research paper for career path

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• Published: 01 December 2020

Exploring the factors that influence the career decision of STEM students at a university in South Africa

• Ethel Ndidiamaka Abe   ORCID: orcid.org/0000-0003-1670-6801 1 &
• Vitallis Chikoko   ORCID: orcid.org/0000-0001-6135-6172 1  

International Journal of STEM Education volume  7 , Article number:  60 ( 2020 ) Cite this article

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Science, Technology, Engineering, and Mathematics (STEM) educators and stakeholders in South Africa are interested in the ways STEM students make their career decisions because of the shortages in these critical skills. Although various factors including family, teachers, peers, and career interest have been reported as determinants of career decision-making, there is a scarcity of studies that have qualitatively explored the levels of influences of any of these factors in the South African context. The main aim of this study was to investigate the factors that influence career decision-making among STEM student majors in a South African university. By better understanding students’ viewpoint on these factors, educators and policymakers can assist students in making career decisions that fit their experiences, personality, and expectations. Students in their 1st, 2nd, 3rd, and 4th year of study respectively, were invited to respond to a semi-structured questionnaire about the factors that were influential in their decision to pursue a career in STEM. A total of 203 texts (response rate: 63%) were qualitatively analyzed utilising a hermeneutic phenomenology approach to traditional content analysis, whereby themes develop inductively from the data.

We used a hermeneutic phenomenological method to traditional content analysis to examine the factors influencing participants’ career decision-making. Peer interrogation, modified member verification, compact description, code-recode tactics, and assessment trails were engaged to confirm quality and rigour. Three key results emerged, namely interpersonal, intrapersonal, and career outcomes expectancy. The perceptions of STEM students of their career decision-making in the South African context are more multifaceted than reported previously. The insights could inform policies to counter skills shortages in the STEM area.

Conclusions

In this exploratory study, we gave attention to describing the various ranges of students’ perceptions and experiences regarding their career decision-making. Several students reported, among other factors, that their families, personality, and expectations played influential roles in their career decision-making. Here, we discuss the meaning of interpersonal, intrapersonal, and outcome expectations with respect to career decision-making from the perspective of STEM students in a South African university.

Introduction

South Africa ranks among the top nations globally to spend a large amount of her national resources on education with respect to percentage gross domestic product (GDP) (Van der Berg & Burger, 2003 ). Practically, government and stakeholders in Science, Technology, Engineering, and Mathematics (STEM) education try to grow sustainable decisions in STEM among students through the provision of funding from the National Student Financial Aid Scheme (NSFAS) and other supportive initiatives (Manuel, 2019 ). The NSFAS funding, through a ring-fenced system, provided for learning materials, tuition, and subsistence for beneficiaries. However, a recent change in its ring-fenced policy to outright cash transfers to beneficiary accounts seems to have resulted in a notable drop in the rate of textbook purchase and decline in academic performance by students. A non-profit organisation called the Alliance for Academic Success cautions that most beneficiaries of the monetary disbursements are using the funds to address family challenges instead of their academic needs (Duma, 2019 ).

Unfortunately, South Africa was among the four lowest performing nations in STEM at the tertiary level in sub-Saharan Africa between 2011 and 2015 (Tikly et al., 2018 ), with “only 1 in 10” high school learners deciding to pursue a career in STEM at tertiary level (Planet Earth Institute, 2016 ). Furthermore, high attrition and low performance among enrolled STEM students is frequently documented (Prince, 2017 ). Therefore, additional high school and university programmes have been developed to further motivate students to choose STEM courses (Kirby & Dempster, 2018 ; Tikly et al., 2018 ). Although some of these endeavours have been helpful, career decision-making still poses challenges among students (Fogarty & McGregor-Bayne, 2008 ).

Literature review

Global literature is rich in empirical evidence about the factors influencing career decision-making, some of which are family influence, passion, capacity, self-efficacy, apparent difficulty, values, sense of belonging, gender and race (Bieri Buschor, Berweger, Keck Frei, & Kappler, 2014 ; Lent et al., 2005 ; Rainey, Dancy, Mickelson, Stearns, & Moller, 2018 ; Rainey, Dancy, Mickelson, Stearns, & Moller, 2019 ). The bulk of attention for the past two decades has been on investigating career decision-making in STEM in western countries. However, there is potential in examining how the phenomenon is experienced in the South African context.

Career decision-making comprises several domains and complex processes. Gelatt’s ( 1962 ) progressive decision-making model offers a supporting foundation for comprehending how career decisions are made. The model shows the process of decision-making as an on-going activity that changes dynamically with the acquisition of additional information. For instance, a young learner who is exposed to technological tools used by their father could learn how to use them and decide over time to choose a career in technology. Furthering the view of Gelatt ( 1962 ), Niles, Amundson, and Neault ( 2010 ) propose that adolescents are pre-emptive catalysts of the socio-cultural domain. Hence, they dynamically integrate knowledge and texts from others to ultimately develop a repository of decision-making.

Outcome expectancy is one of the major constructs that inform career decision-making. It involves the perceived outcomes of performing specific actions (i.e., “if I do this, what will happen?”). The construct assesses young people’s perceptions of several professions based on their apparent economic, shared, and self-satisfaction outcomes. In established frameworks such as the social cognitive career theory (SCCT), career outcome expectancy is positioned as a key mediator of profession and scholarly interest and skill development (Nugent et al., 2015 ). In addition, there are empirical proofs that outcome expectancy, career interest, and self-efficacy are influential in predicting intentions to pursue a career (Blotnicky, Franz-Odendaal, French, & Joy, 2018 ; Fouad & Smith, 1996 ).

Another construct, career interest, is a predictor of both career preference and outcome (Nugent et al., 2015 ). Scholars found that career interest is positively connected to decisions to enrol in a field (Hulleman, Durik, Schweigert, & Harackiewicz, 2008 ). Students who show interests in STEM early in life often decide to study STEM ultimately (OECD, 2005 ).

Furthermore, self-efficacy has been examined as a predictor of career interest using SCCT theories (Fouad & Smith, 1996 ; Lent, Brown, & Hackett, 1994 ). Personal factors and practical STEM-related behaviors influence the formation of self-efficacy, interests, and values, which impact decisions in STEM (Jacobs, Davis-Kean, Bleeker, Eccles, & Malanchuk, 2005 ; Tate et al., 2015 ). Eccles and her associates propose that educators, peers, and families are well positioned to create prospects for students to participate in several STEM-associated activities via learning experiences and special courses (Eccles, Wigfield, & Schiefele, 1997 ; Wang & Eccles, 2012 ).

Additionally, the decision to pursue a career in STEM associates with parental influence. Mzobe ( 2014 ) confirmed that in a study conducted in South Africa, the role played by family in the career decision of students was more significant than monetary influences. Furthermore, Bandura ( 1977 ) asserts that families, educators, and peers are vitally influential in the enhancement of self-efficacy beliefs. Studies have established that self-efficacy could be developed when families and educators accentuate the significance and worth of career proficiencies (Bandura, Barbaranelli, Caprara, & Pastorelli, 2001 ). The influence of family support and attitudes to STEM have been operationalized in several ways, for example, the development of SCCT to incorporate social-contextual factors (Lent, Lopez Jr, Lopez, & Sheu, 2008 ). Workman ( 2015 ) confirms that parental influence was dominant among the themes in learner decision-making processes. This claim is confirmed by several other scholars (Nugent et al., 2015 ). Jacobs, Chhin, and Bleeker ( 2006 ) report that the girl learner’s self-perceptions and proficiencies were influenced by parental gender labelling and encouraged gender-typed career choices. This could be responsible for the under participation of the female gender in STEM as reported globally (Hartung, Porfeli, & Vondracek, 2005 ; Tikly et al., 2018 ; Wang & Degol, 2017 ).

Studies have shown that educators have a strong influence on learner decision-making (Clotfelter, Ladd, & Vigdor, 2007 ; Rivkin, Hanushek, & Kain, 2005 ). Likewise, the attitudes of students’ peers, their accomplishments, and standards can wield a sharp influence on young people’s interest in choosing and deciding to study a specific course (Olitsky, Flohr, Gardner, & Billups, 2010 ). The period of growing up is a time of acquiring a personality and sense of self, and during this period peers can be very instrumental in guiding each other’s choices, behaviors, and career interests (Vedder-Weiss & Fortus, 2013 ).

The role of personality in career decision-making behavior is well researched (Holland, 1997 , 1959 ; Seibert and Kraimer, 2001 ; Sullivan & Hansen, 2004 ). Holland ( 1959 ) proposed a theory suggesting that an individual’s career interest expresses their personality. The theory suggested that personality is a combination of several factors comprising capabilities, interests, behaviors, and principles.

The overarching aim of the present study was to explore the career decision-making of STEM students in a South African university to understand the students’ perspectives about the factors that significantly influenced their decision to study STEM. This qualitative research explored the influential factors in the career decision-making of the participants. The research integrated a hermeneutic phenomenological method to traditional content analysis. Since the study is exploratory in nature, attention was given to describing the different range of students’ viewpoints and experiences. Several students reported, among other factors, that their families, personality, and expectations played influential roles in their career decision-making. Thus, this paper presents the meaning of interpersonal, intrapersonal, and outcome expectations with respect to career decision-making for STEM students in a South African university. With better insight into students’ perspectives on career decision-making, educators can better educate students about their chosen field.

Research question

The investigators explored what students perceived influenced their career decision-making within the intricate context of STEM education settings in a university in South Africa. The aim was to uncover influential factors entrenched in students’ decision-making. The investigators sought to interpret students’ career decision-making journeys and experiences. The term “career decision-making journey” is used here to describe students’ education experiences and the circumstances, individuals, and actions that impacted on their career decision-making. The major question of this study was what defining situation, event, or individual helped STEM students to make the decision to pursue a career in STEM? The question contains many entrenched and intersecting occurrences needing obvious consideration to comprehend and interpret the key phenomenon of this research.

Methodology

Conceptual approach.

Hermeneutic phenomenology proposed by Martin Heidegger (1889–1976) (Laverty, 2003 ), tries to discover the “essence” of people’s lived encounters with phenomena and the factors that influence those encounters (Bynum & Varpio, 2018 ; Creswell & Poth, 2016 ). The technique reflects other people’s encounters and considerations to explain the deeper meaning of phenomena (Bynum & Varpio, 2018 ). Through the examination of people who went through an experience, researchers acquire greater understanding of factors that influence the wider context of STEM education. This method was selected partly because the topic of this research, “exploring the factors that influence the career decision of STEM students at a university in South Africa,” was of personal interest to the researchers who had themselves experienced career decision-making challenges (Bynum & Varpio, 2018 ).

Hermeneutic analysis additionally permits investigators to explore factors that are “taken for granted” (Bynum & Varpio, 2018 ), like those reported in several prior studies (Bennett & Phillips, 2010 ; Clinite et al., 2014 ; Grayson, Newton, & Thompson, 2012 ; Klingensmith et al., 2015 ; Phillips, Peterson, Fang, Kovar-Gough, & Phillips Jr, 2019 a), that learners and residents in the medical profession are motivated by earnings and debts in their career decision-making process. By using a hermeneutic approach, investigators recognise contextual effects of participants’ encounters that ordinarily couch beneath, undetected (Bynum & Varpio, 2018 ). In line with Bennett and Phillips’ model (Bennett & Phillips, 2010 ) which accentuates that learners experience career preparation in diverse manners, this study focuses on the array of students’ lived experiences as they made their career decisions.

This qualitative research was conducted on one campus of the largest university in the province of KwaZulu-Natal. Study participants were enlisted from undergraduate students in Science, Technology, Engineering, and Mathematics (STEM) at the university investigated in 2019, and they were in their 1st, 2nd, 3rd, and 4th year of study, respectively.

Data collection and participants

Within the viewpoint supporting hermeneutic phenomenology, investigators need to create a research approach that runs directly from the research question and aims of the study. Questions in a semi-structured format (see Additional file 1 ) were designed to determine the factors that influenced STEM students’ career decision-making at the university in South Africa (see Additional file 1 ). According to the reports of Phillips, Wilbanks, Rodriguez-Salinas, and Doberneck ( 2019 b), data gathering using written texts (essays) permits many learners to participate in the research, answering in their own words. In line with Phillips et al. ( 2019 a), six semi-structured questions to uncover factors affecting STEM students’ career decision-making were crafted. Participants were expected to respond to all six questions instead of one to generate meaningful data. The questions were piloted with a group of students who were not among the participating fields for clarity. Thereafter, the questionnaire was published on the university’s website for participants to access and complete. Consent forms for each participant were also posted online.

Based on Krejcie and Morgan’s ( 1970 ) table for determining sample size, from a target population of 2000 undergraduate STEM students, a sample of 322 was selected. Out of the sample of 322 STEM students, a total of 203 (63% response rate) responded to the questionnaire. Data was collected over a six-month period. However, the purposeful sampling technique (Patton, 2002 ) was further used to select 150 responses out of the 203 total responses when saturation occurred.

Saturation happens at the point where “additional data does not lead to any new emergent themes” (Given, 2015 , p. 134). In this study, although a hermeneutic phenomenological analysis was adopted, saturation occurred more across than inside individual cases, owing to the large number of participants who participated. Scholars suggest that saturation’s importance and meaning are variously attributed by researchers contingent upon theoretical role and analytical approach used; hence, it could serve dissimilar purposes for various types of studies (Saunders et al., 2018 ). In this context, saturation in this study was interpreted as the point where the researchers found that the responses from participants seemed to be revolving around the captured themes and no more significantly new information could be derived from the remaining collected data.

