Relationship between Job Satisfaction and Employee Performance in the Construction Industry of Pakistan

Abstract

Organizations focus on human resources to improve performance as a result of high global competition and a dynamic business environment. In today’s competitive environment, employee performance and job satisfaction are critical to the achievement of a company’s goals. Job satisfaction is an organization’s unnoticed success. Employee performance and job satisfaction are powerful tools that help in continuously developing and improving organizational performance to achieve strategic objectives. Job satisfaction is critical to the overall productivity of any given industry. Job satisfaction is important for both the employer and the employee. According to studies, employers greatly benefit from satisfied employees because they are more productive. One of the most important goals of a company is to maximize employee performance to achieve those goals. As a result, the focus of this study was on identifying the factors of job satisfaction and employee performance. It also evaluated the relationship between job satisfaction and employee performance in Pakistani construction projects. A detailed literature review was used to identify various factors, which were then shortlisted based on their relevance to the Pakistani construction industry by interviewing ten experienced practitioners. Totals of 11 job satisfaction and eight employee performance parameters were discovered. In total, 85 samples were collected as part of the data collection process via a questionnaire survey and statistically analyzed using multiple regression analysis. According to the results, all of the models have a high ability to compute the increase in employee performance criteria via the predicting variables. The overall models are significant because a value less than 0.05 indicates that they are. The study’s findings will assist practitioners in understanding the critical criteria that will increase employer satisfaction and improve performance.

1. Introduction

Employee performance and job satisfaction are critical in today’s competitive environment in order for firms to achieve their goals. Previously, materialistic items were regarded as more valuable than any other in the eyes of an organization. Organizations, on the other hand, have realized that human resources are more valuable than other assets. Organizations focus on human resources to improve performance due to high global competition and a dynamic business environment. Organizations can attain social sustainability with a positive working relationship with employees. A competent and motivated worker is a resource that is extremely scarce and not readily available in the market. Because of this, modern businesses seek to use sustainable human resources to gain competitive advantages. Human development, including education, training, a positive work environment, fair pay, and a sound corporate culture, is the key to achieving social sustainability [1]. In recent years, one of the most important goals of any organization has been to effectively manage its employees by encouraging positive attitudes such as increased productivity, job satisfaction, and organizational social behavior, while discouraging negative employee attitudes such as high employee turnover, absenteeism, and deviant behavior in the workplace. This can be accomplished in any organization by employing better management and implementing the best employee empowerment system. Human resources are assets made up of the employee’s skills and experiences. A great job encourages employees to seek knowledge, the most up-to-date information skills, and how and where to apply their expertise to advance their status. A better working environment motivates employees, and the continuous advancement of technology for the best utilization of the workforce benefits them in working and completing their projects [2,3]. One of the factors that can influence employee job satisfaction to achieve company goals is the work environment [4,5]. If the company employees are provided with a suitable work environment, they put more effort in to meet the objectives of their company.

Job satisfaction is an organization’s unnoticed success, which refers to a collection of positive feelings that employees have about their jobs, and it has a cascade of effects on various aspects of a company [6,7]. It combines physiological and environmental factors that cause an individual to be genuinely pleased with his or her work [8]. Job satisfaction is critical to the overall productivity of any industry, which is important for both the employer and the employee. This is because numerous studies have shown that employers greatly benefit from satisfied employees. They are, after all, more productive [9]. Job satisfaction can also be defined as the degree to which a worker is satisfied with the rewards he or she receives from his or her work, particularly in terms of intrinsic motivation. Human Resource (HR) practices in organizations, such as career opportunities, job nature, and overall working environment, have an impact on professionals’ job satisfaction [10]. According to [11], employee satisfaction indicates how satisfied an employee is with his or her job and working conditions. Everyone, from managers to retention agents to HR, needs to understand employee engagement and loyalty to determine how committed the workforce is to the organization and whether employees are satisfied with the way things are done to determine their likelihood of staying with them.

Performance has become a hot topic among decision-makers, who are constantly highlighting how their staff or employees perform. The term “performance” comes from the words “employee performance” or “actual performance”, and it refers to a person’s work or accomplishments. To evaluate employee performance, it is critical to analyze performance through assessment. It is possible to determine whether or not employee performance meets expectations [12]. Employees perform better when they are confident in their ability to complete work-related tasks. When employees are confident in their ability to complete work-related tasks, they have less uncertainty about themselves and their work, which improves job performance [13]. Furthermore, psychological factors such as inter-personal relationships, leadership styles, and opportunities for professional development have a significant impact on employee performance [14]. Employee performance and job satisfaction are useful tools for continuously developing and improving organizational performance to achieve strategic objectives [8]. Job satisfaction is critical to the overall productivity of any industry. Job satisfaction is important for both the employer and the employee. Many studies have shown that satisfied employees benefit employers because they are more productive [9]. Employees perceive the meaning of their work when they are satisfied with their jobs. Workers who understand how their positions affect others perform better because they have faith in their ability to complete work-related tasks. They are more confident in themselves and their work, which improves job performance [13]. Organizations rely on their workforce to achieve maximum productivity, which translates into organizational efficiency. As a result, ensuring employee job satisfaction is critical to organizational success. Pakistan has the ninth largest workforce in the world. According to the Labor Force Survey, the agriculture sector employs 38.49% of the population, followed by the manufacturing sector, which employs 16.05% of the workforce [15], while the construction industry employs a labor force comprising more than 7% of the workforce [16]. In Pakistan, very few researchers have focused on the relationship between job satisfaction and employee performance. Further, those studies were limited to the identification of the parameters of job satisfaction and employee performance. As yet, no model has been found to describe the relation between job satisfaction and employee performance in Pakistan. Understanding the issues and variables that can enhance the performance of the employees is very essential for achieving success in any business. This compelled me to investigate the relationship between job satisfaction and employee performance in Pakistan’s construction industry. This will emphasize the significance of job satisfaction and employee performance development. Thus, regression models are developed to assess the effects of job satisfaction and employee performance. This paper is divided into several sections: the introduction, the literature review, research methodology, the results and discussion, and the conclusion.

