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Introduction
Employee engagement (EE) has been a considerable topic of interest in the field of HR for the last two decades. Engaged employees are those who have an intense connection with their organization and working with great enthusiasm. Such employees promote innovational activities, aligned with novel and creative ideas which result in the organizational success It is believed that employee engagement is inevitably linked to the maintenance of an organization (Juevesa et al., 2020). EE occurs when employees are accorded with appreciation, approval, and satisfaction from their organizations with regard to what they are doing. Moreover, employees are more inclined towards their work if they feel that they are producing something creative for their organization. Wellins & Bernthal (2015) opined that acknowledging the employee contributes to an increase in their engagement towards the organization. According to a Gallup study engaged employees are more devoted to their organizations, are more eager to put forward their efforts, more enthusiastic to collaborate with others, more excited to work within a challenging environment, more committed to talk about the problems they are facing, and more ready to suggest any improvements for their organizations
On the contrary, employees who are not engaged in their working organizations, show no interest in the success of the organization. According to Gallup (O’Boyle & Harter, 2013), 65% of the employees in UAE are not inclined to their work thus resulting in the loss of around $450 billion to $550 billion productivities. However, the employees who are not engaged in their working organizations require a reason to be engaged in their working organization A number of researchers have studied the antecedents of EE. For instance, Almujaini et al. (2021) suggested that in order to get the prerequisite benefits from EE, it is crucial to regulate the HR practices (i.e., policies and procedures) of the company. It can be argued that HR is a basic input which contributes vastly to EE but in the absence of HR, organizations are inanimate creatures Elrehail et al. (2019) believed that HR practices play a critical role in refining EE as they highly influence the performance of employees. Juhdi et al. (2013) disputed on “the critical role of HR practices as tools for organizations to make employees engrossed and engaged in their job performance by providing challenging jobs with available resources and opportunities for growth and management” (p.3002). Robins & Judge (2017) postulated that “HRM practices are one of the factors in the organization that could shape and determine employee responses at work” (p.496). This suggests that if employees receive incentive from their organization in the form of job security (JS), career advancement (CA), or employee participation (EP), the employee will be more inclined towards their work which could result in the improvement of EE (Kuean et al., 2010).
Job security (JS) is one of the most important HRM practices having significant influences on EE (Aggarwal et al., 2007). It was defined as the perception of employees that their job is secured. If employees feel that their job is secured then they tend to be more inclined towards their work (Kim et al., 2005). Job insecurity is one of the most prominent reasons of unengaged employees. It is perhaps due to the nature of employees, because most of the people want a secured job (i.e., permanent job) However, it is not always the case that organizations hire employees on a permanent basis, fixed term or temporary contractual arrangements which are common in organizations. It has been argued that employees perceive that their organization is exploiting their capabilities or employing them to fulfil their short term needs These perceptions of employees somewhat create an unsafe job setting causing employees to feel insecure or have a fear of losing their job. It is well recognized that the fear of losing job security results to negative consequences i.e., lower engagement of employees towards their organization (May et al., 2004) etc. because employees feel that soon they will be replaced with some other person. Therefore, they are not engaged in organizational activities. Thus, there is a need for organizations to secure the jobs of their employees in order to engage them in their organization.
Career Advancement is another important antecedent of EE. It refers to the upward advancement in the career. In other words, it refers to the promotion of employees in the organization. For instance, moving from an “entry level” to “management position” in the same field. If employees are able to perceive field advancement in their career, then they are more inclined to their work (Briggs et al., 2012). However, if the employees feel that the one-sided hindrances are inhibiting their advancement in their career then the organization will encounter poor productivity of employees, lower level of EE and an increased turnover ratio of employees (Marineau, 2017). Pandey (2020) believed that promotion of employees is a crucial factor when increasing the engagement of employees within the organization because employees perceive the promotion as an intangible reward of their work. However, if the employees feel that if they give their best to their organization, then the organization will award him or her with some reward (tangible or intangible) then they will be more convinced towards their work. They will try to work for organizational success. Therefore, it is of significant importance that organization offers opportunities of career advancement to their employees in order to increase their level of engagement.