This research was appraised and approved by the ethics committee of the university as a part of the postdoctoral study funded by the National Research Foundation and the Department of Science and Technology.

Data analysis

The research team included a Ph.D. student (Nneka Akwu) in Sciences, a professional data analyst (Idris Ganiyu) who also holds a Ph.D. in management studies, an education professor (Vitallis Chikoko), and a leadership expert (Isaac I. Abe) also holding a Ph.D. in leadership studies. Since this study was designed as an exploratory one, the responses were analyzed qualitatively through a hermeneutic phenomenological method to typical content analysis, with themes developing inductively from the collected data (instead of by a prearranged, concept-driven coding system) (Crabtree & Miller, 1999 ; Hsieh & Shannon, 2005 ). This method permitted themes and their descriptions to proceed from the data (Hsieh & Shannon, 2005 ), which is vital in exploratory research. Since the data source for this study was a voluminous text pool from many participants instead of in-depth interviews, the researchers could not ask follow-up queries or investigate the vital concepts further. Hence, the analysis fused several student opinions and concepts into developing themes instead of trying to broadly depict each student’s personal experiences and opinions.

Attention was given to describing the different groups of participants’ lived experiences, focusing on minority views. Regardless of the limitations associated with the static data source, a large quantity of data permits for a comprehensive exploration of participants’ viewpoints about career decision-making. The scholars primarily immersed themselves in the collected information by reading the texts repetitively to create meaning out of the entire data (Crabtree & Miller, 1999 ; Hsieh & Shannon, 2005 ). Preliminary codes were generated from repeated readings of individual texts and documented in a comprehensive codebook. Through a continuous comparative procedure, commonalities and divergences were refined and documented in the codebook.

Each participant’s submitted text was subsequently separately coded by a minimum of two members of the team, and their coding choices were assessed, and differences fixed in frequent study team meetings. Final coding was posted into QSR NVivo version 12 and the codes were then categorized into meaningful nascent themes (Miles & Huberman, 1994 ; Ys, 1985 ). All through the analytical process, the scholars reflected on the way each data point (coded reports in texts) furthered the whole (developing themes), i.e., the “hermeneutic cycle” (Bynum & Varpio, 2018 ; Creswell & Poth, 2016 ). Explanations of each coded statement were equally scrutinised carefully by reverting to source texts and appraising them totally to confirm that the individual explanation matches the context of an individual participant’s story.

The use of QSR NVivo software permitted the scholars to confirm that each code and developing theme were backed by the text. Text coding and careful examination of codes and themes were sustained until data saturation was arrived at no novel themes emerged (Miles & Huberman, 1994 ; Ys, 1985 ). The scholars concentrated principally on the subject matter of interest: the factors that influence the career decision-making of STEM students (Bynum & Varpio, 2018 , Creswell & Poth, 2016 ). Nevertheless in the procedure of examining these phenomena, factors linked to students’ career decision-making emerged. Additionally, although the participants had created a lot of texts about several factors, they were silent about peer influence, although it was mentioned in the semi-structured questionnaire. Therefore, the research team agreed not to explore this topic in the study although literature is rich with studies on its influence on students’ career decision-making (Wang & Eccles, 2012 ; Eccles et al., 1997 ; Olitsky et al., 2010 ; Vedder-Weiss & Fortus, 2013 ), acknowledging that since peer influence was directly mentioned in the primary research questionnaire, the exploration of peer influence in this study should be preliminary instead of thorough.

This study ensured that various approaches to confirm quality and rigour were applied (Anfara Jr, Brown, & Mangione, 2002 ; Crabtree & Miller, 1999 ). To confirm credibility, the sciences student brought clarity on students’ ethos as a form of peer examination at every phase of the analytical process. Her viewpoint assisted to explain nuances in student feelings, particularly when the codebooks were being refined. When the analysis was completed, developing themes were presented to a group of postgraduate STEM students and they corroborated that the themes echoed their experiences. Since these students did not participate in the study, this step was a revised type of member checking. The investigators added explicit inclusion and exclusion code standards and findings were conveyed by using deep, rich narrative to strengthen transferability. To confirm dependability, the study team applied the code-recode principle and used QSR NVivo version 12 to create an appraisal path (Anfara Jr et al., 2002 ).

Lastly, hermeneutic phenomenology demands that investigators acknowledge their previous encounters as “embedded in and essential to the analysis process” (Bynum & Varpio, 2018 ). The investigators followed reflexivity by disclosing, pondering on, and listening to their experiences and ideas. Researchers talked about their individual responses to the data all through the investigation process. The study team often scrutinised and inspected their emerging explanations of the texts as a group and urged honest discussion about conflicting or divergent interpretations. These processes were adopted to confirm the researchers’ experiences in education leadership, management, data analysis, sciences, and leadership, and to ensure other uncharted preconceptions did not influence the quality of the analytical process and findings.

Three key themes about STEM students’ career decision-making emerged from the analysis, namely interpersonal factors, intrapersonal factors, and career outcomes expectancy. Interpersonal factors are of varying types and have numerous levels of importance to different students. Intrapersonal factors resonated with many students and they reported a variety of reasons including career interest, personality, and self-efficacy as very influential in their career decision-making. Finally, students also stated that career outcomes expectancy was relevant to their career decision-making. The results are summarised in Table 1 . Below is a presentation of the key themes and sub-themes that emerged from this study.

Key theme 1: interpersonal influences

STEM students who participated in this study generally considered interpersonal influence, but in describing the family, they reported different levels of family influence on their decision-making. Some students wrote that family was of no influence at all in their decision to study STEM.

Family influence

The key finding common to all (100%) participating students in the study was family influence. The phenomenon was embedded in specific situations and in the context of decision-making. They reasoned that they made their best career decisions when around their families or in their learning environment. The students’ perceptions of their families’ influence on their decision to study STEM are summarily described as: “very influential,” “somehow influential,” “no influence,” and “family needs my support.” The responses captured under the “no-influence” subcategory was further grouped into “career prejudice” and “left alone to decide.” The use of these adjectives does not in any way carry measurable significance but explains the meaning of the content derived from participants’ responses.

Sixty-eight (45.33%) students felt that their families were very influential in their decision to pursue a career in STEM, and they stated inter alia :

“My family has a huge positive influence because in my family they’ve advised me that in the field of STEM there are a lot of good opportunities as well as life itself as we use technology in our daily basis.”

“My family encouraged me to enrol for STEM and has supported me 100% in my study choices and I personally enjoy STEM related fields, this has pushed me to achieve great academic success.”

“My family influenced me a lot, everyone in the family believes in STEM, I also think STEM is the future.”

“My family has shown me what to expect in different STEM fields. They also showed me what careers might be good for my personality.”

“My current career was greatly influenced by the fact that my late uncle used to hire me to work with him part time on his Engineering related business.”

“They have a great influence, they even told how it is going to benefit me when I am done studying and how great it is.”

However, 40 other students, representing 27% of the respondents, believed that their families were somehow influential ; however, the final decision to study STEM was made by them. These students felt that their families played supportive roles in their decision to pursue STEM careers. There were innuendos suggestive that some students made their decision without family interference or that the family suggested a course different from the learner’s choice but subsequently agreed to support the student’s decision. These participants reported as follows:

“My family has been somewhat influential in me having a career and being independent. I am very determined to change my way lifestyle, therefore am willing to work hard in my chosen field.”

“I have reached a point where my family is much caring about pursuing my chosen field, they encourage me not to give up but try to tolerate every situation comes across to fulfil my potential desires.”

“I'm the only one who have a qualification at home, I get more encouragement to study from them. I was raised by a farm worker so pursuing studies under STEM is something I grew up wishing although less support from them because they are not educated.”

Yet, a worrisome sub-theme emerged where some students felt that they were under obligation to support their family. This trend could be referred to as an inverse influence on students’ decision to study STEM. The responses of the 19 (13%) participants who submitted the comments in the sub-theme family needs my support are as follows:

“I tend to take career decisions based on how urgent my family needs support. it hasn't paid off so far, but it does have an impact on my decision making.”

“They are very happy because they know that by being under STEM may lead to many job opportunities to help.”

“My family just wants me to have a career that will guarantee a good lifestyle at the end. When you are born under privileged, you are not satisfied by life. Hence, you always believe you must be successful at what you do, even if it is a career within STEM to support the family.”

Conversely, 23 students (15.33%) were convinced that their families made no contribution to their decision to study STEM. Although it seems these participants’ families did not have had any influence on their decision, none of the participants came across as predominantly worried by the lack of family influence; it just did not appear to be a huge factor in their lives, since they reasoned that poor or lack of education for instance, contributed to their non-contributory influence. Further understanding of the intricacies of family influence in the career decision-making behavior of STEM students in this university could yield meaningful results. However, the reports deduced from the texts of participants who reported that their family had no influence over their career decision, therefore, they were left alone to decide are

“My parents are not educated, so they supported and appreciated that I wanted to continue studying after Grade 12; as to what field I chose they had no influence at all.”

“My family has no influence whatsoever on my decision to study STEM.”

“My family doesn’t contribute that much in my life, so I make all the decisions by myself.”

“My family pretty much doesn't care about what I do, as long as I'm studying.” “My family they do not care what studies I take the only thing they want is to see me happy in what I do and study.”

“My family doesn't affect that much about making decisions I only have a say to what I want to learn, and I should be the one knowing about the outcomes of my learning.”

“My family members are mostly uneducated therefore my decision will not be influenced with anything that they may want to say.”

Additionally, career prejudice emerged as one of the reasons explicating why the family had no influence on the decision-making behavior of participating students that said:

“They (family) sometimes have prejudice about my career because of my gender.”

“STEM includes my area of academic learning. I am studying Engineering. My family believes that if you choose a career in STEM, you might never finish your studies because it is difficult.”

These students believed that their families were prejudiced against their decision to follow a career in STEM.

Teacher influence

This second category of theme one (interpersonal influence) showcases the influence that teachers have on the career decision-making behavior of STEM students. The 30 (20%) participants who acknowledged the significant role of their teachers in their career decision-making reported as follows:

“Well during my high school days I taught myself but influence from my teacher made me more interested in STEM As for family they had no idea what I’m doing, all they wanted was me to be successful and that all.”

“To a good extent, choosing a science stream as advised by my teacher in high school propelled me to do science related careers which I enjoy the most.”

Key theme 2: intrapersonal influences

Mzobe ( 2014 ) agrees with Young and Collin ( 2004 ) that there is an intrapersonal level of influence on career decisions. This level depicts the interface of self in the decision-making process of the individual student. The sub-themes here include the following:

“Champion” mentality

The first category under theme two is what is titled “champion” mind set. Individuals with champion mentality often want to “save” or “change” the world. The word was merely chosen to summarily capture the content of the responses of the 45 (30%) participants in this category:

“STEM is most effective way in fast development of our country, since we need more people in STEM related field in South Africa to quickly grow our economy and have a much broad experience in our own to benefit the country and the world at large, I decided to choose a career in STEM.”

“In my family, we’ve never had an Engineer, so If complete my studies, I’ll be the first engineer in my family and surely I will make a difference and my family will be really proud.”

“Engineering seemed like a fun major and that it can lead to great things by helping people.”

“To become one of the scientists in the world and be able to improve the living of people in the world using different skills in science.”

“My family and personal traits influenced me a lot as in the world we are living in families are viewed as inferior or people who won’t do science, so I wanted to prove to the world that I can.”

Commonly participants whose responses are documented in this category desire to make a difference in their family and/or society. They strongly believe that by pursuing a career in STEM they would be changing their family’s status or helping society at large.

Career interest in STEM

This is the second category under theme two. Interestingly, 83 students (55.33%) stated that the decision to pursue a career in STEM was based on their career interest. These participants’ passion, dreams, aspirations, desire, and curiosity to study a career in STEM were highlighted in their responses. Career interest is important in the decision-making process of students and has implication for policy decisions. Participants’ statements include the following:

“My personal interest in this career influenced my decision to study STEM to a great extent.”

“I'm passionate about the field of science.”

“I've always loved science, especially biology. My parents always encouraged me to pursue a career I am passionate about.”

“Passion and curiosity for the environment attracted me to science.”

“I have always been curious and enjoyed STEM.”

“I have always loved nature and what makes it, hence i have always enjoyed biology.”

“My decision to study STEM was influenced more by my own interests and my traits than my family.”

“I've always had a passion for helping other people and a fascination for the human body and this influenced my decision to choose a degree in health sciences.”

“I am interested in evolving things, research and innovations. This encouraged me towards STEM field.”

Personality

This is the third category of factors influencing career decision-making as found in theme two. This term is used purely as a descriptive presentation of interpretations of individual student’s personality, reasoning, or aptitude deduced from their feedback. Fifty-three (35.33%) participants identified their personality as being influential in their decision-making behavior. Their comments are as follows:

“No one other than myself who has the say in my life influences and to what I decide on doing.”

“It’s certainly only personal traits that influenced my career choice and decision.”

“My inquisitive approach to life at large and my family supportive nature on supporting my journey in obtaining such information.”

