Fuzzy Analytical Hierarchy Process for Analysing the Factors that Influence the Graduate Students’ Career Choice First Author\[1\]\*, Second Author2 (Double Blind Review: Please do not type or edit anything here until final-camera ready submissions) *1First affiliation, City and Country (Please do not type or edit anything here, until final camera-ready paper submission)* *2Second affiliation, City and Country (Please do not type or edit anything here, until final camera-ready paper submission)*
ARTICLE INFO ABSTRACT

Article history:

Received

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Published 1 March 2024

 There are various types of careers that graduated students can pursue today. However, the career selection could be quite confusing and challenging, due to some factors such as parental and academic influences, personal interest and work environment. This study is aimed to rank the main factor and sub-factor that influence the students’ career choices. Interviews are conducted in obtaining the input for the study. Based on the input obtained, the study utilized the approach based on Saaty’s scale and fuzzy analytic hierarchy process (AHP). The factor that has the highest value of the normalized weight is that most influences factor, which is the personal interest factor. For sub-factors, workplace is the most influential factor, while the sub-factor with the least influence is safety under the parental factor, which is also the least ranked of the factors influencing students' career choices.

Keywords:

Students’ career choice

Saaty’s scale

Fuzzy analytic hierarchy process

DOI:

10.24191/jcrinn.v9i1
# introduction Choosing a suitable career is crucial for the graduate student that can have a profound impact on various aspects of their future life. With an increasing number of job scope available, some students especially for those who are maintain a good Cumulative Grade Point Average (CGPA) for every semester, would be expecting that they can be easily choosing their career according to their qualifications. However, after they graduated, they actually might have difficulty in getting or choosing their job, due to some factors. Numerous elements, including the graduate student's working environment, personal interests, academic influence, and familial influence, may play a role in their employment decisions. Koçak et al. (2021) claim that graduate students' academic and familial backgrounds have a big impact on their self-efficacy in choosing a career. Parents are important because they can affect their children's career choices in a variety of ways, including direct inheritance and role modeling. Furthermore, since academic accomplishment shows the results of students' labor during their academic careers, it also has an impact on employment. Personal interests, however, are also a major consideration when choosing a career. Afzaal Humayon et al. (2018) assert that a person will be inspired and motivated to work hard by whatever it is that they are truly interested in. Boring employment that goes beyond one's capabilities and interests may negatively impact the person and may lead to poor performance, or even worse, anxiety and stress. Besides that, the work environment is also crucial since it affects output and promotes happier working conditions. A decent workplace embodies a culture of mutual respect, empathy, and understanding among coworkers. It is important to take into account environmental factors including friends, race, and gender. Gender roles in the workplace have historically been unfair and discriminatory (Fizer, 2013). Nonetheless, a growing number of people are realizing that gender disparities are acceptable in demanding professions like networking and engineering. Based on the aforementioned constraints and factors, it is obvious that, choosing a suitable career is not as easy as taking candy from a baby. Furthermore, there are also some sub-factors that also influencing the graduate students’ career choice. The sub-factors such as family tradition, scholarship, salary, gender and etc have closed relation to the factors that have been mentioned. These will make the career choice became more tedious and stressful. Therefore, in this study, we are aimed to determine and rank the main factor and sub-factor that most influencing to the graduate students’ in choosing their career. A few studies have been carried out on determining the factors that influencing students’ career choice, which utilized fuzzy Analytic Hierarchy Process (AHP) method (Kilic & Cevikcan, 2011). Besides that, Chen et al. (2018) used TOPSIS and fuzzy cognitive map in identifying the factors (Chen et al., 2018). However, both studies only focusing on the identifying the factors without considering on the ranking of the factors. The ranking of the factors is important to help the graduate students in making the best decision according to their own situation that related to the factors. Hence, the ranking will be providing a benefit to assist the graduate students in choosing a sensible professional path. The fuzzy Analytic Hierarchy Process (AHP) method is used in this study. Since Van Laarhoven and Pedrycz (1983), the fuzzy set theory has been integrated into the traditional analytic hierarchy process (AHP), which was first introduced by Saaty (1980), due to its simplicity, ease of use, and tremendous versatility. Since then, a great deal of studies has been carried out on FAHP in relation to multiple criteria decision-making issues. The latest studies conducted are selecting the effective ways in preventing COVID-19 (Idris et al., 2023), assessment on nasyid competition (Aziz et al., 2023), selection of best student award (Aziz et al., 2023) and so on. Furthermore, there are a lot of real-world scenarios that entail vague and unclear conditions, especially when making decisions, which makes the FAHP extremely applicable to solving issues. The rest of the paper is organized as follows. The methodology used for the study is provided in Section 2. The results are presented in the Section 3 and finally in Section 4, the conclusion is drawn. # methodology The methodology of this study involves a structured framework as illustrated in the Figure 1. Fig. 2. Framework of the study (Source: Emrouznejad & Ho, 2022) Details of the framework are given in the following steps. Step 1: The methodology of the study is begun with the identification of the factors involved in the students’ career choice. Based on literature reviews, the most common factors and sub-factors that contributing to the career choice are presented in the following Table 1. Table 1. Factors and Subfactors Influencing Students’ Career Choice
Factor Sub-factors Source
Parental Influence