2. Literature Review

2.1. Employee Performance

Employee performance has long been a source of concern in organizational management. Any organization’s primary goal is to implement innovative approaches to motivate employees to achieve and produce higher job performance while increasing organizational productivity [17]. Construction organizations suffer from several critical problems, such as time issues [18]. In overcoming these issues, employee performance can play a significant role. According to [19], an employee’s apparent performance reflects their complete trust in their actions and contributions to the achievement of the organization’s goals and mission. They went on to say that the indicators for a worker’s performance are compensation practices, performance evaluation policies, and employee promotion procedures. Employee performance is critical to the success of a construction project for site-based workers [20]. Internal and external factors both influence employee performance. To improve employee performance, the company should investigate the external factors that influence employee performance. Construction companies should develop a strategy for improving employee performance, such as increasing the number of workers to reduce workload, compressing work weeks to achieve a healthier work–life balance, and promoting employees more frequently to improve job satisfaction. According to [21], work performance is critical in determining an employee’s quality in a company/organization. In addition, employee placement seeks to match the right person with the right job based on their skills and interests. Employee job performance can be improved if the company focuses on improving its employees’ abilities, mindsets, and behaviors. Proper employee placement is one of the keys to achieving peak job performance. A manager’s job is to evaluate employee performance and decide on future policies through performance appraisals. A company’s performance assessment is a systematic method for evaluating employees, contributions, and areas of interest.

During his research, Jufrizen [22] discovered that employee performance is very important in a government organization because it supports and aids in the achievement of each employee’s job goals. As a result, the organization must constantly monitor its employees to ensure that they are working effectively and efficiently to meet their objectives. Several factors influence employee performance, including organizational culture, organizational engagement, and job motivation. Organizational culture becomes a habit and custom that every company employee must follow to improve the organization’s performance. Individual accomplishment and levels of achievement in carrying out organizational tasks are frequently referred to as “individual performance”. Employee performance is defined as the quality and quantity of work performed by employees in carrying out and completing tasks delegated to them by their supervisor or leader based on their position in the organization. Organizational culture has a positive and significant impact on employee performance. Organizational culture is an archetype that must be taught to all members, including new members, for them to behave and solve problems, to develop workers who can adapt to their surroundings, and to bring organizational members together. Compensation includes all forms of monetary payment as well as all forms of services received as a result of employee involvement [23]. An individual’s performance is a process by which their actions are related to the task they have been assigned. The entire input, method, output, and result will be reflected in this performance.

Kustinah et al. [12] discovered that job success is dependent on each company’s level of performance, which is supported by workers’ contributions. Employee performance can be improved by improving leadership style, interpersonal communication, and job satisfaction. Because human resources are critical to the success of a project, the organization must hire experts and create a positive work environment to increase employee motivation. Because fieldwork is a dynamic task involving various project aspects, leadership is critical in the construction industry. Good communication is one of the interpersonal skills that contribute to the success of construction projects [24]. The level of consistency and comfort in communication, as well as the worker’s perception of job satisfaction, are some of the factors that influence a leader’s style in relation to the work team. Employee performance is the backbone of an organization because it contributes to its success [25]. A healthy working environment is critical for employee performance because it keeps employees from being overburdened, which reduces job productivity. Several factors influence job satisfaction at work. Employee performance is influenced by factors such as working hours, interpersonal relationships, job safety and security, and the importance perceived. Job safety and security are among the most important workplace concerns that must be strictly enforced to provide employees with a safe and versatile working environment. A company’s overall productivity is connected with the well-being of its employees. Employees at the company will have a more positive working environment, allowing them to focus on their tasks and be more productive. Previous research has discovered a direct relationship between job satisfaction and general factors such as pay and promotion, psychological empowerment, remuneration, healthcare facilities, work stress, and working conditions.

Table 1. Mapping of variables affecting employee performance.

S. No.	Variables Affecting Employee Performance	Source
1	Employees’ knowledge of the job	[26,27,28,29]
2	Quality of the work	[26,28,30]
3	Quantity of the work	[26,28,30]
4	Problem-solving and decision-making	[26,27,28,29,31]
5	Teamwork and cooperation	[26,27,28,31]
6	Leadership	[28,29,32,33]
7	Rate of absenteeism	[28,30]
8	Late attendance	[26,28]
9	Communication skill	[27,28,31,32]
10	Time management	[28]
11	Adaptability and flexibility	[26,27,28,31]
12	Appearance and grooming	[27,28]
13	Professional attitude	[26,27,28]
14	Initiative and innovation	[26,31]
15	Confidence	[26,27,28]
16	Dependability	[26,28]
17	Steadiness under pressure	[27,28,34,35]
18	Ethics and integrity	[28]
19	Planning capability	[28,29]
20	Versatility	[28]

summarizes the factors influencing employee performance as identified in the literature.

2.2. Job Satisfaction

Job satisfaction is a universal attitude that results from many simple attitudes in three areas: (i) individual characteristics, (ii) group relationships outside the job, and (iii) specific job factors. These components cannot be separated for analysis. Employee satisfaction is the term used to describe employees who have different needs and expectations relating to whether they are satisfied, contented, and fulfilled at work [11]. According to numerous studies, employee satisfaction is an important factor in employee engagement, goal achievement, and positive workplace morale. Employee satisfaction measures how happy workers are with their jobs and working conditions. Everyone from managers to retention agents to HR must understand employee engagement and loyalty to determine how committed the workforce is to the organization and whether employees are satisfied with how things are done so as to determine their likelihood of staying with the company. Employee satisfaction is a critical aspect of human resource management. Companies must ensure employee satisfaction because it is the foundation for increasing productivity, responsiveness, quality, and customer service. Employee satisfaction in the workplace cannot be underestimated. Job satisfaction is influenced by a variety of workplace factors, including compensation packages, opportunities for advancement, working environments, and work community. Furthermore, the determinant’s impact serves as a barometer for job satisfaction or dissatisfaction, as well as the outcome. To achieve balance, job dissatisfaction should be considered when addressing job satisfaction issues. Employees who are dissatisfied with their jobs may not quit, but their feelings of disappointment may have an impact on them and their co-workers, and the quality of performance and service they provide. Dissatisfied employees are hostile toward their co-workers [36]. Furthermore, Ref. [37] conducted a study to understand job satisfaction from the perspective of employees and discovered that five factors determine job satisfaction: independence, skill discretion, superior support, opportunities for further education, and relationships with co-workers. Empowerment is a method of approaching feeling and behaving as if one is in power and maintaining the organization [38]. A high turnover rate or withdrawal activity, increased costs in recruiting new employees, lower sales and earnings, and lower customer satisfaction are all consequences of low job satisfaction [39].