“Individual traits: my (particular sort of) intelligence and manner of thinking resulted in an affinity for mathematics and physics.”

“I am a very logical thinker and naturally very curious. These traits lead me to study STEM and makes learning easier as I am interested in what I'm learning.” “I think my critical reasoning skills are the pain driver towards STEM.”

Personal development

The fourth category of factors identified in theme two is personal development. Participants’ desire to develop themselves with knowledge and skills attributed to STEM fields underpinned their decision to pursue a career in STEM. The 17 students (11.33%) that responded in this category thought that a career in STEM would challenge and develop their potentials.

“STEM is incorporated in our everyday lives, pouring a litre of milk, baking a cake to sell to make a living, providing electricity for households. It is nice to know what goes on in the smaller parts of life which become the greater ones. I love learning about all that to improve the lives of others and mine.” “To keep myself updated with new and incoming technology.”

“I like to be challenged so that’s why I choose a course in STEM which is a challenging course to bring out my potential.”

Self-efficacy

This is the fifth category of concepts under theme two. Self-efficacy is the confident belief in one’s self about one’s ability to achieve goals and it develops from earlier experiences and verbal persuasions attributable to the environment of upbringing. In this study, 38% (57) of the participants appeared to believe that they could be successful in a career in STEM. They seemed to understand what they could do as stated below:

“I believe in me. Being in harmony with my family and with myself, I've known to accept my strengths and weaknesses and through assessing those, I know I wouldn't want to study anything else. And accepting that I'm studying what I believe I was born to do, makes me appreciate more and work harder.”

“Family satisfaction makes for a motivating environment which allows me to grow and believe in myself during my studying journey.”

“My family believes in me, I believe and know that I can succeed in almost everything that I set my mind into, which is why I went to science even though it wasn't my first or even second option. I'm doing well my results are good.”

Spirituality

This is the sixth concept in the category of factors found in theme two. Participants seemed to believe that they were influenced by their spiritual life to pursue a career in STEM. Others saw morality and values as being supreme to financial benefits deriving from a successful completion of study in STEM. These 21 (14%) participants said:

“I pray about all my decisions and entrust them to Jesus.”

“Being in the STEM requires one to be in tune with their moral and spiritual values more than financial needs.”

Theme 3: career outcomes expectancy

Career outcomes expectancy expresses young people’s perception of some careers based on their apparent financial, societal, and self-satisfaction outcomes. Sub-themes that emerged here are as follows:

Financial matters

Financial matters describe the first category of factors that emerged in theme three. This study did not set out to evaluate the effect of finance on career decision-making behavior of students in STEM, but it emerged as a theme. However, 64 (43%) students appeared to perceive a career in STEM as economically very rewarding. Therefore, the expectation of better pay when studies are completed could have stimulated their decision to pursue a career in STEM. Participants’ statements are as follows:

“It’s a good career path and it’s paying well since it’s a scarce skill.”

“I chose my career according to my ability and interests and future financial stability.”

“Finance greatly affected my learning decision, especially family related issues that demanded financial contribution.”

“I wanna be happy in what I do and be glad of my finances being able to help and support my parents in every way possible for me, so I’d be happy.”

Career opportunities and prospects

These factors emerged as the second category of theme three. Forty-three students (29%) who participated in this study felt that families understood the benefits and prospects of pursuing a career in STEM. Their comments are stated below:

“My family believes Science has more opportunities, benefits and career prospects more than other fields of study.”

“STEM there are lots of job opportunities and you can get a job. Some of the jobs are similar and you can use skills from one job in the other job.”

“My family had always told me about the opportunities that sciences provided, the money and also the respect for STEM learners.”

This qualitative research provides insight and perspective into the factors that influenced the career decisions of participating STEM students in a South African university.

• Interpersonal factors

The finding in this study that the interpersonal relationships that students had formed with family, teachers, and peers are vital in relation to career decision-making is supported by Bennett and Phillips’ ( 2010 ) model, which confirmed that in making their career decisions, students consider various values and experiences that impact individual decisions differently. For instance family and teacher influences were found to have had varying degrees of influence on participants’ career decision-making in this study. This result is also supported by previous evidence that showcases family influence as a leading theme among the themes in career decision-making (Jacobs et al., 2006 ; Nugent et al., 2015 ; Workman, 2015 ).

Unlike prior studies on career development of students (Mzobe, 2014 ; Zahra & Malik, 2017 ), using a qualitative approach, this study uniquely identified a dimensional angle to family influence on the phenomenon investigated. For several participants, family was found to be very influential in their career decision-making, as commonly reported by scholars (Mzobe, 2014 ; Nugent et al., 2015 ; Workman, 2015 ). However, it was interesting to find in this study that some participants distanced themselves from the family as an influential factor on their career decision-making. Those students firmly reported that other factors such as the need to support their family took greater priority in their career decision-making. Summarily, interpersonal factors were found to be the most prominent reason cited by participants for career decision-making in this study. This implies that educators and stakeholders who have an interest in closing the STEM skills gap by understanding how students make their decision to major in STEM can take note of the levels of influence that the family has on student career decision-making, create constructive initiatives, and offer structures that foster robust interpersonal connections in a productively strategic manner.

Although participants indicated that support received from their families influenced their decision to study STEM, the present study did not classify the form of support received. Further studies could unravel this relationship.

• Intrapersonal factors

STEM students also cited champion mind set, career interest, personality, personal development, self-efficacy, spirituality, and morality, which were categorized as intrapersonal factors, to explain why they decided to pursue a study in the STEM field. This is an essential discovery to note because it agrees with the idea that interest, self-efficacy, and personality are influential in career decision-making (Tzu-Ling, 2019 ; Wu, Zhang, Zhou, & Chen, 2020 ; Yu & Jen, 2019 ), and implies that focus on individual cognitive factors in investigations on career decision-making is founded. However, champion mind set, spirituality, and morality also mentioned by participants as reasons for their career decision-making—even though cognitive factors have meaningful influence on career decision-making—is notable. This finding importantly implies that operational and cultural factors in addition to individual cognitive and interpersonal factors should be considered in future investigations of representation in STEM.

Outcome expectancy

An outcome expectancy as a construct measuring students’ perception of some careers based on their perceived financial, societal, and self-satisfaction effects (Nugent et al., 2015 ) was confirmed to be influential in STEM student career decision-making in this study. Participating students expect to gain financial stability and independence by exploiting the career opportunities and prospects they foresee in the STEM fields. For the participants who place value on financial and economic expectations, the earnings could offer them the ability to meet the financial needs of their family members. The findings also clarify the understanding of the lens through which participants view the STEM field for opportunities and prospects. This characterisation of outcome expectancy is specifically useful because it could assist career counselors in supporting the students in defining their career pursuit in STEM.

Furthermore, the findings of the present study showed that in addition to outcomes expectancy; family, teachers, self-efficacy, interest, spirituality, morality, and personality, among other factors, are influential in students’ decision to pursue a career in STEM. Several studies on career interest, career growth, self-efficacy, and career outcomes expectancy have been conducted among students in high schools and tertiary institutions. A study was conducted among university students in Spain to investigate the effect of perceived supports and hindrances to self-efficacy convictions and other social-cognitive variables associated with STEM students’ career development (Peña-Calvo, Inda-Caro, Rodríguez-Menéndez, & Fernández-García, 2016 ). While another study among Taiwanese college students investigated their career interests and career goals for majoring in STEM (Mau, Chen, & Lin, 2020 ), Baglama and Uzunboylu ( 2017 ) examined the association between career decision-making self-efficacy and career outcomes expectancy among Turkish preservice teachers. They found that career decision-making self-efficacy significantly predicts career outcome expectancy.

However, STEM students need assistance in finding information concerning the world of work, transforming from students to professionals, planning for work, and coping with pressure (Güneri, Aydın, & Skovholt, 2003 ). The transitioning process may not be easy on the students. A study conducted by Gizir ( 2005 ) among graduating university students found that they feel apprehensive about getting employed after graduation and are also uncertain about what the future holds for them. For this purpose, this study may be of value-adding benefit in describing the career counseling needs of STEM students. It could be implied then that knowing what to do post-graduation and the way to approach the world of work could make STEM undergraduates commit to their career.

A study carried out by Vertsberger and Gati ( 2016 ) discovered that adolescents facing career decision challenges and pessimistic outcomes expectancy concerning their potential careers are inclined to seek help in the process. This has a significantly important implication with regards to career counseling initiatives designed to assist students and heightens the cognisance of the value of offering support for students in their career decision-making process. Ascertaining the variables that influence career-associated opinions and behaviors of STEM students in tertiary institutions could result in the control of these variables and the learners being assisted. Because of the importance of providing career guidance and support, it could be inferred that the present study will add to the improvement of counseling interventions. In addition, numerous scholars have focused on student career decision processes elsewhere globally, it is therefore expected that the present study would offer a dissimilar cultural viewpoint to findings from Sub-Saharan Africa. Scholars from elsewhere globally, including the USA, China, Turkey, Taiwan, Spain, and other regions in Africa, would derive benefit from the results of this study.

STEM students approach their career decision-making from diverse perspectives and experiences. Likewise, they appraise the influence of interpersonal and intrapersonal factors to different levels and for a variety of reasons, and interestingly, the family emerged as a dominantly influential element among a host of others found in this study. By comprehending students’ perspectives on career decision-making, STEM educators can assist students in making decisions that reflect their values and experiences.

Limitations

A few limitations should be acknowledged. This research was undertaken at a single tertiary institution. Learners at other institutions could have dissimilar opinions on interpersonal and intrapersonal factors and career outcomes expectation. Texts generated from undergraduate STEM students offered insights into their perceptions at that period; these ideas could change as career plans develop, for instance in postgraduate years. Participants wrote their responses in the context of semi-structured questions. Their answers could have been influenced by the desire to provide generally satisfactory information. As stated above, the data gathering method—the assessment of student texts—differs from the typical hermeneutic phenomenology approach, whereby data is gathered from people using in-depth interviews (Phillips et al., 2019 a). The investigators had no chance to ask follow-up questions to make more enquiry into matters of interest as would have been done in a procedure involving interviews. Lastly, since the questionnaire did not ask participants to respond to financial issues and gender, the findings may not mirror the full range of participants’ ideas of the effects of finances and gender on career decision-making. Further investigation is required to explore these constructs further to confirm the study’s results as generalizable.

Implications

These findings involving interpersonal, intrapersonal, and career outcomes expectancy in the decision to pursue a career in STEM have important theoretical and practical implications. Firstly, this study, like several other studies, has yet again been supported using a phenomenological hermeneutic approach. However, the researchers are quick to agree that this finding is limited to the university investigated and the peculiarity of the environment, bearing in mind Holland’s ( 1959 ) position. He was of the conviction that the experience that an individual acquires in the environment of his/her upbringing creates the inclination towards specific interests or behaviors that combine with the individual’s values to shape their personality trait.

Secondly, this study invites awareness to the finding that although peer influence was prominent in extant literature as an influence on students’ career decision-making (Eccles et al., 1997 ; Olitsky et al., 2010 ; Vedder-Weiss & Fortus, 2013 ; Wang & Eccles, 2012 ), the present study found a different result—peer influence was not notable. Further studies are recommended to explicate the reason behind this finding.

Interestingly, the need to support family was an unexpected sub-theme that emerged from family influence on career decision-making in this study. The students who reported that they needed to support their families were not very pointed about the way in which they needed to support their families and why. Further study would be needed to explore this phenomenon and could be meaningful in assisting educators and policymakers in making more informed decisions on how best to serve this category of STEM students. However, individuals interested in motivating students to pursue STEM careers could consider the fact that majority of the students affirmed that their family was influential in their career decision-making, while some other students considered it financially rewarding. These, in addition to the other factors identified in this study, could be taken into consideration and integrated into future STEM outreach and initiatives. The factors influencing students’ career decision-making have implications for how institutional practices, educational caretakers, and stakeholders shape students’ support.

Availability of data and materials

University ethics approval does not include release of the raw information. Data was collected from the STEM students under the stringent condition of anonymity and cannot be shared. Please contact the corresponding author for more information.

Abbreviations

National Student Financial Aid Scheme

Social Cognitive Career Theory

Doctor of Philosophy

Technology & Software Solutions, owners of NVivo software

Science, Technology, Engineering, and Mathematics

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Acknowledgements

Funding was provided to author by a grant from the National Research Foundation of South Africa. We are thankful to the individuals who helped our team with vetting the coding structure and proof-reading the manuscript. Dr. Isaac I. Abe and Dr. Idris Ganiyu made extensive contributions to data analysis and interpretation and reviewed the paper critically. Nneka Akwu made substantial contribution to the code-recode process bringing the viewpoint of student nuance into consideration in the process of data collection and analysis.

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Abe, E.N., Chikoko, V. Exploring the factors that influence the career decision of STEM students at a university in South Africa. IJ STEM Ed 7 , 60 (2020). https://doi.org/10.1186/s40594-020-00256-x

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The field of careers studies is complex and fragmented. The aim of this paper is to detail why it is important to study careers, what we study and how we study key issues in this evolving field.

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Career path decisions and sustainable options.

