career maturity research paper

• Open access
• Published: 20 October 2023

Relationship between career maturity, psychological separation, and occupational self-efficacy of postgraduates: moderating effect of registered residence type

• Jianchao Ni 1 ,
• Jiawen Zhang 2 , 3 ,
• Yumei Wang 4 ,
• Dongchen Li 5 &
• Chunmei Chen 6  

BMC Psychology volume  11 , Article number:  246 ( 2023 ) Cite this article

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With the slowdown of economic growth and the increasing pressure of employment competition worldwide during the normalized epidemic prevention and control, the job-hunting intention and behavior of college graduates deserve in-depth study. This study explores the relationship between the career maturity, psychological separation and occupational self-efficacy of postgraduates, and provides a theoretical basis for improving their career maturity.

A questionnaire survey was carried out on postgraduates with 584 valid data in China by using the Career Maturity Scale, Psychological Separation Scale and the Occupational Self-efficacy Scale. A structural equation model and bias-corrected self-sampling method were adopted to explore their relationship. The moderating effect of registered residence type was tested.

The results show that: (1) The higher the level of psychological separation of postgraduates, the higher their career maturity. (2) Occupational self-efficacy plays a mediating role in the process of psychological separation promoting career maturity. (3) The registered residence type moderates the latter half of the mediating process of psychological separation, occupational self-efficacy, and career maturity. Moreover, occupational self-efficacy plays a more significant role in promoting the career maturity of postgraduates with rural registered residence.

Conclusions

This study reveals the relationship between the career maturity, psychological separation and occupational self-efficacy of postgraduates. At the same time, it also verifies the mediating role of occupational self-efficacy and the moderating role of registered residence type. The result is helpful for postgraduates to understand the level of their career maturity and improve their career decision-making level and career development ability.

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Introduction

The employment of college graduates is related to people’s well-being, social stability and high-quality development. It is also an important indicator to measure the quality of talent training in colleges and universities. Career maturity, initially proposed by career guidance expert Super in the 1950s (Super, 1953), is a crucial evaluative indicator for assessing individual career development [ 1 ]. He defines career maturity as the psychological, social and physical readiness of young people to choose a career (Super, 1981) [ 2 ]. Building upon Super’s research, Crites (1978) puts forward a more mature theory on the basis of Super’s research [ 3 ]. He holds that career maturity could be used to represent the degree of individual career development and the state of preparation for making career choices. Its key factor is to have clear, rational and correct career goal design and career planning. Furthermore, Kleine et al. (2021) emphasize that career maturity refers to the ability to independently and responsibly make career decisions based on integrating oneself and the work environment [ 4 ]. In this view, career maturity represents not only the individual’s preparedness to choose a career but also their capacity to navigate and adapt to the complexities and dynamics of the work world. By integrating Super’s foundational work, Crites’ emphasis on career development, and Kleine expanded perspective on decision-making and integration, a comprehensive understanding of career maturity emerges. It encompasses the psychological, social, and physical readiness to select a career, the level of an individual's career development and preparedness for decision-making, and the ability to make autonomous and responsible career choices while integrating oneself with the work environment.

Various scholars have conducted in-depth research on career maturity. Scholars find that career maturity has a great impact on the selection of individual positions, and it is the key factor to measure college students’ employment success (Ju & Shin, 2020; Zhang et al., 2018) [ 5 , 6 ]. Tong Huijie’ (2013) studies show that career maturity could predict the probability of an individual successfully obtaining a position [ 7 ]. Moreover, it could effectively predict the job adaptation and job performance of newly recruited college students. The higher the career maturity, the easier it is for an individual to make a suitable career choice, which is correspondingly more conducive to the individual’s career success (Liu Hongxia, 2009) [ 8 ]. In addition, research has supported the idea that self-concept seems to have an effect on career maturity (Greenhaus, 1971) [ 9 ]. Helbing (1984) holds that career maturity is correlated with work orientation and a sense of personal identity [ 10 ]. Dillard (1976) indicates that the relationships between career maturity and self-concepts are relatively weak-positive [ 11 ]. Shelley (1977) concludes that as to the relationship between self-concept, self-actualization and career maturity, a positive self-concept is necessary [ 12 ].

At present, the research on career maturity mainly focuses on undergraduates. There is little research on graduate students, especially postgraduates. Compared with undergraduates, postgraduates have different psychological development and major contradiction in life. They have received a deeper level of higher education and more systematic learning and understanding of professional knowledge. Therefore, the structure, as well as the development characteristics of their career maturity might be different. Therefore, the career maturity of this group has potential value for further research. According to the “National Statistical Bulletin on the Development of Education” issued by China’s Ministry of Education, the country’s graduate enrollment has risen year by year in the past three years. Therefore, the scarcity and competitive advantage of a master’s degree in the job market is gradually decreasing, and the employment pressure they face is gradually increasing. Nowadays, the overall employment market of postgraduates has presented problems such as the mismatch between professional ability and quality with the requirements of employers, mismatch of disciplines and majors with emerging industries, unsynchronized job search and recruitment of employers, and gaps between traditional employment concepts and employment requirements in the new era (Li Jian, 2020) [ 13 ]. Some postgraduates have high expectations of salary and benefits, and are easily affected by the psychological impact of “Being unfit for a higher post but unwilling to take a lower one”, and their professional ability and as well as self-evaluation are prone to deviations, that is, some postgraduates have not yet reached their due level of career maturity. Therefore, it is important to explore the characteristics and influencing factors of postgraduates’ career maturity.

The factors that affect individual career maturity mainly include individual psychology factors (such as psychological separation, career efficacy, etc.) (Lv Aiqin et al., 2008) [ 14 ], family background factors (such as parental occupation type, family economic status, etc.) (Sun & You, 2019; Puebla, 2022) [ 15 , 16 ] and social characteristics factors (such as gender, age, registered residence, etc.) (Bae, 2017; Park & Jun, 2017) [ 17 , 18 ]. Lee & Hughey (2001) hold that a large part of the healthy development of occupation depends on the degree of psychological separation between individuals and their parents [ 19 ]. They believe that psychological separation has an important impact on career maturity. Patton et al. (2005) hold that career efficacy might affect career maturity [ 20 ]. Then, how does psychological separation affect career maturity through occupational self-efficacy? To this end, this study introduced the variable related to family factors “psychological separation”, the variable related to individual psychological factors “occupational self-efficacy”, the variable related to social characteristics factors “registered residence type”. Postgraduates are selected as the research object, and career maturity is taken as an indicator to measure the willingness and ability of personal career development. The mechanism of psychological separation, and occupational self-efficacy on postgraduates’ career maturity was discussed. The mediating effect of occupational self-efficacy and the moderating effect of registered residence registration type were clarified.

Theoretical hypotheses

Career maturity, psychological separation, and occupational self-efficacy.

Super formally put forward the concept of career maturity. Career maturity is now at the center of career counseling and education programs in various schools, as well as being incorporated into many business, industry, and government career development programs. Career maturity is also the most commonly used outcome measure in career counseling and is widely used internationally. With the continuous development of career maturity theory, multinational researchers led by Crites, Savickas and Westbrook have discussed career maturity from different perspectives and accumulated a series of research results. Although there are various categories of career maturity nowadays, it can be generally classified into the following three points: Firstly, almost all scholars recognize the dynamic nature of career development and believe that it is a process of continuous development and advancement. Secondly, scholars’ definition of career maturity pays attention to the role of individual cognitive ability on career maturity. Third, the definition of career maturity emphasizes the individual's subjective initiative in the process of career development. This study is based on Crites’ definition of career maturity. Crites comprehensively summarizes the structure of career maturity and helps people understand their stage and development tasks in the career development process. Individuals’ career maturity is closely related to the family environment. Vondracek et al. (1986) hold that if there is variable that can predict an individual’s occupational status, this variable is the socioeconomic status of the individual’s original family [ 21 ]. Individuals’ career maturity is not only affected by the intergenerational transmission of family status, but also by the relationship with parents. The degree of individual separation from parental dependence, that is, psychological separation, will significantly affect their career maturity, which in turn affects their occupational development level (Lee & Hughey, 2001) [ 19 ]. The higher the degree of separation between adolescents and their original families, the higher the individuals’ sense of professional competence (Frank et al., 1988) [ 22 ]. Son Hyun Sook (2009) explores the relationship between career maturity and psychological separation [ 23 ]. He finds that the higher the level of individual maternal psychological separation, the higher the level of career maturity. Accordingly, this study proposes the following hypotheses:

Hypothesis H1: The psychological separation of postgraduates is positively related to their career maturity.