Contributing to job satisfaction could be a profitable venture for any company [40]. Employee satisfaction is one of the most reliable predictors of long-term positive company performance. High job satisfaction is linked to higher productivity, which is linked to higher profitability. Employees who are satisfied with their jobs carry out their responsibilities with sincerity and dedication. Ref. [1] pointed out that employees who face job-related issues are more likely to be dissatisfied with their jobs and intend to leave. The pro-organizational behaviors that an organization wants to instill in its employees are destroyed by these two attitudes. Organizations urgently need to encourage employees to feel good, and repeated positive emotions usually have a positive impact on social sustainability. Employees will feel a sense of belonging to your company, and they will understand that any work they do for you will have an impact on the overall success of the company. Organizations in both the private and public sectors around the world rely on their employees to achieve maximum productivity, which leads to increased organizational efficiency. Dissatisfaction is regarded as one of the primary factors that demotivate and demoralize employees in the workplace, resulting in lower morale and a negative impact on overall efficiency. Job satisfaction has a positive and necessary relationship with employee performance, implying that improving workers’ job satisfaction will improve performance. A thorough literature review was conducted to identify the variables influencing employee job satisfaction from previous research. These studies were conducted in various parts of the world. The variables affecting employee job satisfaction in the context of Pakistan’s construction industry were identified from these variables through semi-structured interviews.

Table 2. Mapping of variables measuring job satisfaction.

S. No.	Variables Affecting Employee Performance	Source
1	Communication level	[8,11]
2	Salaries	[11,25,41,42,43,44,45,46]
3	Promotions	[11,24,41,42,45,46]
4	Productivity	[32,45]
5	Educational level	[8,11]
6	Meaningful work	[47]
7	Organization development	[11]
8	Self-efficacy or competence	[11,47]
9	Rewards	[11]
10	Job security	[11,41,44,45,46]
11	Job design (scope, depth, interest, perceived value)	[11,48]
12	Social relationships	[48]
13	Opportunities	[11,42,43,48]
14	Relationship with supervisor	[11]
15	Safe working environmental conditions	[11,25,43,44,46,48]
16	Gender and fair compensation level	[8,11,41,46]
17	Company HR policies and practices	[11]
18	Expectation of employees from the job	[11]
19	Achievement	[43]
20	Recognition	[11,43,46]
21	Supervision	[43]
22	Compensation	[11,42]
23	Motivation	[42,46,47]
24	Appreciation	[41]
25	Empowerment	[11,25]
26	Quantity of tasks	[11]

shows the mapping of the job satisfaction variables identified in the literature.

3. Research Methodology

The two most common methods of conducting scientific research are qualitative and quantitative research. A qualitative approach to research aims to develop an interpretation and find significance in a specific problem. This is mainly accomplished by conducting interviews with professionals in different areas where questions are asked. However, the qualitative method is not often used in social science analysis because it is vulnerable to human biases. The amount of data gathered is small, resulting in no conclusive findings that can represent a larger population. Thus, the study was carried out in two stages: qualitative and quantitative. In the qualitative study, structured interviews were conducted to assess the importance level. A total of 10 experience personnel were interviewed and in the quantitative phase of the study, a questionnaire survey method was used to collect data as a form of intervention to acquire a variety of perspectives from project managers working in the construction industry. Most researchers utilize a closed-ended questionnaire with a pre-determined value for each question. In this study, the questionnaire forms were sent to 120 construction experts who had a lot of expertise. A total of 85 replies from 90 responses were used for analysis. The sample size (SS) required for this study was estimated based on the Cochran [49] equation, as follows:

$$\mathrm{Sample}\mathrm{Size}(\mathrm{SS})=\frac{Z^2\ast p\ast (1-p)}{C^2}$$

where:

• SS = sample size;
• Z = Z value (1.65 for 90 percent confidence level);
• p = percentage picking a choice expressed as a decimal (0.5 used for sample size needed);
• C = margin of error (9%), the maximum error of estimation, which can be 9 or 8%.

$$\mathrm{S}\mathrm{S}=\frac{{1.65}^2\ast 0.5(1-0.5)}{{0.09}^2}=84$$

The responses collected for this study number 85, which is more than 84. This means that the collected responses are adequate for the analysis to be concluded. The respondents participating in the data collection possess expertise in their field. The demographic information of the respondents participating in the qualitative study is presented in

Table 3. Demographic information of respondents participating in the qualitative phase.

S. No.	Education	Experience	Designation	Organization Type
1	BE	27	Director	Client
2	BE	10	QC Engineer	Client
3	ME	13	Executive Engineer	Client
4	BE	11	Project Engineer	Contractor
5	BE	21	Owner	Contractor
6	BE	16	Resident Engineer	Consultant
7	BE	22	Director	Client
8	BE	21	Owner	Contractor
9	BE	27	Executive Engineer	Client
10	BE	13	Senior Civil Engineer	Client

.

Table 3 shows that the total experience of the respondents is 181 years, with an average experience of 18.1 years. All the respondents have completed graduate education and are hold several senior positions in their organizations. The respondents are considered relevant and suitable for providing significant information. Similarly, the respondents participating in the quantitative study represent different types of organizations and have been working for several years, as summarized in Figure 1a and Figure 2b, respectively.

Figure 1a shows that the majority of respondents work with contractors, while 33 respondents are engaged in client organization, and the remaining 17 respondents work in a consultant organization. Among these respondents, a significant number of respondents, i.e., 32, have been working for more than 8 years, while 24 have been working for more than 4 years, and the remaining 29 have experience of less than 4 years. These respondents have experience working on different types of projects, and have obtained different levels of academic qualifications, as presented in Figure 2a,b.

Figure 2a shows that 54 respondents are involved in infrastructure projects, 15 in residential projects, 10 in non-residential projects, and 6 in social amenities projects. The practitioners participating in the data collection process of this study have completed different levels of academic qualifications. As depicted in Figure 2b, 33% of respondents (28 respondents) have completed a master’s degree, while 1% of respondents (1 respondent) is PGD-certified and 66% of respondents (56 respondents) are qualified as engineers, with a BE degree.