  • Parents' Expectations

  • Family Tradition

  • Safety

 Afzal et al. (2018); Leung et al. (2011)
Academic Influence

  • CGPA level

  • Scholarship

  • Difficulty

Kass and Miller (2018);

Kazi and Akhlaq (2017)
Self Interest

  • Passion

  • Workplace

  • Salary

 Afzal et al. (2018); Dyrbye et al. (2020)
Work Environment

  • Gender

  • Race

  • Friends

Twidwell et al. (2022);

Kazi and Akhlaq (2017)
Based on the factors and sub-factors that have been found out, interviews have been conducted involving two experts, which are psychological officers from the Career and Counselling Department of UiTM Arau, Perlis, and a human resources executive officer from the Human Resources Department of Safwa Clinic, Kangar, Perlis. The interview involved several questions related to the experts such as demographic profile, including his or her gender, age, and education level, and also some questions related to the factors and subfactors that have influenced the career choice based on the experts’ experience. Step 2: The input from the interviews is transformed into the pairwise comparison matrices (PCM), according to the number of experts. The general form of the PCM is given as follows. ## (1) ## where represents the number of criterion or factors involved in the study. Whileis the input or value for each factor given by the experts in the scale of 1 to 9. It should be noted that and for every . In other words, if the essential preferences is located in the upper triangle of the matrix, then the reciprocal value must be at the lower triangle or vice versa. Hence, the PCM or the matrix in Eq. (1) is always positive and symmetric (Bozanic, et al., 2013). Step 3: The consistency ratio (CR) is calculated in order to make sure that the input obtained from the experts is acceptable. It should be noted that CR should be less than or equal to 10% (0.1), then verifies that the results of comparison are acceptable. The CR is computed using the equation below: (2) where (3) andis the largest eigenvalue of the comparison matrix and *n* is the number of factors. While, the random consistency index (*RI*) is based on the number of factors (Saaty, 1980), as given in Table 2. Table 2. Random consistency index. | Number of factors, *n* | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | | ---------------------- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | | Ratio Index, *RI* | 0.00 | 0.00 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 | Next, the entries of the PCM for both experts are fuzzified. In other words, all the entries are transformed to be in the form of the triangular fuzzy number (TFN) of . *Definition 1*: (Zadeh, 1965) A triangular fuzzy number (TFN) of has a membership function of provided by where and stand for the fuzzy number's lower and upper bounds, respectively, while is the median value. Fig. 1 illustrates the TFN in its standard form. ![A graph of a function Description automatically generated](659c05f02c76c_media/media/image20.png) Fig. 1. Representation of a TFN . On the other hand, the scales of 1 to 9 that are given by the experts that corresponds to its TFN, reciprocal TFN and linguistic variables are given the following Table 3. Table 3. Linguistic variable for pairwise comparison of each criterion | Classical/Non-fuzzy Number | Triangular Fuzzy Number | Triangular Fuzzy Reciprocal Number | Linguistic Variables | | -------------------------- | ----------------------- | ---------------------------------- | -------------------------- | | 