Individuals’ career maturity is also affected by individuals’ career psychological factors, of which the influence of occupational self-efficacy is particularly prominent (Hazel, 2022) [ 24 ]. Bandura (1977) proposes a theory of self-efficacy to explain the reasons for people’s motivation in certain situations [ 25 ]. Spencer & Bandura (1987) believe that self-efficacy is individuals’ assessment of the degree of confidence in one's ability to complete a task. The results of the assessment will affect their subsequent motivations and choices [ 26 ]. Taylor & Betz (1983) propose career decision-making self-efficacy on the basis of Bandura’s self-efficacy theory [ 27 ]. They believe that career decision-making self-efficacy refers to the self-evaluation or confidence of decision-makers in the process of career decision-making in their ability to complete various tasks. Hou Chunna et al. (2013) show that sound and independent personality development (such as a sense of responsibility) would affect the development level of individual college students. This makes them have higher self-efficacy, which could be reflected in the face of career decision-making for occupational self-efficacy [ 28 ]. The “Social Cognitive Career Theory” proposed by Song & Chon (2012) suggests that the career maturity of individuals might be related to their occupational self-efficacy [ 29 ]. Individuals with high occupational self-efficacy tend to have positive expectations for their career development. This positive expectation will drive them to take measures to meet various challenges in their career. Individuals with high occupational self-efficacy have clearer career goals and are more active in exploring career self, career information and career planning (Du Rui, 2006) [ 30 ]. Empirical research also proves that occupational self-efficacy is positively related to career maturity (Abdullah, 2023) [ 31 ]. YongHee & HyunSoon (2019) show that self-encouragement and occupational self-efficacy play a complete mediating role in the relationship between adolescent peer attachment and career maturity [ 32 ]. Kim Daeyoung & Joeng Ju ri. (2018) show the mediating role of career decision-making self-efficacy between parents’ active learning participation and career maturity [ 33 ]. Zhang Hua (2008) shows that the success of the psychological separation process would also help individuals to establish independence and autonomy, and get rid of parental attachment [ 34 ]. Therefore, individuals are more likely to obtain successful experiences, a positive psychological state, a positive external environment of trust, resulting in strong self-efficacy. The positive attitude of actively pursuing success is conducive to the development of a higher sense of career choice efficacy (Ye Baojuan et al., 2020) [ 35 ]. The success of the individualization process after psychological separation is conducive to the formation of healthy occupational psychology. Positive occupational psychology is also the driving force for career maturity. Good occupational self-efficacy plays a catalytic role. Combined with the above discussion, we infer that occupational self-efficacy might be the key factor linking the psychological separation and career maturity of postgraduates. Accordingly, this study proposes the following hypotheses:

Hypothesis H2: Occupational self-efficacy mediates the positive relationship between psychological separation and career maturity.

The moderating effect of registered residence type

In most countries, registered residence registration is mainly used to register the change of residence. The difference between urban and rural registered residence only exists as the difference of residence. The registered residence system is unique in China, since China implements a dual registered residence system in urban and rural areas, which links individual registered residence with specific regions, and divides registered residence into urban and rural types. The original purpose of the system was to restrict the cross-regional mobility of residents. In the long-term development, the residence registration system not only plays the role of registered residence management, but also affects various aspects of society, such as the occupation, medical care, education and social security of residents (Li Zhenjing&Zhang Linshan, 2014) [ 36 ]. The differences of registered residence have different impacts on public resources and social welfare (Jiancai Pi & Pengqing Zhang, 2016) [ 37 ]. With the rapid development of the economy in China, there are more differences in economic conditions and resource allocation among different regions. Due to the different household registered residence types of individuals, the external differences in the region tend to have influence on their career awareness, career knowledge and career attitude. Therefore, individual career maturity might have different performances in urban and rural samples. The research results around the differences in career maturity between urban and rural areas have drawn different conclusions. Chinese scholars Jia Pengfei & Chen Zhenbang (2011) find that there is no significant difference between urban and rural sources of college students’ career maturity [ 38 ]. Scholars in other counties generally believe that the career maturity of students is affected by their registered residence. Research by Alam (2016) shows that there are significant differences in career maturity between rural and urban students [ 39 ]. Junga & Yuntae (2015) find that adolescents living in cities tend to have higher career maturity. Adolescents living in rural areas have lower career maturity due to a lack of relevant social support [ 40 ]. Vibha & Ushakiran (2016) measure the career maturity of adolescents and find that the career maturity score of urban adolescents is higher than that of rural samples [ 41 ]. In addition, there are differences between urban and rural areas in individual occupational self-efficacy. The research of Conceicao et al. (2016) shows that there are significant differences between rural and urban teachers’ occupational self-efficacy [ 42 ]. Casapulla (2017) finds that in the process of participating in urban and rural services, students’ self-efficacy in providing vocational services in urban and rural areas is affected by their type of residence [ 43 ]. Students in different places of residence showed different self-efficacy in this process. To sum up, we believe that differences in registered residence attributes bring about different performances of postgraduates’ occupational self-efficacy and career maturity. The relationship between postgraduates’ occupational self-efficacy and career maturity is affected by registered residence attributes. Accordingly, this study proposes the following hypotheses:

Hypothesis H3: Registered residence type moderates the positive relationship between occupational self-efficacy and career maturity, and such relationship is stronger in rural registered residence rather than in urban registered residence.

To sum up, this study aims to study the relationship between the career maturity, psychological separation and occupational self-efficacy of postgraduates, and examine the mediating role of occupational self-efficacy and the moderating role of the registered residence type. The hypothesis model is shown in Fig.  1 .

Hypothetical model of the mediating effect of occupational self-efficacy and the moderating effect of registered residence type

Data collection

On the basis of ensuring the scientific design of the survey, considering the feasibility of the design and the principle of economy and effectiveness, this study adopts the convenient sampling method. Samples are taken from easily available subjects. This method is fast, simple, easy to obtain and cost-effective (Henry, 1990) [ 44 ]. By this method, a survey of postgraduates from different regions and levels of universities in China such as Xiamen University, Guangxi University (the source universities are widely distributed) was carried out and 600 questionnaires were collected by our research team. The questionnaire was mainly a paper version, supplemented by an electronic version, and data were collected synchronously through a combination of online and offline. The online questionnaire is distributed by using the website of Wenjuanxing ( https://www.wjx.cn/ ) to forward the questionnaire to the group and invite students to fill in the form of red packets. The offline questionnaire is distributed by giving small gifts to students. After sorting, 584 valid questionnaires were finally obtained, 16 invalid questionnaires with characteristics such as short filling time, missing data, and suspected insincere answers were excluded, and the effective recovery rate of the questionnaire was 97.33%. The study is only a questionnaire survey and does not involve human clinical trials or animal experiments, which conforms to ethical standards.

Research tools

Career maturity scale.

The master graduates’ career maturity questionnaire was used to measure the postgraduates’ career maturity (Wang Yumei, 2020) [ 45 ]. 18 typical items (with the sample items such as: “I know what kind of work I like”, “I can describe the main work contents of the occupation I am interested in” etc.) were selected to measure the career choice ability, career choice attitude, career choice knowledge of postgraduates, aiming to reflect the various abilities of postgraduates in the process of career choice. The 18 questions were graded on a 4-point scale: "1 = very inconsistent", "2 = relatively inconsistent", "3 = relatively consistent", and "4 = very consistent". The higher the score, the higher the degree of agreement. Confirmatory Factor Analysis (CFA) was performed on the scale, and the fitting index parameters were obtained as follows: χ 2 /df (ratio of chi-square to degrees of freedom) = 2.361, CFI (Comparative Fit Index) = 0.972, IFI (Incremental Fit Index) = 0.972, GFI (Goodness of Fit Index) = 0.952, RMSEA (Root Mean Square Error of Approximation) = 0.048. The Cronbach’s α coefficient of the scale was 0.943, which showed good consistency and the measurement results were valid. The 18 items were summed up and averaged to obtain the variable of career maturity, which was used to represent the career maturity of postgraduates. The higher the score, the higher the degree of career maturity.

Psychological separation scale

The psychological separation questionnaire was used to measure the degree of psychological separation of postgraduates (Wu Huiqing, 2012) [ 46 ]. This questionnaire has been used and proved to be effective. Although this questionnaire is designed for undergraduates’ psychological separation, psychological separation is a relatively stable concept, which is applicable to people of different ages and different educational levels. Both undergraduates and postgraduates are in a relatively similar environment, facing similar pressures and challenges. And currently, there are no other psychological separation scales more suitable for postgraduates. Therefore, this questionnaire still has reference value in measuring the psychological separation of postgraduates. In a practical study, selecting all the items might lead to the scale being too long, increasing the time, cognitive burden and discomfort of the subjects, thus affecting the quality of their responses. Some questions are less difficult or lack differentiation. Therefore, on the premise of ensuring the reliability and validity of the measurement, eliminating some items can improve the differentiation of the scale. In this study, a total of 8 typical items (with the sample items such as: “I feel especially in need of comfort when I encounter setbacks”, “I seek support when making decisions or plans” etc.) were selected from emotion separation, attitude separation and behavior separation respectively, and the corresponding items could effectively reflect the concept to be measured, help to improve the validity of the scale and reduce measurement errors. Each question adopted a 4-point scale: "1 = very inconsistent", "2 = relatively inconsistent", "3 = relatively consistent", and "4 = very consistent". The higher the score, the higher the degree of agreement. The CFA was performed on the scale, and the parameters were obtained as follows: χ 2 /df = 1.419, CFI = 0.997, IFI = 0.997, GFI = 0.993, RMSEA = 0.027. The Cronbach’s α coefficient of the scale was 0.869, which showed good consistency and the measurement results were valid. The 8 items were summed up and averaged to obtain the variable of psychological separation, which was used to represent the degree of psychological separation of postgraduates. The higher the score, the higher the degree of psychological separation.

