Printer Friendly

Determinants Influencing Employability Skills: Undergraduate Perception.


Since half of century ago, employers and industry stressed that graduates had been lacking in certain employability skills as demanded by the employers. Employers have been in opinion that graduates are not prepared venture the complexities and stressful situations of working environment (Freudenberg, Brimble, and Cameron, 2011; Marais and Perkins, 2012; Schyns, Kiefer, Kerschreiter, and Tymon, 2011). Malaysia strive in becoming a developed country by year 2020 has practiced open financial system that stressed on foreign direct investment and export growth. Due to the open economic system, graduates are having tough time to get job or to be hired. University college students, who are the future era of the labor force, need to equip themselves for a challenging journey inside the prevalent economic conditions (Eleventh Malaysia Plan, 2015). Recently, most of the educational institutions emphasis on the quantity more than the satisfactory of graduates. This is one of the factors why graduates these days are lacking the skills as required by the labor market (Al-Alawneh, 2014).

Today, employers are also concerned about finding suitable employees who no longer only have basic academic competencies but better order thinking competencies like mastering, reasoning, thinking creatively, decision making and problem solving (Shafie and Nayan, 2010a). The current marketplace is becoming extra competitive and challenging for graduates; a huge diversity of job applicants is competing for a limited range of vacancies. Consequently, in the global labor marketplace employability skills are more important for present graduates to secure employment (Puad, 2015). The ministry has also adopted the attributes as Employability Attributes Framework (EAF) within the national Graduate Employability Blueprint 2012-2017 (Ministry of Higher Education Malaysia, 2012). The blueprint recommends that the graduate employability attribute is essential for all graduates to secure and ought to be encouraged and developed throughout the better learning institutions experience (Ministry of Higher Education Malaysia, 2012). Thus, the objective of this study is to identify the most important employability skills possessed by students from higher education institutions.

Literature Review

Scholars have related employability skills to personal skills. Employability skill is often described as the preparation of graduates to get jobs effectively and to expand in their careers, and allow individuals to show their value to an organization as the important thing to job survival (Mohammad Sattar Rasul, 2013). Employability skills can also be defined as the transferable skills needed by an individual to make them employable. Along with appropriate technical understanding and subject knowledge, employers often outline a set of skills that they want from an employee. Those skills are what they believe will equip the employee to perform their role to the best in their capability (Green et al., 2013). One study discovered that 75% of long-term job achievement relied upon on people skills, while only 25% relied upon technical knowledge (Klaus, 2010). As industries change, employers are looking for employees who are adaptable and have skills beyond technical expertise (Wibrow, 2011; Deloza, 2013). Employability skills are important to individuals as they help them to adapt to a changing, complex and interdependent world. They need to be relevant so that individuals can utilize them, learn and adapt to a continually and rapidly changing technological world (Thake, 2016).

Industry leaders' feel that the "skills" and "quality" of the workforce need numerous enhancements. Plagued with issues like curriculum, lack of qualified faculty, poor quality of content, and no longer-so-effective exam system, technical institutions do not provide signaling fee in the job market. Therefore a disparity exists inside the styles of skills taught at colleges and those which is probably demanded in industry (Zaharim et al., 2010). Measurement that used to determine the level of employability skills among the students additionally plays a crucial function. Mohamad Sattar (2010) suggested that teachers in educational institutions need to improve the understanding, implementation and measurement methods of employability skills. For the purposes of this study, a list of graduate employability skills was developed by reviewing studies conducted by previous researchers which are communication skills, critical thinking, developing initiatives, developing professionalism, problem solving, self-awareness, self-management, social responsibility, using technology and working effectively (de Guzman and Choi, 2013; Shukran, Hariyati Shariman, Saodah, and Noor Azlan, 2006; Wickramasinghe and Perera, 2010; Yorke and Knight, 2007; Yusof, Mustapha, Mohamad, and Bunian, 2012).