1 | (1, 1, 1) | (1, 1, 1) | Equally Important | | 3 | (2, 3, 4) | (1/4, 1/3, 1/2) | Moderate Important | | 5 | (4, 5, 6) | (1/6, 1/5, 1/4) | Strong Important | | 7 | (6, 7, 8) | (1/8, 1/7, 1/6) | Very Strong Important | | 9 | (9, 9, 9) | (1/9, 1/9, 1/9) | Extremely Strong Important | | 2 | (1, 2, 3) | (1/3, 1/2, 1) | Intermediate Values | | 4 | (3, 4, 5) | (1/5, 1/4, 1/3) | | | 6 | (5, 6, 7) | (1/7, 1/6, 1/5) | | | 8 | (7, 8, 9) | (1/9, 1/8, 1/7) | | Source: Kannan et al (2013). Thus, the PCM becomes a fuzzy PCM,as follows. (4) From that, the average of all factors provided by the experts which already in the form of TFN and reciprocal TFN are calculated. Step 4: Calculation of fuzzy geometric mean of each factor *i* is performed using the following Eq. (5). (5) where *n* is the number of factors. Step 5: Compute the fuzzy weight of each factor *i* using: (6) Step 6: Defuzzified the fuzzy weight to obtain the non-fuzzy weight, using: (7) Step 7: Normalized the non-fuzzy weight, so that the factor can be ranked, based on the following formula. (8) where is the normalized weight. Apart from that, the normalized weight of sub-factors is obtained by multiplying the normalized weight of each factor to the weight of each sub-factor. It should be noted that both factors and sub-factors are ranked from the highest value to the lowest value of the normalized weight. Hence, the highest value would be the most influence factor or sub-factor that contributes to the students’ career choice. # result and discussion This section provides some results and discussions in analysing the factors and sub-factors that influence the student’s career choice. Table 4 and 5, provides the PCM for factors and sub-factors respectively, and their consistency ratios of each expert. Table 4. Pairwise comparison matrix for factors for both experts and their consistency ratios | **Expert 1** | | | | | | | ------------------------------- | ------------------ | ------------------ | ------------- | ---------------- | --------------------- | | **Pair Comparison** | Parental Influence | Academic Influence | Self Interest | Work Environment | **Consistency Ratio** | | Parental Influence | 1 | 1/3 | 1/7 | 1/4 | 0.0854 | | Academic Influence | | 1 | 1/7 | 1/3 | | | Self Interest | 7 | 6 | 1 | 5 | | | Work Environment | 4 | 3 | 1/5 | 1 | | | **Expert 2** | | | | | | | **Pair Comparison Subfactor 2** | Parental Influence | Academic Influence | Self Interest | Work Environment | **Consistency Ratio** | | Parental Influence | 1 | 1/3 | 1/6 | 1/3 | 0.0605 | | Academic Influence | | 1 | 1/5 | 1/2 | | | Self Interest | 6 | 5 | 1 | 4 | | | Work Environment | 3 | 2 | 1/4 | 1 | | Table 5. Pairwise comparison matrices for sub-factors of each expert and their consistency ratios | **Expert 1** | | | | | | -------------------------------- | ------------------- | ---------------- | ---------- | --------------------- | | **Pair Comparison Sub-factor 1** | Parents Expectation | Family Tradition | Safety | **Consistency Ratio** | | Parents Expectation | 1 | 1/5 | 1/3 | 0.0836 | | Family Tradition | 5 | 1 | 4 | | | Safety | 3 | 1/4 | 1 | | | **Pair Comparison Sub-factor 2** | CGPA Level | Scholarship | Difficulty | **Consistency Ratio** | | CGPA Level | 1 | 1/2 | 2 | 0.0517 | | Scholarship | 2 | 1 | 2 | | | Difficulty | 1/2 | 1/2 | 1 | | | **Pair Comparison Sub-factor 3** | Passion | Workplace | Salary | **Consistency Ratio** | | Passion | 1 | 1/2 | 5 | 