The collected data were analyzed with multiple regression analysis. Regression analysis is a statistical technique used to find and measure the relationship between a dependent variable and one or more independent variables. The independent variable(s) are the variable(s) that are used to predict or explain the dependent variable, whereas the dependent variable is the variable that is being predicted or explained. Regression analysis’ objective is to develop a mathematical model that can forecast the value of the dependent variable, based on the values of the independent variable or variables [50]. Regression analysis enables us to predict future outcomes based on the predictor variables. Kurniawan [51] used multiple regression in developing a delay factor model for substructure works in building construction. The type of relationship between the initial and actual or final contract duration in South Africa was determined using regression analysis [52]. Thomas and Thomas [53] used regression modeling for the prediction of construction cost and duration. Analysis of the research data was carried out with the Statistical Package for Social Sciences (SPSS) software package V20. Before multiple regression analysis, it is essential to check the data’s multicollinearity (heavily related variable). The multicollinearity test assesses how variables are closely associated with one another. The Pearson correlation coefficient test, the tolerance level, and the variance inflation factor (VIF) tests are run to assess these predictive variables [54]. Multicollinearity arises when independent variables in the regression model are highly correlated. The level of multicollinearity can be assessed by looking at the predictor variables. The predictor variables should not have a strong relationship with each other. The stronger the relationship between the predictors, the higher the degree of multicollinearity of the betas. The Pearson correlation coefficient values should be less than 0.8. It is strongly advised to solve the issue if severe collinearity exists (e.g., correlation > 0.8 between two variables or variance inflation factor (VIF) > 20). Tolerance is associated with each independent variable and ranges from 0 to 1. Allison [55] reported that there is no strict cut-off for tolerance, but suggests a tolerance of below 0.40 is cause for concern.

• High tolerance (e.g., 0.84) = low multicollinearity.
• Low tolerance (e.g., 0.19) = high (serious) multicollinearity.

If the VIF value is higher than 10, it is usually correlated with other independent variables. However, the acceptance range is subject to requirements and constraints. The results show that most features are highly correlated with different independent variables, and only two features can pass the <10 thresholds. A variance inflation factor (VIF) of 1 means no correlation among the predictor variables. The general rule is that VIF values exceeding 4 warrant further investigation, while VIFs exceeding 10 are signs of serious multicollinearity requiring correction. For regression analysis, the significance level was set at p < 0.05, and the average level was used when working on significance [55]. The statistical significance and relative importance of each predictive variable were examined with the unstandardized coefficient beta weights and the standardized beta weights of each predictive variable. In addition, an R squared was used to investigate the relationships between the various predictive variables and the dependent variable.

4. Results and Discussion

Initially, the data collected qualitatively were analyzed with the Relative Importance Index (RII) method to prioritize the parameters of job satisfaction and the criteria of employee performance, s, depicted in

Table 4. Ranking of job satisfaction parameters.

S. No.	Job Satisfaction Factors	Occurrence Level	Effectiveness Level	Importance Index	Rank
1	Promotions	0.98	0.96	0.9408	1
2	Salaries and wages	0.94	0.96	0.9024	2
3	Rewards (bonuses, increased pay, additional time off or other awards) given by organization to employees	0.9	0.98	0.882	3
4	Opportunities (financial growth, career growth, professional growth, personal growth)	0.9	0.94	0.846	4
5	Safe working environmental conditions	0.86	0.98	0.8428	5
6	Relationship with supervisor at workplace	0.9	0.92	0.828	6
7	Motivation for employee in organization	0.9	0.9	0.81	7
8	Compensations provided by organization	0.84	0.96	0.8064	8
9	Safety and security at workplace	0.86	0.86	0.7396	9
10	Supervision at workplace	0.84	0.86	0.7224	10
11	Job security in organization	0.9	0.9	0.72	11

and

Table 5. Ranking of employee performance criteria.

S. No.	Employee Performance Criteria	RII Effectiveness	Rank
1	Quantity of the work	0.90	1
2	Employees’ knowledge of the job	0.88	2
3	Steadiness under pressure	0.88	3
4	Time management	0.84	4
5	Planning capability	0.82	5
6	Problem-solving and decision-making	0.80	6
7	Teamwork and cooperation	0.74	7
8	Communication skill	0.70	8

.

The ranking of the factors affecting job satisfaction is shown in Table 4. According to reports, opportunities, rewards given to employees, salaries and wages, and promotions all have a big impact. Safe working conditions are then followed by the relationship between the employee and the supervisor at work, employee motivation within the organization, compensation offered by the organization, safety and security at the workplace, supervision at the workplace, and job security within the organization. The ranking of the performance criteria for employees is shown in Table 5. The criteria are as follows: quantity of work, knowledge of the job, composure under pressure, time management, capacity for planning, ability to solve problems and make decisions, teamwork and cooperation, and communication skill. Based on the results in Table 5 for employee performance criteria, multiple regression analysis was carried out to develop the models for the top five criteria in relation to the effects exerted by job satisfaction criteria.

Multiple regression analysis is a statistical method used to examine the relationship between one dependent variable and two or more independent variables. It is used to predict the value of a dependent variable based on the values of one or more independent variables [56]. The relationship is modeled through a linear equation that estimates the relationship between the variables. The regression coefficients represent the change in the dependent variable for each unit change in an independent variable, holding all other variables constant. This technique is widely used in various fields such as economics, finance, psychology, and social sciences. This study examined relationship between the employees’ performance and job satisfaction parameters. For this, the parameters measuring employees’ performance were considered dependent variables, which were affected by the job satisfaction indicators considered as independent variables. In other words, the developed regression models defined the effect of job satisfaction indicators via the various parameters of employee performance.

4.1. Relation of Quantity of the Work Managed by the Employee with Job Satisfaction Parameters

Employee performance is measured by various parameters, among which the quantity of the work managed by the employee is one of the most important. The data were analyzed with SPSS software package V20 to obtain the regression analysis parameters, as in

Table 6. Model statistics for quantity of work managed by employee using job satisfaction.

Model	Unstandardized Coefficients		Standardized Coefficients	t	Sig.	Collinearity Statistics
	B	Std. Error	Beta			Tolerance	VIF
(Constant)	0.387	0.183		2.11	0.038
Salaries and wages payed to employees in organization	0.215	0.052	0.276	4.17	0.000	0.554	1.807
Safety and security at workplace	0.128	0.045	0.178	2.83	0.006	0.612	1.635
Compensation and benefits provided by organization	0.161	0.049	0.218	3.26	0.002	0.546	1.832
Opportunities (financial growth, career growth, professional growth, personal growth) in organization for employees	0.135	0.045	0.184	2.97	0.004	0.635	1.576
Job security in organization	0.137	0.046	0.199	2.98	0.004	0.546	1.831
Motivation for employee in organization	0.132	0.051	0.168	2.58	0.011	0.574	1.743

.

