# Factors Affecting Social Science Students’ Career Choices: A Web-Based Cross-Sectional Study in Bangladesh

Authors: Sadia Sharmin Suhi; Md. Abdul Jabbar; Fariha Farjana; Nishad Nasrin; Md. Tanvir Hossain
Journal: Education Research International
 (2022)

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## Abstract

This web-based cross-sectional study aimed to investigate university students’ career choices and their determinants. Data were collected from four disciplines within the Social Science School at the Khulna University of Bangladesh. The findings from Pearson’s Chi-square revealed a significant association between career choice and age, sex, discipline, level of education, and socioeconomic status. Exploratory factor analysis indicated a three-factor solution, explaining the variance of over 50% and the overall reliability ofα = 0.748. The findings from a multinomial logistic regression showed that older and male students had a lower likelihood of becoming teachers, while Sociology students were more interested in teaching. Furthermore, career choices were substantially influenced by students’ level of education, job quality, job prospect, and job motive. Considering the global demand for specific skills and knowledge, universities should revise their curricula, integrating the cognitive domain of students with practical knowledge-based education in order to widen the horizon of employment options for university graduates.


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## Body

## 1. Introduction

In the last 50 years, the progress in higher education has been remarkable in Bangladesh. In the 1980s, there were only six public universities and less than 40,000 students [1]. In the following 20 years, the number of universities and students increased dramatically to over 30, including public and private universities, where the number of students rose to over 1,00,000 [1]. At present, there are 157 universities and 4,69,086 students, of which 25% are enrolled in different departments/disciplines within social science faculties/schools [2].However, after completing higher education and graduating, students need to plan their future careers. An individual’s career choice decision is subjective and situation-specific. In fact, career choices for students are mainly based on individuals’ interests and current job market settings. Literature claims that when selecting a specific career or job, security, and stability in employment [3, 4] and financial benefits [5] as well as job prospects, i.e., promotion, are also the key determinants for career choices [4]. Sometimes, culture and gender-specific roles define career choices. For example, some individuals may choose a profession that allows them to spend more time with families [4]; for instance, part-time hours are often deemed essential for potential happiness by women [6]. In Asian-American communities, the perception of teaching as a “women’s job” has led to cultural pressures on men to pursue high-paying jobs over teaching careers [7]. Moreover, family support factors, i.e., financial emotions, expectations, and responsibilities [8] as well as social status and prestige, also influence career choice [4]. Likewise, the influence of teachers and peer groups on career decisions cannot be ignored [4, 9]. The same can be said for age; generally, older students are more likely to consider practical issues and credentials when choosing careers, while younger students rely on imagination [10, 11]. Likewise, students from affluent backgrounds are more motivated to find decent careers than students from economically indigent families [12]. For example, pupils from well-educated, high-income families providing educational expenses are more likely to choose engineering as a future career [13], whereas people from disadvantaged families have less desire to succeed in their careers [14].Furthermore, students’ career aspirations are influenced by their academic majors and achievements [4]. Work and subject experiences positively impact the choice of career [6]. For example, some medical students, especially men, are more interested in cardiothoracic surgery [15–17], while others, women, in particular, prefer primary care specialties and nursing [18, 19]. In contrast, students from business studies backgrounds prefer private jobs to public/government jobs [11]. Although there are a few studies on university students’ career choices in Bangladesh [4, 11, 20], there has been no empirical study on the career choice of university students from social science backgrounds. Therefore, this study was designed to address the existing research gap and identify the determinants that stimulate demand for specific careers among university students in Bangladesh.


## 2. Theorizing Career Choice with Existing Literature

The core idea of rational choice theory (RCT) is that the action of an actor is subjected to a ‘cost and benefit’ analysis before acting, which purports to derive from an individual’s self-interest in maximizing benefits. The basic premises of RCT are derived from neo-classical economic theory, suggesting that self-maximizing individuals are utility-driven actors, unconstrained by others and social norms [21]. An individual actor’s rational choice is purposively carried out based on the current assets and possible consequences of alternative lines of action [22]. Moreover, the actor chooses a course of action that maximizes the long-term benefits while minimizing costs based on preferences and values [23]. It is important to note that an individual directs action to maximize the utility while confirming peer values and norms [24].Leaving aside the influence of “cost and benefit” on career decision, it is also apparent that an individual’s social aspects, i.e., human, financial, physical, and social capital, are associated with the development of human capital, e.g., career choice [25, 26]. explaining the influence of social capital, argued that social capital in different forms is a resource for a person within the family and the community, and that it is linked to an individual’s potential educational and professional success. For Coleman social capital within the home is the time and effort invested by parents, which is critical for an individual’s academic and professional growth. Moreover, an individual’s overall socioeconomic status (SES), e.g., human, and financial capital–parental education, occupation, and income–is also a strong predictor of an individual’s ultimate progress in life, including academic and professional. The personal characteristics of an individual, such as age, sex, education, learning capacity and faith, according to Coleman also influence intellectual progress and future professional lives. In addition, the social capital within the community, i.e., the social relationships and interactions, social values and cultural norms, and sources of information that exist in an individual’s social network affect the individual’s academic progress and professional career based on their qualifications and academic excellence (see Figure 1).Figure 1
Conceptual framework based on Coleman’s interpretation of rational choice (1990) and social capital (1988) theories.The aforementioned theories make it clear that a person thinks critically about the “cost and benefit” before acting on self-interest to maximize benefits. Previous studies suggest that individuals often decide on a career by considering the possible benefits and risks. For example, Wu et al. [27] and Han et al. [28]; in their respective studies, concluded that young people are often drawn to jobs that provide a challenge and ensure certain job qualities that motivate them to complete the work efficiently [4]. Likewise, Smith et al. [6] and Booth and Myers [29] observed that job prospects, including promotion opportunities, financial incentives, and social prestige [4], also determine the career decisions of university students. Job motive has also proven to be an essential determinant of career choice. Siddiky and Akter [4] found that the most critical issues encouraging university students to choose their future jobs were teacher inspiration and family motivation. Another study indicates that a desire to spend more time with family is a powerful motivator in selecting a career [3]. In fact, some students seek employment that allows a balance between work and family in order to satisfy both their professional and personal interests [8, 30]. Again, teachers’ motivation and peer group influence are crucial factors in an individual’s professional growth [9].In addition to the “cost and benefit” issues regarding a career choice, it is also evident that an individual’s career decision is influenced by personal characteristics and socioeconomic status (SES). For example, Gore et al. [14] and Kabil et al. [3] found age to be a critical factor in forming opinions and making career choices. Likewise, the sex of an individual influences career choices. Women often choose culturally approved feminine professions, such as teaching and similar jobs, which assure a secure future, a steady wage, and a flexible schedule that allows extra time with family [20, 31]. In contrast, men are advised to pursue high-paying jobs related to medicine, technology, and other challenging work, and are discouraged from teaching–which is considered “women’s work” [7, 11]. It was found by some studies that junior students were serious about their career choices [3], while senior students showed less concern about payment; they were more interested in working in the public sector [11, 18]. Aside from age, sex, and level of education, students’ experience in profession-related academic and technical fields substantially influences their career choices. Studies have revealed that academic majors and subject-related work experience positively impact career choices [4, 6, 11].As evidenced in earlier research, residence, socioeconomic background, and personal characteristics significantly impact students’ career-related decisions. For example, it was found that students from small towns were less concerned about high pay and were more interested in securing any type of job [18]. Similarly, it has been found that students from affluent families are more driven to acquire a respectable jobs than students from low-income families [12]. Moreover, students with more educated parents are more optimistic about their chances of success in professional life than those with less-educated parents. The latter were less motivated to achieve professional success [14].