1. Introduction

1.1. trait–factor and related approaches, 1.2. theory of work adjustment, 1.3. psychological/socio-psychological approach, 1.4. sociological approach, 1.5. developmental approach, 2. materials and methods, 2.1. methodology, 2.2. utilization of the proposed approach, 2.3. computer-assisted career guidance (cacg), 3. results and findings, 3.1. discussion of the results, 3.2. findings, 3.3. benefits and operational aspects of the proposed approach through cacg, 4. conclusions, author contributions, institutional review board statement, informed consent statement, conflicts of interest.

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Share and Cite

Hassan, H.; Hussain, M.; Niazi, A.; Hoshino, Y.; Azam, A.; Kazmi, A.S. Career Path Decisions and Sustainable Options. Sustainability 2022 , 14 , 10501. https://doi.org/10.3390/su141710501

Hassan H, Hussain M, Niazi A, Hoshino Y, Azam A, Kazmi AS. Career Path Decisions and Sustainable Options. Sustainability . 2022; 14(17):10501. https://doi.org/10.3390/su141710501

Hassan, Hamid, Mujahid Hussain, Amna Niazi, Yasuo Hoshino, Akbar Azam, and Ahmad Shabbar Kazmi. 2022. "Career Path Decisions and Sustainable Options" Sustainability 14, no. 17: 10501. https://doi.org/10.3390/su141710501

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How To Write A Career Research Paper

• July 2, 2020
• How To's

Here's What We'll Cover

A career research paper is a write-up that gives students a better idea of what to expect in a career field they’d like to explore. The paper helps them familiarize themselves with careers they knew nothing or little about. Besides, it also gives them a chance to prove the career choice is indeed suitable for them. And if it isn’t, they can then reconsider their area of specialization . 

For an effective research paper, find a career that suits your interest to score better grades. You also need to conduct detailed research and create an outline to help you organize your work. Examples of career research papers include; How to get an internship and careers that don’t require a college education. Other topics are what should your resume contain and The benefits of working from home.

There are diverse careers out there designated for different people based on their personalities and capabilities. Thus when writing the career research paper, your focus will be a career you have thought about before. It might also be a job that suits you perfectly.

How To Develop a Career Research Paper

If you’ve had any challenges writing your research paper, worry no more. This article will teach you how to write a great career research paper. To do so, consider the following;

• Identify a career
• Gather sufficient information on the chosen career
• Identify professionals in your career line and interview them
• Write your career essay

Identify A Career

Every person has an aptitude and personality. This must be considered when determining an ideal career. If you are not sure what career fits you best, consider taking a personality test quiz. These answers will eventually help you discover careers that best match your personality. In the test, you will answer questions like; are you friendly towards people? Do you prefer working alone? Do you enjoy writing and have tremendous writing abilities ? 

Be keen on your interests and strengths. Carefully examine them with different opportunities. More so, through the research paper, you will be able to access the unfamiliar career opportunities available. A career research paper does not necessarily mean you will pursue that career in life. Nonetheless, it helps present adequate information about the careers.

Gather Sufficient Information On The Chosen Career

Use the internet, library, and career center materials to gather additional information. Your research plays an integral role in gathering sufficient data and facts about the career at large. You can also consult with librarians and career advisors for career recommendations. Therefore, have questions designated for your research. 

Identify Professionals In Your Career Line And Interview Them

Experts in a particular field must be considered for such interviews. They have extensive knowledge and experience. So, find the professionals and ask your burning questions concerning your career choice. Ask relevant questions that will help you understand their experiences, likes, dislikes, and the challenges they face. 

Write Your Career Essay

Because you have already gathered sufficient information, use your outline to start writing your paper. The essay will contain information about the career, the requirements needed to pursue it, the pay range, and its pros and cons. In a situation where you conducted interviews, designate a paragraph for the reporting.

What Should a Career Research Paper Have?

The purpose is to have a paper that is informative and resonates with the reader . Therefore, it should explore the following; 

Highlight Your Career Goals 

Talk about what you want to pursue or achieve in life and the steps to get you there. Here, you’ll need to have long-term and short-term goals. This will answer questions like, where do you see yourself in 5 or 10 years. Examples of short-term goals are; gaining a new professional skill or a self-improvement skill like networking. On the other hand, long-term goals are opening a business, making a career switch, or attaining a leadership position. 

Discuss One Major Career

Talk about one career you’d love to pursue. If you want to become a doctor, you can narrow it down to a cardiologist or a pediatrician. However, if you want to be a reporter, you can focus on being a business or sports reporter.

Talk About Facts Concerning The Career

Some careers don’t necessarily need a degree or professional skills. If yours falls under this category, you can talk about it. And because every career has its strengths and weaknesses, talk about both without sugarcoating anything. For example, if you write about the healthcare industry, you might discuss working beyond the extra hours or lack of advancement opportunities. Discussing the pros and cons will prepare you for the challenges ahead or issues you had overlooked before. 

Discuss Hobbies, Talents, and Interests

A career research paper requires you to talk about what you love to do in your free time. It might be something you are good at or something you’d love to be an expert in. Therefore, this helps you match your personality to a career you are interested in. It also makes you aware of other possibilities you can explore. 

Cite Sources Correctly

Because the paper will require you to do a lot of research, you must document your search sources. Therefore, it is important to cite your sources correctly to add credibility to your paper. 

Guidelines To Help You Write A Catchy Paper

• Examine your goals
• Analyse your skills and interests
• Make a chart of pros and cons
• Explore career sites
• Reseach current trends
• Create an outline
• Identify a conducive environment to write from
• Work with a timeline.
• Edit and proofread your work
• Confirm that all career questions are answered

Examine Your Goals

What are the goals you want to achieve? Is there a particular level you’d love to reach? Is your objective to have money or find a career that will give your life meaning and purpose? Taking time to think about the future can help you identify jobs that will be a long-term fit. You should also consider the salary, working hours, advancement opportunities, and location.

Analyse Your Skills And Interests

A skill is something you have learned to do or something you are good at. You might have a skill in writing, knitting, or fixing appliances. On the other hand, interest is something you like . An area you’ve always loved to pursue. It might be fashion, photography, drawing—anything you like or prefer to do. Most importantly, ask yourself what you are interested in the most, what gives you joy, or what areas you would like to perfect.

Make A Chart Of Pros And Cons

Everything in life has its advantages and disadvantages. If something makes you happy, it will inevitably make you sad. That is why it is important to list the positives and the negatives of a particular career. You might choose an interesting field that is quite demanding. It might require long hours of work or a high level of confidentiality, or maybe creativity. Hence, it is essential to know what you are willing to work with.

Explore Career Sites 

There are many sites you can use to get detailed information regarding certain careers. For instance, some sites will help you take personality tests to match what career types match your personality the best.

Research Career Trends 

What major careers are people pursuing? What careers will bloom in the next five years? Is it technology? Is it healthcare because of the pandemic? The hospitality industry or finance ? If you consider this aspect, it will help you make a wise move.

Create An Outline

An outline is the framework of any good paper. Developing an outline helps you understand the sequence of the paragraphs. Additionally, it will help you know what idea to establish, hence creating a good flow. Having an outline can also help you develop a draft for your paper as well as ensure you have a powerful introduction and a strong thesis that backs up all of your points with good research.

Identify A Conducive Environment To Write From

Writing an essay demands keenness and a high level of concentration. Therefore, choose a welcoming environment to write from. For instance, you could write from the library, a silent coffee shop at the corner, or even your bedroom, where you experience zero disruptions and distractions. 

Work With a Timeline.

Understand the deadline for handing in the research paper and set your timeline. A timeline will certainly help you meet deadlines and also help you submit a high-quality research paper that has not been written in a hurry.

Edit And Proofread Your Work

Edit and proofread your work to correct grammatical mistakes that make you look like a careless writer. Similarly, you can even read your work twice or thrice to ensure that you haven’t left out any errors.

Confirm That All Career Questions Are Answered

How thorough is the essay? Does it cover all the angles about the career? The career research paper needs to present a detailed report about the career. Therefore, reexamine the relevance of the paper as far as the career is concerned.

Crafting this paper has never been a simple task. However, if the guidelines above are followed, the process becomes easier to tackle. So, understand each instruction, and you will be able to write your essay about careers.

An Outline For a Career Research Paper 

A good outline is important for any paper. Most importantly, it helps you create a good flow of thoughts and ensures you have a powerful introduction, body, and conclusion. 

The Introduction 

An introduction has one or two paragraphs. Of course, this depends on the simplicity or the complexity of the paper. The introduction should introduce the paper’s topic, have a good definition of the career, and a thesis statement that clearly explains the paper’s focus. Above all, remember to hook the reader to your paper and give them an easy transition to your work. 

The Body 

The body can have three or more paragraphs depending on the points you want to address. Here are some things that need to be included;

• The most important features of the career.
• The nature of the job and a list of the responsibilities.
• The minimum qualification required for the purpose.
• The challenges involved in pursuing the career.
• A discussion of how easy or how difficult it is to find placement in the sector.
• The possibilities for growth in the career sector.
• An explanation of why that particular career looks more appealing to you than the rest.
• The skills which complement the career responsibilities.
• The shortcomings that might come in the way of your career and how you plan to tackle those situations.

The Conclusion

The conclusion should summarize what you have learned. It should summarise the most important points, a reinstatement of the thesis stated in the introduction, and a concluding statement that effectively winds up the discussion.

An Example Of a Career Reserch Paper

Career Research Paper

Institution

Media ideally refers to means of delivering information to a targeted audience. Throughout the lessons, we have learned of the different types of media that are there. These include print media, broadcast media, and the internet.

Print media uses printed items to pass the information, including magazines, books, newspapers, brochures, and pamphlets.

On the other hand, broadcast media involves using avenues like television and radio to pass information to people. The internet is the other form of media whereby people share and communicate through online platforms.

Some people use the internet as a media channel by using podcasts, YouTube videos, and virtual programs. As time goes by, some types of media lose audiences because of better and convenient platforms for communication.

When the internet had not been developed, print media was the most prevalent avenue for passing information. However, this got to change with the invention of the internet.

I have chosen the type of media is the broadcast media, as a journalist who aspires to work as a news anchor. Journalism career at the most basic level revolves around journalists investigating, collecting, and presenting the information.

This can be done in newspapers and magazines or radio, television, and online sites of passing information. It is the work of journalists to inform the public of important news and activities.

Therefore, I would like to learn more about journalists and the news anchoring profession and their importance. I believe that news anchors are essential in the world of information and communication. They are the link between the listeners and the public.

I hope to learn how they can read the news eloquently with the most minimal errors. Intriguingly, an individual will read news knowing well that a million people are watching yet still not become shy and afraid of the vast multitude.

I am also interested in learning more about how they can maintain professionalism in their interviews without letting their emotions get in the way. News anchoring is undoubtedly an art of presentation.

Broadcast media entails the use of radio and television to convey information. This form of media uses journalists to pass the information to other people concerning the target audience.

Television and radio remain the principal source of information and entertainment for people exposed to mass media. These channels are both influential because they reach a broader audience.

Additionally, they combine visual images, sound, motion, and color to empathize with the viewers and listeners. Broadcast journalism involves researching and reporting the news.

Several jobs are available in broadcast journalism. One can work as a reporter or work behind the scenes as researchers and producers who find background details about stories and interview other people.

These jobs include researchers, editors, news reporters, camera specialists, graphic designers, producers, and directors. Researchers are the people who go out and source for information to be passed through either television, radio, or online platforms.

On the other hand, editors edit the information received to ensure that it is safe to be viewed by the audience. Graphic designers and camera specialists ensure that the images displayed on television and online platforms are good for viewing.

News anchoring is what I am interested in, mainly reporting the news through the channels like television, radio, and online platforms.

News presentation is an art of journalism whereby an individual presents news during a news program either on the television, the radio, or online. A news reporters’ role has developed over time to what it now is.

However, these news anchors occupy a contestable role in news broadcasts. The news reporters can be working journalists who assist in the collection of news material. Also, the news presenters may provide commentary assistance during the news program session.

Often, news presenters and anchors work from the studios. These studios can be either television or radio studios. Recently, there has been a new group of news anchoring, which is done online. These online news reporters present the news from remote areas of their interest because not all of them own studios.

I take much interest in learning about journalism, particularly in news reporting. I want to study anchoring in the next two years of college and hopefully work as a news anchor one day in one of these radio or television companies.

News anchors work during news program sessions and when breaking news needs to be communicated to people. News anchors also have the opportunity to run specific programs. 

There are news anchors who report on specific topics like either politics, sports. I want to learn about these anchoring groups and maybe choose one that I can work on in the future.

Working as a news anchor journalist appears to me to be a profession that can take many different aspects from day-to-day life activities. It involves learning about what an issue is and identifying the blur lines while presenting to an audience.

Appealing to the audience is the main aim of a journalist since it is a profession that depends on the public’s interest. News anchors have to learn how to unpackage the stories using headlines that will catch the attention of the listeners and viewers. 

In conclusion, I am very interested in learning more about this profession and hopefully working in this career field in the future.

A career research paper is important for students because it opens their eyes and helps them see the employment world. It also helps them know what skills will be of help to them, what challenges they will face, and how they will overcome them. This way, they will step into the world of opportunities with an idea about how things work.

To help you explore careers, you might be interested in, learn the challenges and maybe reconsider your choice if it’s not an area you’d like to pursue.

Learn about your values, interests, personality type, and aptitude. Then identify areas you’d like to explore. Please do thorough research on them, then after figuring out the pros and cons, make your career choice and write down your goals.