Occupational self-efficacy scale

The occupational self-efficacy scale was used to measure the occupational self-efficacy of postgraduates (Schyns & Collani, 2002) [ 47 ]. According to the actual situation of postgraduates in China, 9 typical items (with the sample items such as: “I have a way of getting what I want even if others are against me”, “It's easy for me to stick to my ideals and achieve my goals” etc.) were finally retained to measure the degree of self-confidence of postgraduates in completing corresponding professional behaviors and achieving career goals. The 9 questions were graded on a 4-point scale: "1 = very inconsistent", "2 = relatively inconsistent", "3 = relatively consistent", and "4 = very consistent". The higher the score, the higher the degree of agreement. The CFA was performed on the scale, and the parameters were obtained as follows: χ 2 /df = 1.345, CFI = 0.998, IFI = 0.998, GFI = 0.994, RMSEA = 0.024. The Cronbach’s α coefficient of the scale was 0.901, which showed good consistency and the measurement results were valid. The 9 items were summed up and averaged to obtain the variable of occupational self-efficacy, which was used to represent the degree of the occupational self-efficacy of postgraduates. The higher the score, the higher the degree of occupational self-efficacy.

Statistical analysis

Consideration was given to the possibility of common method bias arising from the use of self-report data collection. Therefore, the procedure of this study was controlled by an anonymous survey and reverse scoring of some questions. At the same time, Harman’s single-factor test was used to test the data for common method bias. The results showed that there were 4 factors with eigenvalues greater than 1, and the total variance explained by the first common factor was 35.63%, which was less than the critical value of 40%. Therefore, the data in this study did not have the problem of common method bias (Zhou Hao&Long Lirong, 2004) [ 48 ]. SPSS26.0 was used for reliability analysis, confirmatory factor analysis and correlation analysis. The macro PROCESS of SPSS procedure was used to test the hypothesis of the moderated mediation model.

Research results

The basic characteristics of the sample are shown in Table 1 . The distribution of samples in demographic variables is relatively balanced, showing good representativeness.

Correlation analysis between main variables

Correlation analysis was carried out on the main variables for the career maturity, occupational self-efficacy and psychological separation. Considering that the main variables were continuous, the Pearson correlation coefficient test was used. The results in Table 2 showed that the psychological separation, occupational self-efficacy and career maturity were positively correlated. Psychological separation was positively correlated with occupational self-efficacy ( r  = 0.26, p  < 0.01), and positively correlated with career maturity ( r  = 0.19, p  < 0.01). Occupational self-efficacy was positively correlated with career maturity ( r  = 0.67, p  < 0.01).

The relationship between psychological separation and career maturity: a moderated mediation test

The test of the research hypothesis refers to the procedure of Wen Zhonglin’s moderated mediation test (Wen Zhonglin, 2014) [ 49 ]. The mediation model is constructed and the moderating variables are introduced to explore the model. According to this judgment standard, this section examines the moderating effect of registered residence type on the mediating process of "psychological separation, occupational self-efficacy, and career maturity". Control variables such as major, whether the only child and gender type were virtualized.

Firstly, the mediating effect of occupational self-efficacy between psychological separation and career maturity was tested under the control of major, whether the only child and gender type. The specific results were shown in Table 3 below. The results showed that psychological separation had a significant promoting effect on career maturity (β = 0.195, t = 4.721, p  < 0.001), which passed the 99.9% significance level test, Therefore, hypothesis 1 was supported. Psychological separation also had a promoting effect on occupational self-efficacy (β = 0.26, t = 6.410, p  < 0.001), which also passed the 99.9% significance level test. Referring to the idea of Wen Zhonglin’s mediation effect test, it can be considered that occupational self-efficacy played a mediating role between psychological separation and career maturity. It was a complete mediator, that was, the proportion of the mediation effect was 100%. Therefore, occupational self-efficacy mediated the relationship between psychological separation and career maturity, and Hypothesis 2 was supported.

Secondly, in order to further verify the mediating effect of occupational self-efficacy, the bootstrap sampling method (1000 times of sampling) was adopted to obtain the bootstrap test results of the mediating effect, which were arranged in Table 4 below. The results showed that the 95% confidence interval of the indirect effect did not contain 0, and the 95% confidence interval of the direct effect contained 0, that was, the indirect effect existed, the direct effect did not exist, and the occupational self-efficacy played a complete mediating role.

Again, model 14 in the macro PROCESS of SPSS plug-in compiled by Hayes (2012) (model 14 assumes that the second half of the indirect effect in the mediation model is moderated, which is in line with our hypothesis expectations) is used. Hayes developed the plugin PROCESS based on SPSS for mediating and moderating effect analysis. Process is a plug-in that specializes in mediating and moderating effects analysis, providing more than 70 models, the analysis process needs to select the corresponding model, set the corresponding independent variables, dependent variables, mediating or moderating variables, which can facilitate the operation and analysis of mediation models, moderated mediation models, etc. The moderating effect of registered residence type was tested under the control of major, whether the only child and gender, and the following Table 5 was obtained. The results showed that psychological separation significantly promoted occupational self-efficacy (β = 0.260, t = 6.410, p  < 0.001), and occupational self-efficacy significantly promoted career maturity (β = 0.771, t = 17.655, p  < 0.001). The complete mediation of occupational self-efficacy between psychological separation and career maturity was still valid. There was a positive relationship between registered residence type and career maturity (β = 0.113, t = 1.696, p  < 0.1), and the career maturity of postgraduates with rural registered residence was higher than that of postgraduates with urban registered residence. The interaction item of occupational self-efficacy and registered residence type was significant in the model (β = -0.209, t = -3.42, p  < 0.001), that was, the interaction item between occupational self-efficacy and registered residence type could significantly affect career maturity. Therefore, registered residence type moderated the relationship between occupational self-efficacy and career maturity.

Finally, in order to more vividly interpret the moderating effect of registered residence type on occupational self-efficacy and career maturity, the research subjects were grouped according to registered residence type. A simple slope test was performed to obtain Fig.  2 below. The results showed that occupational self-efficacy played a positive role in promoting the career maturity of postgraduates (all slopes were greater than 0). In general, at all stages of occupational self-efficacy, the career maturity of postgraduates with rural registered residence was higher than that of postgraduates with urban registered residence. With the rise of occupational self-efficacy, the career maturity of postgraduates with rural registered residence increased faster. In fact, the rate of career maturity improvement of postgraduates with urban registered residence was slower than that of postgraduates with rural registered residence. This indicates that the occupational self-efficacy of postgraduates with rural registered residence had a stronger role in promoting their career maturity. Therefore, Hypothesis 3 was supported.

The moderating effect of registered residence type on occupational self-efficacy and career maturity

In this study, we constructed and tested a moderated mediation model to examine the moderating effect of registered residence type on the mediating process of “psychological separation, occupational self-efficacy, and career maturity”. The results showed that the moderating variable “registered residence type” had a significant moderating effect on the mediating path.

Firstly, the results of correlation analysis and structural equation analysis proved that the positive effect of psychological separation on career maturity was significant and robust. This is consistent with previous research results. Previous studies showed that there was a close relationship between psychological separation and career maturity. Lopez & Andrews (2014) found that the degree of separation between children and families had a significant impact on individuals’ career decision-making, and a better level of psychological separation might reduce the difficulty of individuals’ career decision-making [ 50 ]. Zhang Xinyong et al. (2014) pointed out in their research that the more able an individual was to take responsibility in the event of a conflict with parents, the higher their level of career maturity [ 51 ]. Therefore, college educators or psychological consultants should focus on strengthening the psychological counseling work for postgraduates. Guide them to achieve emotional, attitude and behavioral independence on the premise of maintaining emotional contact with their parents, and promoting their career maturity.

Secondly, the results of the mediation effect test showed that psychological separation not only directly promoted the career maturity of postgraduates, but also promoted the occupational self-efficacy of postgraduates. Moreover, it could indirectly affect career maturity through occupational self-efficacy. Occupational self-efficacy played a completely mediating role between psychological separation and career maturity. This was consistent with the previous studies. The research of Kang & Hyun-Wook (2016) showed that psychological separation had a positive effect on individuals’ occupational self-efficacy[ 52 ]. Chen Yuaner & Ma Xiaoqin (2016) found that the two dimensions of occupational self-efficacy, occupational cognition and occupational value, had a positive role in promoting career maturity [ 53 ]. Gao Shanchuan & Sun Shijin (2005) found that the influence of occupational self-efficacy on individual career maturity was not only reflected in a direct role, but also played an indirect role [ 54 ]. The research of Liu Yang et al. (2022) on the career maturity of college students showed that occupational self-efficacy played a mediating role between their craftsman psychology and career maturity, that was, craftsman psychology could indirectly predict career maturity through occupational self-efficacy [ 55 ]. Psychological separation could positively promote career maturity, but there were various factors that affect career maturity, such as occupational self-efficacy, and career maturity was a dynamic change process (Betz et al., 1981) [ 56 ]. Occupational self-efficacy could help individuals achieve more positive results in the process of job hunting. Specifically, individuals with high occupational self-efficacy were more confident in achieving career goals. They could objectively and comprehensively conduct self-analysis and evaluation, and have a more objective understanding of their personality traits, abilities, interests, etc. (Li Zhengwei et al., 2010) [ 57 ]. In addition, they could position their career direction more accurately and their career goals were clearer (Ochs & Roessler, 2004) [ 58 ]. At the same time, they were usually more involved in the process of career selection, and were able to explore and learned more actively (Blustein, 1989) [ 59 ]. They collected occupational and industry information related to their career goals to have a more comprehensive understanding of professional knowledge and positions. They could timely understand and discover the needs and changes of the occupational environment (Qu Kejia et al., 2015) [ 60 ], flexibly respond to difficulties encountered in the process of career selection, and be willing to adjust the target appropriately according to the actual situation (Savickas et al., 2002) [ 61 ]. Therefore, individuals with high occupational self-efficacy would have more confidence in their careers, have more active job-seeking behaviors, have stronger career decision-making abilities, and have a higher level of career maturity. Colleges and universities could formulate scientific career planning courses according to the development law of occupational self-efficacy of postgraduates. Through cultivating good career self-efficacy of postgraduates, the career development level of postgraduates can be improved.