## 3. Materials and Methods

### 3.1. Study Sites and Participants

The Khulna district is the third-largest metropolis in the southwestern region of Bangladesh [32], and this study was conducted at Khulna University, a public university located in Khulna district. Khulna University started its academic program on 31 August 1991 providing four academic disciplines to less than 100 undergraduate students, and now it provides 29 disciplines through eight different schools/faculties. Of the 6,965 students, 80% are enrolled in undergraduate programs, whereas male students accounted for 59% [33]. In this study, data were collected from students in four disciplines with the Social Science School, namely Economics Discipline (ED), Sociology Discipline (SD), Development Studies Discipline (DSD), and Mass Communication and Journalism Discipline (MCJD). Participants were recruited based on some necessary specifications: (i) studying at Khulna University, (ii) enrolled in one of the four disciplines within the Social Science School, (iii) in a regular undergraduate and postgraduate program, (iv) not a Term repeater (students who failed to attend the Term Final examination, and re-registered for the Term) or suspended from academic activities for academic cheating or other similar offenses. Based on the aforementioned criteria, a self-administered e-questionnaire (SAeQ) written in English, using a Google Form developed after an intensive review of the relevant literature, was forwarded to the class representatives for each academic year of the selected disciplines through online platforms/social media (i.e., Messenger, WhatsApp) and through e-mails. The class representatives and the participants were requested to forward the SAeQ to their respective academic groups and share it with their friends within the Social Science School. A total of 372 responses were recorded from which 304 were retained, and 68 were excluded due to incompleteness or repetitive responses. It is important to note that this study was conducted from late April to mid-May 2021, during the COVID-19 pandemic, the SAeQ was used to maintain “social distancing” to avoid “face-to-face” interviews and interaction. It is important to note that the SAeQ was pretested on 20 students, five from each Discipline within the Social Science School, to identify if there is any inconsistency or repetition of question items as well as to check the reliability of SAeQ to extract a valid response from the participants.Among the participants, 43.4% were aged between 22 and 23 years, while half (50.0%) were male (see Table1). Four out of five participants were Muslims (81.6%) and 50.3% were residents of urban areas. More than 40% of the participants were SD students (41.4%) and came from a family with medium SES. In addition, most of the participants were second (25%) and fourth-year (21%) students. A quarter of the participants were second-year students (25%), while postgraduate students accounted for 28.9%.Table 1
Characteristics of the participants and their association with career choice.
Variablesn (%)Choice of careerTest statistics (χ)2p valueOther jobsTeachingCivil serviceAge≤21106 (34.9)55 (43.7)25 (41.0)26 (22.2)14.793a0.005∗∗∗22–23132 (43.4)51 (40.5)23 (37.7)58 (49.6)24 ≥66 (21.7)20 (15.9)13 (21.3)33 (28.2)SexFemale152 (50)49 (38.9)46 (75.4)57 (48.7)22.053a0.001∗∗∗Male152 (50)77 (61.1)15 (24.6)60 (51.3)ReligionSanatan56 (18.4)24 (19.0)6 (9.8)26 (22.2)4.150a0.126Islam248 (81.6)102 (81.0)55 (90.2)91 (77.8)LocationRural153 (50.3)64 (50.8)26 (42.6)63 (53.8)2.038a0.361Urban151 (49.7)62 (49.2)35 (57.4)54 (46.2)DisciplineED66 (21.7)29 (23.0)14 (23.0)23 (19.7)12.800a0.046∗∗SD126 (41.4)39 (31.0)31 (50.8)56 (47.9)DSD54 (17.8)27 (21.4)6 (9.8)21 (17.9)MCJD58 (19.1)31 (24.6)10 (16.4)17 (14.5)Level of educationFirst year19 (6.3)5 (4.0)4 (6.6)10 (52.6)14.850a0.062∗Second year76 (25.0)32 (25.4)11 (18.0)33 (43.4)Third year56 (18.4)24 (19.0)17 (27.9)15 (26.8)Fourth year65 (21.4)35 (27.8)10 (16.4)20 (30.8)MSS/MDS88 (28.9)30 (23.8)19 (31.1)39 (44.3)SESLow (<7)35 (11.5)9 (7.1)7 (11.5)19 (16.2)7.960a0.093∗Medium (8–12)143 (47.0)65 (51.6)23 (47.0)55 (38.5)High (13>)126 (41.4)52 (41.3)31 (36.8)43 (34.1)Note.∗∗∗p < 0.01; ∗∗p < 0.05; ∗p < 0.10. EDEconomics Discipline; SDSociology Discipline, DSDDevelopment Studies Discipline; MCJDMass Communication and Journalism Discipline; MSS/MDSMaster of Social Science/Master of Development Studies; SESSocioeconomic status.


### 3.2. Ethical Statement

The Ethical Clearance Committee of Khulna University, Bangladesh approved this study (Reference No. KUECC–2022/02/02). The e-questionnaire contained an informed consent form in the first section, detailing participant’s right to withdraw from this web-based study without giving any explanation or providing any compensation. Moreover, the e-questionnaire also contained information regarding the anonymity of the participants and the confidentiality of the information. The participants did not receive any remuneration or compensation and participated voluntarily.


### 3.3. Measures

#### 3.3.1. Outcome Variable

The participants selected their potential careers from a list of professions developed following a review of the Bangladesh labor force survey [34] and other relevant studies [11,20]. The participants identified more than 20 potential careers, including “teaching,” “business/entrepreneurship,” “civil service,” “corporate careers,” “research and innovation,” and others. Three major career categories, i.e., “other jobs = 1,” “teaching = 2,” and “civil service = 3,” were identified and used for the bivariate and multivariate analyses (see Table 2).Table 2
Operational definitions of variables and their measurements.
VariablesDescriptionMeasurement scaleDependent variableCareer choiceThe profession that the participants were willing to pursue the following completion of higher educationNominal; 1 = other jobs; 2 = teaching; 3 = civil serviceIndependent variablesAgeAge of the participants at the time of the surveyContinuousSexBiological identification of the participantsNominal; 0 = female; 1 = maleReligionReligious affiliation of the participantsNominal; 0 =Sanatan; 1 = islamLocationThe residence of the participantsNominal; 0 = rural; 1 = urbanDisciplineThe department in the university where the participants were enrolled as studentsNominal; 1 = economics; 2 = sociology; 3 = development studies; 4 = Mass communication and journalismLevel of educationThe year/level of education the participants were enrolled during the surveyOrdinal; 1 = first year; 2 = second year; 3 = third year; 4 = fourth year; 5 = MSS/MDSFamily typeType of family based on nature of familyNominal; 1 = extended family; 2 = nuclear familyFamily sizeThe total number of family members of the participantsContinuousSESSocioeconomic status index measuredOrdinal; 1 = low (<7); 2 = medium (8–12); 3 = high (13>)Job qualityFactors related to a job prospectContinuousJob prospectsFuture surveillance-related factorsContinuousJob motiveFactors related to surroundings influenceContinuousNote. MSS/MDS. Master of Social Science/Master of Development Studies; SES. Socioeconomic status.