Table 6 shows that none of the predicting variables have a tolerance value less than 0.2, indicating that there is no severe multicollinearity problem. Furthermore, among all the predicting variables, the predicting variable safety and security at workplace has the highest VIF value of 1.835, which is within the limits. Hence, a regression model equation was generated for the given data with SPSS software to assess the variation impact on the quantity of work due to job satisfaction parameters, and a summary of the model parameters is presented in

Table 7. Model summary for calculating increase in quantity of work managed by employee using job satisfaction.

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	Change Statistics					Durbin–Watson
					R Square Change	F Change	df1	df2	Sig. F Change
1	0.713 a	0.508	0.502	0.532	0.508	85.831	1	83	0.000
2	0.817 b	0.667	0.659	0.440	0.159	39.203	1	82	0.000
3	0.866 c	0.750	0.740	0.384	0.082	26.563	1	81	0.000
4	0.880 d	0.774	0.763	0.367	0.025	8.810	1	80	0.004
5	0.891 e	0.795	0.782	0.352	0.020	7.748	1	79	0.007
6	0.900 f	0.811	0.796	0.340	0.016	6.705	1	78	0.011	2.027

^a Predictors: (Constant), Salaries and wages payed to employees in organization. ^b Predictors: (Constant), Salaries and wages payed to employees in organization, Safety and security at workplace. ^c Predictors: (Constant), Salaries and wages payed to employees in organization, Safety and security at workplace, Compensation and benefits provided by organization. ^d Predictors: (Constant), Salaries and wages payed to employees in organization, Safety and security at workplace, Compensation and benefits provided by organization, Opportunities (financial growth, career growth, professional growth, personal growth) in organization for employees. ^e Predictors: (Constant), Salaries and wages payed to employees in organization, Safety and security at workplace, Compensation and benefits provided by organization, Opportunities (financial growth, career growth, professional growth, personal growth) in organization for employees, Job security in organization. ^f Predictors: (Constant), Salaries and wages payed to employees in organization, Safety and security at workplace, Compensation and benefits provided by organization, Opportunities (financial growth, career growth, professional growth, personal growth) in organization for employees, Job security in organization, Motivation for employee in organization. Dependent variable: Quantity of work managed.

. Based on the model data shown in Table 7, the regression equation can be expressed as:

Y = 0.387 + 0.215(Salaries and wages pay to employees in organization) + 0.128(Safety and security at workplace) +
0.161(Compensation and benefits provided by organization) + 0.135(Opportunities) + 0.137(Job security in
organization) + 0.132(Motivation for employee in organization)

4.2. Relation of Employee Knowledge with Job Satisfaction Parameters

Another criterion that plays very important role in measuring the performance of the employee is employee knowledge. Employee knowledge has a significant effect on job satisfaction. If the employee is satisfied with the job, they will always try to enhance their knowledge level and implement it for the betterment of the work. Hence, a regression model was developed for defining the relationship of job satisfaction indicators with employee knowledge. The results obtained from the regression analysis are presented in

Table 8. Model statistics for employee knowledge of the job using job satisfaction.

Model	Unstandardized Coefficients		Standardized Coefficients	t	Sig.	Collinearity Statistics
	B	Std. Error	Beta			Tolerance	VIF
(Constant)	0.090	0.188		0.477	0.634
Rewards (bonuses, increased pay, additional time off or other awards) given by organization to employees	0.314	0.046	0.404	6.819	0.000	0.569	1.75
Relationship with supervisor at workplace	0.236	0.053	0.243	4.436	0.000	0.666	1.50
Job security in organization	0.163	0.045	0.215	3.643	0.000	0.572	1.74
Safe working environmental conditions	0.145	0.049	0.173	2.957	0.004	0.585	1.70
Opportunities (financial growth, career growth, professional growth, personal growth) in organization for employees	0.130	0.047	0.151	2.761	0.007	0.669	1.49

.

Table 8 shows that none of the predicting variables have a tolerance value greater than 0.2, indicating that there is no severe multicollinearity problem. Furthermore, the predicting variable rewards (bonuses, increased pay, extra time off, or other awards) given to employees has the highest VIF value of 1.757, which is within the limit. Hence, a regression model equation was generated for the given data with SPSS software to assess the variation impact on employee knowledge due to job satisfaction parameters, and a summary of the model parameters is presented in

Table 9. Model summary for calculating increase in employee knowledge of the job using job satisfaction.

Model Summary f
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	Change Statistics					Durbin–Watson
					R Square Change	F Change	df1	df2	Sig. F Change
1	0.790 a	0.624	0.619	0.480	0.624	137.597	1	83	0.000
2	0.866 b	0.750	0.744	0.393	0.126	41.498	1	82	0.000
3	0.893 c	0.798	0.790	0.356	0.048	19.056	1	81	0.000
4	0.910 d	0.827	0.819	0.331	0.030	13.690	1	80	0.000
5	0.918 e	0.843	0.833	0.318	0.015	7.621	1	79	0.007	1.910

^a Predictors: (Constant), Rewards (bonuses, increased pay, additional time off or other awards) given by organization to employees. ^b Predictors: (Constant), Rewards (bonuses, increased pay, additional time off or other awards) given by organization to employees, Relationship with supervisor at workplace. ^c Predictors: (Constant), Rewards (bonuses, increased pay, additional time off or other awards) given by organization to employees, Relationship with supervisor at workplace, Job security in organization. ^d Predictors: (Constant), Rewards (bonuses, increased pay, additional time off or other awards) given by organization to employees, Relationship with supervisor at workplace, Job security in organization, Safe working environmental conditions. ^e Predictors: (Constant), Rewards (bonuses, increased pay, additional time off or other awards) given by organization to employees, Relationship with supervisor at workplace, Job security in organization, Safe working environmental conditions, Opportunities (financial growth, career growth, professional growth, personal growth.) in organization for employees. ^f Dependent variable: Employee knowledge of the job.

.

The dependent variable for this model is employee knowledge of the job, and there are five predictor (independent) variables, which are: rewards (bonuses, increased pay, additional time off, or other awards) given by the organization to employees, relationship with supervisor at the workplace, job security in the organization, safe working environmental conditions, opportunities (financial growth, career growth, professional growth, personal growth) in organization for employees. Below is equation, used in the model to calculate the increase in job employee knowledge using the predictor variables.

