# Work Readiness, Decision-Making Self-Efficacy, and Career Exploration among Engineering Students: A Two-Step Framework

Authors: Bilal Iftikhar Makki; Feng Feng; Muhammad Azam Waqar; Iswar Mani Adhikari
Journal: Mathematical Problems in Engineering
 (2023)

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## Abstract

Prior to entering the workforce, engineering students are expected to be highly skilled and contribute to decision-making with confidence in their abilities. Despite this, most students are lacking in these areas. Engineering students typically have a hard time finding work because they lack the necessary skills and are unable to take decisions with confidence. Accordingly, the Washington accord created job-ready features for engineering students that contain core knowledge and design (FKD), project management and finance (PMF), communication (C), modern tool use (MTU), teamwork (TW), engineers’ society and environment (ESE), ethics (E), and lifetime learning (LL). Work readiness (WR) literature will be examined in this study in an effort to promote decision-making self-efficacy (DMSE), which in turn leads to more successful career exploration (CE). Career discovery is then examined as a two-step process, with work readiness influencing decision-making self-efficacy and decision-making self-efficacy influencing career exploration. Malaysian private engineering universities were surveyed using a quantitative way to acquire the data. Results found a strong correlation between work readiness and decision-making self-efficacy, according to scientific evidence. Decision-making self-efficacy was also found to have a significant impact on career exploration. This study is to be useful to curtail unemployment by adopting the required skill set, which will help universities to produce engineers who are able to contribute to decision making with confidence towards exploring their careers. Overall, the results of this study might provide significant information to the related institutions and policymakers on the scarcity of decision-making of talented engineering students in Malaysia.


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## Body

## 1. Introduction

To become a developed nation, Malaysia requires a workforce with a wide range of skills and abilities [1–4]. Though, Malaysia has been experiencing a scarcity of competent people in recent years [5–7]. There is a growing fear that students are not meeting the demands of businesses, which is the one cause for this growing concern [8–11]. In spite of the fact that Malaysian students are competent in their chosen fields, they lack the necessary work-ready skills [8, 12–15], as work preparedness is essential for students to employ their technological ability efficiently [16–18].Accordingly, due to the lack of job preparation, graduates are more likely to be unemployed, as evidenced by current data [9, 19, 20]. In 2013, Malaysia’s job loss rate amplifies from 2.7 percent to 3.4 percent, according to the Malaysian Statistics Department [21]. Approximately, 460,000 graduates were out of work in 2015, and 21.7 percent of those were in engineering-related fields [22]. Local graduates, particularly those with engineering degrees, face a high percentage of unemployment, according to this data. Scholars argue that the high unemployment rate among engineering graduates is due to the lack of employability skills [9, 18]. Further research shows that Malaysian students acquired poor self-confidence, and as a result, students are less motivated to look for employment prospects [23–25]. Research has shown that graduates’ self-confidence increases when they have the right skills, which in turn encourages them to pursue a professional path, such as exploring a new field or field of study [26, 27].As a result, there is a shortage of competent employees, a greater unemployment rate, and low self-efficacy among graduates when it comes to making career-related decisions because of a lack of job preparation. Work-ready skills, self-efficacy towards career, and exploration of career are the main goals of this study. Therefore, having a wide range of abilities is predicted to improve the confidence and belief of engineering students, which in turn improves their job-hunting habits. Washington Accord work readiness qualities/skills were used for engineering graduates to gauge WR, DMSE, and CE. The findings of this study will help engineering graduates and higher education institutions to develop the necessary skills and confidence to meet the needs of the market.


## 2. Literature Review

### 2.1. Work Readiness (WR)

WR has been described in a variety of ways because of the many properties it has with other traits [19, 28]. Generally speaking, it is described as having the proper abilities, qualities, and practical knowledge that lead to a greater chance of finding a job [29]. There are several factors that contribute to a person’s ability to be a successful employee. These include a person’s overall talents, their ability to work in a specific field, and their professional traits and drive. In general, graduates’ perceptions of the learned traits and attitudes that guarantee success in the workplace are described by WR [18, 30].On WR, there is a wealth of information for students who want to get a job [4, 31–39]. Work readiness, on the other hand, is a relatively recent idea that originated as a way to understand a graduate’s success in the workplace [40, 41]. In the published literature, there are a wide variety of frameworks for employability [31, 33, 37–39]. Despite this, academics cannot agree on the particular skill set that engineering students should learn with the intention to start their first job.As a result, the International Engineering Alliance (IEA) created job readiness qualities for engineering students to cope with this weakness under the Washington Accord, i.e., essential knowledge and design (FKD), engineering society and environment (ESE), modern tool Usage (MTU), ethics (E), project management and finance (PMF), communication (C), teamwork (TW), and lifelong learning (LL) are all included in Washington’s list of job-ready traits. The regulatory body of Malaysian engineering education, the Engineering Accreditation Council (EAC), has created a set of engineering qualities as a guideline for engineering graduates in accordance with the Washington Accord. Having these skills is expected to considerably boost the self-confidence of engineering students and lead to a valuable product.