#### 3.3.2. Explanatory Variables

Based on previous studies on professional choice, several interpersonal and socioeconomic factors were evaluated as explanatory variables. Age [14], sex [11], religion [11], location [18], and educational track/discipline [4, 11] are all critical factors that influence career choice, according to prior research. Additionally, four basic pieces of information were used to measure the SES index, as suggested by Hollingshead [35]. The first variable was the education of parents–both father and mother–measured in years of schooling; this ranged from “0” (no formal education) to “17” (postgraduate degree). The education of parents was classified into five consecutive categories, with “1” assigned for “no formal education,” “2” for “primary education (1–5),” “3” for “secondary education (6–10),” “4” for “higher secondary education (11–12)” and “5” for “graduate and postgraduate education (13-above).” In this study, the participants reported 33 different types of occupation for their fathers; following the labor force survey [34], fathers’ occupations were re-categorized into five classes, with “1” assigned for “physically unfit or retired,” “2” for “agro farmer or manual worker,” “3” for “different service sector other than the government,” “4” for “professionals” and “5” for “business or entrepreneurship.” Father’s monthly income was divided into three consecutive categories, with ‘1’ assigned for “BDT ≤20,000,” “2” for “BDT 20,001–40,000” and “3” for “BDT ≥40,001.” Finally, the recoded variables were computed together, and the SES index was constructed. The SES scores were further categorized into “low,” “medium,” and “high” SES. In addition to using the exploratory factor analysis (EFA), three domains of the career choice index–job quality (creative work, work diversity, challenging work, and job availability), job prospects (promotion prospects, retirement rules, job incentives, social prestige, and high social demand), and job motive (motivation from teachers, vacation, encouragement from peers and time for family)-were developed from 39 “five-point” Likert-scale items.Following Greene [36] and Hosmer Jr. et al. [37]; a multinomial logit model was used to analyze how a student’s career choice was determined by a vector of explanatory variables. This model is just a further extension of the binary logit model, which can address more than two possible outcomes as a dependent variable. The multinomial logistic regression model is demonstrated below:(1)Pri=1=eβ1xi1+∑K=1K−1eβ1xi,(2)Pri=2=eβ2xi1+∑K=1K−1eβ1xi,(3)Pri=3=eβ3xi1+∑K=1K−1eβ1xi.In equation set 1–3 of the multinomial logit models,Pri=1 stands for the base category, i.e., “other jobs,” while Pri=2andPri=3 implying “teaching at different institutions” and “civil service,” respectively. Here, xi is the identical vector of the explanatory variable for the complete model and βi is the set of the regression coefficient. A detailed explanation of the explanatory variables is delineated in Table 2.


### 3.4. Analysis

In this study, the data were analyzed using the IBM SPSS Statistics, version 25.0 and STATA, version 13.0 for Windows, for statistical and econometric analysis. Descriptive statistics, i.e., percentage, and Pearson’s Chi-square (χ2) test of independence, were deployed, respectively, to describe the personal characteristics and SES of the participants, and to explore the association between career choices, personal characteristics, and SES. Exploratory factor analysis (EFA) was performed to ascertain a meaningful and effective factor structure for 39 “five-point” Likert-scale items. Finally, a multinomial logit model with marginal effect (ME) was used to estimate the vector of explanatory variables corresponding to each set of career choices for social science students.


## 3.1. Study Sites and Participants

The Khulna district is the third-largest metropolis in the southwestern region of Bangladesh [32], and this study was conducted at Khulna University, a public university located in Khulna district. Khulna University started its academic program on 31 August 1991 providing four academic disciplines to less than 100 undergraduate students, and now it provides 29 disciplines through eight different schools/faculties. Of the 6,965 students, 80% are enrolled in undergraduate programs, whereas male students accounted for 59% [33]. In this study, data were collected from students in four disciplines with the Social Science School, namely Economics Discipline (ED), Sociology Discipline (SD), Development Studies Discipline (DSD), and Mass Communication and Journalism Discipline (MCJD). Participants were recruited based on some necessary specifications: (i) studying at Khulna University, (ii) enrolled in one of the four disciplines within the Social Science School, (iii) in a regular undergraduate and postgraduate program, (iv) not a Term repeater (students who failed to attend the Term Final examination, and re-registered for the Term) or suspended from academic activities for academic cheating or other similar offenses. Based on the aforementioned criteria, a self-administered e-questionnaire (SAeQ) written in English, using a Google Form developed after an intensive review of the relevant literature, was forwarded to the class representatives for each academic year of the selected disciplines through online platforms/social media (i.e., Messenger, WhatsApp) and through e-mails. The class representatives and the participants were requested to forward the SAeQ to their respective academic groups and share it with their friends within the Social Science School. A total of 372 responses were recorded from which 304 were retained, and 68 were excluded due to incompleteness or repetitive responses. It is important to note that this study was conducted from late April to mid-May 2021, during the COVID-19 pandemic, the SAeQ was used to maintain “social distancing” to avoid “face-to-face” interviews and interaction. It is important to note that the SAeQ was pretested on 20 students, five from each Discipline within the Social Science School, to identify if there is any inconsistency or repetition of question items as well as to check the reliability of SAeQ to extract a valid response from the participants.Among the participants, 43.4% were aged between 22 and 23 years, while half (50.0%) were male (see Table1). Four out of five participants were Muslims (81.6%) and 50.3% were residents of urban areas. More than 40% of the participants were SD students (41.4%) and came from a family with medium SES. In addition, most of the participants were second (25%) and fourth-year (21%) students. A quarter of the participants were second-year students (25%), while postgraduate students accounted for 28.9%.Table 1
Characteristics of the participants and their association with career choice.
Variablesn (%)Choice of careerTest statistics (χ)2p valueOther jobsTeachingCivil serviceAge≤21106 (34.9)55 (43.7)25 (41.0)26 (22.2)14.793a0.005∗∗∗22–23132 (43.4)51 (40.5)23 (37.7)58 (49.6)24 ≥66 (21.7)20 (15.9)13 (21.3)33 (28.2)SexFemale152 (50)49 (38.9)46 (75.4)57 (48.7)22.053a0.001∗∗∗Male152 (50)77 (61.1)15 (24.6)60 (51.3)ReligionSanatan56 (18.4)24 (19.0)6 (9.8)26 (22.2)4.150a0.126Islam248 (81.6)102 (81.0)55 (90.2)91 (77.8)LocationRural153 (50.3)64 (50.8)26 (42.6)63 (53.8)2.038a0.361Urban151 (49.7)62 (49.2)35 (57.4)54 (46.2)DisciplineED66 (21.7)29 (23.0)14 (23.0)23 (19.7)12.800a0.046∗∗SD126 (41.4)39 (31.0)31 (50.8)56 (47.9)DSD54 (17.8)27 (21.4)6 (9.8)21 (17.9)MCJD58 (19.1)31 (24.6)10 (16.4)17 (14.5)Level of educationFirst year19 (6.3)5 (4.0)4 (6.6)10 (52.6)14.850a0.062∗Second year76 (25.0)32 (25.4)11 (18.0)33 (43.4)Third year56 (18.4)24 (19.0)17 (27.9)15 (26.8)Fourth year65 (21.4)35 (27.8)10 (16.4)20 (30.8)MSS/MDS88 (28.9)30 (23.8)19 (31.1)39 (44.3)SESLow (<7)35 (11.5)9 (7.1)7 (11.5)19 (16.2)7.960a0.093∗Medium (8–12)143 (47.0)65 (51.6)23 (47.0)55 (38.5)High (13>)126 (41.4)52 (41.3)31 (36.8)43 (34.1)Note.∗∗∗p < 0.01; ∗∗p < 0.05; ∗p < 0.10. EDEconomics Discipline; SDSociology Discipline, DSDDevelopment Studies Discipline; MCJDMass Communication and Journalism Discipline; MSS/MDSMaster of Social Science/Master of Development Studies; SESSocioeconomic status.