As with JS and CA, EP is another important determinant of EE. It refers to the participation of employees in the decision-making process of the organisation. It is predictable that the employee’s involvement in the decision-making process of organization may be asserting but it increases their eagerness to work resulting in the higher level of EE (Scott-Ladd & Marshall, 2004) believed that EP is one of the most fundamental factors which motivates the employees to engage in their organizational activities because if they will engage in their organization then they will be able to be involved in the decision-making processes of the organization. EP is also significantly related to the satisfaction of the employees. For instance, an employee will perceive himself/herself as an important asset of the organization and will work more eagerly for the success of his/her organization. Therefore, it is important that the organization provides EP opportunities to their employees so that they will engage themselves energetically with organizational activities.
The significance of EE cannot be exaggerated as EE strategies have been confirmed in lessening the rate of employee turnover, retaining the customer at high rate, making maximized profits and improving efficiency and productivity; predominantly, both at their place of living as well as working (Hughes & Rog 2008). When an individual is engaged, it permeates everything he/she does with a purpose, enthusiasm and energy thus making EE crucial for further study.
The objective of the present study is investigating the empirical affiliation between job security and employee engagement, To explore the empirical link between career advancement and employee engagement and To inspect the empirical association between employee participation and employee engagement.
Methodology
Design and Approach of Research Study
The current research study focuses on investigating the influence of soft HRM practices (such like employee participation, career advancement and job security) on engagement of employee on the basis of SET theory in oil and gas industry in UAE. Thus, this research study falls under the deductive research approach that aims to test the hypotheses in order to explore the linkage between employee engagement and soft HRM practices (employee participation, career advancement and job security). It has been identified that “deductive means reasoning from the particular to the general. If a causal relationship or link seems to be implied by a particular theory or case example, it might be true in many cases. A deductive design might test to see if this relationship or link was obtained on more general circumstances. Deductive approach can be explained based on the means of hypotheses, which can be derived from the propositions of the theory. In other words, deductive approach is concerned with deducting conclusions from premises or propositions” (Donaldson et al., 2009, p.92), see the diagram Fig 1.
For defining the participants of the current study, it is necessary to identify the population of the study and its sample.
Population
The population of a study is described as a group of objects, things, entities or persons (individuals) having the desired ability to reply the study questions (Lind et al., 2012). As this study aims to investigate the insights of the employees of oil and gas industry in UAE on soft practices of HRM (employee participation, career advancement and job security) for their workstation engagement, consequently, this study only takes into consideration full time (permanent and contract) employees as the study population. In the meantime, this research also focuses on those employees (permanent) who have completed their period of probation at oil and gas industry in UAE. It is a significant belief that full time workers create a long term link with their respective business organization. Additionally, this study deliberately excludes the top level managers/executives as they are accountable for implementing HR policies. It is, therefore, not appropriate to gather information from such participants concerning their established practices as they may provide favourable views to be implemented/included.
Sampling and Sample Size
For a successful, effective and fruitful research conduct it is crucial and necessary to choose the right and effective representative sample. Sampling is described as a process, technique or method in which a few parts of the selected population is chosen to represent the entire population (Lind et al., 2012); indicating that it (sampling) is a sub-part of a particular total population in a research conduct. On the basis of Rosceo’s (1975) findings, a general rule for selecting a sample size is that “it should be between 30 to 500 respondents” covering the design of sampling and questions of research under consideration. Another approach that determines the size of sample is using the sampling Table on the basis of 0.05 alpha (α) value accompanied by a level of accuracy of 0.05 (Chuan & Penyeldikan, 2006;). Furthermore, in the case of probability sampling, it is crucial to select a minimum sample size of 100 respondents, that otherwise causes obscurity concerning the data (that the researcher is going to collect) validity. Thus, the current conduct of research adopts the probability sampling method. There exists a probability for sample distribution concerning the qualification, groups and genders namely stratified sampling. Currently, around 55,000 workers are performing their duties in oil and gas industry in UAE. To gather the required data with the help of probability sampling method using Krejci and Morgan’ Table, 382 employees/respondents were chosen as the sample for this research study. In accordance with Krejci and Morgan’s Table, when there is a population with a size of 50,000 to 70,000, the sample size should be 382 participants (Chuan and Penyelidikan, 2006).