0.0518 | | Workplace | 2 | 1 | 5 | | | Salary | 1/5 | 1/5 | 1 | | | **Pair Comparison Sub-factor 4** | Gender | Race | Friends | **Consistency Ratio** | | Gender | 1 | 1 | 1/4 | 0.0177 | | Race | 1 | 1 | 1/6 | | | Friends | 4 | 6 | 1 | | | **Expert 2** | | | | | | **Pair Comparison Sub-factor 1** | Parents Expectation | Family Tradition | Safety | **Consistency Ratio** | | Parents Expectation | 1 | 7 | 5 | 0.0744 | | Family Tradition | 1/7 | 1 | 1/5 | | | Safety | 1/5 | 4 | 1 | | | **Pair Comparison Sub-factor 2** | CGPA Level | Scholarship | Difficulty | **Consistency Ratio** | | CGPA Level | 1 | 6 | 4 | 0.0521 | | Scholarship | 1/6 | 1 | 1/3 | | | Difficulty | 1/4 | 3 | 1 | | | **Pair Comparison Sub-factor 3** | Passion | Workplace | Salary | **Consistency Ratio** | | Passion | 1 | 1/7 | 1/5 | 0.0630 | | Workplace | 7 | 1 | 3 | | | Salary | 5 | 1/3 | 1 | | | **Pair Comparison Sub-factor 4** | Gender | Race | Friends | **Consistency Ratio** | | Gender | 1 | 1 | 2 | 0.0516 | | Race | 1 | 1 | 1 | | | Friends | 1/2 | 1 | 1 | | As shown in the above tables, the consistency ratio for each factor and sub-factor is less than 0.1, which means that the comparisons made by the experts are acceptable. Hence the calculation is proceeded. First, calculate the average of all values in the fuzzy PCM, which all the entries are in the form of triangular fuzzy numbers , as shown in the following Table 6 and 7. Table 6. Average pairwise comparison matrix for all experts | **Pair Comparison** | Parental Influence | Academic Influence | Self Interest | Work Environment | | ------------------- | ------------------ | ------------------ | ------------------ | ------------------ | | Parental Influence | (1, 1, 1) | (0.29, 0.42, 0.75) | (0.12, 0.13, 0.15) | (0.20, 0.25, 0.33) | | Academic Influence | (1.50, 2.50, 3.50) | (1, 1, 1) | (0.15, 0.18, 0.23) | (0.29, 0.42, 0.67) | | Self Interest | (6.50, 7.50, 8.50) | (4.50, 5.50, 6.50) | (1, 1, 1) | (3, 4, 5) | | Work Environment | (3, 4, 5) | (1.50, 2.50, 3.50) | (0.33, 0.27, 0.38) | (1, 1, 1) | Table 7. Average pairwise comparison matrix for sub-factors in the triangular fuzzy number form | **Pair Comparison Sub-factor 1** | Parents Expectation | Family Tradition | Safety | | -------------------------------- | ------------------- | ------------------ | ------------------ | | Parents Expectation | (1, 1, 1) | (3.08, 3.60, 4.13) | (2.13, 2.67, 3.25) | | Family Tradition | (2.06, 2.57, 3.08) | (1, 1, 1) | (1.58, 2.10, 2.63) | | Safety | (1.08, 1.60, 2.13) | (1.60, 2.13, 2.67) | (1, 1, 1) | | **Pair Comparison Sub-factor 2** | CGPA Level | Scholarship | Difficulty | | CGPA Level | (1, 1, 1) | (2.67, 3.25, 4.00) | (2, 3, 4) | | Scholarship | (0.57, 1.08, 1.60) | (1, 1, 1) | (0.63, 1.17, 1.75) | | Difficulty | (0.27, 0.38, 0.67) | (1.17, 1.75, 2.50) | (1, 1, 1) | | **Pair Comparison Sub-factor 3** | Passion | Workplace | Salary | | Passion | (1, 1, 1) | (0.23, 0.32, 0.58) | (2.08, 2.60, 3.13) | | Workplace | (3.50, 4.50, 5.50) | (1, 1, 1) | (3, 4, 5) | | Salary | (2.08, 2.60, 3.13) | (0.21, 0.27, 0.38) | (1, 1, 1) | | **Pair Comparison Sub-factor 4** | Gender | Race | Friends | | Gender | (1, 1, 1) | (1, 1, 1) | (0.60, 1.13, 1.67) | | Race | (1, 1, 1) | (1, 1, 1) | (0.57, 0.58, 0.60) | | Friends | (1.17, 1.75, 2.50) | (3, 3.50, 4) | (1, 1, 1) | Next, calculation of the fuzzy geometric mean of each factor and sub-factor are performed using the Equation (5). The results are presented in Table 8 and 9. Table 8. Fuzzy