## 3.2. Ethical Statement

The Ethical Clearance Committee of Khulna University, Bangladesh approved this study (Reference No. KUECC–2022/02/02). The e-questionnaire contained an informed consent form in the first section, detailing participant’s right to withdraw from this web-based study without giving any explanation or providing any compensation. Moreover, the e-questionnaire also contained information regarding the anonymity of the participants and the confidentiality of the information. The participants did not receive any remuneration or compensation and participated voluntarily.


## 3.3. Measures

### 3.3.1. Outcome Variable

The participants selected their potential careers from a list of professions developed following a review of the Bangladesh labor force survey [34] and other relevant studies [11,20]. The participants identified more than 20 potential careers, including “teaching,” “business/entrepreneurship,” “civil service,” “corporate careers,” “research and innovation,” and others. Three major career categories, i.e., “other jobs = 1,” “teaching = 2,” and “civil service = 3,” were identified and used for the bivariate and multivariate analyses (see Table 2).Table 2
Operational definitions of variables and their measurements.
VariablesDescriptionMeasurement scaleDependent variableCareer choiceThe profession that the participants were willing to pursue the following completion of higher educationNominal; 1 = other jobs; 2 = teaching; 3 = civil serviceIndependent variablesAgeAge of the participants at the time of the surveyContinuousSexBiological identification of the participantsNominal; 0 = female; 1 = maleReligionReligious affiliation of the participantsNominal; 0 =Sanatan; 1 = islamLocationThe residence of the participantsNominal; 0 = rural; 1 = urbanDisciplineThe department in the university where the participants were enrolled as studentsNominal; 1 = economics; 2 = sociology; 3 = development studies; 4 = Mass communication and journalismLevel of educationThe year/level of education the participants were enrolled during the surveyOrdinal; 1 = first year; 2 = second year; 3 = third year; 4 = fourth year; 5 = MSS/MDSFamily typeType of family based on nature of familyNominal; 1 = extended family; 2 = nuclear familyFamily sizeThe total number of family members of the participantsContinuousSESSocioeconomic status index measuredOrdinal; 1 = low (<7); 2 = medium (8–12); 3 = high (13>)Job qualityFactors related to a job prospectContinuousJob prospectsFuture surveillance-related factorsContinuousJob motiveFactors related to surroundings influenceContinuousNote. MSS/MDS. Master of Social Science/Master of Development Studies; SES. Socioeconomic status.


### 3.3.2. Explanatory Variables

Based on previous studies on professional choice, several interpersonal and socioeconomic factors were evaluated as explanatory variables. Age [14], sex [11], religion [11], location [18], and educational track/discipline [4, 11] are all critical factors that influence career choice, according to prior research. Additionally, four basic pieces of information were used to measure the SES index, as suggested by Hollingshead [35]. The first variable was the education of parents–both father and mother–measured in years of schooling; this ranged from “0” (no formal education) to “17” (postgraduate degree). The education of parents was classified into five consecutive categories, with “1” assigned for “no formal education,” “2” for “primary education (1–5),” “3” for “secondary education (6–10),” “4” for “higher secondary education (11–12)” and “5” for “graduate and postgraduate education (13-above).” In this study, the participants reported 33 different types of occupation for their fathers; following the labor force survey [34], fathers’ occupations were re-categorized into five classes, with “1” assigned for “physically unfit or retired,” “2” for “agro farmer or manual worker,” “3” for “different service sector other than the government,” “4” for “professionals” and “5” for “business or entrepreneurship.” Father’s monthly income was divided into three consecutive categories, with ‘1’ assigned for “BDT ≤20,000,” “2” for “BDT 20,001–40,000” and “3” for “BDT ≥40,001.” Finally, the recoded variables were computed together, and the SES index was constructed. The SES scores were further categorized into “low,” “medium,” and “high” SES. In addition to using the exploratory factor analysis (EFA), three domains of the career choice index–job quality (creative work, work diversity, challenging work, and job availability), job prospects (promotion prospects, retirement rules, job incentives, social prestige, and high social demand), and job motive (motivation from teachers, vacation, encouragement from peers and time for family)-were developed from 39 “five-point” Likert-scale items.Following Greene [36] and Hosmer Jr. et al. [37]; a multinomial logit model was used to analyze how a student’s career choice was determined by a vector of explanatory variables. This model is just a further extension of the binary logit model, which can address more than two possible outcomes as a dependent variable. The multinomial logistic regression model is demonstrated below:(1)Pri=1=eβ1xi1+∑K=1K−1eβ1xi,(2)Pri=2=eβ2xi1+∑K=1K−1eβ1xi,(3)Pri=3=eβ3xi1+∑K=1K−1eβ1xi.In equation set 1–3 of the multinomial logit models,Pri=1 stands for the base category, i.e., “other jobs,” while Pri=2andPri=3 implying “teaching at different institutions” and “civil service,” respectively. Here, xi is the identical vector of the explanatory variable for the complete model and βi is the set of the regression coefficient. A detailed explanation of the explanatory variables is delineated in Table 2.


## 3.3.1. Outcome Variable

The participants selected their potential careers from a list of professions developed following a review of the Bangladesh labor force survey [34] and other relevant studies [11,20]. The participants identified more than 20 potential careers, including “teaching,” “business/entrepreneurship,” “civil service,” “corporate careers,” “research and innovation,” and others. Three major career categories, i.e., “other jobs = 1,” “teaching = 2,” and “civil service = 3,” were identified and used for the bivariate and multivariate analyses (see Table 2).Table 2
Operational definitions of variables and their measurements.
VariablesDescriptionMeasurement scaleDependent variableCareer choiceThe profession that the participants were willing to pursue the following completion of higher educationNominal; 1 = other jobs; 2 = teaching; 3 = civil serviceIndependent variablesAgeAge of the participants at the time of the surveyContinuousSexBiological identification of the participantsNominal; 0 = female; 1 = maleReligionReligious affiliation of the participantsNominal; 0 =Sanatan; 1 = islamLocationThe residence of the participantsNominal; 0 = rural; 1 = urbanDisciplineThe department in the university where the participants were enrolled as studentsNominal; 1 = economics; 2 = sociology; 3 = development studies; 4 = Mass communication and journalismLevel of educationThe year/level of education the participants were enrolled during the surveyOrdinal; 1 = first year; 2 = second year; 3 = third year; 4 = fourth year; 5 = MSS/MDSFamily typeType of family based on nature of familyNominal; 1 = extended family; 2 = nuclear familyFamily sizeThe total number of family members of the participantsContinuousSESSocioeconomic status index measuredOrdinal; 1 = low (<7); 2 = medium (8–12); 3 = high (13>)Job qualityFactors related to a job prospectContinuousJob prospectsFuture surveillance-related factorsContinuousJob motiveFactors related to surroundings influenceContinuousNote. MSS/MDS. Master of Social Science/Master of Development Studies; SES. Socioeconomic status.