A stratified random sampling is adopted for this study. To test the hypotheses, a survey with 5-point Likert scale range from (5) strongly agree, (4) agree, (3) uncertain, (2) disagree, and (1) strongly disagree is developed. A total of 500 survey question is distributed among students in public and private university, however only 356 is returned. Westland (2010) and Cohen (1988) approves that power analysis is appropriate to determine the sample size while using structural equation modelling technique. A statistical power analysis was carried out using G Power software (Faul et al. 2007) in which a correlation test was performed to assess the adequacy of the sample size to undertake quantitative analysis. The sample size recommendation by Cohen (1992) in his statistical power of 80% with the significant level of 5% and minimum [R.sup.2] is 0.25. Therefore, as suggested by Cohen (1992) the results indicated that a sample size of at least 118 (refer figure 1) was sufficient to conduct the analysis, which means that the 356 sample size was in excess of that considered adequate to carry out the statistical analysis in this study. To test the model, the structural equation modelling (SEM) technique using SmartPLS 3.0 software are used (Henseler, Ringle, & Sarstedt, 2012). Based on the rule of thumb to run PLS-SEM analyses, the minimum sample size required is 210 (Hair, Hult, and Ringle, 2014). As a result, 356 usable questionnaires obtained yielded.



As presented in Table 2, most of the respondent are female (63.9%), while male made up 36.1% of the sample. The age group that have the highest percentage are 21-25 years old with 93.9%, and least number of respondent age range from 18-20 years old with 2.6%. Race can be divided into four category, which is first Malay (85%), second, Chinese (1.2%), third, Indian (13%) and others (0.9%). In term of years of study, highest number of respondent are from the final year with 75.4%. For area of specialization, highest are from human resources (44.2%), followed with marketing (23.1%), and least number of respondent are from entrepreneur (0.1%). Majority of the respondents (97.1%) have Bachelor Degree as their highest level of education and 1.4% have obtained Masters and Diploma. Lastly, for nationality, majority of the respondent are Malaysian which holds 99.7%.

Measurement Model Analysis

Internal consistency reliability of the measurement model as shown in Table 3, indicated that all construct is reliable at threshold > 0.7 for composite reliability (CR) and cronbach alpha (Hair, Black, Babin, Anderson, and Tatham, 2010). Convergent validity of the constructs was gauged by average variance extracted (AVE) (Hair, Ringle, and Sarstedt, 2011). Convergent validity is used to measure to determine whether any indicators that estimated a valid measure the dimensions of the concept being measured, the loading value of 0.5 is considered sufficient according to (Wiyono, 2011). As in Table 2 below, the result displayed that, there is no AVE value, less than 0.5.

Discriminant validity is an extent to which a construct is truly distinct from other constructs (Hair et al., 2014). Discriminant validity for this study is measured using heterotraitmonotrait ratio (HTMT) as suggested by Henseler, Ringle, and Sarstedt (2016). This recent approach shown the estimation of the true correlation between two latent variables. A threshold value of 0.90 has been suggested for HTMT (Henseler et al., 2016). Above 0.90 shows a lack of discriminant validity. Table 4 shows that HTMT criterion has been fulfilled.

Evaluation of Significance and Relevance of Indicator Weights

The objective of this study is to identify the importance determinants for employability skills. The significance of weight of each indicator reveals the relative importance and the loading represents the absolute importance that can be examined through bootstrapping. The bootstrapping procedure requires cases of at least equal in number to original sample's observation (Hair et al., 2011). Smart PLS was used to examine the significance and relevance of indicators' weights. The bootstrapping procedure using 1000 resamples was used (Chin, 2010; Surienty, Ramayah, Lo, and Tarmizi, 2014) to assess the significance of weights of the reflective indicators. Lohmoller (1989), recommended >0.1 weight for an indicator. The results reveal that the indicators' weights were above the recommended value of 0.1. Table 5 reveal that all weights of reflective indicators were having significant t-values that have provided an empirical support to retain all the indicators (Hair et al., 2011). These findings are also supported by the [Q.sup.2] value (Geisser, 1975) of the predictive relevance. After running the blindfolding procedure (Henseler et al., 2012) with an omission distance D 7, the [Q.sup.2] value of employability skills (0.376), which is well above zero, indicating the predictive relevance of the PLS path model (refer Table 6)

Discussion and Conclusion

In the globalized work place, everyone has the equal opportunity to get employed appropriately based on various factors that are inclusive of educational qualification and strength of the individual. Although it would not be appropriate from this limited study to suggest comprehensive changes to higher education system, particularly one that necessarily recommends more emphasis on employment skills, especially in competitive job market. The role of higher education is to produce graduates that can perform well in a 21st century workplace, which means that employability attributes should be emphasized. Graduates would like to leave university with the attributes needed by the contemporary labor market so that they can become productive members of the workforce, which will eventually lead to a competitive national economy. In this study, employability skills are the target construct, which is predicted by ten indicators which is communication skill, critical thinking, developing initiatives, developing professionalism, problem solving, self-awareness, self- management, social responsibility, using technology and working effectively (refer to Fig. 3).