Finally, the moderated mediation effect test showed that registered residence type played a moderating role between occupational self-efficacy and career maturity. The results showed that the career maturity of postgraduates with rural registered residence was higher than that of postgraduates with urban registered residence, which was consistent with the research results of He Weijie et al. (2022) [ 62 ]. However, the results are inconsistent with Junga & Yuntae (2015) [ 40 ], Vibha & Ushakiran (2016) [ 41 ]. Occupational self-efficacy not only played a positive role in promoting the career maturity of postgraduates, but also the magnitude of this effect varied among postgraduates with different registered residence types. Occupational self-efficacy had a more significant role in promoting the career maturity of postgraduates with rural registered residence. With the rise of occupational self-efficacy, the career maturity of postgraduates with rural registered residence increased faster than that of postgraduates with urban registered residence. The reason might be as follows: The long-term urban–rural dual policy in China has resulted in a social and economic imbalance between rural and urban areas. Due to the influence of the economy, family cultural structure, parental education methods, etc., compared with urban registered residence postgraduates, rural registered residence postgraduates are more likely to show insufficient confidence and inferiority. This also affects the career choice of urban and rural postgraduates to some extent. For example, rural registered residence postgraduates might reduce their career opportunities, and they are less likely to enter the government organs and state-owned enterprises, engage in elite occupations and obtain high-income industries than urban registered residence postgraduates. From the perspective of social capital, this phenomenon is directly related to the lack of social capital owned by rural registered residence postgraduates. Bourdieu Pierre (1980) holds that social capital means that when a person has a certain kind of lasting relationship network, the relationship network composed of people who are familiar with each other means the resources he or she actually or potentially owns [ 63 ]. Yu Hui & Hu Zixiang (2019) believe that young people’s career choice is also influenced by strong relational social capital, especially strong relational capital of talent [ 64 ]. Individuals with better family conditions often rely on strong family social relationship capital to obtain employment opportunities. However, strong family social relationship capital will gradually develop into weak relationships over time, while individual social capital will gradually show strong relationship over time. Most urban registered residence postgraduates have better social capital than rural registered residence postgraduates, this kind of social capital is mainly provided by the previous generation, which has been a fact. For rural registered residence postgraduates, the lack of abundant social capital makes them pay more attention to the accumulation of psychological capital, so they are more independent, hard-working, especially focus on the improvement of occupational self-efficacy, that is, they do not rely too much on the power of social capital. The belief in achieving career goals is an internal drive. Those rural registered residence postgraduates who have a strong belief in achieving career goals have a stronger motivation for career exploration and more career exploration behaviors. They are more fully prepared to collect career information and formulate career goals, and dare to face challenges in career development. They have a clear grasp of their professional abilities. Career maturity will also be higher. However, urban registered residence postgraduates are generally more confident because of their relatively favorable family environment and educational conditions. Their belief in achieving career goals has a weaker influence on their career maturity level. Therefore, occupational self-efficacy has a greater role in promoting the career maturity of rural registered residence postgraduates.

As research on career maturity mainly focuses on undergraduate students, there is little research on postgraduates. This study further enriches the research field of career maturity. Based on Crites’ career maturity theory, this paper uses occupational self-efficacy to explain the mechanism of psychological separation on career maturity, clarifies the logical relationship between postgraduates’ psychological separation and career maturity, and explores the mediating effect of occupational self-efficacy and the moderating effect of registered residence type. It further supplements and enriches the relevant theoretical research on career maturity, and provides theoretical support for the targeted career planning education of postgraduates, improving and enhancing the level of postgraduates’ career development. Meanwhile, this paper still has the following limitations: Firstly, this study uses cross-sectional data, so we couldn’t see the trend of time changes, and the impact of time effects on the conclusion is ignored. Secondly, due to the limitations of the questionnaire design, the measurement of some variables might not be precise enough to cover all aspects of the relevant concepts, and there might still be omissions. Third, limited by familiarity with the relevant topics, there might be omissions in the selection of control variables, which might have a certain impact on the research conclusions. Finally, although this paper uses a robust test mechanism for mediating and moderating effects, it fails to explore the causal mechanism in-depth and lacks the identification of causal relationships. Therefore, future related research can be improved from the following aspects: Firstly, expand the scope of the study, strive to include more types of schools as the sampling frame, and select the final sample by random sampling, so as to ensure a completely random data as much as possible and avoid bias in model estimation due to sampling error. Meanwhile, strive to collect data for the same sample for multiple years, so as to control the impact of time effects on the relevant variables of the sample, and obtain a more robust and reliable research conclusion. Secondly, improve the design of the questionnaire and strive to design a more realistic scale to ensure more accurate measurement. At the same time, increase the control variables in the questionnaire to include some demographic characteristics and social structure characteristics, so as to avoid the estimation error caused by various dependent variables. Third, improve the research method and use the model that can identify the causal mechanism to discuss the relationship between the research objects, so as to ensure that the obtained regression relationship is accurate and reliable.

The results show that: (1) Psychological separation has a significant positive effect on the occupational self-efficacy and career maturity of postgraduates. The higher the level of psychological separation, the higher the level of career maturity of postgraduates. Occupational self-efficacy also has a significant positive effect on the career maturity of postgraduates. (2) Occupational self-efficacy plays a complete mediating role between the psychological separation and career maturity of postgraduates. Psychological separation not only affects the career maturity of postgraduates directly, but also has an indirect effect on career maturity through occupational self-efficacy, and this indirect effect has a 100% mediating effect. (3) Registered residence type plays a moderating role between occupational self-efficacy and career maturity. Occupational self-efficacy not only has a positive predictive effect on the career maturity of postgraduates, but also the magnitude of this effect varies among postgraduates with different registered residence types. Occupational self-efficacy has a more significant role in promoting the career maturity of postgraduates with rural registered residence.

In summary, the level of career maturity not only reflects the employability of postgraduates, but also reflects their future career development, and is an important factor affecting the quality of postgraduate employment. The analysis of the characteristics and influencing mechanism of postgraduates’ career maturity will help postgraduates understand the level of their own career maturity, explore and plan their own career as early as possible, and improve their career decision-making level and career development ability. At the same time, it is also helpful for colleges and universities to provide more targeted employment guidance, improve the efficiency and effectiveness of counseling and guidance, and provide implementation direction for the improvement of postgraduates’ career maturity, so as to help postgraduates alleviate their employment anxiety and achieve high-quality employment.

Availability of data and materials

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

We would like to thank the participants for their involvement in this study.

This study was the research results of the 2022 Annual Project of the Training and Training Center of College Ideological and Political Work Team of the Ministry of Education, PRC (Southwest Jiaotong University, SWJTUKF22-02), supported by the Fundamental Research Funds for the Central Universities (20720231076), the Research of the Young and Middle-aged Teachers' Educational Research Project (Social Science) of Fujian Provincial Education Department in 2021(JAS21710), the Research Initiation Fund of Jimei University (Q201907), Planning Project (FJ2021B211).

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Ni, J., Zhang, J., Wang, Y. et al. Relationship between career maturity, psychological separation, and occupational self-efficacy of postgraduates: moderating effect of registered residence type. BMC Psychol 11 , 246 (2023). https://doi.org/10.1186/s40359-023-01261-9

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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.

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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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Career Adaptability Research: A Literature Review with Scientific Knowledge Mapping in Web of Science

Huaruo chen.

1 School of Education Science, Nanjing Normal University, Nanjing 210046, China; nc.ude.unjn.uts@120106091 (H.C.); nc.ude.unjn.uts@390206091 (F.L.); nc.ude.unjn.uts@980206091 (L.P.); nc.ude.unjn.uts@620106071 (Y.W.); nc.ude.unjn.uts@120106081 (S.C.)