## 3.3.2. Explanatory Variables

Based on previous studies on professional choice, several interpersonal and socioeconomic factors were evaluated as explanatory variables. Age [14], sex [11], religion [11], location [18], and educational track/discipline [4, 11] are all critical factors that influence career choice, according to prior research. Additionally, four basic pieces of information were used to measure the SES index, as suggested by Hollingshead [35]. The first variable was the education of parents–both father and mother–measured in years of schooling; this ranged from “0” (no formal education) to “17” (postgraduate degree). The education of parents was classified into five consecutive categories, with “1” assigned for “no formal education,” “2” for “primary education (1–5),” “3” for “secondary education (6–10),” “4” for “higher secondary education (11–12)” and “5” for “graduate and postgraduate education (13-above).” In this study, the participants reported 33 different types of occupation for their fathers; following the labor force survey [34], fathers’ occupations were re-categorized into five classes, with “1” assigned for “physically unfit or retired,” “2” for “agro farmer or manual worker,” “3” for “different service sector other than the government,” “4” for “professionals” and “5” for “business or entrepreneurship.” Father’s monthly income was divided into three consecutive categories, with ‘1’ assigned for “BDT ≤20,000,” “2” for “BDT 20,001–40,000” and “3” for “BDT ≥40,001.” Finally, the recoded variables were computed together, and the SES index was constructed. The SES scores were further categorized into “low,” “medium,” and “high” SES. In addition to using the exploratory factor analysis (EFA), three domains of the career choice index–job quality (creative work, work diversity, challenging work, and job availability), job prospects (promotion prospects, retirement rules, job incentives, social prestige, and high social demand), and job motive (motivation from teachers, vacation, encouragement from peers and time for family)-were developed from 39 “five-point” Likert-scale items.Following Greene [36] and Hosmer Jr. et al. [37]; a multinomial logit model was used to analyze how a student’s career choice was determined by a vector of explanatory variables. This model is just a further extension of the binary logit model, which can address more than two possible outcomes as a dependent variable. The multinomial logistic regression model is demonstrated below:(1)Pri=1=eβ1xi1+∑K=1K−1eβ1xi,(2)Pri=2=eβ2xi1+∑K=1K−1eβ1xi,(3)Pri=3=eβ3xi1+∑K=1K−1eβ1xi.In equation set 1–3 of the multinomial logit models,Pri=1 stands for the base category, i.e., “other jobs,” while Pri=2andPri=3 implying “teaching at different institutions” and “civil service,” respectively. Here, xi is the identical vector of the explanatory variable for the complete model and βi is the set of the regression coefficient. A detailed explanation of the explanatory variables is delineated in Table 2.


## 3.4. Analysis

In this study, the data were analyzed using the IBM SPSS Statistics, version 25.0 and STATA, version 13.0 for Windows, for statistical and econometric analysis. Descriptive statistics, i.e., percentage, and Pearson’s Chi-square (χ2) test of independence, were deployed, respectively, to describe the personal characteristics and SES of the participants, and to explore the association between career choices, personal characteristics, and SES. Exploratory factor analysis (EFA) was performed to ascertain a meaningful and effective factor structure for 39 “five-point” Likert-scale items. Finally, a multinomial logit model with marginal effect (ME) was used to estimate the vector of explanatory variables corresponding to each set of career choices for social science students.


## 4. Results

### 4.1. Characteristics of the Participants and their Association with Career Choices

Table1 shows the association between the personal characteristics and SES of the participants and their career choices. The findings suggest that students between 22 and 23 years of age (49.6%) were more interested in the civil service, while younger students (≤21 years) preferred teaching (41%) and other professions (43.7%) (χ2 = 14.793, p = 0.005). It was observed that male students prioritized the civil service (51.3%) and other jobs (61.1%), whereas female students were more interested in the teaching profession (75.4%) (χ2 = 22.053, p < 0.001). Students from SD showed interest in teaching (50.8%) and the civil service (47.9%), while students from ED, DSD, and MCJD exhibited greater interest in jobs other than teaching and the civil service (χ2 = 12.800, p = 0.046). Likewise, students from all four-year groups exhibited an interest in the civil service; however, fourth-year students opted for other job options (27.8%) compared to other year groups (χ2 = 14.850, p = 0.062). Moreover, students of higher socioeconomic status were more interested in teaching (36.8%) and other jobs (41.3%) than civil service (χ2 = 7.960, p = 0.093).


### 4.2. Exploring the Career Choice Index

EFA, using the maximum likelihood extraction method followed by the orthogonal rotation method (varimax rotation), was carried out in order to sort the critical items under a latent factor emphasized by the participants. Prior to the maximum likelihood extraction, principal components extraction was conducted in order to assess the number of factors, multicollinearity, and factorability of the correlation matrices [38, 39]. Kaiser’s criterion [40] and Cattell’s scree plot test [41] were used to determine the number of factors. However, Kaiser’s criterion was inconclusive based on eigenvalues as there were 11 factors with an eigenvalue of 1 and above. As reported by the scree plot test, a six-factor solution was allowed by taking into account the ‘upper’ scree, and ignoring the ‘lower’ one [42]. For a meaningful and consistent factor structure, a pattern coefficient of ≥0.50 and an internal consistency of ≥0.70 [43] was considered. Based on the loading, the items were sorted and grouped together. 26 items were removed because their loadings were less than 0.50.In accordance with the established criteria and, using maximum likelihood extraction with varimax rotation, a three-factor solution was retained from the exploratory factor analysis, which recommended the following dimensions: job quality, job prospect, and job motive (see Table3). For the three-factor solution, the Kaiser-Meyer-Olkin (KMO) was 0.77, while Bartlett’s test of sphericity was χ2 [78] = 1623.785, p < 0.001, signifying sampling adequacy. Factor 1, labeled as “job quality”, explains 20.4% of the total variance with Cronbach’s α = 0.833, contained four items that individuals often consider as an essential element of an ideal job. Factor 2, labeled as ‘job prospect’, consisted of five items and explained 16.2% of the variance with an internal consistency of 0.772. The third and final factor comprised four items, labeled as ‘job motive’-explaining 15.5% of the variance with Cronbach’s α = 0.725. Cumulatively, the three-factor measurement of career choices explained 52.1% of the total variance, with overall Cronbach’s α = 0.748.Table 3
Factor loadings, communalities (h2), Cronbach’s alpha (α) and percentages of total variance for maximum likelihood extraction with varimax rotation (n = 304).
Sl. No.ItemsFactorh2Job qualityJob prospectJob motive09Creative work0.8650.30710Work diversity0.8210.39208Challenging work0.8020.40907Job availability0.5100.65315Retirement policy0.7040.75122Social prestige0.6560.68606Promotion prospects0.6070.48818High social demand0.5680.76604Job incentives0.5480.32932Motivation from teacher0.6840.48614Vacation0.6560.40833Encouragement from peers0.6130.50028Time for family0.5910.591% of variance20.35416.16615.535Cronbach’sα0.8330.7720.725