Y = 0.090 + 0.314(Rewards) + 0.236(Relationship with supervisor) + 0.163(Job security in organization) + 0.145(Safe
work environment) + 0.130 (Professional and personal growth)

4.3. Relation of Increase in Employee Steadiness under Pressure with Job Satisfaction

Employee steadiness under pressure is an important aspect when measuring the performance. It has a significant effect on job satisfaction. A regression model was developed for assessing employee steadiness under pressure, and the results obtained from the regression analysis are presented in

Table 10. Model statistics for employee steadiness under pressure using job satisfaction.

Model	Unstandardized Coefficients		Standardized Coefficients	t	Sig.	Collinearity Statistics
	B	Std. Error	Beta			Tolerance	VIF
(Constant)	0.065	0.210		0.312	0.756
Job security in organization	0.276	0.047	0.371	5.901	0.000	0.587	1.703
Opportunities (financial growth, career growth, professional growth, personal growth) in organization for employees	0.164	0.049	0.211	3.370	0.001	0.592	1.688
Compensation and benefits provided by organization	0.139	0.049	0.187	2.842	0.006	0.535	1.870
Relationship with supervisor at workplace	0.156	0.054	0.178	2.906	0.005	0.619	1.615
Promotions given to employee at workplace	0.145	0.048	0.177	3.054	0.003	0.692	1.446
Supervision at workplace	0.114	0.052	0.125	2.196	0.031	0.711	1.406

.

Table 10 shows that none of the predicting variables have a tolerance value less than 0.2, indicating that there is no severe multicollinearity problem. Furthermore, among all the predicting variables, the organization’s predicting variable compensation and benefits has the highest VIF value of 1.870, which is within the limits. Hence, a regression model equation was generated for the given data with SPSS software to assess the employee steadiness under pressure due to job satisfaction parameters, and a summary of the model parameters is presented in

Table 11. Model summary for calculating the increase in employee steadiness under pressure using job satisfaction.

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	Change Statistics					Durbin–Watson
					R Square Change	F Change	df1	df2	Sig. F Change
1	0.735 a	0.540	0.534	0.522	0.540	97.294	1	83	0.000
2	0.839 b	0.704	0.697	0.421	0.164	45.496	1	82	0.000
3	0.866 c	0.750	0.741	0.389	0.046	15.037	1	81	0.000
4	0.885 d	0.783	0.772	0.365	0.032	11.913	1	80	0.001
5	0.899 e	0.808	0.796	0.346	0.025	10.400	1	79	0.002
6	0.905 f	0.819	0.805	0.337	0.011	4.824	1	78	0.031	2.229

^a Predictors: (Constant), Job security in organization. ^b Predictors: (Constant), Job security in organization, Opportunities (financial growth, career growth, professional growth, personal growth) in organization for employees. ^c Predictors: (Constant), Job security in organization, Opportunities (financial growth, career growth, professional growth, personal growth) in organization for employees, Compensation and benefits provided by organization. ^d Predictors: (Constant), Job security in organization, Opportunities (financial growth, career growth, professional growth, personal growth) in organization for employees, Compensation and benefits provided by organization, Relationship with supervisor at workplace. ^e Predictors: (Constant), Job security in organization, Opportunities (financial growth, career growth, professional growth, personal growth) in organization for employees, Compensation and benefits provided by organization, Relationship with supervisor at workplace, Promotions given to employee at workplace. ^f Predictors: (Constant), Job security in organization, Opportunities (financial growth, career growth, professional growth, personal growth) in organization for employees, Compensation and benefits provided by organization, Relationship with supervisor at workplace, Promotions given to employee at workplace, Supervision at workplace. Dependent variable: Employee steadiness under pressure.

.

Y = 0.065 + 0.276(Job security in organization) + 0.164(Opportunities in organization) + 0.139(Compensation and
benefits) + 0.156(Relation with supervisor at workplace) + 0.145(Promotions given to employee) +
0.114(Supervision at workplace)

4.4. Relation of Time Management Skills Using Job Satisfaction

Time is a very fundamental aspect of success measurement. Time management plays a vital role in improving the productivity of the employee. Ultimately, it improves the overall performance of an organization. The time management skills of an employee are greatly affected by the job satisfaction, and this relationship was studied through regression analysis as presented in

Table 12. Model statistics for time management skills using job satisfaction.

Model	Unstandardized Coefficients		Standardized Coefficients	t	Sig.	Collinearity Statistics
	B	Std. Error	Beta			Tolerance	VIF
(Constant)	0.254	0.217		1.169	0.246
Rewards (bonuses, increased pay, additional time off or other awards) given by organization to employees	0.222	0.052	0.310	4.243	0.000	0.497	2.012
Promotions given to employee at workplace	0.185	0.057	0.229	3.273	0.002	0.542	1.845
Supervision at workplace	0.220	0.059	0.221	3.717	0.000	0.750	1.333
Motivation for employee in organization	0.175	0.061	0.210	2.849	0.006	0.491	2.038
Job security in organization	0.139	0.064	0.165	2.188	0.032	0.468	2.135

.

Table 12 shows that none of the predicting variables have a tolerance value less than 0.2, indicating that there is no severe multicollinearity problem. Furthermore, among all the predicting variables, the organization’s predicting variable safety and security at the workplace has the highest value of VIF among all the predicting variables, which is 1.835, which is within the limits. Hence, a regression model equation was generated for the given data with SPSS software to assess the employee steadiness under pressure due to job satisfaction parameters, and a summary of the model parameters is presented in

Table 13. Model summary for calculating increase in time management skills using job satisfaction.

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	Change Statistics					Durbin–Watson
					R Square Change	F Change	df1	df2	Sig. F Change
1	0.747 a	0.558	0.553	0.498	0.558	104.840	1	83	0.000
2	0.825 b	0.681	0.673	0.426	0.123	31.518	1	82	0.000
3	0.862 c	0.743	0.734	0.384	0.063	19.792	1	81	0.000
4	0.882 d	0.777	0.766	0.360	0.034	12.111	1	80	0.001
5	0.889 e	0.790	0.777	0.352	0.013	4.789	1	79	0.032	1.817

^a Predictors: (Constant), Rewards (bonuses, increased pay, additional time off or other awards) given by organization to employees. ^b Predictors: (Constant), Rewards (bonuses, increased pay, additional time off or other awards) given by organization to employees, Promotions given to employee at workplace. ^c Predictors: (Constant), Rewards (bonuses, increased pay, additional time off or other awards) given by organization to employees, Promotions given to employee at workplace, Supervision at workplace. ^d Predictors: (Constant), Rewards (bonuses, increased pay, additional time off or other awards) given by organization to employees, Promotions given to employee at workplace, Supervision at workplace, Motivation for employee in organization. ^e Predictors: (Constant), Rewards (bonuses, increased pay, additional time off or other awards) given by organization to employees, Promotions given to employee at workplace, Supervision at workplace, Motivation for employee in organization, Job Security in organization. Dependent variable: Time Management skills.