Variables
For investigating the impact of soft HRM practices on employee engagement, two types of variables are taken into consideration; DPV (employee engagement) and INDV (employee participation, career advancement and job security).
Employee Engagement (EE)
EE is defined as a set of commitment to the company and the values of that company, in addition, having the willingness to assist the colleagues. It is stated that engagement is more desirable than satisfaction and is considered as a complex phenomenon rather than motivation (CIPD, 2006). Another definition given by Right Management (2006) explained that the engagement of each person in a business firm is committed to the success of that business organization’s strategy, and that it takes more attention than satisfaction and integrates pride, advocacy and commitment about a firm’s products and brands. Kahn (1990) defined EE as tying together the members of an organization to their roles of work, in engagement, individuals express and employ themselves emotionally, cognitively and physically while performing their roles.
Employee Participation (EP)
EP is considered as a procedure through which the influence is shared among the people who are hierarchically not equal the practices of participatory management equal the managers’ and their subordinates’ participation in processing of information, problem solving and decision-making activities
Career Advancement (CA)
CA is defined as an upward progress or movement of an individual’s career. An employee can progress by moving upwards from an initial (entry-level) career to the position of a manager within the same field e.g., from one profession to the next
Job Security (JS)
Job security is the likelihood that a person will keep his job; as a job with higher security level is liken to an individual having a job but with little chance of dropping it
Analytical Modelling
In order to test the impact of JS, CA and EP on EE, this study applied the equation 1 in the econometric model:
Where: “EE is employee engagement, JS is job security, EP is employee participation and CA is career advancement, β0 is intercept, β1 to β3 are coefficients and µ is normally distributed error term.”
Results and discussion
Demographic Profile of the Respondents
Table 1 gives an insight on the demographics of the participants. The Table shows that 66.49% (33.51%) respondents are male (female). Also, the respondents were categorised according to their age which depicted that 16.49% respondents are less than 30 years of age, 28.27% participants are between the ages of 30 to 35 years, 32.20% respondents are between the ages of 36 to 40 while the remaining (23.04%) of the participants are in the range of 40 plus years of age. According to years of working experience, only 29.84% respondents have 2 to 3 years of working experience, 39.01% respondents have 4 to 5 years of working experience while the remaining 31.15% participants have more than 5 years of working experience. Moreover, 71.73% (28.27%) respondents are married (unmarried).
Table 1 - Demographic Profile of Respondents
| Demographics | Frequency | Percentage | Cumulative %age | |
|---|---|---|---|---|
| Gender | Male | 254 | 66.49 | 66.49 |
| Female | 128 | 33.51 | 100 | |
| Age (Years) | Less than 30 | 63 | 16.49 | 16.49 |
| 30-35 | 108 | 28.27 | 44.76 | |
| 36-40 | 123 | 32.20 | 76.96 | |
| Above 40 | 88 | 23.04 | 100 | |
| Working Experience | 2-3 years | 114 | 29.84 | 29.84 |
| 4-5 years | 149 | 39.01 | 68.85 | |
| More than 5 years | 119 | 31.15 | 100 | |
| Marital Status | Married | 274 | 71.73 | 71.73 |
| Single | 108 | 28.27 | 100 | |
Source: own elaboration
Descriptive Statistics
The analysis of the present study begins with the descriptive statistics, which is used to describe the basic structures of data. It provides a simple summary of the data which is used to summarise the larger group data in a rational way. The results of the descriptive statistics are reported in Table 2 which shows the mean, standard deviation, minimum and maximum values of the data. The mean values show the average response of each item while the value of standard deviation shows the dispersion of data. Additionally, the minimum and maximum value shows the minimum and maximum responses of each item. The result shows that the survey is accomplished using the 5-point Likert scale, which comprised of 23 items. Of which the responses of 20 items vary from one to five, the response of 2 items vary from two to five, and the repsonse of one item vary from one to four. The average response of different items ranges from 2.624 to 4.407, while the value of standard deviation ranges from 0.783 to 1.235.