Finding identified that students in this university are aware of the importance skills required, as it is evident from Table 5 that the three highest indicators which would contribute towards employability skills are working effectively (0.199), communication skills (0.163) and using technology (0.144). Attention should be given to the following employability skills by educators and educational institutions, employers and business and industry, and graduates themselves. This means that lecturer need to give attention on working effective skills as it could promote improvement in job performances. Olsen and Bryant (2012) expressed that in educational settings, it should not only build collaborative skills, but also fosters other positive interpersonal relationships and skills which could help graduates to work effectively. Besides, lectures also need to work on communication skills for employability among students, as it could help students in preparing themselves in working environment. Besides, graduates also need to have communication skills that enable them to interact with different levels of people in the workplace. Other researchers agreed that in a global competitive market with its technological advancements and management skills, employees must be fully equipped with excellent communication skills (Shafie and Nayan, 2010b). In addition, with an advancement of technology, in the current extremely-competitive local and global labor market, graduates need to market themselves to be employable, which will not happen without equipping them with technology skills needed in the workplace. Lecturer should focus on equipping graduates with computer applications that develop their thinking, problem-solving skills, and also by mastering necessary computer applications.

Other than that, the findings have also revealed that the lowest weight indicator is on problem solving with 0.072, problem solving is important for tomorrow's workers as they must be able to think creatively in order make decisions and solve problems. The result of this study however cannot be generalized since the questionnaires were distributed in the state of Pahang only. Future research should cover whole Malaysia and a comparative study would be best to identify different perception among undergraduates in public and private universities in Malaysia.


Al-Alawneh, M. K. (2014). Measuring students' employability skills as they are perceived at Yarmouk University. Canadian Social Science, 10(1), 10-20.

Chin. (2010). How to write up and report PLS analyses. In Handbook of partial least squares (pp. 655-690): Springer.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences. 2nd. de Guzman, A. B., and Choi, K. O. (2013). The relations of employability skills to career adaptability among technical school students. Journal of Vocational Behavior, 82(3), 199-207.

Eleventh Malaysia Plan. (2015). Eleventh Malaysia plan 2016-2020 anchoring growth on people. Malaysia: Prime Minister's Department.

Freudenberg, B., Brimble, M., and Cameron, C. (2011). WIL and generic skill development: The development of business students generic skills through work-integrated learning. Asia-Pacific Journal of Cooperative Education, 12(2), 79-93

Geisser, S. (1975). The predictive sample reuse method with applications. Journal of the American statistical Association, 70(350), 320-328.

Green, A., de Hoyos, M., Barnes, S.-A., Owen, D., Baldauf, B., and Behle, H. (2013). Literature review on employability, inclusion and ict, part 1: The concept of employability, with a specific focus on young people, older workers and migrants. Centeno C, Stewart J (Eds.). JRC Technical Report Series, EUR 25794 EN. Institute for Prospective Technological Studies, Joint Research Centre, European Commission (2013). Available at:

Hair, Black, Babin, Anderson, and Tatham. (2010). Multivariate data analysis (Vol. 7). In: Upper Saddle River, NJ: Pearson Prentice Hall.

Hair, Hult, and Ringle. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). In: Thousand Oaks (CA): Sage Publications.

Hair, Ringle, and Sarstedt. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152.

Henseler, Ringle, and Sarstedt. (2012). 12 Using partial least squares path modeling in advertising research: basic concepts and recent issues. Handbook of research on international advertising, 252.

Henseler, Ringle, C. M., and Sarstedt, M. (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33(3), 405-431.

Lohmoller, J.-B. (1989). Predictive vs. structural modeling: Pls vs. ml. In Latent Variable Path Modeling with Partial Least Squares (pp. 199-226): Springer.

Marais, D., and Perkins, J. (2012). Enhancing employability through self-assessment. Procedia-Social and Behavioral Sciences, 46, 4356-4362.

Ministry of Higher Education Malaysia. (2012). Education development plan

Olsen, D. H., and Bryant, P.-D. (2012). Business intelligence and information systems: enhancing student knowledge in database courses. The Review of Business Information Systems (Online), 16(1), 1.

Puad, M. H. M. (2015). The role of employability skills training programs in the workforce of Malaysia. Purdue University,

Schyns, B., Kiefer, T., Kerschreiter, R., and Tymon, A. (2011). Teaching implicit leadership theories to develop leaders and leadership: How and why it can make a difference. Academy of Management Learning and Education, 10(3), 397-408.