geometric mean for all factors. | Factors | | | | | ------------------- | ------ | ------ | ------ | | Parental Influence, | 0.2881 | 0.3437 | 0.4435 | | Academic Influence, | 0.5101 | 0.6611 | 0.8512 | | Self Interest, | 3.0606 | 3.5840 | 4.0769 | | Work Environment, | 1.1067 | 0.8165 | 1.0890 | | | 4.9655 | 5.4053 | 6.4606 | | | 0.2014 | 0.1850 | 0.1548 | Table 9. Fuzzy geometric means for all sub-factors. | Sub-factors of Factor 1 (Parental Influence) | | | | | -------------------------------------------- | ------ | ------ | ------ | | Parents Expectation, | 1.8712 | 2.1253 | 2.3756 | | Family Tradition, | 1.4836 | 1.7544 | 2.0078 | | Safety, | 1.2012 | 1.5037 | 1.7828 | | | 4.5561 | 5.3834 | 6.1662 | | | 0.2195 | 0.1858 | 0.1622 | | Sub-factors of Factor 2 (Academic Influence) | | | | | CGPA Level, | 1.7472 | 2.1363 | 2.5198 | | Scholarship, | 0.7095 | 1.0812 | 1.4095 | | Difficulty, | 0.6776 | 0.8690 | 1.1856 | | | 3.1343 | 4.0865 | 5.1149 | | | 0.3191 | 0.2447 | 0.1955 | | Sub-factors of Factor 3 (Self Interest) | | | | | Passion, | 0.7816 | 0.9419 | 1.2216 | | Workplace, | 2.1898 | 2.6207 | 3.0184 | | Salary, | 0.7571 | 0.8851 | 1.0543 | | | 3.7285 | 4.4477 | 5.2943 | | | 0.2682 | 0.2248 | 0.1889 | | Sub-factors of Factor 4 (Work Environment) | | | | | Gender, | 0.8434 | 1.0400 | 1.1856 | | Race, | 0.8298 | 0.8355 | 0.8434 | | Friends, | 1.5183 | 1.8297 | 2.1544 | | | 3.1916 | 3.7052 | 4.1835 | | | 0.3133 | 0.2699 | 0.2390 | Subsequently, Table 10 and 11 show the fuzzy weight, non-fuzzy weight, and normalized weight that have been calculated using Eqs. (6-8). Finally, from the normalized weight, the factors and subfactors and ranked. Table 10. Fuzzy weight, non-fuzzy weight, and normalized weight of all factors | **Factor** | **Fuzzy Weight** | **Non-Fuzzy Weight** | **Normalized Weight** | **Rank** | | ------------------ | ------------------------ | -------------------- | --------------------- | -------- | | Parental Influence | (0.0446, 0.0636, 0.0893) | 0.0658 | 0.0643 | 4 | | Academic Influence | (0.0790, 0.1223, 0.1714) | 0.1242 | 0.1214 | 3 | | Self Interest | (0.4737, 0.6631, 0.8210) | 0.6526 | 0.6378 | 1 | | Work Environment | (0.1713, 0.1511, 0.2193) | 0.1806 | 0.1765 | 2 | | SUM | 1.0232 | | | | Table 11. Fuzzy weight, non-fuzzy weight, and normalized weight of all sub-factors | **Factor** | **Weight of Factor** | **Sub-factor** | **Fuzzy Weight of Sub-factor** | **Weight of Sub-factor** | **Normalized Weight of Factor × Weight of Sub-factor** | **Normalized Weight of Sub-factor** | **Rank** | | ------------------ | -------------------- | ------------------- | ------------------------------ | ------------------------ | ------------------------------------------------------ | ----------------------------------- | -------- | | Parental Influence | 0.0658 | Parents Expectation | (0.3035, 0.3948, 0.5214) | 0.4066 | 0.0268 | 0.0251 | 10 | | | | Family Tradition | (0.2406, 0.3259, 0.4407) | 0.3357 | 0.0221 | 0.0207 | 11 | | | | Safety | (0.1948, 0.2793, 0.3913) | 0.2885 | 0.0190 | 0.0178 | 12 | | Academic Influence | 0.1242 | CGPA Level | (0.3416, 0.5228, 0.8040) | 0.5561 | 0.0691 | 0.0647 | 5 | | | | Scholarship | (0.1387, 0.2646, 0.4497) | 0.2843 | 0.0353 | 0.0330 | 8 | | | | Difficulty | (0.1325, 0.2127, 0.3783) | 0.2411 | 0.0299 | 0.0280 | 9 | | Self Interest | 0.6526 | Passion | (0.1476, 0.2118, 0.3276) | 0.2290 | 0.1494 | 0.1398 | 2 | | | | Workplace | (0.4136, 0.5892, 0.8096) | 0.6041 | 0.3942 | 0.3690 | 1 | | | | Salary | (0.1430, 0.1990, 0.2828) | 0.2083 | 0.1359 | 0.1272 | 3 | | Work Environment | 0.1806 | Gender | (0.2016, 0.2807, 0.3715) | 0.2846 | 0.0514 | 0.0481 | 6 | | | | Race | (0.1984, 0.2255, 0.2643) | 0.2294 | 0.0414 | 0.0388 | 7 | | | | Friends | (0.3629, 0.4938, 0.6750) | 0.5196 | 0.0938 | 0.0878 | 4 | | SUM | 1.0683 | | | | | | | # conclusion This study aims to analyse the main factors and sub-factors that influence graduated students’ career decisions using fuzzy AHP by selecting certain factors, which are parental influence, academic influence, personal interest, and work environment. Additionally, the study uses fuzzy AHP to rank the factors and sub-factors that affect the career decisions of graduates. The information utilized was gathered through interviews with two professionals; a psychological officer from the career and counselling department of UiTM Arau, Perlis, and a junior human resources executive officer from the human resources department of Safwa Clinic. The study's conclusions indicate that the most crucial factors are personal interest, followed by the work environment, academic influence, and parental influence. Meanwhile, the workplace is the sub-factor that matters the most, followed by passion, salary, friends, CGPA level, gender, race, scholarship, difficulty, parents’ expectations, family tradition, and safety. Future studies are advised to analyse the variables impacting students' profession choices using a more pertinent methodology, such as Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) or fuzzy inference system. On the other hand, future studies can also look into the variables impacting students' career decisions for particular faculties. Faculty mathematics students, for instance, can choose to shift occupations or go into actuarial science, statistics, or mathematics. Students from various faculties may hold varying views regarding the elements that influence their choice of employment. Therefore, more research can yield more accurate results regarding the elements that affect students' career decisions across various faculties. New factors and sub-factors, such as work-life balance, travel needs, abilities and skills, job opportunities, and personality-driven factors, can also be added by future researchers. These would aid in obtaining more precise outcomes. # acknowledgement The authors would like to acknowledge the support of Universiti Teknologi Mara (UiTM), Cawangan Perlis and Institute of Engineering Mathematics, UniMAP for providing the facilities support on this research. The authors would also like to express their gratitude to the anonymous referee for the constructive comments to improve this study. # Conflict of interest statement The authors agree that this research was conducted in the absence of any self-benefits, commercial or financial conflicts and declare the absence of conflicting interests with the funders. # References Afzal, H. A., Raza, S., Aamir Khan, R., & ul ain Ansari, N. (2018). Effect of family influence, personal interest and economic considerations on career choice amongst undergraduate students in higher educational institutions of Vehari, Pakistan. International Journal of Organizational Leadership, 7(2), 129–142. . Aziz, K. A. A., Idris, M. F. I. 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Information and Control, 8, 338-353. 1. \* Corresponding author. *E-mail address*: (Please do not type or edit anything here, our editors will do the work for you)