Table 2 - Descriptive Statistics
| Items | Mean | Min | Max | SD |
| EE1 | 3.447 | 1 | 5 | 1.000 |
| EE2 | 3.420 | 1 | 5 | 1.048 |
| EE3 | 3.478 | 1 | 5 | 0.967 |
| EE4 | 3.627 | 1 | 5 | 1.030 |
| EE5 | 3.485 | 1 | 5 | 0.924 |
| EE6 | 3.393 | 1 | 5 | 1.065 |
| EE7 | 3.349 | 1 | 5 | 0.937 |
| EE8 | 3.390 | 1 | 5 | 0.883 |
| EE9 | 3.498 | 1 | 5 | 1.018 |
| EE10 | 3.395 | 1 | 5 | 0.995 |
| JS1 | 3.125 | 1 | 5 | 1.153 |
| JS2 | 3.251 | 1 | 5 | 1.264 |
| JS3 | 4.407 | 2 | 5 | 0.830 |
| JS4 | 3.027 | 1 | 5 | 1.235 |
| EP1 | 3.024 | 1 | 5 | 1.124 |
| EP2 | 2.692 | 1 | 5 | 0.877 |
| EP3 | 2.817 | 1 | 5 | 0.781 |
| EP4 | 2.786 | 1 | 4 | 0.874 |
| CA1 | 3.810 | 2 | 5 | 1.057 |
| CA2 | 2.624 | 1 | 5 | 0.937 |
| CA3 | 2.708 | 1 | 5 | 0.783 |
| CA4 | 2.624 | 1 | 5 | 0.788 |
| CA5 | 3.437 | 1 | 5 | 1.241 |
Source: own elaboration
Assessment of Measurement model
Assessment of the measurement model (outer model) is the initial step in PLS-SEM. The purpose of this model is to test how well the modeled items are loaded into the theoretically defined construct. An evaluation of the outer model consists of a one-way predictive relationship between the latent constructs which are associated with the perceived indicator (Hair et al., 2011). Generally, PLS-SEM is comprised of two measurement models which are reflective and formative The reflective measurement model tests the reliability of individual items (convergent validity), reliability of each construct (CBa), internal consistency of each construct (CR), average variance extracted (AVE), and discriminate validity (Fornell-Larker/ HTMT ratio). The measurement model for this study is shown in Figure 2, which is obtained with the help of PLS algorithm. The model contains four latent variables such as JS, CA, EP (independent variables), and EE (dependent variable). Each latent variable comprises of different items i.e., EE (10 items), JS (4 items), EP (4 items), and CA (5 items), adapted from existing studies. The results of the measurement model are shown in the following sections.
Convergent Validity
The results of convergent validity are reported in Table 3. The study used factor loadings to test the presence of convergent validity in the data. The value of factor loading ranges from 0 to 1. According to the rule, the value of factor loading of each item should be greater than 0.4 in order to satisfy the condition of convergent validity of each item (Hair et al, 2011). If the loading of any individual item is less than 0.4, then the item should be excluded from the model in order to derive optimal results. Results in the table show that the loading value of each individual item is greater than the upper threshold (i.e., 0.4). For instance, the highest loading value is 0.966 (EE6), and the lowest loading value is 0.635 (CA5). To sum up, the results concluded that the convergent validity is present in each individual item of the construct.