Your career goals, Interests, and talents, the thesis statement, the pros and cons of the particular career, and an outline.

Start by discussing your career goals, describe your talents and interests, focus on one career, cite sources, and then explore the career’s advantages and disadvantages.

Choose a career, then describe your talents and interests, focus on one career, and outline the advantages and disadvantages of the career.

A career research is an extensive research to determine which job is best for you.

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The Vital Role of Career Pathways in Nursing: A Key to Growth and Retention

Abdulqadir j nashwan.

1 Nursing Department, Hamad Medical Corporation, Doha, QAT

This editorial highlights the importance of career pathways for nurses, emphasizing their role in fostering personal and professional growth, building a diverse and adaptable nursing workforce, and promoting staff retention. Healthcare organizations can empower nurses to reach their full potential and address the nursing shortage by offering a clear roadmap for advancement. The development and promotion of career pathways contribute to a stable and experienced workforce and ensure the delivery of high-quality patient care in today's complex healthcare environment. Prioritizing career pathways is crucial for nursing education, professional development, and long-term success in the healthcare sector.

The nursing profession has long been a cornerstone of our healthcare system, providing the essential care and expertise that patients need during critical moments in their lives [ 1 ]. As the demand for highly skilled nurses continues to grow, it is crucial that we consider the importance of career pathways for nurses and the impact they have on professional development and staff retention.

Career pathways for nurses are not merely a way to map out potential progressions; they also foster personal and professional growth [ 2 ]. By providing a clear and structured roadmap, nurses can visualize their long-term career goals, identify the necessary steps to achieve them, and make informed decisions about their professional development. This sense of purpose and direction empowers nurses to reach their full potential and enables them to adapt to the ever-evolving healthcare landscape.

Moreover, career pathways are essential to building a resilient and diverse nursing workforce [ 3 ]. When nurses are encouraged to explore different areas of practice, they can acquire a wide range of skills and knowledge, making them more versatile and valuable to their organizations. This adaptability is crucial in today's complex healthcare environment, where the challenges faced by nurses are as diverse as the patients they serve.

Investing in career pathways for nurses is also a strategic move for healthcare organizations, as it directly correlates with staff retention. By providing clear opportunities for growth and development, organizations demonstrate their commitment to nurturing the talents of their nursing staff. This support is crucial in maintaining employee satisfaction and engagement. A study by the American Association of Critical-Care Nurses (AACN) found that hospitals with professional development programs in place experienced a 34% decrease in nurse turnover rates [ 4 ]. The implementation of these programs not only reduces the financial burden of high turnover but also contributes to a stable and experienced workforce that can consistently deliver high-quality care.

There are several reputable programs available that support nurses in advancing their careers. One such example is the Magnet Recognition Program®, created by the American Nurses Credentialing Center (ANCC). This program promotes excellence in nursing practice and fosters professional growth within a supportive environment. The National Health Service (NHS) in the United Kingdom also offers initiatives like the Preceptorship Framework and the Clinical Academic Careers Framework to aid nurses in career advancement. Additionally, Hamad Medical Corporation (HMC) in Qatar, one of the largest healthcare providers in the Middle East, has implemented a career framework that provides structured support and guidance to nurses throughout the career planning process. This framework enables nurses to acquire new skills and achieve their professional goals.

Additionally, career pathways can help combat the nursing shortage [ 5 ]. As older nurses retire and the need for healthcare services increases, the gap between the number of nurses required and the number available will only grow wider. Healthcare organizations can attract new talent and retain their experienced staff by offering enticing career opportunities and promoting professional development.

In conclusion, the development and promotion of career pathways for nurses are vital for the nursing profession's growth and the healthcare system's stability. By recognizing and supporting nurses' career aspirations, healthcare organizations can foster a highly skilled, diverse, and motivated workforce better equipped to navigate the complex challenges of modern healthcare. To ensure the longevity and excellence of our nursing workforce, it is crucial that we prioritize career pathways as a key component of nursing education, professional development, and staff retention strategies.

The authors have declared that no competing interests exist.

Career Path Prediction System Using Supervised Learning Based on Users’ Profile

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• Hrugved Kolhe 41 ,
• Ruchi Chaturvedi 41 ,
• Shruti Chandore 41 ,
• Gopal Sakarkar 42 &
• Gopal Sharma 43  

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 968))

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In the past few years, technology has changed drastically and due to COVID-19 pandemic, people spend more time on screen. The use of social media platforms has also been increased and this affects the human mind and decision taking ability. Online career counseling is largely supported these days and hence this paper proposes an online career prediction system using supervised machine learning based on the user’s profile. This research attempted to develop a model for the user which predicts the career path in a precise manner and gives actionable feedback and career recommendations to encourage them to make significant career judgments.

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Kolhe, H., Chaturvedi, R., Chandore, S., Sakarkar, G., Sharma, G. (2023). Career Path Prediction System Using Supervised Learning Based on Users’ Profile. In: Shukla, A., Murthy, B.K., Hasteer, N., Van Belle, JP. (eds) Computational Intelligence. Lecture Notes in Electrical Engineering, vol 968. Springer, Singapore. https://doi.org/10.1007/978-981-19-7346-8_50

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Research Article

Mapping career patterns in research: A sequence analysis of career histories of ERC applicants

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Independent Expert, Affiliated with Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

Contributed equally to this work with: Sara Connolly, Stefan Fuchs, Channah Herschberg, Brigitte Schels

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Norwich Business School, University of East Anglia, Norwich, United Kingdom

Affiliation Institute for Employment Research, Nuremberg, Germany

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Institute for Management Research, Radboud University, Nijmegen, The Netherlands

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliations Institute for Employment Research, Nuremberg, Germany, Friedrich-Alexander University of Erlangen-Nuremberg, Nuremberg, Germany

• Claartje J. Vinkenburg, 
• Sara Connolly, 
• Stefan Fuchs, 
• Channah Herschberg, 
• Brigitte Schels

• Published: July 29, 2020
• https://doi.org/10.1371/journal.pone.0236252
• Peer Review
• Reader Comments

22 Jun 2021: The PLOS ONE Staff (2021) Correction: Mapping career patterns in research: A sequence analysis of career histories of ERC applicants. PLOS ONE 16(6): e0253832. https://doi.org/10.1371/journal.pone.0253832 View correction

Despite the need to map research careers, the empirical evidence on career patterns of researchers is limited. We also do not know whether career patterns of researchers can be considered conventional in terms of steady progress or international mobility, nor do we know if career patterns differ between men and women in research as is commonly assumed. We use sequence analysis to identify career patterns of researchers across positions and institutions, based on full career histories of applicants to the European Research Council frontier research grant schemes. We distinguish five career patterns for early and established men and women researchers. With multinomial logit analyses, we estimate the relative likelihood of researchers with certain characteristics in each pattern. We find grantees among all patterns, and limited evidence of gender differences. Our findings on career patterns in research inform further studies and policy making on career development, research funding, and gender equality.

Citation: Vinkenburg CJ, Connolly S, Fuchs S, Herschberg C, Schels B (2020) Mapping career patterns in research: A sequence analysis of career histories of ERC applicants. PLoS ONE 15(7): e0236252. https://doi.org/10.1371/journal.pone.0236252

Editor: Ting Ren, Peking University, CHINA

Received: October 25, 2019; Accepted: July 2, 2020; Published: July 29, 2020

Copyright: © 2020 Vinkenburg et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Unrestricted and uncontrolled access to the complete career history data (in terms of position, institution, contract type, location etc. of all spells since PhD) compromises the confidentiality and privacy of research participants, and violates the conditions on ethics approval obtained for this study from the Ethical Committee of the European Research Council. Simplified de-identified data sets that contain minimal but relevant personal (age, gender, children, etc) and career related variables, including a career pattern denominator, are available upon request. The data sets are available through the University of East Anglia: https://people.uea.ac.uk/en/datasets/mapping-career-patterns-in-research-a-sequence-analysis-of-career-histories-of-erc-applicants(a64c76cc-da8f-4ab1-b19f-7a3b3a814d7f).html . Please contact [email protected] to explain why you need the data and purposes for which they will be used. The data will be made available through one of the beneficiaries of our ERCAREER grant, Professor Sara Connolly.

Funding: This work was supported by the European Research Council (ERC https://erc.europa.eu/ ) Coordination and Support Action (CSA) [ERC-CSA-2012-317442], project acronym ERCAREER, awarded to CJV SC SF. The funder was instrumental in data collection.

Competing interests: The authors have declared that no competing interests exist

Data on the career paths of young researchers would help […] . There is a pressing need for greater transparency about the likelihood of PhD students and postdocs following an academic career to the higher levels . […] . vn [ 1 ].

Introduction

The need to map research careers is tied to policy efforts to stimulate career mobility and enhance career development for researchers [ 2 – 4 ], with the ultimate goal to strengthen innovation and the knowledge economy. However, despite some efforts to map research careers in the European context [ 5 – 9 ], exactly how research careers develop in terms of patterns or moves through positions and institutions remains largely uncharted territory [ 1 ]. Research careers are often described in terms of outcomes (i.e. publications) [ 10 ] or mobility events (i.e. international moves) [ 11 ]. Following Abbott, we view the career pattern itself as an outcome [ 12 ]. After obtaining a PhD, researchers move through job positions within and between institutions. From a holistic life course perspective, careers are not (only) marked by singular specific events but also a sequence of states that may differ in progression and timing [ 13 ]. However, details on differences in career trajectories of individual researchers are lacking [ 14 ]. Based on full career histories of European Research Council (ERC) Starting and Advanced grant applicants, we contribute to earlier studies of research careers by mapping the career patterns of men and women researchers from their PhD to more established careers. We do not start from theoretical or anecdotal assumptions about career patterns but use a relatively new analytical strategy developed to empirically capture the nature of career patterns over time and place, providing an overview of research careers in different disciplinary and national settings across Europe.

Career patterns can be interpreted as objectively observable paths of movement through occupational hierarchies [ 15 ]. However, despite the ubiquitous presence of the term “career patterns” in discourse and writings about careers in research, earlier efforts to track research careers yielded limited evidence on exactly how research careers develop over time. We often assume that researchers follow a very similar and traditional career path after obtaining their PhD degree [ 16 ]. The normative expectation of upward mobility has changed from a stylized career path [ 17 ] based on a very limited number of academic “rites of passage” (e.g. PhD defense, inaugural lecture) toward a new career model of cumulative promotions [ 18 ]. However, such expectations and assertions are rarely built on an evidence base of actual career patterns in research. Our analysis reveals how research careers develop over time, in terms of moving through positions and institutions, and whether career patterns beyond the “traditional” can be identified among researchers who apply to the ERC.

The ERC in looking for “excellence only” aims at selecting “groundbreaking” and “truly novel research” for funding [ 19 , 20 ]. By funding and thus organizing excellent science at the European level [ 21 – 23 ], the ERC extends national funding schemes with unique conditions: generous, long-term, flexible, and risk-tolerant [ 24 ]. The ERC’s prestigious individual research grants [ 20 , 25 ] are awarded based on a peer-reviewed evaluation of the quality of the principal investigator and the research proposal [ 19 ]. Similar to other grant schemes, ERC evaluators rank applications taking into account both the science and the scientist [ 26 , 27 ]. The career histories of applicants, thus, play an important role in the ERC peer review process. Previous studies have shown that funded applicants (grantees) and non-funded applicants in various research funding schemes do not differ (much) on objective quality criteria [ 28 , 29 ] and therefore we include both funded and non-funded applicants in our study. Applications to the ERC are made through a host institution, where the research will be undertaken, and there is typically an internal sorting within institutions resulting in support for only the highest quality applications [ 30 , 31 ]. We therefore argue that both the funded and non-funded applicants are among the most excellent researchers of their generation as their applications have been submitted to the most prestigious European research funding organization. Using an exploratory, empirical approach we study how the careers of these researchers develop and whether they develop in a similar manner–in accordance with the assumed traditional career path in research and matching normative expectations of upward mobility.

In addition, we study another commonly held assumption, namely that the careers of men and women in research tend to develop differently. In their initial report on research careers in Europe, ESF [ 2 ] states that “almost all obstacles and bottlenecks identified during a research career affect the careers of women scientists more severely than those of men”, with the main underlying cause of this difference being care responsibilities, which fall disproportionally to women. This assumption is found extensively in the literature and also resonates in the call for proposals sent out by the ERC gender balance thematic working group in 2011 to map “the paths and patterns, differences and similarities in the career paths of women and men ERC grantees”. Our proposal was selected by the ERC to explore gender aspects in career structures and career paths of applicants.

However, despite women’s relative underrepresentation at the highest levels in most research fields [ 32 ], and given that women ERC grantees have lower publication rates than men [ 33 ], we do not know whether women researchers’ career develop at a different speed or in a different way than men’s, nor do we know the actual impact of care responsibilities on career patterns. We therefore empirically test the likelihood of men and women following different career patterns, as well as the extent to which certain personal and institutional characteristics affected this likelihood differentially for men and women.