### 2.2. Decision-Making Self-Efficacy (DMSE)

The theory of self-efficacy in the context of career-related behavior was created by Betz and Hackett [42]. Taking decisions with self-belief and assurance in one’s ability to undertake career-related behavior is known as DMSE [42–44]. It is the confidence or judgment about their talents in relation to their work conduct and professional growth and choices [43, 45]. According to Parsa et al. [27], graduates’ self-efficacy and behaviors related to decision-making improved when they had the required skills. DMSE also promotes individuals to think, motivate, and act in a way that is consistent with their beliefs [46]. As a result, those who achieve a high level of DMSE are career-driven and often communicate with a positive outlook [47]. A low DMSE restricts job possibilities and choices [48–51]. As a result, graduates are unable to explore their professional options since they lack self-confidence in their talents [48, 52–56].


### 2.3. Career Exploration (CE)

During the late adolescent years, when young individuals begin to form a job identity, the CE notion begins to emerge [57]. Currently, it is viewed as a vibrant, lifetime process that includes every phase of career development [58]. CE, according to Johnston and Moniz-Lecce [59], is defined as those activities that expose students to potential professional paths and possibilities. Professional panels, career-information workshops, library, online searches, and volunteer opportunities are just some of the CE activities that students can participate in. Job shadowing and tours of a job site are also included [26, 60].A recent survey indicated that students are missing out on many job chances because of the lack of CE activities [61, 62]. As a result of this research, it can be concluded that a strong conviction in one’s abilities and capabilities will lead to an increase in career exploration alternatives.A two-step study approach has been constructed based on the aforementioned evaluation of the literature to examine the influence of WR on DMSE and CE. Figure1 depicts the study’s framework.Figure 1
Proposed conceptual framework.


## 2.1. Work Readiness (WR)

WR has been described in a variety of ways because of the many properties it has with other traits [19, 28]. Generally speaking, it is described as having the proper abilities, qualities, and practical knowledge that lead to a greater chance of finding a job [29]. There are several factors that contribute to a person’s ability to be a successful employee. These include a person’s overall talents, their ability to work in a specific field, and their professional traits and drive. In general, graduates’ perceptions of the learned traits and attitudes that guarantee success in the workplace are described by WR [18, 30].On WR, there is a wealth of information for students who want to get a job [4, 31–39]. Work readiness, on the other hand, is a relatively recent idea that originated as a way to understand a graduate’s success in the workplace [40, 41]. In the published literature, there are a wide variety of frameworks for employability [31, 33, 37–39]. Despite this, academics cannot agree on the particular skill set that engineering students should learn with the intention to start their first job.As a result, the International Engineering Alliance (IEA) created job readiness qualities for engineering students to cope with this weakness under the Washington Accord, i.e., essential knowledge and design (FKD), engineering society and environment (ESE), modern tool Usage (MTU), ethics (E), project management and finance (PMF), communication (C), teamwork (TW), and lifelong learning (LL) are all included in Washington’s list of job-ready traits. The regulatory body of Malaysian engineering education, the Engineering Accreditation Council (EAC), has created a set of engineering qualities as a guideline for engineering graduates in accordance with the Washington Accord. Having these skills is expected to considerably boost the self-confidence of engineering students and lead to a valuable product.


## 2.2. Decision-Making Self-Efficacy (DMSE)

The theory of self-efficacy in the context of career-related behavior was created by Betz and Hackett [42]. Taking decisions with self-belief and assurance in one’s ability to undertake career-related behavior is known as DMSE [42–44]. It is the confidence or judgment about their talents in relation to their work conduct and professional growth and choices [43, 45]. According to Parsa et al. [27], graduates’ self-efficacy and behaviors related to decision-making improved when they had the required skills. DMSE also promotes individuals to think, motivate, and act in a way that is consistent with their beliefs [46]. As a result, those who achieve a high level of DMSE are career-driven and often communicate with a positive outlook [47]. A low DMSE restricts job possibilities and choices [48–51]. As a result, graduates are unable to explore their professional options since they lack self-confidence in their talents [48, 52–56].