2 Center for Research and Reform in Education, Johns Hopkins University, Baltimore, MA 21286, USA

Tingting Fang

3 School of Psychology, Nanjing Normal University, Nanjing 210046, China; nc.ude.unjn.uts@730203291

With the rapid development of society and technology, personal adaptability is becoming more and more important. Learning how to adapt to a changing world is becoming one of the necessary conditions for success. Career adaptability can help individuals to smoothly adapt to changes when coping with their career roles, and maintain their ability to balance their career roles, which will affect their important psychological resources for career development and achieve more meaning in life. In recent years, career adaptability has gradually attracted the attention of researchers. Therefore, in order to explore the main factors, such as research focus, the main researchers, its evolution, and the important results of career adaptability in the last ten years, this study used the scientific knowledge mapping software CiteSpace as a research tool, and select related articles from the Web of Science between 2010 to 2020 under the theme of “career adaptability” for data analysis, which can help future researchers to understand current and future career adaptability research and control the research direction of career adaptability. The results of this research indicate that there are direct or indirect connections between different themes, such as the career adaptability scale, career construction, positive personalities, and so on, but few articles integrate multiple research topics. At the same time, the main researchers, research frontiers and network relationships were also obtained. Based on the above findings, the correlative main concept, theoretical structure, evolution, and research progress of career adaptability in the past ten years are discussed.

1. Introduction

Under the influence of COVID-19, many fields around the world, such as education and the economy, have been impacted. Many people argued that they cannot smoothly adapt to the transition from offline work and learning to online, which also leads to researchers’ increased concern about adaptability. Therefore, career adaptability is becoming one of hottest research topic in the field of careers in recent years. Career adaptability originated from the core concept of Super’s career development theory, namely career maturity, which has been constantly updated and revised by researchers (Super & Knasel, 1981) [ 1 ]. Some experts proposed “career adaptability” instead of “career maturity” after 1981 (Super & Knasel, 1981; Savickas, 1997) [ 1 , 2 ]. The concept of career adaptability was first proposed by Super, which evolved from another core concept of career development, career maturity. Career adaptability refers to the ability of individuals to adapt to changes smoothly and maintain the balance of their career roles when coping with the transition of their own career roles (Super & Knasel, 1981) [ 1 ]. As the work world shifts from stable to fluid, how individuals can improve their career resilience to cope with unpredictable situations and make appropriate adjustments has been explored (Savickas, 1997; Savickas, 2005) [ 2 , 3 ]. Simply put, career adaptability are the resources that can successfully manage individuals’ current and anticipated career transitions (Savickas, 1997; Savickas, 2005) [ 2 , 3 ]. These resources are not the core characteristics of the individual, but exist as a meeting point between humans and the environment, so they are psychosocial (Samuel, 2015) [ 4 ]. As an adaptive resource, career adaptability is a self-regulatory ability that one can use to solve unfamiliar, complex, and ill-defined problems arising from developmental career tasks, career transitions, and job trauma (Tolentino et al., 2014) [ 5 ]. Career adaptability enables individuals to broaden, improve, and ultimately realize self-concept in professional roles, thus creating a working life, bettering life satisfaction, and building a career framework (Porfeli & Savickas, 2012; Ginevra et al., 2018; Ginevra et al., 2017) [ 6 , 7 , 8 ].

From the 1950s to the 1990s, Super established career development theory [ 9 ], career developmental self-concept theory [ 10 ], and life-span life-space career theory [ 2 ], all of which became the best interpretations and theoretical models of individual career development at that time (Zhao & Guo, 2010; Guan & Li, 2015) [ 11 , 12 ]. Over time, the theory of career adaptability was continuously revised and developed based on previous studies. Later, Savickas, a representative figure in the study of career adaptability, developed a richer and more connotative and extended career adaptability theory (Savivkas, 1997) [ 2 ]. Based on this, a career construction theory from the perspectives of individual constructivism, social constructivism, and postmodernism was proposed and gradually established (Savickas, 2005; Guan & Li, 2015; Savickas, 2002; Savickas & Profeli, 2012) [ 3 , 12 , 13 , 14 ]. After that, some researchers initially defined career adaptability as the ability to adapt to job needs or transfer, which can suit individual needs (Pratzner & Ashley, 1985) [ 15 ]. With the development of society, different researchers have different definitions of occupational adaptability.

Super and Knasel argued that career adaptability is a state of readiness which is required to cope with tasks that can be predicted by current or future job roles and to adapt to unpredictable work or changes in the work environment (Super & Knasel, 1981) [ 1 ]. Savickas modified it to be the individual’s state of readiness for predictable career tasks, the career roles involved, and career problems that are unpredictable in career changes or career situations, which is also a quality that allows for change without much difficulty to conform to the new environment (Savivkas, 1997) [ 2 ]. Later, Savickas made a more concise definition and supplement to the concept that was the state of preparation and resources needed to respond to current and anticipated career development tasks, including the attitudes, abilities, and behaviors individuals need to match them with work that suits them, which are psychological resources for managing career change, new tasks, and job trauma (Savickas, 2005; Savickas, 2002) [ 3 , 13 ].

Research aiming at the important potential impact of career adaptability on individuals, career development, and the international community has paid attention to the issue of improving youth career readiness. At the same time, career adaptability has become an important component of international research. Therefore, understanding and mastering the latest development trends of career adaptability is not only helpful for future research, but also for discovering the components of career adaptability that have not been received attention.

Justification and Objectives

It should be noted that career adaptability should not only be considered in the preparation of study and work, but it should also be considered that career adaptability is an important part of life design and satisfaction (Zhou & Lin, 2016) [ 16 ]. On the one hand, at the level of theoretical research, the concept of career adaptability has been established, and relevant theories and models have been published and accepted by researchers. The division of career adaptability into each dimension is increasingly clear (Wilkins et al., 2014) [ 17 ]. However, as time goes by, especially in the past decade, it should be stated that the life design paradigm was the most important theoretical framework for career adaptability (Maree & Symington, 2015) [ 18 ]. Therefore, in the study of career adaptability, it is very important to understand the latest theoretical research. On the other hand, at the level of empirical research, as an important variable, career adaptability has been applied and practiced in many studies. In the field of education, Chen believed that career adaptability is conducive to the promotion of students’ sustainable development education (Chen et al., 2020) [ 19 ]. In terms of family factors, Guan pointed out career-specific parenting behaviors linking parents’ vocational characteristics and children’s career adaptability, which means parental support is positively related to parents’ intrinsic fulfillment values and work–life balance values (Guan et al., 2018) [ 20 ]. In the field of work, Spurk’s interindividual study concluded that career adaptability and proactive care behaviors showed positive relations between initial individual levels, intraindividual changes in career adaptability, and proactive care behaviors, pointing to a parallel development (Spurk et al., 2019) [ 21 ].

Based on the above researchers’ perspectives, it can be clearly understood that career adaptability emphasizes the interaction between individuals and their living environment, and also need to focus on the non-deterministic problems that individuals faced. Therefore, career adaptability can be regarded as a kind of psychological ability for individuals to maintain a balance these elements when they change their career roles. The ability of career adaptability can be cultivated and developed, which is the result of the interaction between the individual and the environment, and it is also an ability that enables individuals to develop. However, the dimensions of career adaptability research are relatively scattered, and there is no relevant research to systematically sort out career adaptability.

After summarizing the relevant studies, this research reviews and analyzes the international studies on occupational adaptability with the help of CiteSpace and describes the research direction, research hotspots, and representatives of international occupational adaptability. The purpose of this research is to help future researchers to understand the concept, theoretical framework, and other factors of career adaptability, and find out the deficiencies of current research, so as to make more valuable contributions to future research in the field of career adaptability.

2. Materials and Methods

2.1. research design.

The research methodology to achieve the formulated objectives was bibliometrics, which can be understood as the branch of scientometrics that analyzes scientific publications. The methodological branch is assumed to be based on the potentialities of scientometrics, which can be defined as the statistical and sociometric analysis of the scientific literature through the use of scientific knowledge mapping and questions related to the processes of searching, recording, analyzing, and predicting the academic literature (Martínez et al., 2015) [ 22 ].

More specifically, this research was based on analysis of co-words (Hirsch, 2005) [ 23 ] and of various bibliometric indicators and indexes (Cobo et al., 2011) [ 24 ]. These data allow for the attainment of a set of maps with nodes that show the performance and the location of sub-domains of the constructs connected to “career adaptability”. In addition, the graphic preparation facilitates the development of the themes of career adaptability in the initially established database (López-Robles et al., 2019) [ 25 ].

2.2. Procedure

This research followed several procedures:

• (1) choice of the database to be analyzed (Web of Science).
• (2) determination of the key words to be considered (“career adaptability” or “adaptability”). This research used CiteSpace software as a research tool, and selected related articles for data analysis from the beginning of 2010 to March 2020 in order to explore the research topics and future trends, so that future researchers can understand the current and future career adaptability research from the analysis results, and control the research direction of career adaptability.
• (3) elaboration of the search equation (“career adaptability” in the categories of “Education Educational Research”, “Education Scientific Disciplines”, “Psychology Educational”, and “Education Special”).
• (4) selection of the search process by bringing together the topic process to report documents that included the concept to be analyzed in the metadata, comprising the title, abstract, and keywords. This action allowed access to a first data report of 39,037 publications. Then, this research set up relevant inclusion/exclusion criteria. In order to meet the inclusion criteria, the article must have had career adaptability as the theme or main variable. This inclusion criterion excluded articles like patents, early access, case reports, data papers, editorials, letters, retracted publications, meetings, unspecified, news, biographies, reference materials, report s, retractions, abstracts, and so on. It is worth noting that, in view of the number of international versions of this article and the language limitations of the researchers, this study mainly included research published in English, Chinese, and a small number of other languages (such as Simplicio, 2014, a report in Portuguese) [ 26 ].