To map career patterns in research across disciplines around Europe, we use a specific kind of sequence analysis called Optimal Matching Analysis (OMA). OMA incorporates timing alongside transition between occupational states, offering an appropriate analytical tool for the study of careers [ 34 , 35 ]. Abbott [ 36 , 37 ] proposed using OMA, as an appropriate method for measuring life courses “as they are”, calling this descriptive approach a paradigm shift from causes to events. OMA is used to identify order in sequences by analyzing the similarity of sequences to one another and sorting them into groups of similar sequences [ 13 ]. Using data on career histories of ERC grant applicants, OMA provides insights into career patterns among early and established researchers, highlighting differences and similarities. For each grant scheme we identify patterns reflecting combinations of positional and institutional sequences, different progression logics, and movements–including leave or spells of unemployment. In distinguishing five career patterns for early and five for established researchers across Europe, we explore whether certain patterns are more common or “conventional” than others, whether some patterns are associated with greater likelihood of application success, and how gender and other personal, disciplinary, and PhD-related factors affect the likelihood and appearance of career patterns. This mapping of research career patterns should inform research policy, in terms of promoting career development, mobility, and gender equality in funding.

Career patterns in research

The origins of the construct of career patterns can be found in industrial sociology where “it was viewed, objectively, as the number, duration, and sequence of jobs in the work history of individuals” [ 38 , 39 ]. Career conventions, or general agreements on descriptions of common career patterns, are likely to be normative, in the sense that they provide prescriptions of what careers in research should look like. The notion of an ideal career in research likely translates into career conventions in terms of linearity or steady progress [ 40 ], early successes [ 41 ], institutional prestige [ 42 ], and (inter) national mobility [ 43 , 44 ].

These conventions have been surprisingly stable despite the increasing demographic diversity of those who do research and the challenges to the conventional view of research careers associated with this diversity, most notably perhaps with respect to the representation of women [ 32 , 45 , 46 ]. It is evident that career conventions matter in selection decisions (including funding). Decision makers use signals such as linearity and mobility (upward and across borders), sometimes even as a proxy for excellence [ 44 , 47 ]. Careers as represented by CVs play an important part when funding decisions are made [ 26 , 27 , 48 , 49 ] and are viewed through lenses that are affected by the context, culture, and gender of the candidate and the evaluator. Knowledge of career progress in terms of moving between positions within and across different types of institutions (e.g., universities, research institutes) is important for the evaluation of researchers’ standing and independence [ 25 ].

Despite more than a decade of efforts to track research careers across disciplinary and national contexts, conclusive answers on career patterns of researchers are missing. To gain insight into the existing empirical evidence on career patterns in research, we performed an extensive literature review (see S1 File for search strategy and detailed findings; and [ 14 ] for an earlier version of the review). From the final set of 40 peer reviewed sources, we conclude that the number of existing empirical studies that shed light on what career patterns in research “objectively” look like is very small. While many sources refer to the existence of “career patterns”, there are actually only three studies that empirically distinguish unique patterns in research based on temporal combinations of positions and institutions. Two of these use CVs to identify distinct career patterns for senior administrators in U.S. universities [ 34 , 50 ]. The third is a recent paper [ 51 ], which differentiates five early career patterns based on narratives from young academics crossing disciplinary, institutional, and national borders.

The majority of the sources reviewed in fact do not distinguish patterns, but rather characteristics of careers, predictors of career advancement, or mobility events. What authors call “patterns” are typically counts of mobility events collected from CVs or surveys. Most sources by virtue of their data are limited to one location or one discipline. The dominant theme is gaining an understanding of how (international) mobility, early success (e.g. grants), publications and/or citations contribute to promotion, prestige, and income. A second dominant theme is gender [ 52 , 53 ]. The common assumption that women’s careers in research are less likely than men’s to resemble an uninterrupted linear pattern, due to women’s typically larger share in care responsibilities, is both a rationale for and a finding of studies looking at gender in research career. Gender differences in career advancement or representation are hypothesized to result from gender gaps in publication or mobility. Given that career indicators are used to evaluate grant applications, the finding that women receive lower evaluations on their “quality of researcher” assessment than men [ 26 , 27 ] may reflect both a greater actual diversity in career patterns amongst women than men and assumptions made about such diversity in career patterns.

In conclusion, our literature review (see S1 File ) reveals a profound disconnect between compelling notions of what a conventional career in research looks like, and the lack of insight into the appearance and frequency of “actual” career patterns in research across distinct institutional, disciplinary, and national contexts. Our study sheds light on the reality behind normative career expectations and conventions, and takes a holistic view across different contexts. Based on the limited empirical evidence on research careers, we test the likelihood of following a particular career pattern depending on the context (in- or outside academia, institutional prestige) and personal characteristics (gender, children, cohort).

Data and methods

Research context.

The European Research Council (ERC) established its grant schemes in 2007 in order to “support investigator-driven frontier research across all fields, on the basis of scientific excellence” [ 54 ]. The Starting Grant scheme (StG) was intended for researchers up to 12 years after their PhD, with subcategories for “starters” (within 7 years of the PhD) and “consolidators” (8–12 years after the PhD). Since 2013, the Starting Grant scheme has been divided into the separate Starting and Consolidator grants, but at the time we collected our data, this was a single scheme. The Advanced Grant scheme (AdG) is aimed at established researchers with a strong research record who are considered to be leaders in their field. Funding entails a long-term, individual grant in order to conduct groundbreaking, curiosity-driven, high-risk high-gain research–from 1.5 to 2.5 million Euros. Applications are accepted across disciplines and reviewed by expert sub-panels within the umbrella of three domains: LS–life sciences; PE–physical sciences and engineering; SH–social sciences and humanities (details in S3 File ).

Participants and procedure

We used data on individual career histories that we collected in a survey of ERC applicants. The advantage of the survey is that respondents were directly asked whether they experienced career interruptions such as unemployment, parental leave. These career breaks may be underreported in their CVs. Due to data protection regulations, the ERC gave us permission to survey those who had applied for the StG in 2012 –as applicants from previous years were surveyed as part of an earlier ERC funded project [ 55 ]–and all AdG applicants between 2007 and 2012. Therefore, our potential sample comprised applicants who gave consent for the use of their data at the time of application to the ERC (33% of StG and 39% of AdG applicants). Our data collection and protection procedures were described in the declaration on ethics considerations of ERC-CSA-2012-317442 ERCAREER, approved by the ERC Executive Agency, in compliance with the terms of Regulation EC 45/2001, and included written consent of survey participants.

The surveys were constructed to collect data on the paths that researchers take from PhD to their current position. The survey design for StG and AdG applicants was slightly different, to reflect the relative length and complexity of the career. For both surveys, we included questions on job positions and institutional affiliations of all spells of employment after completing the PhD, as well as other states, such as unemployment and different types of leave. To account for differences in career length and complexity, the survey for the AdG applicants started with the first job position, for StG applicants directly after the PhD. The surveys also included questions on reasons for mobility or changes in position, family situation, parental leave and other career breaks, perceived institutional support, and career aspirations (StG only). For replication purposes, a pdf version of the online surveys is provided (see S2 File ). The information collected via the surveys was matched with information provided by the applicant on their application form (contact information, host institution, gender, nationality, year of PhD) and some administrative information (sub-domain, application outcome) provided by the ERC.

A personalized email invitation with a link to an online survey was sent in October and November 2013 via email to 1,588 StG 2012 applicants (460 women, 29%) and to 4,088 AdG applicants (632 women, 15.46%) from the cohorts 2008 to 2012. Respondents who did not finish the survey were excluded from the analysis. For our analysis of career patterns, we used 322 completed responses from StG applicants (20% response rate, 126 from women, 39%) and 737 completed responses from AdG applicants (18% response rate, 145 from women, 20%). The StG 2012 and AdG applicant samples are representative of their respective populations in terms of discipline composition (see S1 File ). However, funded grantees and women are over-represented in the sample, possibly because grantees may have felt an obligation to the ERC, and women may have been more motivated by the topic of the survey and thus more likely to respond to the invitation. We calculated probability weights relating the sample population with the ERC applicants’ population based on gender, discipline and grantees, which we apply in our bivariate descriptive analysis. Weighting changes the share of women and grantees in each cluster; however, the findings are robust when comparing the results from unweighted or weighted data.

Identifying research career patterns

In the first step of our analysis, we used Optimal Matching Analysis (OMA) with cluster analysis in order to identify and compare groups of typical research careers. Following this approach, we conceptualize the unfolding of careers as outcomes [ 56 ] reflecting researchers’ trajectories through different positions and institutions. OMA is an exploratory method to identify patterns, in terms of sequences of states (position and succession) in longitudinal data [ 37 ], and, thus, a recommended analytical method in careers research [ 57 ] (see [ 13 ] for a critical overview of applications of OMA).

To model research careers, we defined ten positional and seven institutional states that capture the variance we are interested in. We include five different job positions that reflect differences in status, hierarchy and tasks: (1) Postdoc; (2) Lecturer; (3) Senior Lecturer; (4) Professor; and (5) Other job. Each of the categories also includes comparable job descriptions from different national and discipline-specific contexts. The categorization was based on a coding scheme that we developed from a preliminary analysis of 180 CVs of ERC applicants. It was cross-checked with existing European frameworks for research careers [ 4 , 58 ], (details in S1 File ). While the position labels used in the analysis reflect common denominations in university settings (e.g. senior lecturer), the survey provided examples of equivalent labels used in non-university settings (e.g. senior researcher). The five other positional states were: (6) Unemployed; (7) Research leave; (8) Parental leave; (9) Other status (e.g., illness or military service); and (10) Gap, if no information is provided. We defined the following seven institutional states: (1) Universities and other institutions of higher education; (2) Non-profit research institutions; (3) Commercial research institutes; (4) Hospitals or clinics; (5) Government; (6) Private organizations; and (7) Other.

In the analysis, we identified the positional state and the institutional state for each person in each month from PhD to application for ERC grant. An example for three researchers A, B and C is given in Table 1 . Each combination of numbers (e.g. 8–7) reflects a combination of position and institution. A and B have been in a postdoc position at a university (1–1) in the first month after PhD; after 36 months, A is a lecturer at a university (2–1) while B is on parental leave (8–7); after 72 months, A is a senior lecturer at another university (3–1) while B is a government policy officer (5–5). In contrast, C started in a job in a commercial research institute (5–2) after PhD and stayed in this job for several years, before they are, 72 months after PhD, in an executive position in the same institute (3–2). While the careers of A and B start in the same way, they develop differently. In contrast, the career of C runs through different positions from the beginning. OMA is an explorative method that allows to investigate whether there are comparable structures and differences within individual research careers that are aggregated to typical patterns.

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https://doi.org/10.1371/journal.pone.0236252.t001

OMA compares each sequence in a sample with any other sequence and calculates distances between the sequences. To do so, OMA calculates the costs of transferring one sequence into another by deleting, inserting, or substituting the states of a sequence. Costs are assigned to each of these transformations. The OMA calculates the distance between any two sequences as the minimum possible costs of transformation and generates a matrix of distances for all sequence combinations. In other words, the distance between the sequences of two individuals is lower when fewer steps are needed to make them equal (as can be illustrated using the example in Table 1 ). For this analysis, we used the information on the sequence of job positions and institutional affiliations for each month after finishing the PhD until the date of application for the ERC grant, so that the length of the sequences varies between individuals. Due to the structure of the survey, the period of observation for the AdG applicants started with the first job position, for StG applicants directly after the PhD. Robustness test show that restricting the StG observation to the first job position would not change our results. The appearance of the career patterns is not driven by differences in career length (details in S3 File ).

For our analysis, the sequences of job positions and institutional affiliations for each person were treated as two channels: in a first step of the analysis, the costs are specified separately for each channel: second, the substitution costs for each time point are aggregated in order to calculate a combined substitution cost matrix [ 59 ]. The costs for insertion and deletion (“indel costs”) were set at 1 and substitution costs were set at 2. In our setting, substitution operations are as expensive as one insertion and one deletion operation so that they can be interchangeable in their use [ 57 ]. The calculated distances measures were normalized to account for differences in sequence lengths. We applied the OMA for each sample, StG and AdG separately, using the statistical software R and “TraMineR”.

In order to identify the main typical career patterns after OMA, hierarchical Ward cluster analysis was used to group sequences according to their similarity based on the matrix of distances generated. Sequences bundled within a particular group are close to one another and distant to other sequences. From the cluster dendrograms (see S3 File ), the space of meaningful distinctions and then the possible number of groupings were derived [ 60 ]. Furthermore, the grouping of sequences was chosen that offered the best explanatory power for the overall research questions [ 61 ]. For each sample, StG and AdG applicants, we identified five distinct clusters representing unique career patterns, described in the results section.

Analyzing characteristics of researchers in the career patterns

In the second step of the analysis, we estimated which characteristics influence the likelihood of researchers belonging to one pattern using multivariate multinomial logistic regressions, estimated separately for StG and AdG samples. Results of the multinomial logistic regressions are presented in the results section. The five distinct career patterns were used as the dependent variable. We applied a robustness test with a two-step selection model to take account of possible response bias to the survey; this did not change our conclusions. Further details on this test are available upon request from the authors.