## 2.3. Career Exploration (CE)

During the late adolescent years, when young individuals begin to form a job identity, the CE notion begins to emerge [57]. Currently, it is viewed as a vibrant, lifetime process that includes every phase of career development [58]. CE, according to Johnston and Moniz-Lecce [59], is defined as those activities that expose students to potential professional paths and possibilities. Professional panels, career-information workshops, library, online searches, and volunteer opportunities are just some of the CE activities that students can participate in. Job shadowing and tours of a job site are also included [26, 60].A recent survey indicated that students are missing out on many job chances because of the lack of CE activities [61, 62]. As a result of this research, it can be concluded that a strong conviction in one’s abilities and capabilities will lead to an increase in career exploration alternatives.A two-step study approach has been constructed based on the aforementioned evaluation of the literature to examine the influence of WR on DMSE and CE. Figure1 depicts the study’s framework.Figure 1
Proposed conceptual framework.


## 3. Methodology

### 3.1. Sampling and Data Collection

This study employed a survey method to obtain data. It was a cross-sectional study as the data were obtained at a single moment in time. They were students who had recently finished their internships and were now ready to enter the workforce as engineers at three private institutions. Students from the Electrical, Mechanical, Chemical, Civil, and Petroleum Engineering departments were surveyed for this study. This study utilized a stratified sample strategy since the population is divided into five “strata” based on the number of departments. Randomly picked elements from each stratum can then be taken from the population as a whole [66]. It was determined that a questionnaire could be used to address the study questions listed in Table 1. Only 345 of the 745 questionnaires that were handed out were returned. Fifty-five surveys were deemed ineligible because they included insufficient information. The outstanding 290 questionnaires were taken into account for the final data examination after fifty-five incomplete samples were excluded.Table 1
Foundation of the scale.
No.VariablesNo. of itemsReference1Work readiness46[64]2Decision-making self-efficacy21[65]3Career exploration27[53]


### 3.2. Reliability of the Scale

A, B, C, and D are the four sections of a closed-ended questionnaire used in this study. Section A of the questionnaire includes demographic information about the respondents. Sections B, C, and D offer information about WR, DMSE, and CE, respectively. Section B items were tested for consistency by calculating the survey’s dependability. Using the 0.70 criterion, Cronbach’s alpha was determined. This alpha reliability value is considered satisfactory [66]. According to Table 2, the dependability of all the framework’s constructions was found to be within an acceptable range.Table 2
Reliability results.
NoVariablesNo. of itemsCronbach’s alpha1Fundamental knowledge and design80.6752Modern tool usage50.7023Project management and finance60.7384Engineer society and environment60.7925Teamwork60.6986Ethics40.7617Communication60.7768Lifelong learning50.8129Decision-making self-efficacy210.86310Career exploration270.814


## 3.1. Sampling and Data Collection

This study employed a survey method to obtain data. It was a cross-sectional study as the data were obtained at a single moment in time. They were students who had recently finished their internships and were now ready to enter the workforce as engineers at three private institutions. Students from the Electrical, Mechanical, Chemical, Civil, and Petroleum Engineering departments were surveyed for this study. This study utilized a stratified sample strategy since the population is divided into five “strata” based on the number of departments. Randomly picked elements from each stratum can then be taken from the population as a whole [66]. It was determined that a questionnaire could be used to address the study questions listed in Table 1. Only 345 of the 745 questionnaires that were handed out were returned. Fifty-five surveys were deemed ineligible because they included insufficient information. The outstanding 290 questionnaires were taken into account for the final data examination after fifty-five incomplete samples were excluded.Table 1
Foundation of the scale.
No.VariablesNo. of itemsReference1Work readiness46[64]2Decision-making self-efficacy21[65]3Career exploration27[53]


## 3.2. Reliability of the Scale

A, B, C, and D are the four sections of a closed-ended questionnaire used in this study. Section A of the questionnaire includes demographic information about the respondents. Sections B, C, and D offer information about WR, DMSE, and CE, respectively. Section B items were tested for consistency by calculating the survey’s dependability. Using the 0.70 criterion, Cronbach’s alpha was determined. This alpha reliability value is considered satisfactory [66]. According to Table 2, the dependability of all the framework’s constructions was found to be within an acceptable range.Table 2
Reliability results.
NoVariablesNo. of itemsCronbach’s alpha1Fundamental knowledge and design80.6752Modern tool usage50.7023Project management and finance60.7384Engineer society and environment60.7925Teamwork60.6986Ethics40.7617Communication60.7768Lifelong learning50.8129Decision-making self-efficacy210.86310Career exploration270.814