### 4.3. Determinants of Career Choice

Table4 delineates the coefficient of the multinomial logit regression model; the robust standard error of the respective variables was added in the parenthesis. In the very first stage, multicollinearity and heteroscedasticity tests were executed. The test result confirmed that there was no issue of multicollinearity since the mean-variance inflation factor (VIF) score was 2.32. However, the heteroscedasticity issue was present in the model (χ2 = 3.83, p < 0.050). Therefore, to minimize the bias of the estimated model, the robust standard error was calculated, as reported in Table 4. In this model, “other jobs” was considered the reference category for differentiating the relative chance of choosing one of the other two career preferences from the base category. Since the coefficient of the multinomial regression analysis could not be interpreted in analysis with heteroscedasticity, Table 5 represents the ME of the multinomial regression model.Table 4
Multinomial logistic regression showing the coefficient with a 9% confidence interval of teaching and other career choices of the graduates of Social Science School.
VariablesTeachingCivil serviceCoef. (SE)Coef. (SE)Age<21RC22–−23−2.87∗∗∗ (0.78)-0.22 (0.46)24>−0.83 (0.83)1.32∗∗ (0.51)SexFemaleRCMale−1.75∗∗∗ (0.56)−0.22 (0.32)LocationRuralRCUrban1.33∗∗ (0.57)0.30 (0.33)DisciplineEDSD2.72∗∗∗ (0.81)0.85∗ (0.47)DSD−0.08 (0.97)−0.26 (0.50)MCJD0.94 (0.74)0.66 (0.57)Level of educationFirst yearRCSecond year−2.57∗∗ (1.14)−0.96 (0.76)Third year0.0983 (1.17)−1.16 (0.80)Fourth year−1.97∗ (1.19)−1.41∗ (0.79)MSS/MDS−0.24 (1.13)−0.38 (0.78)SESLowRCMedium−152∗ (0.78)−1.17∗∗ (0.52)High−1.54∗∗ (0.83)−1.16∗∗ (0.56)Job quality−1.75∗∗∗ (0.32)−1.12∗∗∗ (0.21)Job prospect−0.41 (0.36)0.77∗∗∗ (0.23)Job motive3.05∗∗∗ (0.56)0.83∗∗∗ (0.23)Note.∗∗∗p < 0.01; ∗∗p < 0.05; ∗p < 0.10. SEStandard error; RCReference category; EDEconomics Discipline; SDSociology Discipline, DSDDevelopment Studies Discipline; MCJDMass Communication and Journalism Discipline; MSS/MDSMaster of Social Science/Master of Development Studies; SESSocioeconomic status.Table 5
Marginal effect of the multinomial logit model.
VariablesBase (other jobs)TeachingCivil serviceMarginal effect (dy/dx)Marginal effect (dy/dx)Marginal effect (dy/dx)Age<21RC22–230.11∗ (0.06)−0.21∗∗∗ (0.05)0.09947 (0.07)24>−0.12∗ (0.06)−0.15∗∗ (0.06)0.27∗∗∗ (0.07)SexFemaleRCMale0.08∗ (0.05)−0.13∗∗∗ (0.04)0.05 (0.05)LocationRuralRCUrban−0.08 (0.05)0.09∗∗ (0.04)−0.02 (0.05)DisciplineEDRCSD−0.18∗∗∗ (0.06)0.18∗∗∗ (0.05)0.001 (0.07)DSD0.04 (0.08)0.0045 (0.06)−0.04 (0.08)MCJD−0.11 (0.08)0.03 (0.04)0.07 (0.08)Level of educationFirst yearRCSecond year0.17∗ (0.0987)−0.14∗ (0.08)−0.03 (0.11)Third year0.12 (0.10)0.08 (0.09)−0.1996∗ (0.11)Fourth year0.23∗∗ (0.10)−0.08 (0.09)−0.14 (0.11)MSS/MDS0.05 (0.10)0.003 (0.08)−0.05 (0.11)SESLowRCMedium0.17 (0.06)−0.06 (0.06)−0.11 (0.08)High0.17 (0.07)−0.06 (0.06)−0.11 (0.08)Job quality0.18∗∗∗ (0.02)−0.08∗∗∗ (0.02)−0.0982∗∗∗ (0.02)Job prospect−0.08∗∗ (0.03)−0.07∗∗∗ (0.02)0.15∗∗∗ (0.03)Job motive−0.18∗∗∗ (0.03)0.20∗∗∗ (0.03)−0.02 (0.03)Note. delta− method standard error in the parenthesis.∗∗∗p < 0.01; ∗∗p < 0.05; ∗p < 0.10. RCReference category; EDEconomics Discipline; SDSociology Discipline, DSDDevelopment Studies Discipline; MCJDMass Communication and Journalism Discipline; MSS/MDSMaster of Social Science/Master of Development Studies; SESSocioeconomic status.In Table5, “other jobs” was compared with “teaching” and “civil service.” The findings show that for students aged 22–23 years, their probability of choosing teaching as a career choice was reduced by 21% (ME: 0.21; p < 0.001) compared to those aged 21 and below. Again, in comparison to other professions, the probability of students aged over 24 years entering the teaching profession was 15% (p < 0.014) lower than that of students aged 21 and below. Furthermore, compared to the base category, students aged 24 and above preferred the civil service by 26% (p < 0.001) more than the base age category. Interestingly, the career preferences of male and female undergraduate and postgraduate students were different. For instance, compared to the base category (other jobs), the probability of male students choosing to teach as their career was 13% (p < 0.001) less than that of female students. The likelihood of students from urban areas preferring teaching was 9% (p < 0.04) more than students from rural areas. In addition, students from SD preferred teaching (ME: 0.18; p < 0.01) to a greater extent than students from other disciplines within the Social Science School. Students in the second year preferred teaching by 14% (p < 0.08) more than the first-year students. However, the probability of third-year students preferring the civil service was 19% (p < 0.07) less than for other jobs. Regarding job quality (ME: −0.08; p < 0.001) and job prospects (ME: −0.07; p < 0.01), students preferred other jobs than teaching. However, job motive has a favorable influence on choosing the teaching profession over other jobs; it was 20% (p < 0.001) more for selecting teaching as a career than for other jobs. A higher score for job prospects increased the probability of students choosing the civil service by 15% (p < 0.03) more than other jobs. However, students’ perceptions of better job quality scores decreased (ME: −0.0982; p < 0.02) their likelihood of choosing the civil service as a career option compared to other jobs.


## 4.1. Characteristics of the Participants and their Association with Career Choices

Table1 shows the association between the personal characteristics and SES of the participants and their career choices. The findings suggest that students between 22 and 23 years of age (49.6%) were more interested in the civil service, while younger students (≤21 years) preferred teaching (41%) and other professions (43.7%) (χ2 = 14.793, p = 0.005). It was observed that male students prioritized the civil service (51.3%) and other jobs (61.1%), whereas female students were more interested in the teaching profession (75.4%) (χ2 = 22.053, p < 0.001). Students from SD showed interest in teaching (50.8%) and the civil service (47.9%), while students from ED, DSD, and MCJD exhibited greater interest in jobs other than teaching and the civil service (χ2 = 12.800, p = 0.046). Likewise, students from all four-year groups exhibited an interest in the civil service; however, fourth-year students opted for other job options (27.8%) compared to other year groups (χ2 = 14.850, p = 0.062). Moreover, students of higher socioeconomic status were more interested in teaching (36.8%) and other jobs (41.3%) than civil service (χ2 = 7.960, p = 0.093).


## 4.2. Exploring the Career Choice Index

EFA, using the maximum likelihood extraction method followed by the orthogonal rotation method (varimax rotation), was carried out in order to sort the critical items under a latent factor emphasized by the participants. Prior to the maximum likelihood extraction, principal components extraction was conducted in order to assess the number of factors, multicollinearity, and factorability of the correlation matrices [38, 39]. Kaiser’s criterion [40] and Cattell’s scree plot test [41] were used to determine the number of factors. However, Kaiser’s criterion was inconclusive based on eigenvalues as there were 11 factors with an eigenvalue of 1 and above. As reported by the scree plot test, a six-factor solution was allowed by taking into account the ‘upper’ scree, and ignoring the ‘lower’ one [42]. For a meaningful and consistent factor structure, a pattern coefficient of ≥0.50 and an internal consistency of ≥0.70 [43] was considered. Based on the loading, the items were sorted and grouped together. 26 items were removed because their loadings were less than 0.50.In accordance with the established criteria and, using maximum likelihood extraction with varimax rotation, a three-factor solution was retained from the exploratory factor analysis, which recommended the following dimensions: job quality, job prospect, and job motive (see Table3). For the three-factor solution, the Kaiser-Meyer-Olkin (KMO) was 0.77, while Bartlett’s test of sphericity was χ2 [78] = 1623.785, p < 0.001, signifying sampling adequacy. Factor 1, labeled as “job quality”, explains 20.4% of the total variance with Cronbach’s α = 0.833, contained four items that individuals often consider as an essential element of an ideal job. Factor 2, labeled as ‘job prospect’, consisted of five items and explained 16.2% of the variance with an internal consistency of 0.772. The third and final factor comprised four items, labeled as ‘job motive’-explaining 15.5% of the variance with Cronbach’s α = 0.725. Cumulatively, the three-factor measurement of career choices explained 52.1% of the total variance, with overall Cronbach’s α = 0.748.Table 3
Factor loadings, communalities (h2), Cronbach’s alpha (α) and percentages of total variance for maximum likelihood extraction with varimax rotation (n = 304).
Sl. No.ItemsFactorh2Job qualityJob prospectJob motive09Creative work0.8650.30710Work diversity0.8210.39208Challenging work0.8020.40907Job availability0.5100.65315Retirement policy0.7040.75122Social prestige0.6560.68606Promotion prospects0.6070.48818High social demand0.5680.76604Job incentives0.5480.32932Motivation from teacher0.6840.48614Vacation0.6560.40833Encouragement from peers0.6130.50028Time for family0.5910.591% of variance20.35416.16615.535Cronbach’sα0.8330.7720.725