We were interested whether the likelihood of following a specific career pattern is associated with the research discipline, PhD-related characteristics, and personal characteristics. Descriptive statistics for relevant characteristics can be found in the S3 File ). We included the broad disciplinary areas of the research captured by the ERC categorization between Life Sciences (LS), Physical Sciences and Engineering (PE), and Social Sciences and Humanities (SH). Regarding PhD-related factors, the research prestige of the PhD granting institution for all respondents was measured by assigning the 2014 “Leiden score” (the proportion of the publications of each research institution or university belonging to the top 10% of their field [ 62 ]). A dummy variable was used to control for the cases for which we have no information on the Leiden score of their PhD institution. To control for career-specific factors preceding the period of observation, we controlled for work experience before the PhD and age at the time of the PhD. We considered care responsibilities by parenthood status and age of the youngest child (under the age of three or older). We strictly used information before obtaining a PhD to ensure clear interpretation of which researchers enter which pattern and not to mix up conclusions with outcomes of career processes, for example, parenthood during the period of observation. Personal characteristics include gender and birth cohort. Nationality of the applicants is an additional control variable.

Identified career patterns

Each of the five distinct Starting Grant (StG) and Advanced Grant (AdG) career patterns represents a unique and temporal combination of positions and institutions. The cluster figures provided in Figs 1 and 2 illustrate the order and timing of job positions and institutional affiliations. The upper graphs plot the individual sequences of positions and affiliations for each observation in the cluster and, thus, illustrate the career complexity among researchers. The bottom graphs plot the monthly breakdown of the different status in each cluster. These figures provide an aggregate picture of the share of researchers in each job position and institutional affiliations and the change of these shares over the career progress. Tables 2 and 3 provide additional information on cluster characteristics.

Sequence index plots and status proportion plots for positions and institutions.

https://doi.org/10.1371/journal.pone.0236252.g001

https://doi.org/10.1371/journal.pone.0236252.g002

https://doi.org/10.1371/journal.pone.0236252.t002

https://doi.org/10.1371/journal.pone.0236252.t003

Starting grant career patterns.

In the first StG pattern ( Fig 1 ), the postdoc position is concentrated at the beginning and lectureships at the end. The careers are predominantly located in universities. These movements reflect a career model of upward mobility and we label this pattern as steady progress at universities . In contrast, the second StG pattern shows a relative dense sequence of postdoc, lecturer, senior lecturer, and professor positions. Although this cluster has the longest average duration, the figures indicate that researchers move relatively rapidly from one position to the next up the hierarchy. Again, we find a relative stable institutional university affiliation–we label this pattern quick advances in universities . In the third StG pattern, the postdoc is the dominant position and this is relative stable over time. At the end of the observation period, relatively few researchers have moved to lecturer or senior lecturer positions. Again, the careers are predominantly located at universities and we label this pattern delayed advances in universities . In the StG fourth pattern, the postdoc position is concentrated at the beginning and lecturer at the end. The job position progression is very similar to those in our first cluster–steady progress in universities–but careers are predominantly located in research institutes. We identify this fourth distinct pattern as steady progress in research institutions . The final StG pattern consists of several job positions that are concentrated within relative short careers. Although postdoc positions are the most frequent status, the careers in this cluster also consist of higher shares of unemployment and other jobs compared to the other clusters. Researchers also moved across different institution types. Thus, the final pattern is labeled complicated moves across institutions .

Of the five StG career patterns, three account for more than 20% of the sample and 74% in total: steady progress in universities (27%), complicated moves across institutions (24%), and delayed advances in universities (23%).

Regarding the composition of the patterns (see descriptives in Table 2 ), we can see there are relatively more women in the steady progress in research institutions and complicated moves across institutions clusters than in the quick advances in universities pattern. The grant success rate varies between the different career patterns from 10 per cent in the complicated moves across institution pattern to 19 per cent in the quick advances in universities pattern. However, none of these differences are statistically significant. Excellence, it could be said, can be found in all career patterns.

Advanced grant career patterns.

In the first AdG career pattern ( Fig 2 ), the postdoc position is concentrated at the beginning followed by lecturer, senior lecturer, and professor. The careers are predominantly located at universities; we label this pattern (as we do in for the StG) steady progress at universities . The second AdG career pattern is similar to the first, but reflects longer careers at universities and especially in professorial positions. We label this pattern mature progress in universities , as it reflects steady progress that has reached maturity or even a ceiling. The sequence of job positions in the third cluster is similar to the first cluster but is predominantly located in research institutes. We label this pattern steady progress at research institutes . The fourth AdG pattern reflects steady progress from one position to the next, including a higher share of ‘other’ job positions, within government institutions. We label this pattern steady progress in government institutes . Finally, in the fifth AdG pattern, we again find complicated moves across various institutional settings. Many individuals in this cluster start with postdoc positions, moves to other jobs, and this cluster consists of significantly more states, including unemployment, than others. Thus, the final pattern is labeled complicated moves across institutions .

In comparison to the composition of the StG sample, there is greater coherence amongst the AdG applicants, with over half belonging to a single pattern– steady progress at universities (57%). The two university based career patterns ( steady and mature progress ) account for over three-quarters of the entire sample (see descriptives in Table 3 ). Across different clusters, there are significant differences in career length. Furthermore, women account for about 15% of the sample and are significantly underrepresented in the mature progress at universities pattern. The grant success ranges from 9 per cent in the steady progress in government pattern to 16 per cent in the mature progress at universities pattern. Again, the differences are not statistically significant.

Who follows which pattern?

We examined what factors influence whether a researcher follows one career pattern or another. For each pattern identified, we estimated average marginal effects (AME) from multinomial logistic regressions ( Table 2 for StG and Table 3 for AdG). For categorical variables, the AME indicated by how many percentage points the probability of being in a certain pattern is on average higher or lower for a researcher with certain personal and PhD characteristics compared to the reference category. For example: In the StG sample ( Table 2 ), female researchers have an 8 percentage point higher probability of being in the steady progress in research institutes pattern and a 10 percentage point lower probability of being in quick advances in universities than male researchers. For continuous variables, such as age and Leiden score, the AME indicated by how many percentage points the probability of being in a certain cluster increases (decreases) if the variable increases (decreases) by one unit. For example: In the StG sample, being older by one year when receiving the PhD is associated with a 2 percentage point increase in the probability of being in the complicated moves across institutions pattern.

Starting grant applicants.

Given the role of institutions in deriving career paths, it is unsurprising that there are some discipline-based differences: researchers from the Life Sciences (LS) have a significantly higher probability of making steady progress in research institutes when compared to researchers from Social Sciences and Humanities (SH).

Those who were older when receiving their PhD are more likely to make complicated moves across institutions , and less likely to be in the quick advances at universities pattern. Moreover, researchers who had other work experience before commencing their PhD are less likely to make quick advances at universities . These findings indicate that there are path dependencies between fast progression towards the PhD and quick advances in the career after PhD. Presence in the complicated moves across institutions pattern is negatively correlated with the prestige of the institution from which researchers received their PhD. Whether researchers have already been internationally mobile during their PhD or not does not make a significant difference in terms of career pattern. Finally, compared with those who were not parents at the time of completing their PhD, those with older children (over three years) at the time of receiving the PhD have a lower probability of being in steady progress at universities and a higher likelihood, only significant at the 10 percent level to be in the delayed advances at universities pattern.

Regarding personal characteristics, scientists from the later birth cohorts, born after 1970, are less likely to be making quick advances in universities , and more likely to be in the delayed advances in universities , the steady progress in research institutes , as well as in the complicated moves pattern. Women are less likely than men to make quick advances in universities . Furthermore, women are more likely to be in the steady progress in research institutes pattern than men (only significant at a 10 percent level). This difference is not only linked to the high proportion of Life Sciences (LS) in research institutes, where women are proportionally overrepresented, but also to a generally higher likelihood of women to be employed in research institutes than men.

We also examine whether the careers of male and female scientists tend to develop differently by estimating the multinomial logistic regressions with interaction terms between gender and PhD-related characteristics (results presented in S3 File ). The results indicate whether PhD-related characteristics make differences in the probability of being in a certain pattern for a male and female researcher. There are few gender differences. Research prestige of the PhD institution based on the Leiden score is a significant factor for men only, in particular for their likelihood to be making complicated moves across institutions . Those who were parents at by the time that they completed their PhD are generally less likely to be in the steady progress at universities pattern. However, having care responsibilities for children over the age of three when receiving the PhD is a stronger factor for women. It is associated with a lower likelihood of women of making complicated moves cluster as well as increasing the likelihood of making delayed advances at universities compared to the women in the sample who are not mothers.

Advanced grant applicants

There is a significant difference between patterns regarding research discipline. Given the predominance of LS research undertaken in research institutes, it is unsurprising that we again observe a higher likelihood of life scientists making steady progress at research institutes . SH is more dominant at universities so that social scientists or humanities scholars have a higher probability than those in LS of being in the steady or mature progress at universities pattern and a lower probability of being in all other patterns.

Those who were older when starting their first job after their PhD are more likely to make complicated moves across institutions , as reported for the StG sample. Furthermore, there are differences in the likelihood of being in mature or steady progress at universities observed by age of completing PhD, given otherwise equal age as controlling for birth cohort. Those in the mature progress in universities pattern in general were younger when starting their scientific career in contrast to those making steady progress in universities . Birth cohort, not surprisingly, is an additional differentiating factor between those having made mature progress at universities and those who have not. Moreover, steady progress in government is negatively correlated with the prestige of the institution from which researchers received their PhD (significant at 10 percent level). Neither international mobility during the PhD, work experience before receiving the PhD nor having children at the start of the career after PhD are significant factors in the likelihood of being in a particular career pattern. There is some significance for parenthood at the time of PhD. Finally, we find some gender differences as women, ceteris paribus , are less likely to be making matured progress at universities .

There are hardly any gender-specific relationships between relative early parenthood and career patterns (results presented in S3 File ). Women researchers who had older children when starting their first job after the PhD are less likely to be making steady progress in government when compared to researchers who are not parents.

Discussion and conclusion

Using sequence analysis of self-reported career histories of ERC applicants, we have identified multiple and distinct career patterns that represent combinations of positional and institutional sequences, different progression logics, and movements. Our contribution responds to the gap in the empirical literature, and the need expressed by policy makers and the broader scientific community [ 1 ], by mapping research careers and providing evidence-based insight into not only the variety in research careers but also into the breadth of institutional environments in which research is undertaken–thereby challenging conventional wisdom on research careers in the European context. Our results confirm that cumulative upward mobility is (still) the norm for research careers. In our study this is reflected in the predominant steady progress career patterns. However, the ‘road to excellence’ cannot be characterized only by this traditional pattern–as conventions would have it. We found divergent career patterns including complicated moves that do not follow conventions of smooth progress. In particular among early career researchers in the Starting Grant sample (StG), differences in career patterns reflect differences in timing as illustrated by quick versus delayed advances . This variety in research careers is visible in our sample of applicants to the most prestigious individual research grant scheme in Europe. While the proportion of funded versus non-funded applicants is not the same across patterns, grantees are found in each; therefore one of our key results is that excellence in terms of ERC grant success is found across all career patterns. Both the variety of patterns and the presence of grantees across all patterns add to the validity of our findings. Even if based on a narrow population (because only an elite group of potentially excellent researchers applies for this kind of competitive funding), we have a broad sample that is representative of the ERC applicant population covering applicants from all disciplines, from EU and non-EU countries, and including both early and later career stages. A different sample may be distributed differently across patterns but would only produce limited additional patterns.

Across both samples of early and established researchers, we have identified two conventional and common career patterns of steady progress in universities or research institutes and a third, less conventional pattern, of complicated moves across institutions. In addition we find three career patterns that are uniquely related to the career stage: quick and delayed advances for researchers applying to the Starting Grant (StG) and mature progress for the Advanced Grant (AdG)–all within universities. The pattern of steady progress in government appears only for the AdG, but forms part of the complicated moves across institutions pattern for the StG. Steady progress is thus more common for AdG than StG, reflecting not only the more exclusive nature of this sample of established researchers but also career length–enough time has passed to detect steady progress. Although we observe cohort effects, delayed progress in universities or steady progress in research institutes are more common than the 5 other patterns amongst those born after 1970 and steady progress in universities is more common that mature progress . Our robustness analysis of the pooled samples (see S3 File ) suggests that the differences in the appearance and the frequency of patterns between the StG and the AdG are to a large extent an age or tenure effect, meaning that those in the StG patterns will develop towards the equivalent AdG patterns over time.

Positions (and moves between positions) are more important in differentiating between patterns than institutions. Our parallel analysis of job positions and institutional affiliation shows that career progression primarily means changing positions, while movements across institutions are less common. One exception is the pattern of complicated moves across both positions and institutions. Institutions thus host research careers, and where careers develop (inside which kind of institution) is often a matter of discipline. We also see that different institutions host similar career patterns of steady progress –universities, research institutes and government—a finding that extends our understanding of research careers beyond those in universities.

In contrast to the prominent assumption that women’s careers in research develop differently from men’s, gender in itself makes little difference in terms of which career patterns men and women follow. Women among the StG applicants are less likely to be in the quick advances cluster; the small numbers of women among the AdG applicants are less likely to have achieved mature progress in universities. There are some indications that having children at the time of PhD affects men and women’s career differently and that differences are more pronounced in the StG sample. One possible explanation for this finding is that the AdG sample is more selective as a consequence of low(er) representation of women in the older cohorts. The intersection of career mobility, children’s ages, and timing of funding [ 63 ] is something that deserves further exploration. With more detailed information on family formation and partners’ careers available, sequence analysis could be applied to a joint analysis of work and family trajectories (e.g.; [ 56 ]), to explore the interlocked nature of family patterns and research career patterns. The fact that we find only very limited gender differences in career patterns, undermines the common assumption held by policy makers and contradicts the (limited) empirical evidence that careers of men and women in research develop differently. However, this could be an effect of the exclusive nature of our sample of ERC applicants. If career patterns do not differ between men and women applicants, but success rates in research funding do, we must reconsider the importance of CVs and gendered assumptions in selection decisions.