## 4. Results and Discussion

### 4.1. Demography

A total of 183 men (63.1%) and 107 women (36.9%) out of 290 final-year engineering graduates were found. The majority (124) were 20-21 years of age (42.75%); 69 students were 22-23 years old (23.80%) and 97 students were older than 23 (33.45%). More than 87 percent (88.27%) were Malaysian citizens, with the remainder (34) being from other countries (11.72%). Of the 256 Malaysian students, 113 were ethnic Malays (38.97%), 93 were Malaysian Chinese (32.06%), and 60 were Malaysian Indians (22.68%). 34 other overseas students of various ethnicities made up the final tally (11.72 percent). The following is a breakdown by discipline: Chemical (15.52%), Petroleum (16.20%), Electrical (16.20%), and Mechanical (20.68%) engineers make up the majority of the nation’s workforce (25.51%).The study’s goal was to evaluate the WR on DMSE and CE using a two-step methodology that was provided above. In the first step, a multiple regression analysis was carried out to observe the association between the predictors of WR, which contain FKD, PMF, MTU, TW, E,C, ESE, and LL with DMSE.The regression model summary is given in Table3, and the predictor coefficients for one dependent variable and the eight predictors are shown in Table 4.Table 3
Regression model summary.
ModelRR squareAdj.R squareStd. error of estimate10.8320.7620.7150.293Predictors (constant): FKD, PMF, MTU, TW, ESE,C, E, LL. DV: DMSE.Table 4
Predictor coefficients.
ModelUnstandardized coefficientsStandardized coefficientstValue of significanceBStd. errorBeta(Constant)−0.0420.211−0.2030.830FKD0.0250.0270.0320.9490.343MTU0.2140.0660.2343.1290.001PMF0.1230.0270.1552.7190.004ESE0.1200.0470.1142.1210.025TW0.0580.0220.0592.1750.035C0.1330.0620.1242.0130.042E0.0580.0280.0932.8960.012LL0.2190.0710.2363.5680.000DV: DMSE.To find out how close a dependent variable correlates with its independent counterparts, we usedR, R squared, and an adjusted R square. A positive link between decision-making self-efficacy and the variables (basic knowledge and design, project management and finance, modern tool use, engineer society and environment, ethics, communication, teamwork, and lifelong learning) is demonstrated in Table 2 as R-value of 0.832. Another statistic in the regression model summary is R square, which indicates that 76.2 percent of the total variance is elucidated by this equation. The adjusted R squared value is 0.715, indicating that the independent factors account for 71.5% of the variation in the dependent variable’s value (decision-making self-efficacy) (fundamental knowledge and design, modern tool usage, project management and finance, engineer society and environment, teamwork, ethics, communication, and lifelong learning).The value of standardized coefficient 0.032 indicates the association between FKD and DMSE. However, thep value of significance is 0.343, which indicates (p>0.05) that it has an unfavourable effect. There is no ample support to conclude that FKD is considerably associated with DMSE. It also shows that there are insufficient data to suggest that FKD is strongly linked to DMSE. Beta result of 0.243 indicates that modern tool utilization and decision-making self-efficacy have a positive link that is significant at 0.001 (p<0.05). This illustrates that the use of modern technology is a strong predictor of one’s job success. The findings are consistent with the hypothesis [67, 68]. Having a beta of 0.155 (p<0.05) indicates that the association between PMF and DMSE is significant. It indicates a strong link between PMF and DMSE. Ardolino (2015) and Ellis et al. [69] also found similar results (2010). Engineers and society have a beta value of 0.114. A p value of 0.025 indicates that the association between ESE and DMSE is significant. It shows that there is a strong link between the engineer’s social and environmental context and their sense of self-worth. Schwarzer (2014) has confirmed the findings. The beta coefficient for teamwork is 0.059. (p<0.05) indicates a strong association between teamwork and decision-making self-efficacy. According to this study, collaboration and decision-making self-efficacy are strongly linked. Findings are corroborated by many research investigations in diverse fields [70, 71]. Ethics has a beta value of 0.093 (p<0.05) which indicates a strong relationship between ethics and DMSE. Ethics and self-efficacy in the workplace have a strong connection, as seen below. It was also consistent with the findings of May et al., (2014) and Nelson et al., [72]. The beta value of communication is 0.124. At 0.042 (p<0.05), the positive connection between communication and decision-making self-efficacy is statistically significant. A strong correlation between communication and decision-making self-efficacy was found. Nørgaard et al. [73]; Khaddouma et al. [79], and Miller et al. [80] are in agreement with the findings. LL has a beta value of 0.236. The connection between LL and DMSE is significant at 0.000 (p<0.05). Decision-making self-efficacy has a strong correlation with lifelong learning. Klug, et al. agreed with the conclusion [76–78].In the second phase, multiple regressions were used to examine the relationship among the predictors of DMSE and CE. Tables5 and 6 show the regression testing findings.Table 5
Regression model summary.
ModelRR squareAdjustedR squareStd. error of the estimate10.5860.3630.3540.445Predictors (constant): DMSE.Table 6
Predictor coefficients.
ModelUnstandardized coefficientsStandardized coefficientsTSigBStd. errorBeta(Constant)1.1130.2230.000DMSE0.6740.0270.0530.5870.000DV: CE.The result ofR in Table 5 is 0.586, representing a positive relationship between predictors of DMSE with CE. In the regression model summary, the value of R square is 0.363 demonstrating that 36.3% of the total change is explained by this equation. The value of adjusted R-square is 0.354 which illustrates that a 35.4% difference or change in the values of CE is due to DMSE.Decision-making self-efficacy is seen in Table6 with a beta value of 0.587. DMSE and CE have a positive connection at 0.000, which is (p<0.05). There is a strong correlation between DMSE and CE. Consistent with previous findings [3, 26, 46, 53, 62, 79, 80]. Figure 2 depicts the two-step structure of the study.Figure 2
Two-step framework of the study.