Table 3 - Factor Loading
| Items | EE | JS | EP | CA | VIF |
|---|---|---|---|---|---|
| EE1 | 0.735 | 1.533 | |||
| EE2 | 0.834 | 1.234 | |||
| EE3 | 0.866 | 2.234 | |||
| EE4 | 0.894 | 3.453 | |||
| EE5 | 0.954 | 2.433 | |||
| EE6 | 0.966 | 1.645 | |||
| EE7 | 0.855 | 2.452 | |||
| EE8 | 0.875 | 3.356 | |||
| EE9 | 0.744 | 2.567 | |||
| EE10 | 0.845 | 1.232 | |||
| JS1 | 0.796 | 2.249 | |||
| JS2 | 0.755 | 2.272 | |||
| JS3 | 0.724 | 3.231 | |||
| JS4 | 0.846 | 3.372 | |||
| EP1 | 0.876 | 2.272 | |||
| EP2 | 0.883 | 2.362 | |||
| EP3 | 0.842 | 1.127 | |||
| EP4 | 0.825 | 2.263 | |||
| CA1 | 0.822 | 3.237 | |||
| CA2 | 0.865 | 3.282 | |||
| CA3 | 0.835 | 2.382 | |||
| CA4 | 0.723 | 1.732 | |||
| CA5 | 0.635 | 2.723 |
Source: own elaboration
Reliability and Internal Consistency
Table 4 gives an insight on the reliability and consistency of the constructs. Cronbach’s Alpha (CBa) is used to test the reliability of each construct. It is a widely used measure of reliability in applied literature. CBa tests the reliability of the data on the basis of interrelation with the modeled variables. The value of CBa also ranges from 0 to 1. A higher value indicates the higher reliability, while the lower value indicates the lower reliability. However, according to the rule, the value of CBa should be greater than 0.5 in order to satisfy the condition of reliability (Hinton et al., 2014). While the value of CBa which ranges from 0.60 to 0.70 is highly acceptable For this present case, the value of CBa ranges from 0.711 to 0.873 i.e., the value of CBa for EE, JS, EP and CA is 0.792, 0.711, 0.873, and 0.704 respectively. The findings indicate that the data of each multi-item construct is highly reliable.
The coefficient of CR is also used to test the reliability and internal consistency of each item of the construct. According to Hair et al., (2016), the value of CR should be greater than 0.6 in order to satisfy the condition of internal consistency. In the present case, the value of CR ranges from 0.780 to 0.882 i.e., the coefficient of CR for EE, JS, EP and CA is 0.798, 0.780, 0.882, and 0.804 respectively which satisfies the upper threshold level of internal consistency.
The value of AVE is used to test the convergent validity of each construct. The upper threshold level of AVE is 0.5. For the present case, the values of AVE for all the constructs are greater than 0.5 i.e., (EE = 0.821), (JS = 0.733), (EP = 0.834), and (CA = 0.811). The findings, thereby, confirm that convergent validity is present in each item of the construct.
Table 4 - Reliability and Internal Consistency
| Construct | CBa | CR | AVE |
| EE | 0.792 | 0.798 | 0.821 |
| JS | 0.711 | 0.780 | 0.733 |
| EP | 0.873 | 0.882 | 0.834 |
| CA | 0.704 | 0.804 | 0.811 |
Source: own elaboration
Discriminate Validity
Discriminate validity refers to the spread in which the construct is genuinely different from one another. It also measures the extent of variances among the corresponded constructs. The present study uses two different criteria i.e., Fornell-Larker criteria and the criteria of HTMT ratio in order to measure the discriminate validity in the data. The results of discriminate validity with the Fornell-Larker criteria are reported in Panel A of Table 5, while the results of discriminate validity with the HTMT ratio are reported in Panel B of Table 5.
Fornell-Larker’s criteria measure the discriminate validity with the help of correlation matrix. According to this criteria, discriminate validity will be present in the data if the diagonal values of correlation matrix (shown in bold) are greater than the remaining values. For the present case, all the diagonal values i.e., 0.815, 0.883, 0.873, and, 0.841 (shown in panel A of Table 5) are greater than the remaining values. Thus, it is concluded that the findings satisfy the condition of discriminate validity according to the Fornell-Larker criteria.
HTMT ratio measures the discriminate validity with the help of correlation coefficient. Voorhees et al. (2016) suggested that the coefficient of HTMT ratio must be less than 0.85 in order to satisfy the condition of discriminate validity. This criterion also satisfies the condition of discriminate validity as the HTMT ratio for all the variables are less than 0.85 i.e., the highest value of HTMT ratio is 0.692, whilst the lowest value of HTMT ratio is 0.474 (as shown in Panel B of Table 5)
Table 5 - Discriminate Validity
| Panel A: Fornell-Larker criteria | ||||
| Constructs | EE | JS | EP | CA |
| EE | 0.815 | |||
| JS | 0.634 | 0.883 | ||
| EP | 0.721 | 0.718 | 0.873 | |
| CA | 0.683 | 0.623 | 0.673 | 0.841 |
| Panel B: HTMT Ratio | ||||
| Constructs | EE | JS | EP | CA |
| EE | ||||
| JS | 0.583 | |||
| EP | 0.574 | 0.474 | ||
| CA | 0.624 | 0.567 | 0.692 | |
Source: own elaboration
Structural Mode
After successful valuation of the measurement model, the structural model of the study is then assessed. The structural model is used to test the proposed hypotheses of this study. Also, it deals with the issue of multicollinearity. Furthermore, the structural model comprises of path modeling which provides the direct and indirect coefficients of the variables. The structural model of this study is shown in Figure 3 which is obtained through the process of bootstrapping. The findings of the structural model is shown in the succeeding sections.