Our analysis also shows that discipline matters for career patterns. When looking at the researchers in each pattern, it is clear that those following careers in research institutes are typically from the Life Sciences. Path dependence makes a difference in terms of following particular career patterns. Prior work experience, age when receiving their PhD, PhD obtained from different prestigious institutions, and having children at the time of PhD are differentiating factors in the StG sample. However, we were not able to explore disciplinary differences within patterns, nor could we take underlying social and economic factors related to host country, country of origin, or international mobility (other than moving to do the PhD) into account. Evaluators looking for “excellence only” use career signals from applicants’ CVs including mobility as proxies–a pattern of moves across institutions may be viewed positively when it includes various prestigious institutions across national borders. A route for future research would be to examine career patterns as an individual predictor of grant application success, alongside other personal and prestige indicators. Another would be to examine the stability of patterns both within and between patterns by extending the analysis to further cohorts of grant applicants and by following the StG applicants over time to see whether they continue the same trajectory in the future. The further funding and careers of those who applied but were rejected could also be examined. This would also shed light on the complex interactions of grant funding on the national and European level [ 21 ], as well as career consequences of reaching the quality threshold but not getting funded [ 24 , 29 ]. From a policy perspective, it would also be interesting to study the level of institutional support and the degree to which institutions discourage or even deny researchers the opportunity to apply for an ERC grant, something that may have affected the selectivity of our sample. In Spain, for example, universities’ commitment to ERC “values of excellence” varies from evident to neglected [ 21 ].

Methodologically, our study has limitations but also opens possibilities. Using a survey to capture full career histories may affect response rates and thus limit coverage of an already selective sample in terms of career patterns identified. Sophisticated methods to extract information from CVs as submitted alongside applications have since been developed and tested, which could be used for further research [ 64 ]. A more structured CV format used in the application materials would certainly help in terms of consistency and comparability of career data. Sample size affecting statistical power, sample selection bias, and the analysis of only a single applicant cohort for the StG suggests caution in terms of generalizing our career pattern findings. However, the multichannel sequence analysis method we have used [ 59 ], could be used to identify career patterns among other samples of researchers or scientists, as well as other professions in which a common career start (e.g. initial professional qualification) and/ or ceiling (e.g. making partner) can be established.

This is the first application of sequence analysis to map contemporary European research careers across disciplinary, institutional, and national borders. We have shed light on career patterns in research and we provide a firm basis to explore implications of (un) conventional career patterns for grant application success of men and women in research. We hope our findings on the occurrences and nuances of career patterns in research will inform policymaking, career development, mobility, and gender equality in the European Research Area.

Supporting information

https://doi.org/10.1371/journal.pone.0236252.s001

S2 File. Survey questions.

https://doi.org/10.1371/journal.pone.0236252.s002

https://doi.org/10.1371/journal.pone.0236252.s003

Acknowledgments

We thank the School of Business & Economics at Vrije Universiteit Amsterdam for hosting the ERCAREER project. We appreciate the insightful and supportive comments from our reviewers, and we thank Dr. Christian Brzinsky-Fay (WZB) for his expertise in developing the patterns visualization,

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Overarching guide on writing a career research paper.

What is a career research paper? It is a paper written about a chosen career that appeals to the writer (student in this case.) A research paper on career is one of the most common since that is the end goal of education – a flourishing career!

The tenets of a top-notch career research paper are that it should:

Highlight your career goals. Discuss your hobbies, talents, and interests. Major on one career. Bring out the facts about the career Discuss the pros and cons of the possible career

Now, the conventions of a career research paper assignment are more formulaic than you might think. Nevertheless, it also is as simple as counting one to five. How is the latter possible? With this expert articulated article, you will find it smooth writing such a paper.

Are you ready to unravel the secret writing formula? Well, keep on reading. Remember, we always save the best for the last part.

How to Write a Career Research Paper

Why can’t I use a career research paper sample and get done with this once and for all? Before you start aiming all those criticism guns, allow me to tell you why such a comprehensive guide is necessary:

It brings out all the information readers need to know It shows readers the order in which they need to write

You will never find these things when you decide to jump right into a career research paper example. That said and done, let us see how to structure a college research paper on careers. We will begin with the most critical part:

A Career Research Paper Outline

The outline of a top career research paper should entail all the career’s positive and negative perspectives. Also, a thorough evaluation of your skills and shortcomings relevant to the subject are essential when coming up with the outline.

How do you achieve an entertaining, informative, and practical outline for your career research paper? Read on.

The Introduction

Someone once said, “show me your introduction and I will tell you whether I will read your paper or not.” A top-grade career research paper introduction should:

Have information about yourself such as your talents, goals, and interests Include a good definition of a career such as nursing, journalism, and engineering – what does the job entail? Contain a thesis statement that clearly explains the focus of the paper – from which perspective are you handling your paper?

Be sure to end the introduction with a strong declarative statement on your research paper’s career choice.

Depending on the topic you chose for your career paper, the body content may vary. However, these are the standard guidelines to help you write it effectively:

• Hierarchically present the information – begin with the essential details, such as the features of the career.
• Have a topic sentence for each body paragraph of your paper.

If you have a research paper on nursing career, these pointers can come in handy for you:

• What is the nature and responsibilities of nursing as a career?
• Which minimum qualifications do I need for this job?
• What are the challenges involved in pursuing this career?
• What are the positives of nursing?
• Is nursing worth pursuing?

For a research paper on career choice, these use some of the ideas below:

Which skills do I have which complement the career responsibilities? Which schools offer the best programs for the career? How does the job I chose to reflect on my career goals? Where do I need to improve to succeed in this career? How many hours will I need to dedicate to this career?

All these ideas and prompts do not only apply to one career choice; they cut across the divide. Feel free to use them to make your career research paper body as in-depth as possible.

And finally,

The Conclusion

Here, you will make a summary of the most relevant points in your discussion. You should have an appealing concluding statement that effectively wraps up the research paper.

The climax of all this is to justify your decision to pursue a particular career.

Careers Research Paper: Pro Tips

To spice up your research paper on careers; these professionally brainstormed tips will act as your anchor:

• Write on a career that appeals to you; this way, you will have more points to discuss.
• Explore career sites such as Careerbuilder.com or Monster.com to get career ideas.
• Delve deep into the benefits and limitations of possible careers. You can make a chart to achieve this quickly.
• Your career + your goals + your skills + your interests = your topic.
• It should be informative and subjective as opposed to a boring story about a career you like.
• Include the trends in the career path you wish to take

Hopefully, at the end of your career paper, you will have a clear picture of what you would like to do in the future.

Career Research Paper Nailed With Ease

Organizing your thoughts is vital in coming up with a perfect research paper on careers. Fortunately, with this guide, you can accomplish that and get your paper started right away!

Nevertheless, if you are still having challenges, you can count on our top-notch research paper writing services. The rates are pocket friendly, and you will not regret one single bit. Post your order now and see your grades soar higher than the eagles.

From College Community to Career Path

Joyce Kim, an advanced doctoral student in sociology and education, wants to know what motivates undergraduates—especially those who are the first in their families to attend college—to choose the career trajectories that they do.

When PhD candidate Joyce Kim, C’15, arrived at Penn as a first-year undergraduate, she found the campus environment liberating. “Having primarily grown up in a homogeneous suburb of Dallas, it was a breath of fresh air to be in a racially diverse, not only campus, but also city,” she says.

Other early impressions were the challenging academics, the intense pressures affecting her Penn classmates and friends, and the fact that so many graduating seniors chose careers in one of three highly lucrative fields: finance, tech, or consulting, “When I first got to Penn I wondered, ‘What in the world is consulting?’ I had no idea,” says Kim, who earned her bachelor’s in political science and, after completing a Fulbright and a master’s degree and working for a time, returned to Penn for a joint PhD in sociology and education.

“When we write our college admissions essays, we often talk about wanting to change the world, which suggests a whole array of career paths,” she says, “but I wondered how it is that students end up getting funneled into these very specific careers.” That question informs Kim’s current research, which examines the college-to-career transition, with a focus on how race and class affect students’ decision-making.

Finding Her Own Path

Kim’s own career path has taken many turns. After graduating from Penn, she won a Fulbright Research Fellowship and lived in Seoul, where she studied how North Korean defectors adapted to civic norms in South Korean society. That work was informed by family history; her father’s parents were refugees from North Korea.

After the Fulbright, Kim worked for an educational nonprofit called Year Up, a workforce development program for underserved young adults that provides skills training, internships, and community college credits. Her next stop was the University of Cambridge for a master’s in education under a Rotary Fellowship, where she briefly continued her work on North Korean defectors before ultimately deciding to return to the U.S. She also spent three years as a researcher at Harvard Business School, where she worked on projects related to how higher education can address intractable social problems.

When she first applied to Penn’s doctoral program, her plan was to study cross-racial student activism on college campuses—an interest that stemmed from her involvement with student minority coalitions when she was in college—but her earlier questions about career choices kept bubbling up. Ultimately, those questions set her on her current path. Her dissertation, which she is just beginning, will examine the different ways inequality can factor into the college-to-career transition.

Objections, Obligations, and “Selling Out”

As a first step for this work, Kim interviewed 62 students at a highly selective college she calls “Eastwood” (a pseudonym) about their career plans. About half the students in the study identified as first-generation and low-income (FGLI) and half as middle class. And, Kim says, because most research on FGLI students has focused on white, Black, and Latinx students, she intentionally sought out Asian students as part of the FGLI group. The study has been recognized with awards from the American Sociological Association and the Society for the Study of Social Problems, and a paper based on the research is forthcoming in the journal Social Problems .

While not all institutions define FGLI in the same way, Kim laid out specific criteria for identifying such students for her study, enrolling only those who were first in their immediate family to attend a four-year university and those whose families met the threshold to receive full financial aid. She acknowledges that some people view the designation as stigmatizing but says the students who participated in this project did not seem bothered by it, viewing it as a way to find community with one another.

Instead, the main theme that quickly emerged was the idea of “selling out.” “It came up repeatedly,” says Kim, “but the flavor and texture this idea took on varied by students’ racial and class backgrounds. Much existing literature focuses on individualistic job values such as finding a ‘good fit,’ pursuing a passion, or work-life balance. I did find evidence for that, but I also found a moral component that was colored by students’ class and racial backgrounds in ways that previous research hadn’t explored.”

She identified, for instance, what she calls “objections based on a value of social good,” such as the desire to avoid working for companies perceived as responsible for some kind of social harm. But she says it was primarily those in her study who identified as Asian and Black FGLI students whose interview responses cited these objections.

For some FGLI students, however, financial considerations took precedence, pitting their social objections against a sense of obligation to family or to their ethnic or racial community. “I found that the Asian and Black students more often cited these familial and ethno-racial obligations as part of why they wanted to pursue certain careers,” Kim says.

“I think the tension that comes with students grappling with the moral aspects of what they want to do and who they want to be is really important.”

She recalls one young woman who went to work for a large online retailer because the job helped her pay her family’s rent, despite her negative feelings about the company. “I remember she kept sighing during our interview,” says Kim. “I think the tension that comes with students grappling with the moral aspects of what they want to do and who they want to be is really important.” Students also reported as points of tension the ratcheting up of on-campus recruitment and competition over club memberships. Club memberships are the topic of a study Kim is working on this summer with Tristan Ly, C’24, a sociology major who graduated in May.

“The Eastwood interviews pointed not only to how students grappled with the moral dimension of their career decisions, but also how at elite private institutions, clubs can impede or facilitate access to resources for the most lucrative, high-paying jobs in finance, consulting, and tech,” she says.

Helping Students Think Critically

Kim’s dissertation fieldwork, beginning this fall, will expand on the Eastwood and club studies to look more deeply into various mechanisms of inequality in the college-to-labor market transition and how they differ by students’ backgrounds—particularly race, class, and gender—and institutional setting. She’ll conduct interviews at one private, highly selective institution and one public, broad-access institution.

“It’s a big umbrella,” she says. “I’ll be looking at what factors either facilitate or impede upward mobility in the college-to-career transition.”

Kim says she hopes her research will have tangible applications. She would like campus staff, faculty, and administrators to be more cognizant of considerations beyond “finding a passion,” such as family needs and other motivations, when advising students. She also hopes that her research can encourage policymakers to take a holistic approach in national conversations about the college-to-career transition.

One of her most formative undergrad experiences was working with the Netter Center, which was a strong draw for her to come back to Penn. She is currently a Provost’s Graduate Academic Engagement Fellow at the Center, and some of the insights from her research will eventually be translated into an Academically Based Community Service class to address the civic purposes of higher education.

As for her own future work aspirations, Kim is looking at the broader picture.

“I’d like to have a career where, through my research and teaching, I can help students and members of the academic community focus on encouraging students to think critically about the purposes of higher education and what it means to them, not only for their careers, but also ultimately as citizens,” she says. “That’s very important to me.”  

Jane Carroll

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