Shafie, L. A., and Nayan, S. (2010a). Employability awareness among Malaysian undergraduates. International Journal of Business and Management, 5(8), 119.

Shafie, L. A., and Nayan, S. (2010b). The Roles of University English Teachers in Malaysia. Journal of Language Teaching and Research, 1(3), 262-265.

Shukran, A., Hariyati Shariman, A., Saodah, W., and Noor Azlan, M. (2006). Employee knowledge, skills, abilities, and others characteristics: expectation of industries. Paper presented at the First International Conference of Muslim Psychologist, Khartoum, Sudan.

Surienty, L., Ramayah, T., Lo, M.-C., and Tarmizi, A. N. (2014). Quality of work life and turnover intention: a partial least square (PLS) approach. Social indicators research, 119(1), 405-420.

Thake, A. M. (2016). A critical review of graduate employability skills: Lessons from the Maltese experience. University of Bath,

Westland, C. (2010). Lower bounds on sample size in structural equation modeling. Electronic Commerce Research and Applications, 9(6), 476-487.

Wickramasinghe, V., and Perera, L. (2010). Graduates', university lecturers' and employers' perceptions towards employability skills. Education+ Training, 52(3), 226-244.

Wiyono, G. (2011). Merancang penelitian bisnis dengan alat analisis SPSS 17.0 & SmartPLS 2.0. Yogyakarta: STIM YKPN.

Yorke, M., and Knight, P. (2007). Evidence-informed pedagogy and the enhancement of student employability. Teaching in higher education, 12(2), 157-170.

Yusof, H. M., Mustapha, R., Mohamad, S. A. M. S., and Bunian, M. S. (2012). Measurement model of employability skills using confirmatory factor analysis. Procedia-Social and Behavioral Sciences, 56, 348-356.

Zaharim, A., Yusoff, Y. M., Mohamed, A., Omar, M. Z., Muhamad, N., and Mustapha, R. (2010). Practical framework of employability skills for engineering graduate in Malaysia. Paper presented at the Education Engineering (EDUCON), 2010 IEEE.

Mafuzah Mohamad (*)

College of Business and Accounting, Universiti Tenaga Nasional, Malaysia


Hamiza Jamaludin

College of Business and Accounting, Universiti Tenaga Nasional, Malaysia

Zuraini Abdullah Zawawi

College of Business and Accounting, Universiti Tenaga Nasional, Malaysia

Wan Noordiana Wan Hanafi

Universiti Tenaga Nasional, Malaysia

(*) Corresponding Author
Table 2: Demographic Profile

Criteria               Category     Number  Percentage

Gender                   Male        125         36.1
                        Female       231         63.9
Age                     18-20          9          2.6
                        21-25        325         93.9
                        50-54         12          3.5
Race                    Malay        294         85
                       Chinese         4          1.2
                        Indian        45         13
                        Others         3          0.9
Year of study         Foundation       1          0.3
                      First year       0          0
                     Second year      15          4.3
                      Third year      69         19.9
                      Final year     261         75.4
Area of             Human resource   153         44.2
specialization      International     51         14.7
                     Entrepreneur      1          0.3
                      Marketing       80         23.1
                      Accounting       7          2.0
                       Finance        54         15.6
Level of education      Master         5          1.4
                       Bachelor      336         97.1
                       Diploma         5          1.4
                     Certificate       0          0
Nationality           Malaysian      354         99.7
                    Non-Malaysian      1          0.3

Table 3: Measurement Model (Construct Reliability and Convergent

Construct               Item   Loading  Cronbach's  Composite
                                        Alpha       Reliability

Communication Skill     CS1    0.802    0.864       0.902
                        CS2    0.795
                        CS3    0.807
                        CS4    0.828
                        CS5    0.792
                        CT1    0.941    0.866       0.937
                        CT2    0.938
Developing Initiatives  INI1   0.804    0.798       0.881
                        INI3   0.885
                        INI4   0.842
Developing              P1     0.781    0.894       0.919
Professionalism         P2     0.81
                        P3     0.818
                        P4     0.854
                        P5     0.793
                        P6     0.796
Problem Solving         PS1    0.864    0.837       0.902
                        PS2    0.852
                        PS3    0.89
Self-Awareness          SA1    0.857    0.831       0.899
                        SA2    0.876
                        SA3    0.861
Self-Management         SM1    0.84     0.854       0.901
                        SM2    0.833
                        SM3    0.847
                        SM4    0.817
Social Responsibility   SR1    0.818    0.854       0.901
                        SR2    0.833
                        SR3    0.867
                        SR4    0.817
Using Technology        TECH1  0.874    0.85        0.909
                        TECH3  0.867
Working Effectively     WE1    0.763    0.88        0.909
                        WE2    0.813
                        WE3    0.819
                        WE4    0.79
                        WE5    0.812
                        WE6    0.742