## 4.1. Demography

A total of 183 men (63.1%) and 107 women (36.9%) out of 290 final-year engineering graduates were found. The majority (124) were 20-21 years of age (42.75%); 69 students were 22-23 years old (23.80%) and 97 students were older than 23 (33.45%). More than 87 percent (88.27%) were Malaysian citizens, with the remainder (34) being from other countries (11.72%). Of the 256 Malaysian students, 113 were ethnic Malays (38.97%), 93 were Malaysian Chinese (32.06%), and 60 were Malaysian Indians (22.68%). 34 other overseas students of various ethnicities made up the final tally (11.72 percent). The following is a breakdown by discipline: Chemical (15.52%), Petroleum (16.20%), Electrical (16.20%), and Mechanical (20.68%) engineers make up the majority of the nation’s workforce (25.51%).The study’s goal was to evaluate the WR on DMSE and CE using a two-step methodology that was provided above. In the first step, a multiple regression analysis was carried out to observe the association between the predictors of WR, which contain FKD, PMF, MTU, TW, E,C, ESE, and LL with DMSE.The regression model summary is given in Table3, and the predictor coefficients for one dependent variable and the eight predictors are shown in Table 4.Table 3
Regression model summary.
ModelRR squareAdj.R squareStd. error of estimate10.8320.7620.7150.293Predictors (constant): FKD, PMF, MTU, TW, ESE,C, E, LL. DV: DMSE.Table 4
Predictor coefficients.
ModelUnstandardized coefficientsStandardized coefficientstValue of significanceBStd. errorBeta(Constant)−0.0420.211−0.2030.830FKD0.0250.0270.0320.9490.343MTU0.2140.0660.2343.1290.001PMF0.1230.0270.1552.7190.004ESE0.1200.0470.1142.1210.025TW0.0580.0220.0592.1750.035C0.1330.0620.1242.0130.042E0.0580.0280.0932.8960.012LL0.2190.0710.2363.5680.000DV: DMSE.To find out how close a dependent variable correlates with its independent counterparts, we usedR, R squared, and an adjusted R square. A positive link between decision-making self-efficacy and the variables (basic knowledge and design, project management and finance, modern tool use, engineer society and environment, ethics, communication, teamwork, and lifelong learning) is demonstrated in Table 2 as R-value of 0.832. Another statistic in the regression model summary is R square, which indicates that 76.2 percent of the total variance is elucidated by this equation. The adjusted R squared value is 0.715, indicating that the independent factors account for 71.5% of the variation in the dependent variable’s value (decision-making self-efficacy) (fundamental knowledge and design, modern tool usage, project management and finance, engineer society and environment, teamwork, ethics, communication, and lifelong learning).The value of standardized coefficient 0.032 indicates the association between FKD and DMSE. However, thep value of significance is 0.343, which indicates (p>0.05) that it has an unfavourable effect. There is no ample support to conclude that FKD is considerably associated with DMSE. It also shows that there are insufficient data to suggest that FKD is strongly linked to DMSE. Beta result of 0.243 indicates that modern tool utilization and decision-making self-efficacy have a positive link that is significant at 0.001 (p<0.05). This illustrates that the use of modern technology is a strong predictor of one’s job success. The findings are consistent with the hypothesis [67, 68]. Having a beta of 0.155 (p<0.05) indicates that the association between PMF and DMSE is significant. It indicates a strong link between PMF and DMSE. Ardolino (2015) and Ellis et al. [69] also found similar results (2010). Engineers and society have a beta value of 0.114. A p value of 0.025 indicates that the association between ESE and DMSE is significant. It shows that there is a strong link between the engineer’s social and environmental context and their sense of self-worth. Schwarzer (2014) has confirmed the findings. The beta