In addition, repeated or improperly indexed documents were deleted. This resulted in a final unit of analysis of 20,871 documents. These actions are reflected in the following flow chart, taking into consideration the protocols of the preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) matrix ( Figure 1 ).

Flowchart according to the PRISMA declaration.

2.3. Software

This research used CiteSpace science knowledge mapping software to conduct a macro analysis of the career adaptability research field. CiteSpace is an information visualization application software for citation analysis written by Dr. Chen from Drexel University, based on the JAVA programming language (Chen et al., 2010) [ 27 ]. Because the structure, regularity, and distribution of scientific knowledge are presented by means of visualization, the visualization graphs obtained by such methods are also called “science knowledge mapping”. The maps drawn by CiteSpace software can reveal the knowledge base, hotspot areas, and frontier evolution of the field of scientific knowledge, and enable researchers to intuitively identify the classic basic literature of the corresponding subject area and the evolutionary path of the subject frontier (Chen et al., 2010) [ 27 ].

3.1. Author Co-Citation Analysis

Author co-citation refers to the phenomenon in which two authors are commonly cited by other articles. The co-citation relationship between authors reflects the close relationship between authors in this research direction. The higher the co-citation frequency of two authors, the stronger the correlation between the authors in this academic research direction (Chen et al., 2010) [ 27 ]. The CiteSpace visual analysis software was used to carry out author co-citation analysis on the data. When processing the data, cited author was selected as the node type, the year was set to 2010–2020, and other settings kept the default values. After running the data, the authors’ co-cited network map of career adaptability was obtained (see Figure 2 ). There were 346 network nodes and 739 connections. In the atlas, according to the intermediary centrality of node authors in the co-citation network, representative scholars in the field of career adaptability with greater centrality were shown. At the same time, this research analyzed these highly cited articles.

Co-cited atlas of career adaptability authors.

By sorting out the data from CiteSpace, the co-citation frequency ranking of career adaptability researchers can be obtained (see Table 1 ). As can be seen from Figure 2 and Table 1 , Savickas, Hirschi, Guan, Koen, Zacher, and Rudolph occupied important node positions in the co-cited network. These representative scholars are important figures in the field of career adaptability research. Among them, Savickas is a senior scholar in the field of career development research. Savickas continuously explored the theoretical construction of career adaptability. From the three-dimensional structure model to the four-dimensional structure, Savickas provided an effective method for evaluating the level of individual career adaptability and an important guiding scheme for researchers and career consultants, which had strong practical value (Hartung et al., 2008) [ 28 ]. Hirschi established the dimensional structure of the career adaptability questionnaire according to Savickas’s adaptability model. Career adaptability consists of four dimensions, including career decision-making, career planning, career exploration, and career self-confidence. Hirschi developed the youth career adaptability questionnaire and conducted empirical research on the functions and influencing factors of career adaptability (Hirschi, 2009) [ 29 ].

List of cited frequencies of career adaptability research authors (top six).

A study by Guan et al. [ 30 ] mainly took Chinese college students as the research subjects and conducted research on the role of career adaptability of Chinese college graduates in the job-seeking process. Among the four dimensions of career adaptability, career concern and career control were the strongest predictors of job-seeking self-efficacy. In addition, career adaptability also significantly predicted the employment situation and person–environment (P–E) adaptability. These findings carried implications for research on career construction theory, as well as career education and career counseling practices.

Therefore, Koen, Klehe, and Van developed training aimed at providing graduates with career adaptability resources (Koen et al., 2012) [ 31 ]. The training included four parts: participants’ self-knowledge, professional environment knowledge, implementation overview, and specific implementation. Koen et al. found that the training successfully enhanced the control and curiosity of college students in the training group by comparing the results of college graduates who received career adaptability training (i.e., the training group) and those who did not receive career adaptability training (i.e., the control group). In addition, among participants who found employment half a year later, training participants reported higher employment quality than did members of the control group. In sum, the results showed that providing graduates with career adaptability resources could raise their chances of finding a qualitatively good job (Koen et al., 2012) [ 31 ].

Zacher mainly studied the relationship between career adaptability and personality traits. In the data analysis of a diary survey of enterprise employees, researchers found that openness and conscientiousness could promote career adaptability, while the other three personality types had no significant influence. Meanwhile, Zacher adopted enterprise multi-sample delayed test and situational test methods, which proved that those individuals with high core self-evaluation and concern for the past and the future had relatively high adaptability levels, which confirmed the core view put forward by career construction theory, that is, the development and progress of an individual’s career, fundamentally speaking, involves the realization of individual–environment coordination through the integration of their experience, current situation perception, and future planning (Zacher, 2014) [ 32 ].

Rudolph (2017) [ 33 ] used meta-analysis to explore career adaptability and adaptation results. One of the studies was based on the career structure model of adaptation and used meta-analysis to examine career adaptability and adaptation measurement, adaptation response, and adaptation relationships. The results supported the significance of career adaptability and related it with personality and other individual difference structures. In terms of adaptation response, the study found that career adaptation was positively correlated with career planning, career exploration, and career decision-making self-efficacy. Career adaptability was not only related to the career success results of subjective evaluation, but also to more objective measures, such as income (Yu et al., 2017) [ 34 ]. Career adaptability had a positive effect on subjective well-being (life satisfaction, positive effects, and a low level of negative effects) (Sovet et al., 2018; Santilli et al., 2014; Wilkins et al., 2014) [ 17 , 35 , 36 ]. Regression analysis clearly showed that some indicators of adaptation outcomes (career and life satisfaction, income, and job performance) were related to, on the one hand, adaptation indicators (such as the Big Five personalities) that were distant (Dalpé et al., 2019) [ 37 ]. On the other hand, these adaptation outcome indicators were related to the closer career adaptability indicators, while the more distant adaptation indicators were controlled (Ocampo et al., 2018) [ 38 ]. In addition, the indirect effects of these adaptation indicators on the adaptation results were analyzed through career adaptability. That study provided theoretical guidance for researchers seeking to develop intensive research projects on the role of adaptive capacity in career development and professional behavior (Briscoe et al., 2006) [ 39 ].

3.2. Keyword Co-Occurrence Analysis

Keywords are the extraction of the main content of a paper. Analyzing the keywords can reveal some hot topics in career adaptability research. In this study, CiteSpace was used to analyze the keywords of career adaptability articles. Article data were imported into CiteSpace software. Keyword was selected as the node type. Time slicing was set as 2010–2020, and the value of time partition of year per slice was set as 1. Selection criteria maintained the default value of 50 and the imported data were run to obtain the co-occurrence map of career adaptability research keywords (see Figure 3 ). After sorting out the data from CiteSpace, 43 high-frequency keywords for career adaptability research were obtained (see Table 2 ). In a later chapter, this research also classifies these high-frequency keywords into the dimensions of career adaptability.

Co-occurrence map of keywords in career adaptability.

Ranking of 43 high-frequency keywords for career adaptability.

In the keyword co-occurrence map in Figure 3 , the size of each circle refers to the frequency of keyword occurrence, and the larger the circle, the bigger the number of keyword occurrences. Table 2 shows the ranking of keywords derived from the data. The ranking of keywords indicates the ranking of research hotspots in this field. The higher the keywords are ranked, the more often this word is used in this field, which represents a problem to which everyone pays more attention. Judging from the ranking of keywords in Table 2 , the top five keywords are “Career Adaptability”, “Adaptability Scale”, “Personality”, “Construction” and “Form Psychological Property”. These keywords have had a relatively high frequency in the past decade. These are hot topics for researchers in this field, who have mainly focused on the issues of career adaptability and scale revision, the relationship with personality, structure establishment, career adaptability, and psychometric attributes.

3.3. Research Frontier Topics

On the basis of keyword processing, cluster view was selected for the visualization of layout to obtain the career adaptability research frontier topics map (see Figure 4 ), in which the modular Q value (modularity Q = 0.6364) and mean silhouette score (mean silhouette = 0.7465) indicate that the clustering is reasonable. From the map, five clusters automatically identified by CiteSpace can be seen, which represent five frontier topics of career adaptability research. The map shows the basic trend of the research and development of career adaptability in the past 10 years (see Figure 4 ). The research found that the field includes five main clusters (described by the clustering features of high-frequency keywords): boundaryless mindset, career adaptability scale, career construction, proactive personality, and life design. It is worth noting that there are direct or indirect connections between different topics. For example, a boundaryless mindset in workplace life often encounters a dilemma. A favorable, unfavorable, or neutral attitude may have adverse consequences, which is reflected in the discussion of career adaptability. In order to measure a person’s level of career adaptability, the scale of career adaptability has attracted more and more attention. Career construction theory consists of three main components: life theme, professional personality, and career adaptability. Paying attention to the development of career adaptability will certainly extend to the study of career construction to ensure the integrity of an individual’s career and the classification of professional personalities includes proactive personalities. With the maturity of social development in recent years, people are no longer just satisfied with career success, and more and more researchers found that a great number of people will pay attention to career success and self-satisfaction. Therefore, they gradually design their own complete life to achieve the best level of life satisfaction. Life design comes naturally and has been widely used in recent years. On the other hand, in addition to proactive people, the other four clustering topics are closely linked. This shows that there is an unbalanced relationship between career adaptability topics. In this research, the development and analysis of these five topics will be discussed.