## 4.3. Determinants of Career Choice

Table4 delineates the coefficient of the multinomial logit regression model; the robust standard error of the respective variables was added in the parenthesis. In the very first stage, multicollinearity and heteroscedasticity tests were executed. The test result confirmed that there was no issue of multicollinearity since the mean-variance inflation factor (VIF) score was 2.32. However, the heteroscedasticity issue was present in the model (χ2 = 3.83, p < 0.050). Therefore, to minimize the bias of the estimated model, the robust standard error was calculated, as reported in Table 4. In this model, “other jobs” was considered the reference category for differentiating the relative chance of choosing one of the other two career preferences from the base category. Since the coefficient of the multinomial regression analysis could not be interpreted in analysis with heteroscedasticity, Table 5 represents the ME of the multinomial regression model.Table 4
Multinomial logistic regression showing the coefficient with a 9% confidence interval of teaching and other career choices of the graduates of Social Science School.
VariablesTeachingCivil serviceCoef. (SE)Coef. (SE)Age<21RC22–−23−2.87∗∗∗ (0.78)-0.22 (0.46)24>−0.83 (0.83)1.32∗∗ (0.51)SexFemaleRCMale−1.75∗∗∗ (0.56)−0.22 (0.32)LocationRuralRCUrban1.33∗∗ (0.57)0.30 (0.33)DisciplineEDSD2.72∗∗∗ (0.81)0.85∗ (0.47)DSD−0.08 (0.97)−0.26 (0.50)MCJD0.94 (0.74)0.66 (0.57)Level of educationFirst yearRCSecond year−2.57∗∗ (1.14)−0.96 (0.76)Third year0.0983 (1.17)−1.16 (0.80)Fourth year−1.97∗ (1.19)−1.41∗ (0.79)MSS/MDS−0.24 (1.13)−0.38 (0.78)SESLowRCMedium−152∗ (0.78)−1.17∗∗ (0.52)High−1.54∗∗ (0.83)−1.16∗∗ (0.56)Job quality−1.75∗∗∗ (0.32)−1.12∗∗∗ (0.21)Job prospect−0.41 (0.36)0.77∗∗∗ (0.23)Job motive3.05∗∗∗ (0.56)0.83∗∗∗ (0.23)Note.∗∗∗p < 0.01; ∗∗p < 0.05; ∗p < 0.10. SEStandard error; RCReference category; EDEconomics Discipline; SDSociology Discipline, DSDDevelopment Studies Discipline; MCJDMass Communication and Journalism Discipline; MSS/MDSMaster of Social Science/Master of Development Studies; SESSocioeconomic status.Table 5
Marginal effect of the multinomial logit model.
VariablesBase (other jobs)TeachingCivil serviceMarginal effect (dy/dx)Marginal effect (dy/dx)Marginal effect (dy/dx)Age<21RC22–230.11∗ (0.06)−0.21∗∗∗ (0.05)0.09947 (0.07)24>−0.12∗ (0.06)−0.15∗∗ (0.06)0.27∗∗∗ (0.07)SexFemaleRCMale0.08∗ (0.05)−0.13∗∗∗ (0.04)0.05 (0.05)LocationRuralRCUrban−0.08 (0.05)0.09∗∗ (0.04)−0.02 (0.05)DisciplineEDRCSD−0.18∗∗∗ (0.06)0.18∗∗∗ (0.05)0.001 (0.07)DSD0.04 (0.08)0.0045 (0.06)−0.04 (0.08)MCJD−0.11 (0.08)0.03 (0.04)0.07 (0.08)Level of educationFirst yearRCSecond year0.17∗ (0.0987)−0.14∗ (0.08)−0.03 (0.11)Third year0.12 (0.10)0.08 (0.09)−0.1996∗ (0.11)Fourth year0.23∗∗ (0.10)−0.08 (0.09)−0.14 (0.11)MSS/MDS0.05 (0.10)0.003 (0.08)−0.05 (0.11)SESLowRCMedium0.17 (0.06)−0.06 (0.06)−0.11 (0.08)High0.17 (0.07)−0.06 (0.06)−0.11 (0.08)Job quality0.18∗∗∗ (0.02)−0.08∗∗∗ (0.02)−0.0982∗∗∗ (0.02)Job prospect−0.08∗∗ (0.03)−0.07∗∗∗ (0.02)0.15∗∗∗ (0.03)Job motive−0.18∗∗∗ (0.03)0.20∗∗∗ (0.03)−0.02 (0.03)Note. delta− method standard error in the parenthesis.∗∗∗p < 0.01; ∗∗p < 0.05; ∗p < 0.10. RCReference category; EDEconomics Discipline; SDSociology Discipline, DSDDevelopment Studies Discipline; MCJDMass Communication and Journalism Discipline; MSS/MDSMaster of Social Science/Master of Development Studies; SESSocioeconomic status.In Table5, “other jobs” was compared with “teaching” and “civil service.” The findings show that for students aged 22–23 years, their probability of choosing teaching as a career choice was reduced by 21% (ME: 0.21; p < 0.001) compared to those aged 21 and below. Again, in comparison to other professions, the probability of students aged over 24 years entering the teaching profession was 15% (p < 0.014) lower than that of students aged 21 and below. Furthermore, compared to the base category, students aged 24 and above preferred the civil service by 26% (p < 0.001) more than the base age category. Interestingly, the career preferences of male and female undergraduate and postgraduate students were different. For instance, compared to the base category (other jobs), the probability of male students choosing to teach as their career was 13% (p < 0.001) less than that of female students. The likelihood of students from urban areas preferring teaching was 9% (p < 0.04) more than students from rural areas. In addition, students from SD preferred teaching (ME: 0.18; p < 0.01) to a greater extent than students from other disciplines within the Social Science School. Students in the second year preferred teaching by 14% (p < 0.08) more than the first-year students. However, the probability of third-year students preferring the civil service was 19% (p < 0.07) less than for other jobs. Regarding job quality (ME: −0.08; p < 0.001) and job prospects (ME: −0.07; p < 0.01), students preferred other jobs than teaching. However, job motive has a favorable influence on choosing the teaching profession over other jobs; it was 20% (p < 0.001) more for selecting teaching as a career than for other jobs. A higher score for job prospects increased the probability of students choosing the civil service by 15% (p < 0.03) more than other jobs. However, students’ perceptions of better job quality scores decreased (ME: −0.0982; p < 0.02) their likelihood of choosing the civil service as a career option compared to other jobs.