coefficient for teamwork is 0.059. (p<0.05) indicates a strong association between teamwork and decision-making self-efficacy. According to this study, collaboration and decision-making self-efficacy are strongly linked. Findings are corroborated by many research investigations in diverse fields [70, 71]. Ethics has a beta value of 0.093 (p<0.05) which indicates a strong relationship between ethics and DMSE. Ethics and self-efficacy in the workplace have a strong connection, as seen below. It was also consistent with the findings of May et al., (2014) and Nelson et al., [72]. The beta value of communication is 0.124. At 0.042 (p<0.05), the positive connection between communication and decision-making self-efficacy is statistically significant. A strong correlation between communication and decision-making self-efficacy was found. Nørgaard et al. [73]; Khaddouma et al. [79], and Miller et al. [80] are in agreement with the findings. LL has a beta value of 0.236. The connection between LL and DMSE is significant at 0.000 (p<0.05). Decision-making self-efficacy has a strong correlation with lifelong learning. Klug, et al. agreed with the conclusion [76–78].In the second phase, multiple regressions were used to examine the relationship among the predictors of DMSE and CE. Tables5 and 6 show the regression testing findings.Table 5
Regression model summary.
ModelRR squareAdjustedR squareStd. error of the estimate10.5860.3630.3540.445Predictors (constant): DMSE.Table 6
Predictor coefficients.
ModelUnstandardized coefficientsStandardized coefficientsTSigBStd. errorBeta(Constant)1.1130.2230.000DMSE0.6740.0270.0530.5870.000DV: CE.The result ofR in Table 5 is 0.586, representing a positive relationship between predictors of DMSE with CE. In the regression model summary, the value of R square is 0.363 demonstrating that 36.3% of the total change is explained by this equation. The value of adjusted R-square is 0.354 which illustrates that a 35.4% difference or change in the values of CE is due to DMSE.Decision-making self-efficacy is seen in Table6 with a beta value of 0.587. DMSE and CE have a positive connection at 0.000, which is (p<0.05). There is a strong correlation between DMSE and CE. Consistent with previous findings [3, 26, 46, 53, 62, 79, 80]. Figure 2 depicts the two-step structure of the study.Figure 2
Two-step framework of the study.


## 5. Conclusion

It was the goal of the study to determine how to work readiness affects a person’s decision-making self-efficacy and career exploration. Empirical data have helped to generate new research in the domains of WR, DMSE, and CE after the two-step paradigm was formed through empirical evidence. The outcomes of the study show that WR helps engineering graduates to take decision with more self-confidence and self-belief in their talents, allowing them to consider a wider range of future options. With the combined WR, DMSE, and CE, the study gives an integrated model employees who are actively employed were the primary focus of previous WR research. A unique viewpoint is gained by focusing on the final-year students in this study. In addition, the study confirmed the validity of a Western-developed research tool in an Eastern context. A research instrument that academics may use to evaluate the degree of WR, DMSE, and CE that was established by the study was also developed by the researchers in this study. As a general rule, research may help higher education institutions establish a curriculum that can help graduates acquire the necessary skills to satisfy the needs of the business. As a result of this research, the industry may be able to alleviate the scarcity of trained workers. Overall, it may be a useful foundation for dealing with graduates’ lack of skills and fears about contributing decision-making with confidence and finding a job in the future.



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*Source: 8166825-2023-02-08.xml*