Career adaptability research frontier topics map.

3.3.1. Boundaryless Mindset

The boundaryless mindset refers to a tendency of employees belonging to an organization to pursue cooperation with individuals or organizations outside the organization and actively adapt to the content of cross-border work. The concept of the boundaryless mindset originated from the study of the borderless career. In the past two decades, the research on careers has gradually formed two popular perspectives, one is the protean career, and the other is the boundaryless career. Briscoe and Hall (2006) developed the Boundaryless Career Attitude Scale on the basis of defining relevant concepts, and for the first time explained the concept of boundaryless mindset, namely, “Thus a person with a decidedly high ‘boundaryless’ attitude toward working relationships across organizational boundaries is comfortable, even enthusiastic about creating and sustaining active relationships beyond organizational boundaries” (Briscoe et al., 2006) [ 39 ]. The main meaning of the boundaryless mindset is that individuals like and pursue cross-departmental or cross-organizational cooperative relationships, challenge and update their knowledge systems and work abilities, and are positively adaptable to new environments. It is a kind of value judgment of employees in the career field, so as to guide and influence employee behavior, and it further had an effect on organizational development (Ali et al., 2016) [ 40 ]. With the deepening of research, researchers also continuously found that career adaptability was related to the boundaryless mindset. For example, in the study of Chan et al., the relationship between career adaptability and the boundaryless mindset was closer (Chan et al., 2015) [ 41 ].

3.3.2. Career Construction Theory

Career construction theory was put forward by Savickas, a senior scholar in the field of career counseling practice and research in America and a rising star in the field of Western vocational psychology over the past 20 years (Nota et al., 2014) [ 42 ]. Based on the research of Super’s career development theory, Savickas moved from career development to career construction by way of career narration (Walker et al., 2019) [ 9 ]. Savickas (2002) proposed 16 exploratory propositions based on the theory of career construction (Savickas, 2002) [ 13 ]. Later, inspired by McAdans’s general framework of personality, Savickas merged and developed personal environment fit theory and life theme theory, and further refined those propositions into three aspects of career construction theory (Savickas, 2013) [ 43 ]. The traits of different individuals are differential. The tasks and coping strategies faced by individuals in different career stages are developmental. Career development is a dynamic process (Savickas, 2005) [ 3 ]. As a result, career construction theory responds to the “what”, “how”, and “why” in individual professional behaviors with vocational personality types, career adaptability, and life themes (Savickas, 2005, 2013) [ 3 , 43 ].

3.3.3. Proactive Personality

The proactive personality first appeared in an article entitled The Proactive Component of Organization Behavior , published by Bateman and Crant in the Journal of Organization Behavior in 1993 (Bateman & Crant, 1993) [ 44 ]. In this study, according to the definition of positive psychology, the concept of the proactive personality was put forward, and it further defined the difference between the active personality and passive personality, whereby individuals with proactive personalities are relatively free from environmental constraints, which thus affect environmental changes. A proactive personality could help people to identify opportunities, take actions, and persevere until they succeed. However, passive individuals show adaptation to the environment and are easily shaped by the environment. They cannot identify and seize the opportunity to change things, seldom show enthusiasm, and rely on the strength of others. Later, other scholars defined the concept of the proactive personality from different perspectives. Greguras and Diefendorff (2010) proposed that individuals with a proactive personality show self-reliance and self-improvement and are mostly future-oriented (Greguras & Diefendorff, 2010) [ 45 ]. Parker, Bindl, and Strauss (2010) proposed that the goal of employees with a proactive personality is to continuously improve the work process and results (Parker et al., 2010) [ 46 ]. Campbell (2000) summed up the five core characteristics or qualities. Firstly, they can be competent in their own work, showing a high level of professional organization, problem-solving ability, and excellent performance. Secondly, they have a high level of interpersonal competence, leadership, and trustworthiness. Thirdly, they show high level of commitment to organizational goals and a strong sense of responsibility for organizational success, with values consistent with the organization and a positive working attitude. Fourthly, they have positive and enterprising qualities, such as initiative, independent judgement, a high level of engagement and involvement, and the courage to express their ideas. Fifthly, they show the quality of integrity and pursue higher values (Campbell, 2000) [ 47 ].

In recent years, the active personality, work performance, and career success are more common. Pan (2018) found that the quality of internships had become an important regulator of the relationship between the active personality and career adaptability, as well as employment success (Pan et al., 2018) [ 48 ]. When the quality of internships was low, the indirect influence of the proactive personality on employment success through career adaptability was greater. Jiang (2017) pointed out that the initiative personality promoted personal work development and thus improved career adaptability. Moreover, it was found that the impact of vigorous development on career adaptability was stronger for those with a less proactive personality (Jiang, 2017) [ 49 ]. In addition, there are studies on the active personality and leadership. For example, Crant and Bateman explored the relationship between the proactive personality and charismatic leadership. One hundred and fifty-six managers and their directed supervisors participated in the study. The results showed that the higher the managers’ self-report scores for proactive personality, the higher the superiors’ evaluation for charismatic leadership. The results also showed that the variance explained by having a proactive personality was larger than that explained by many additional variables. The variance explained by the social approval of behavior in the role of five personality factors was larger (Bateman, 2000) [ 50 ].

3.3.4. Career Adaptability Scale

The Career adaptability scale is the most important research topic in this field. Researchers have developed different scales according to their own research. Therefore, this study has organized the areer adaptability scale across a wider application scope.

• (1) Savickas and Porfeli’s Career Maturity Scale

The Career Maturity Scale (CMI-C), as a concise, reliable, and effective measure of career choice readiness, is mainly used in school populations of grade 12 and below. CMI-C has a total of 24 items and consists of five parts, namely: the total score of career choice readiness, the scores of three scales reflecting the adaptability dimensions of attention, curiosity, and self-confidence, and the scores reflecting the relationship style that informs career choice (Savickas & Porfeli, 2011) [ 51 ].

• (2) Savickas and Others’ Career Adjustment Strength Table

Savickas proposed a four-dimensional construction of career adaptability, which includes four dimensions of career attention, career control, career curiosity, and career confidence and a four-dimensional structure for the career adapt ability scale (CAAS) based on the theoretical model (Savickas & Porfeli, 2012) [ 14 ]. The scale has been tested in 13 countries and regions including Europe, America, Japan, and China. It has passed the consistency test under different cultural backgrounds and has high applicability. Subsequently, researchers from many countries and regions have revised the scale in combination with the local culture and have compiled a career adaptability scale more suitable for the region. The scale consists of 24 items and is divided into four dimensions: career attention, career confidence, career curiosity, and career control. Each dimension has six items (Savickas & Porfeli, 2012) [ 14 ].

• (3) Savickas’ Career Adapt abilities Scale—Short Form (CAAS-SF)

The CAAS-SF is a psychological measurement tool for measuring adaptive resources. The CAAS-SF is a short version of only 12 items based on the CAAS. The development of the CAAS-SF aims to promote the implementation of the CAAS in the research of more different groups and professional backgrounds, reduce the management time for professional consulting and life design practitioners, and promote all aspects of their practical work. The reduction in the number of items also retains the excellent psychometric characteristics of the tool. This scale is composed of four dimensions, and each dimension is evaluated by three items, namely, a job satisfaction survey, general work stress scale, and organizational self-efficacy scale (Maggiori, Rossier, & Savickas, 2017) [ 52 ].

• (4) Hirschi et al.’s Adolescent Career Adaptability Questionnaire

Hirschi revised the “Adolescent Career Adaptability Questionnaire” dimension structure, which is based on Savickas’s adaptability model and consists of four dimensions: career decision-making, career planning, career exploration, and career confidence (Hirschi, 2009) [ 29 ]. In this questionnaire, the career decision-making component is taken from the Career Maturity Inventory (Seifert, 1986) [ 53 ] and consists of 12 items. The career planning component is taken from the Career Development Inventory (Seifert, 1985) [ 54 ], with 22 items in total. Career exploration consists of two parts, one of which is taken from the Career Development Inventory, with a total of 26 items. The second part is taken from the Career Exploration Survey Scale (Stumpf, Colarelli, & Hartman, 1983) [ 55 ] and the Career Exploration Questionnaire (Kracke, 2002) [ 56 ], which has 10 questions, including four questions for self-exploration and six questions for environmental exploration. Career confidence is measured by two career ability belief questionnaires. Among them, one questionnaire has three items and the second questionnaire has five items (Hirschi, 2009) [ 29 ].

• (5) Rottinghaus et al.’s Career Futures Inventory—Revised (CFI-R)

Rottinghaus adopted a rational method to formulate the Career Future Inventory (CFI). The list consists of three subscales: career adaptability (CA), career optimism (CO), and perceived job market knowledge (PK). The scale has a total of 25 items, of which CA has 11 items, CO has 11 items and PK has 3 items (Rottinghaus et al., 2005) [ 57 ]. After that, Rottinghaus revised the CFI (Rottinghaus et al., 2012) [ 58 ]. The revised CFI (CFI-R) has 28 items, including five internally consistent sub-scales: career agency, career awareness, support, work–life balance, and negative career outlook. The CFI-R evaluates various aspects of career adaptability, including positive career planning attitude, general outcome expectation, Parsons’s tripartite model, and Bandura’s personal agency. The convergent validity and discriminate validity of the CFI-R sub-scale are related to career decision-making, difficulties, self-efficacy, personality optimism, and coping styles. The CFI-R can help individuals understand the current attitude towards various career changes and improve the effectiveness of career counseling by solving the concerns of individuals in the changing career world (Rottinghaus et al., 2012) [ 58 ].