## 5. Discussion

In developing countries like Bangladesh, students are usually pressured by their family, community, and social surroundings to choose a professional career [4, 11, 20]. Generally, families invest in education for their children to form human capital, expecting to safeguard better livelihood options for the household as well as for the children [26]. Hence, it is expected in the context of Bangladesh that university students will secure a better professional career to ensure prosperity in the future [11]. However, students’ choice of career depends on various exogenous and endogenous factors. In this study, the aim was to identify the possible career options and their determinants for students in the Social Science School of a public university in Bangladesh.This study found that students between 22 and 23 years of age were less likely to select teaching or the civil service, whereas students aged above 24 years were more likely to choose the civil service over teaching and other jobs. Zhao et al. [44]; reviewing the role of age in an entrepreneurial career, observed similar findings: young people were more uncertain about career choices, whereas older people were more determined about their careers. Generally, as students become older, they recognize the social standing and dignity that comes with joining the civil service in Bangladesh, as evident in this study; therefore, they are increasingly interested in doing so [11, 20]. Ko and Jun [45] identified that for Asian undergraduate students, both job security and salary structure motivated them to select career in the public sector. In Canada, on the other hand, among the millennial generation, it is the high ethical standards, progressive working environment, divergent colleagues, and social responsibilities of the civil service that significantly influence students to choose it over other career options [46].As with age, it is evident from this study that male and female students have divergent perceptions regarding career choices; where female students were more interested in the teaching profession, while male students opted for jobs other than teaching. In a patriarchal society like Bangladesh, teaching is considered a more relaxing and respectable job for women, that provides a steady wage in a flexible schedule with a secured future. Moreover, teaching is considered a feminine profession in traditional patriarchal culture that allows women to spend additional time with family to take care of its members [11, 20, 31]. This finding contradicts that of Bieri Buschor et al. [47] and Buday et al. [48]. Buday et al. [48]; for example, found no significant relationship between career choice and sex, whereas Bieri Buschor et al. [47] found that female students in Switzerland preferred more scientific professions than their male contemporaries. In contrast, men are considered the ultimate breadwinners, and it is culturally easy for men to migrate for livelihood reasons. Thus, men were pressured by their family members and peers to choose higher-paying and more challenging jobs than teaching, which was seen as “women’s work” from a gendered perspective [7, 11, 18].In this study, it was found that urban students had a higher likelihood of becoming teachers than rural students, which is in contrast to other jobs. In the context of Bangladesh, city-dwelling students are more engaged in academic activities and perform better in various public examinations [49–52]. Due to circumstantial differences with rural students, urban students were more interested in teaching than in other professions that offered good life with respect from society. Besides, students in urban areas are more likely to receive private tuition than their rural counterparts [53], for which around 30–60% of household income is spent [54]; thus, they may have developed a positive perception of teaching as a steady source of income with social acceptance. Akosah-Twumasi et al. [55], however, identified professional prestige as the most important deciding factor in career decisions, particularly among urban Asian youth, over higher remuneration. In contrast, Gąsiorowski et al. [18] found that small-town students were less concerned about high pay and were more interested in serving people through primary care specialization in the medical profession.The findings also suggest that educational background or academic track significantly influenced the career choices of university students. For example, students from Sociology backgrounds seemingly preferred teaching and were not interested in other jobs. A possible explanation for such behavior lies within subject-related skills that may have widened high employment possibilities in teaching; therefore, Sociology students are more likely to select teaching. This finding is aligned with previous studies in Bangladesh [4, 11] and other parts of the world [6, 56]. In fact, students’ career choices are strongly associated with their academic track or subject experiences, which create a favorable sense of security and ease their employment choices [6].It is also evident that students’ level of education has a considerable influence on career choice. For instance, second-year students were less interested in ‘teaching’ as a career option than their first-year contemporaries, whereas third-year students were less likely to prefer the civil service to other jobs. Second-year students, considering their academic grades–an important prerequisite for the teaching profession, decided that it would be more practical to pursue other jobs than teaching. As a result, they were less likely to enter the teaching profession than first-year students. A similar finding was observed by Kabil et al. [3]; they suggested that students from different year groups may differ in opinions when selecting a career. Likewise, Gąsiorowski et al. [18] found that final-year students were less concerned about higher payments and more concerned about securing jobs in the public sector.In addition to personal issues, it is apparent that job quality, job prospect, and job motives significantly influence the career choice of social science students in Bangladesh. The findings indicate that “job quality” has a negative influence on choosing teaching or the civil service, while “job prospect” has a negative effect on choosing teaching profession but a positive impact on choosing the civil service. In contrast, “job motive” positively influences the choice of teaching. These results complement the existing literature. For example, Han et al. [28] and Wu et al. [27] found that students often pursue careers that assure job quality, i.e., diversified jobs that allows creativity and professional growth. Likewise, it has been observed that students in medicine, nursing, and other healthcare-related subjects often choose their jobs based on “job prospect,” i.e., social prestige, financial incentives, and promotion prospects [16, 57]. Sundly and Galway [13] observed that engineering students often select their careers based on social prestige or social value, while some medical students emphasize high payment over social prestige or job prospects [17], especially those involved in emergency medicine [15]. Regarding “job motive,” previous studies on university students in Bangladesh showed that students’ career choice was substantially influenced by inspiration from their instructors/teachers’ as well as by the scope of spending quality time with family [4, 11]. In addition to encouragement from teachers, it has also been found that both medical and finance students are influenced by their academic supervisors [16] and peers [58] when making career choices. Furthermore, the influence of family advice/encouragement to take a particular job that allow more time with family cannot be denied [13].


## 6. Strengths and Limitations

In this study, a reasonable assessment based on systematic statistical and econometric analysis was executed in order to identify the rationale for career choice; it offers a foundation for understanding university students’ career choice dynamics in Bangladesh. This research took into account a variety of factors, including students’ age, sex, level of education, discipline, and socioeconomic background. In addition, various tangible rewards were also considered in this study to identify the influencing factors of career choice, such as job quality (work diversity, job availability), job prospects (retirement policies, social prestige, promotion prospects, high social demand, job incentives), and job motive (motivation from teachers, vacation, peer encouragement, family time). Yet the authors encourage readers to be cautious in generalizing the findings of this study. The results are based on a public university student, and it is a cross-sectional study from which a causal conclusion may not be drawn. It was also impossible to regulate the socioeconomic background. Furthermore, nonresponse from specific participants may have resulted in sample bias, making it difficult to assess whether participants’ career choices were always influenced by their age, sex, religion, educational background, or other socioeconomic factors. The use of nonprobability sampling may also have reduced the generalizability of findings. Therefore, more extensive empirical research through mixed methods is required in order to understand the changing aspects of career choice for social science students in Bangladeshi universities.


## 7. Conclusions and Recommendations

Aiming to identify potential career choices and their determinants for students at the Social Science School of a public University in Bangladesh, the findings of this cross-sectional study revealed that majority of social science students preferred the civil service over teaching and other career options. It was also apparent that the career choice of university students was primarily determined by their age, sex, level of education, and discipline. Besides, some other career choice indicators, such as job quality, prospect, and motive also determined career decisions. Considering the outcomes of the study, it is strongly suggested that government policymakers should consider various aspects, including personal and socioeconomic background and specialization of knowledge and skills, when planning employment opportunities for university students, given that these issues substantially influence career choices in Bangladesh. Furthermore, the government and authorities of both public and private universities should critically examine current job requirements and take necessary action to revise their existing academic curriculum in order to meet the criteria of the potential employment standards. The arrangement of job fairs, clarification of employers’ expectations, showcasing the benefits for the employee, and specification of work environment safety are also recommended. Additionally, both government and non-governmental organizations should make sure a congenial working environment that would remove the gender biases in employability and reduce the socio-cultural barriers in career choices. Ultimately, the study has emphasized the necessity for future research, particularly a mixed-method approach, to conduct a more extensive investigation into the issues considered by university students when selecting a career.



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