Multicollinearity
Multicollinearity is defined as a condition where two or more proposed variables of the study are highly or linearly correlated with each other. This correlation can create hurdles during the empirical estimation of the results. Moreover, the estimated results will be biased and spurious in the presence of multicollinearity as it increases the variance/standard deviation of the coefficient resulting in significant t-values/p-values. Therefore, the detection of multicollinearity is one of the most important steps of the estimation. Structural model uses VIF to detect the problem of multicollinearity in the data. According to the rule, the value of VIF should not exceed from 5. The results of VIF as presented in Table 3. show that the value of VIF for each individual item is less than 5 (shown in bold) i.e., the lowest VIF is 1.234, while the highest value of VIF is 3.372. Thus, it is concluded that the data of current study is free from the problem of multicollinearity.
Hypothesis Testing
The structural model contains advance statistical technique, known as path modeling which estimates the direct and indirect relationship among the study variables. This present study applied path modeling to test the proposed hypothesis of the study in Table 6. Furthermore, the path coefficient of model 1 (0.337) shows there is a positive and significant relationship between JS and EE at the level of 5%. The findings indicate that 1-unit increase in JS tends to increase 0.337 units of EE which support the first hypothesis of the study. The results also support the evidences of past studies (The findings suggest that if employees feel secured with reference to their job security then they tend to be more positive towards the work engagement of the organisation The path coefficient of model 2 (0.453) also indicates there is a significant relationship between CA and EE at the level of 1%. In addition, the results suggested that 1-unit increase in CA will increase 0.453 units of EE. The second hypothesis of the study is hence accepted. Finally, the path coefficient of model 3 (0.243) signifies there is a positive relationship between EP and EE. Results shows that 1-unit increase in EP tends to increase 0.243 units of EE at the level of 1%. Thus, the third hypothesis of this study is also supported.
Table 6 - Path Modeling
| Hypothesis/model | Path | Coefficient | T-value | P-value | Decision |
|---|---|---|---|---|---|
| Hypothesis 1 | JS ( EE | 0.337** | 3.764 | 0.034 | SP |
| Hypothesis 2 | CA ( EE | 0.453*** | 16.936 | 0.000 | SP |
| Hypothesis 3 | EP ( EE | 0.243*** | 12.287 | 0.000 | SP |
Source: own elaboration
Note: “JS: job security, EP: employee participation, CA: career advancement, EE: employee engagement, SP: supported; *** and ** indicate level of significance at 1% and 5%, respectively.”
Key Findings
Using SEM-PLS, the findings of this study show there is a positive and significant relationship between JS and EE. The findings indicate that 1-unit increase in JS tends to increase 0.337 units of EE which also support the first hypothesis. Indeed, the findings also support the evidences of past studies The findings suggest that if employees feel secured with their job security then they tend to be more positive towards work engagement of the organization. Results also establish that there is significant relationship between CA and EE which suggests that 1-unit increase in CA will increase 0.453 units of EE. The second hypothesis of the study is hence accepted. There is a positive relationship between EP and EE. The result shows that 1-unit increase in EP tends to increase 0.243 units of EE at the level of 1%. Thus, the third hypothesis of this study is also supported. The findings are in line with SET which argued that practices of HR help in increasing the positive consequences of employees at the place of work. Over a period of time, Saks (2006) considered SET as the theoretical foundation for exploring employee engagement. In line with the earlier studies of Cropanzano & Mitchell, (2005) the findings also highlighted that business organizations provide economic, social and emotional resources to their workers. In return, the employees feel obliged and want to do something in return for the benefit of the organization.