.

Y = 0.254 + 0.222(Rewards) + 0.185(Promotions) + 0.22(Supervision) + 0.175(Motivation for employee) + 0.139(Job
security)

4.5. Relation of Employee Planning Capability Using Job Satisfaction

The success of a project highly depends on effective planning. It is an essential characteristic of a good practitioner in order to enhance the performance of an organization. If an employee is satisfied with their job, it will motivate the employees to think strategically and enhance planning capabilities. The relationship of employee planning capability with job satisfaction was assessed with the multiple regression method, and the results are presented in

Table 14. Model statistics for employee planning capability using job satisfaction.

Model	Unstandardized Coefficients		Standardized Coefficients	t	Sig.	Collinearity Statistics
	B	Std. Error	Beta			Tolerance	VIF
(Constant)	0.154	0.178		0.866	0.389
Rewards (bonuses, increased pay, additional time off or other awards) given by an organization to employees	0.130	0.045	0.193	2.900	0.005	0.422	2.368
Motivation for employee in organization	0.172	0.048	0.214	3.559	0.001	0.515	1.943
Promotions given to employee at workplace	0.138	0.050	0.173	2.777	0.007	0.480	2.081
Relationship with supervisor at workplace	0.071	0.051	0.086	1.388	0.169	0.489	2.043
Compensation and benefits provided by organization	0.217	0.051	0.270	4.235	0.000	0.458	2.183
Supervision at workplace	0.131	0.045	0.155	2.906	0.005	0.659	1.518
Safety and security at workplace	0.104	0.042	0.144	2.454	0.016	0.543	1.843

.

Table 14 shows that none of the predicting variables have a tolerance value less than 0.2, indicating that there is no serious multicollinearity problem. Furthermore, among all the predicting variables, the predicting variable rewards (bonuses, increased pay, additional time off, or other awards) given to employees has the highest value of VIF, which is 2.368—within the limits. As a result, a regression model equation was generated using SPSS software for the given data to assess employee steadiness under pressure due to job satisfaction parameters, and a summary of the model parameters is presented in

Table 15. Model summary for calculating increase in employee planning capability using job satisfaction.

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	Change Statistics					Durbin–Watson
					R Square Change	F Change	df1	df2	Sig. F Change
1	0.764 a	0.584	0.579	0.460	0.584	116.43	1	83	0.000
2	0.852 b	0.725	0.718	0.376	0.141	42.145	1	82	0.000
3	0.880 c	0.775	0.766	0.342	0.050	17.878	1	81	0.000
4	0.897 d	0.805	0.795	0.321	0.030	12.400	1	80	0.001
5	0.910 e	0.827	0.816	0.304	0.022	10.151	1	79	0.002
6	0.919 f	0.845	0.833	0.289	0.018	9.004	1	78	0.004
7	0.925 g	0.856	0.843	0.281	0.011	6.023	1	77	0.016	1.440

^a Predictors: (Constant), Rewards (bonuses, increased pay, additional time off or other awards) given by organization to employees. ^b Predictors: (Constant), Rewards (bonuses, increased pay, additional time off or other awards) given by organization to employees, Motivation for employee in organization. ^c Predictors: (Constant), Rewards (bonuses, increased pay, additional time off or other awards) given by organization to employees, Motivation for employees in organization, Promotions given to employees at workplace. ^d Predictors: (Constant), Rewards (bonuses, increased pay, additional time off or other awards) given by organization to employees, Motivation for employees in organization, Promotions given to employee at workplace, Relationship with supervisor at workplace. ^e Predictors: (Constant), Rewards (bonuses, increased pay, additional time off or other awards) given by organization to employees, Motivation for employees in organization, Promotions given to employee at workplace, Relationship with supervisor at workplace, Compensation and benefits provided by organization. ^f Predictors: (Constant), Rewards (bonuses, increased pay, additional time off or other awards) given by organization to employees, Motivation for employees in organization, Promotions given to employee at workplace, Relationship with supervisor at workplace, Compensation and benefits provided by organization, Supervision at workplace. ^g Predictors: (Constant), Rewards (bonuses, increased pay, additional time off or other awards) given by organization to employees, Motivation for employees in organization, Promotions given to employee at workplace, Relationship with supervisor at workplace, Compensation and benefits provided by organization, Supervision at workplace, Safety and security at workplace. Dependent variable: Employee planning capability.

.

The dependent variable for this model is employee planning capability, and there are seven predictor (independent) variables, which are: rewards (bonuses, increased pay, additional time off or other awards) given by the organization to employees, motivation for employees in organization, promotions given to employees at the workplace, relationship with supervisor at the workplace, compensation and benefits provided by organization, supervision at the workplace, and safety and security at the workplace. Below is equation, used in the model to calculate the increase in employee planning capability from the predictor variables.

Y = 0.154 + 0.13(Rewards) + 0.172(Motivation for employee in organization) + 0.138(Promotion given to employee
at workplace) + 0.071(Relationship with supervisor at workplace) + 0.225(Compensation and benefits provided by
organization) + 0.153(Supervision at work place) + 0.125(Safety and security at workplace)

5. Discussion

This study developed an equation to assess the potential variation in employee performance criteria based on job satisfaction parameters. The model development process as well as the regression analysis equations are presented. According to Table 7, the model for explaining the quantity of work managed by the employee has six predicting variables with R² equal to 0.796, indicating that predictors explained 79.6% of the variance. It demonstrates that this model has a high degree of accuracy in calculating the increase in quantity of work managed from the predicting variables. The overall model is significant at p = 0.011 (less than 0.05), indicating that it is significant. Job satisfaction has a significant impact on many aspects of organizational life, including the quantity of work, opportunities (financial growth, career growth, professional growth, personal growth, and salaries) [48]. Satisfied employees show better performance as compared to dissatisfied employees, and contribute significantly to the uplifting of their organizations [57,58]. Every organization is required to make necessary arrangements to motivate their employees, even if there is economic and political instability [59].

