from other variables data

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STEP-31: Look at the Variable View, New Variable entries are generated by SPPS for Variable data Sets. (One for each dependent and independent variables)

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STEP-32: Populate all new variable items following all previous steps and initiate validation check (Click Analyse tab, Click Correlate and then Select Bivariate)

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STEP-33: Select all Main Variables (Dependent and Independent) and Drag towards Variables Screen, Press OK for further process

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STEP-34: Go to Output View for results. You will have one Correlation table showing strength of variables’ relations (-ve and +ve)

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* 0.000 Sig. (2-tailed) means 0% here (very strong relation) while the result is statistically significant at the 5% (0.050) level. If P ≥0.5 then we fail to reject Null.

** If we increase Job Satisfaction by 100% then job commitment will be increased by a proportion of 0.796. Less than 0.3 value indicates a week correlation relation between any two variables.

STEP-35: Now press Analyse Tab, go to Descriptive Statistics and Select Descriptive for checking means and deviations. Select Main Variable only and press OK

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STEP-36: Look at the Output Results of Descriptive Analysis and find out the mean of overall responses against each Variable

Abbildung in dieser Leseprobe nicht enthalten

Pearson Correlation must be greater than 0.3 to show the strength of predictors/ independent variables over dependent variable (irrespective of the –ve or +ve relation between independent and dependent variables). In the same time, this Pearson Correlation value must not be more than 0.7 for two predictors. Lesser value for two predictors/ independent variables means two different and distinct variables otherwise, two variables in fact are behaving like one variable.

STEP-37: Check frequencies of each Main Variables. Press Analyse Tab, go to Descriptive Statistics and Select Frequencies. Drag all Main Variables for Analysis

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STEP-38: Look at Output view to investigate the details of Demographic items, Variable’s Responses Breakdown, Means, Deviation, Minimum and Maximum

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STEP-39: Press Analyse Tab, Press Regression and Select Linear Tab to check relation between various Variables. Select Dependent & Independent Variables

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1- There is +ve or –ve relationship between two variables.

2- Correlation between two variables (independent and dependent) is Zero

3- There is no supported correlation/ relationship between two variables i.e. unrelated variables.

STEP-40: Look at the Output Result View to either Reject or Validate Null Hypothesis. R Value higher 0.7 means higher similarity between two Variables

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Delete variables with higher “R” values as responses indicate these variables similar in nature i.e. multicollinearity nature of two variables and redundancy of two predictors. Logically either to combine those two predictors to form a composite variable or eliminate one variable. “R Square” generally must be between 0.2-0.3 to validate Hypothesis. “R Square” indicates that selected independent variables has impact of 29.7% on dependent variable (significant relation b/w independent and dependent variables).

STEP-41: Now Perform T-Test between Demographic Items and Variables to see the responses (Press Analyse Tab, Compare Means and then Select Independent

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STEP-42: Select Demographic Item like Gender or Age, drag to Grouping Variables, Define Groups as per its originally defined value (1, 2 or as you have defined)

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STEP-43: After selecting the Grouping Variables and defining it, Select One Main Variable as Test Variable and Press OK for further analysis

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STEP-44: Look at the Demographic Grouping Variables such as Females and Males responses (Means, Sig) against Main Variable such as Job Turnover

Abbildung in dieser Leseprobe nicht enthaltenCheck the Questionnaire items 2 and 3 under Variable Turnover to check the responses of overall males and females for this Main Variable. As Sig. ≥ 0.05 therefore, females and males both don’t have significant relation for Job Turnover variable.

[...]

Details

Pages
103
Year
2017
ISBN (Book)
9783668683235
File size
9 MB
Language
English
Catalog Number
v419494
Institution / College
University of Canberra – School of Management, Faculty of Business, Government & Law
Grade
85
Tags
study construction industry quantitative research report

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Title: Study of the relationship between employees’ commitment, job satisfaction, job safety, job autonomy and employees’ turnover intention in a Construction Industry