Practical Implications
This study contributes to the existing theoretical debate a clear vision to the managers of the development of soft practices of HRM in the domain of EE. Firstly, it proposes a model in order to exhibit a deep understanding regarding the EE-HRM practices link, where the findings provide more generalized may waste lots of money on turnover, lost productivity and absenteeism. The action of engaging the employees depends on the managers. For managers to get their employees engaged, they must also engage themselves at work. Thus, it is the obligation of the firm managers to identify the perceptions of their employees concerning the soft HRM practices and consequently secure their support to enhance the engagement of employees in their jobs.
This pressure of job insecurity of employees negatively affects the organization’s work. Employees with insecure job are not actively involved in the organization’s work and are also avoiding to deliver quality work This practice is more common in high achievers who can look out for new opportunity in lesser time. As a result, the firm may lose their most valuable employees, the one they most want to retain. Therefore, the findings of this current study is focused on improving employees with high job security that will increase their engagement at work.
Conclusion
The significance of EE cannot be exaggerated as EE strategies have been confirmed in lessening the rate of employee turnover, retaining the customer at high rate, making maximized profits and improving efficiency and productivity, predominantly, both at their place of living as well as working. When an individual is engaged, it pervades everything he/she does with a purpose, enthusiasm and energy that makes EE crucial to be explored. EE has been a considerable topic of interest in the field of HR for the last two decades. Engaged employees are those who feel an intense connection with their organisation and work with great enthusiasm. Such employees promote innovational activities, aligned with novel and creative ideas which result in the organizational success. It is believed that employee engagement is inevitably linked with the maintenance of an organization. EE occurs when employees receive appreciation, liking, and satisfaction from their organization with regard to what they are doing. Moreover, employees are more inclined towards their work if they feel that they are producing something creative for their organization. It believed that gratifying the employees for their contributions also increase their engagement towards the organization. Engaged employees are more devoted to their organization, more eager to put forward their efforts, more enthusiastic to collaborate with others, more excited to work within a challenging environment, more intense to talk about the problems they are facing, and, above all, more ready to suggest any improvements for their organization.
The area of work on EE in the field of HR though extensive but is primarily concerned with the organizational consequences. As far as we know, soft HR-EE nexus have received limited attention in the applied literature. Therefore, this present study contributes to the existing body of knowledge of EE literature by examining the empirical relation between HR and EE of oil and gas industry in UAE. In this regard, the study uses three different dimensions of HR; job security, career advancement, and employee participation (known as soft HRM practices) in order to derive more optimal results. This present study does not only contribute to the empirical literature but it also suggests effective managerial policies that help organizations on how to increase the engagement of employees towards the organization. In addition, this study helps future researchers by providing them with a new dimension.
In order to explore EE, the current study adopts the probability sampling method. At the present time, around 55,000 workers are performing their duties in oil and gas industry in UAE. To gather the required data with the help of probability sampling method and using Krejci and Morgan’s Table, 382 employees/respondents are chosen as the sample for this research study. In accordance with Krejci and Morgan’s Table, when there is a population with a size of 50,000 to 70,000, the size of sample should be 382 participants. The source of data for this research is taken from permanent employees who are offering their duties at oil and gas industry in UAE. In addition, the structured questionnaires serve as data collection tool. Moreover, SEM-PLS is used to analyse the results. For investigating the impact of soft HRM practices on employee engagement, the following variables are taken into consideration; employee engagement (outcome variable) and soft HRM practices (explanatory variable). Employee engagement is a set of commitment to the company and values of that company with the willingness to assist the colleague. It states that engagement is more desirable than satisfaction and is considered as a complex phenomenon than motivation. The explained variable (i.e., soft HRM practices) consists of three measures of employee participation, career advancement and job security. Employee participation is a procedure through which the influence is shared among the people who are hierarchically not equal. Career advancement is defined as an upward progress or movement of an individual’s career. An employee can progress upward from an initial (entry-level) career to the position of a manager within the similar field e.g., from one profession to the next. Job security is the probability that individuals will keep their job; a job with a high level of security assures that the person would have a small chance of losing it.