According to Table 9, the model has five predicting variables with R² equal to 0.843, indicating that the predictors explained 84.3% of the variance. It demonstrates that this model is capable of calculating the increase in job employee knowledge as a result of predicting variables. The overall model has p = 0.007 (less than 0.05) significance, indicating that it is significant. Work knowledge is an important aspect of employee performance. Employees who are knowledgeable about their jobs will perform better and be more productive (rewards, job security, and opportunities). A good job skills match is positively associated with (1) high levels of job and life satisfaction, (2) positive perceptions of the current job, and (3) health interference with work [60,61,62]. Table 11 shows that the model has six predicting variables with R² equal to 0.819, indicating that predictors explained 81.9% of the variance. Being able to deal with workplace pressure is a highly sought-after skill. If work-related stress is a part of one’s daily life, there are things one can do to reduce stress, and demonstrate to others that one can handle stress effectively. Excessive stress causes performance to decline, because stress interferes with performance. An employee’s power or ability to cope is lost, and he or she is unable to make decisions, which affects behavior. Good workplace supervision has a positive impact on overall employee performance. On the other hand, López-Cabarcos et al. [63] reported that task significance and employee empowerment are secondary factors in the development of employee job performance.

Table 13 shows that the model has five predicting variables with R² equal to 0.790, indicating that predictors explained 79% of the variance. It demonstrates that this model has a high ability to calculate the increase in time management skills based on the predicting variables. The overall model is significant at p = 0.032 (less than 0.05), indicating that it is significant. Ref. [64] defined time management (TM) as the process of determining needs, goal setting, work supervision, and motivation. A significant relationship exists between job stress and TM variables and employees’ goal setting, planning, and performance evaluation. Today, because money and knowledge are no longer sufficient to ensure high performance, the most important factor in job success is time management, or TM. TM entails controlling and planning time effectively in order to reduce stress, which is one of the most important factors in professional success.

Table 15 shows that the model has seven predicting variables with R² equal to 0.856, indicating that predictors explained 85.6% of the variance. It demonstrates that this model has a high ability to compute the increase in employee planning capability as a result of predicting variables. The overall model is significant at p = 0.016 (less than 0.05), indicating that it is significant. All seven predictive variables were found to be significant, with significance levels of p = 0.05 (rewards (bonuses, increased pay, additional time off, or other awards) given by the organization to employees p = 0.000, motivation for an employee in organization p = 0.000, promotions given to the employee at workplace p = 0.000, relationship with supervisor at workplace p = 0.001, compensation and benefits provided by organization p = 0.002, supervision p = 0.002). Employee development planning, also known as employee growth planning, is a process for assisting individuals in improving their skills for their current job, and acquiring knowledge and skills for new roles and responsibilities in an organization. Any raise in pay or additional time off encourages employees to participate in the planning phase [65,66]. It is not always necessary to train them to be leaders. Managerial skills include object planning and determination, decision-making, human relations, marketing, financial and accounting skills, management, control, negotiation, and development management.

6. Conclusions

This study identified 11 job satisfaction parameters, among which the top five parameters based on importance are (i) promotions, (ii) salaries and wages, (iii) rewards (bonuses, increased pay, additional time off and other rewards given by organizations to employees), (iv) opportunities (financial growth, carrier growth, professional growth and personal growth) and (v) safe working environmental conditions. Moreover, eight employee performance parameters were identified, whereby (i) quantity of work, (ii) employee knowledge of the job, (iii) steadiness under pressure, (iv) time management and (v) employee planning capability were found to be the most important performance criteria. The study developed regression models for these five employee performance factors for assessing the relation between employee performance criteria and job satisfaction metrics. Data analysis for the collinearity analysis showed that the identified parameters are correlated with each other. By taking job satisfaction indicators into account, linear multiple regression models were developed to predict an increase in employee performance. Using all of the above-mentioned results and models, practitioners in Pakistan’s construction industry can predict and calculate how much improvement in employee performance will occur. These models will assist construction organizations in determining the difference in employee performance after providing facilities to employees in order to meet their satisfaction level. These predictive models will direct them to provide only the necessary facilities that contribute the most to employee performance.

The linear multiple regression model equations revealed that “job security” and “reward” are the most common factors that influence employee performance. Six employee performance parameters are heavily influenced by rewards: work knowledge, time management, employee planning capability, problem-solving and decision-making, teamwork and cooperation, and communication skills. Job security also has an impact on six aspects of employee performance: the amount of work managed, the knowledge of the work, the employee’s stamina under pressure, time management, problem-solving and decision-making skills, and communication skills. Following that, workplace supervision affects five parameters: employee steadiness under pressure, time management, employee planning capability, problem-solving and decision-making, and communication skills. Employee steadiness under pressure, planning capability, and communication skills are positively influenced by the relationship with supervisor and promotions. Relationships with supervisors also increase knowledge of the job. Salaries and wages, as well as safe working conditions, have a significant impact on employees’ problem-solving and decision-making abilities, as well as teamwork and cooperation within the organization. Salaries and wages also have an impact on the amount of work that an employee manages at work. Workplace safety and security, as well as compensation and benefits provided by an organization to employees, have all had a positive impact on the amount of work managed by employees, employee planning capabilities, teamwork, and cooperation. Furthermore, compensation and benefits have an impact on employee stability under pressure. Opportunities provided by the organization at work have a significant positive impact on the amount of work managed, employee knowledge of work, and employee steadiness under pressure. Furthermore, the amount of work managed, the amount of time managed by employees, employee planning capabilities, and problem-solving and decision-making abilities of employees at the workplace, are all affected by the motivation provided in the organization.

Using all of the above-mentioned results and models, practitioners in Pakistan’s construction industry can predict and calculate how much improvement in employee performance will occur by looking at statistics on employee job satisfaction. These models will assist construction organizations in determining how much difference in employee performance has occurred after providing facilities to meet their satisfaction. These predictive models will direct them to provide only the necessary facilities that contribute most to employee performance. They will also help the Pakistani construction industry to elevate the status of motivated and hardworking employees, resulting in prosperity in the Pakistani construction industry. This study establishes a foundation for understanding the relationship between various parameters of satisfaction and performance with human resources in the workplace. However, this work needs to be extended by increasing the number of respondents. The respondents participating in this study are located in the Sindh province only. The responses from the practitioners working in the other four provinces of Pakistan should also be perceived as cultural differences, and work environments in different regions could show different levels of effects on performance. Further, case studies could be conducted to study the effectiveness of the developed regression models in explaining the relation between the variables.