Construct               Average

Communication Skill     0.648


Developing Initiatives  0.713

Developing              0.654

Problem Solving         0.755

Self-Awareness          0.747

Self-Management         0.696

Social Responsibility   0.695

Using Technology        0.769

Working Effectively     0.625

Table 4: Discriminant Validity - Heterotraitmonotrait Ratio (HTMT)

                            #1     #2     #3     #4     #5     #6

Communication Skill
Critical Thinking           0.502
Developing Initiatives      0.820  0.506
Developing Professionalism  0.491  0.658  0.808
Problem Solving             0.561  0.720  0.879  0.521
Self-Awareness              0.434  0.562  0.730  0.466  0.565
Self- Management            0.550  0.660  0.869  0.541  0.747  0.766
Social Responsibility       0.529  0.824  0.788  0.574  0.673  0.795
Using Technology            0.507  0.749  0.898  0.516  0.686  0.773
Working Effectively         0.570  0.797  0.860  0.569  0.684  0.736

                            #7     #8     #9     #10

Communication Skill
Critical Thinking
Developing Initiatives
Developing Professionalism
Problem Solving
Self- Management
Social Responsibility       0.608
Using Technology            0.623  0.744
Working Effectively         0.596  0.741  0.897

Table 5: Testing of Significance of Weights

Relationships                           Beta   SE     T value

Communication Skill ->                  0.163  0.012  13.303 (***)
Employability Skill
Critical Thinking -> Employability      0.139  0.011  12.825 (***)
Developing Initiatives ->               0.104  0.005  19.570 (***)
Employability Skill
Developing Professionalism ->           0.109  0.006  18.302 (***)
Employability Skill
Problem Solving -> Employability        0.072  0.005  14.053 (***)
Self-Awareness -> Employability Skill   0.109  0.006  18.874 (***)
Self-Management -> Employability        0.110  0.014   7.716 (***)
Social Responsibility -> Employability  0.140  0.007  19.101 (***)
Using Technology -> Employability       0.144  0.008  18.345 (***)
Working Effectively -> Employability    0.199  0.01   19.821 (***)

Relationships                           P Values

Communication Skill ->                     0
Employability Skill
Critical Thinking -> Employability         0
Developing Initiatives ->                  0
Employability Skill
Developing Professionalism ->              0
Employability Skill
Problem Solving -> Employability           0
Self-Awareness -> Employability Skill      0
Self-Management -> Employability           0
Social Responsibility -> Employability     0
Using Technology -> Employability          0
Working Effectively -> Employability       0

Note: Critical t values (***) 2.57 (significance level= 1%)

Table 6: Q2 of the Employability Skills

                      SSO        SSE        [Q.sup.2] (=1-SSE/SSO)

Communication          2,076.00   2,076.00
Critical Thinking      1,730.00   1,730.00
Developing             1,038.00   1,038.00
Developing             1,038.00   1,038.00
Employability skills  13,840.00   8,638.75  0.376
Problem Solving          692        692
Self-Awareness         1,038.00   1,038.00
Self- Management       1,384.00   1,384.00
Social                 1,384.00   1,384.00
Using Technology       1,384.00   1,384.00
Working Effectively    2,076.00   2,076.00
COPYRIGHT 2018 Global Business and Management Research: An International Journal
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2018 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Mohamad, Mafuzah; Jamaludin, Hamiza; Zawawi, Zuraini Abdullah; Hanafi, Wan Noordiana Wan
Publication:Global Business and Management Research: An International Journal
Geographic Code:9MALA
Date:Apr 1, 2018
Previous Article:Bridging Emotional Intelligence on Academic Performance: Public University's Perspective.
Next Article:Digital Disruption in Higher Learning Education: The Implementation of Blended Learning For Undergraduates.

Terms of use | Privacy policy | Copyright © 2022 Farlex, Inc. | Feedback | For webmasters |