• (6) Nota et al.’s Career and Work Adaptability Questionnaire (CWAQ)

Over the past 10 years, the aim of the presented studies was to develop a specific, new instrument, “Career and Work Adaptability”, to assess the degree of adaptability in adolescents planning their futures. In 2012, Nota, Ginevra, and Soresi conducted three studies, the first of which aimed to formulate the instrument’s items and to verify its factor structure. The second study confirmed the instrument’s multidimensional structure and evaluated its discriminant validity. The third study was conducted to verify the factorial structure’s cross-gender invariance and to evaluate its stability over time. The results showed that the instrument is an effective and multidimensional instrument for accurately measuring career adaptability. Specifically, it can serve as a useful vocational guidance tool in analyzing adolescents’ career adaptability (Nota et al., 2012) [ 59 ].

3.3.5. Life Design

Life design, a new paradigm, was implicit within the constructivist and narrative methods for career intervention that emerged in the 21st century (Savickas, 2012) [ 60 ]. Special attention should be paid to the fact that career adaptability has been recorded as a key self-regulation process in career construction and life design in the 21st century (Wen et al., 2020) [ 61 ]. However, some studies found that after life design intervention, career adaptability did not changed (Savickas & Porfeli, 2012; Maree et al., 2018b; Maree, 2019) [ 14 , 62 , 63 ]. Only a few studies found that life design could improve career adaptability or some dimensions (Nota et al., 2016; DaSilva et al., 2020) [ 64 , 65 ].

Life design plays an important role in career adaptability. However, it cannot be neglected that life design still faces challenges (Nota & Rossier, 2015) [ 66 ]. In order to solve those challenges, some experts came up with the following paths, which can be considered in future research. One is that career adaptability is an important variable affecting an individual’s career, and life design intervention could be designed around career adaptability, such as increasing intervention time, etc. The other is that similar groups may have common characteristics, so future studies could focus on the common needs of clients, and create more research groups based on life design (DaSilva et al., 2020; Maree & Symington, 2015c) [ 18 , 65 ]. In addition, a more personalized life design could be developed by creatively combining adaptability methods, such as career collages, career portfolios, etc. (Barclay et al., 2019; Ginevra et al., 2018; Ginevra et al., 2016) [ 67 , 68 , 69 ].

3.4. Research Hotspot Evolution

Research hotspots refer to topics or issues that are of common interest in a set of articles that have large number of high-frequency occurrences and have internal links within a certain period of time in a scientific field (Chen, 2010) [ 27 ]. Keywords are the embodiment of the content of the article, so the analysis of keywords can show the evolution of the hotspots in the research of career adaptability. After importing the data into CiteSpace, “keyword” was selected as the node type in the function selection area, “2010–2020” was selected for the year range. The other settings were left as their default values. In the running edit box, timezone view was selected in layout visualization, and then the map was adjusted to get the research hotspot evolution map of career adaptability (see Figure 5 ). By objectively showing the dynamics of the research hotspots of career adaptability over time, it can be used to not only grasp the development context of career adaptability research in the time dimension, but also understand the distribution characteristics of the research keywords in different time zones more intuitively.

Network map of hotspots in career adaptability research.

Through the analysis of the literature and charts (see Figure 5 and Table 3 ), from the perspective of time distribution, the 2010 study used “predictor” as the hotspot to predict the predictive effects of variables related to career adaptability. For example, Koen et al. adopted a two-wave design to predict the job search strategy and reemployment quality of the unemployed through their career adaptability (Koen et al., 2010) [ 70 ]. Duffy studied 1991 undergraduate students and found that the sense of personal control is one of the predictors of career adaptability (Duffy, 2010) [ 71 ]. From 2011 to 2013, research mainly focused on the revision, localization, and verification of the career adaptability scale. For example, Savickas revised the CMI (career maturity) (Savickas & Porfeli, 2011) [ 51 ]. It was noteworthy that in 2012, localization studied with the CAAS appeared in various regions. In 2013, research mainly verified the effectiveness of the CAAS in different cultural backgrounds. In 2014, studies focused on research related to career adaptability in different occupational groups. For example, Tolentino targeted business students to study the relationship between career adaptability and individual entrepreneurial intentions (Tolentino et al., 2014) [ 5 ]. Studies have found that, when individuals formed their entrepreneurial intention, they relied on the adaptive resources of their career adaptability and entrepreneurial self-efficacy, and individuals exposed to family businesses had stronger career adaptability (Tolentino et al., 2014) [ 5 ]. Based on career construction theory, Guo’s research examined individual and contextual predictors for the professional competence of Chinese undergraduates majoring in social work and found that career concern and career curiosity predicted social work students’ professional competence, with these relations mediated by the calling in social work (Guo et al., 2014) [ 72 ]. In 2015, studies mainly discussed the influence of the proactive personality on career adaptability. Based on Career Construction Theory and Self-Verification Theory, Cai’s research examined the mediating and moderating models for the relations among self-esteem, the proactive personality, career exploration, future work self, and career adaptability and found that both self-esteem and a proactive personality (measured at time 1) positively predicted the future work self and career adaptability (measured at time 2), with these relationships mediated by career exploration (Cai et al., 2015) [ 73 ]. Chan found that relationships between the CMI-C and CAAS with entrepreneurial, professional, and leadership career motivation profiles showed that the CAAS was more strongly related to the boundaryless mindset and protean career attitudes, while the CMI-C appeared to relate to more traditional (professional and leadership) career motivations (Chan et al., 2015) [ 41 ].

Top 10 keywords in career adaptability studies.

From 2016 to 2020, the research keywords in this field tended to describe diversity, reflected in the diversity of research subjects, including research on different groups of students, refugees, corporate employees, and immigrant groups. For example, Zacher used a quantitative daily diary study design and discovered that daily job demands, daily job autonomy, daily conscientiousness, and daily openness to experience, as well as daily past and future temporal focus, positively predicted daily career adaptability (Zacher, 2016) [ 74 ]. Presbitero and Quita found that the career adaptability of overseas immigrants has a positive correlation with their overseas working intention (Presbitero & Quita, 2017) [ 75 ]. Obschonka and Wehrle studied the career adaptability of refugee groups (Obschonka et al., 2018; Wehrle et al., 2019) [ 76 , 77 ]. At the same time, there were also studies on career adaptability in China, Turkey, Switzerland, Italy, and other regions during this period, which reflected the diversity of research areas in this field. For example, Saido conducted a qualitative analysis of the role of career adaptation in Japan in career change (Saido & Yoshida, 2016) [ 78 ], and Santilli used adolescents from Switzerland and Italy as samples to explore the relationship between career adaptability and life satisfaction (Santilli et al., 2017) [ 79 ]. Ozdemir used a qualitative study of the adaptability of the Turkish youth community (Karacan-Ozdemir, 2019) [ 80 ]. In addition, from 2016 to 2020, there were many small research hotspots in this research area, mainly around individual factors and situational factors influencing career adaptability research. The research on individual factors mainly focuses on personality traits, highlighting the Big Five and the proactive personality. Rudolph, Lavigne, and Zacher found that each dimension in the Big Five personalities was significantly related to career resilience through a meta-analysis test (Rudolph et al., 2017) [ 33 ]. Jiang found that a proactive personality first enabled individuals to thrive at work, which in turn led to improved career adaptability (Jiang, 2017) [ 49 ]. The study of contextual factors is mainly focused on social support. For example, Guan et al. found that the level of support from organizations can affect individual career construction to a considerable extent (Guan et al., 2016) [ 81 ]. Guan et al. studied the moderating role of traditionality beliefs in the indirect relationships among parental support, career decision-making self-efficacy, and career adaptability among Chinese university students, and found that parental support was associated positively with career decision-making self-efficacy and career adaptability (Guan et al., 2016) [ 82 ]. Atac, Dirik, and Tetik found that perceived social support positively predicted career adaptability and that perceived social support plays a moderating role in the relationship between perceptions of self-esteem and career adaptability sub-scales (Atac et al., 2018) [ 83 ].

4. Discussion

4.1. theory construction.

Super was one of the earliest researchers to discuss the construction of career adaptability theory. In the early days, Super proposed that the measurement of career adaptability should include the following areas: work values and work salience, autonomy or sense of agency, planning or future perspective, exploration and establishment, decision-making, and reflection on experience (Super & Knasel, 1979) [ 84 ]. Later, Super revised the construction of adult career adaptability, and proposed a more complete “model of adult career adaptability”, which had five dimensions, including planning, exploration, information, decision-making, and reality orientation. However, the object scope of the career adaptability model constructed by Super was aimed at adults (Super & Knasel, 1981) [ 1 ].

In order to make up for this deficiency, Savickas further enriched and improved the theoretical construction of career adaptability based on Super. Initially, Savickas proposed that career adaptability consists of three important dimensions: planful attitudes, self and environmental exploration, and adaptive decisions (Savickas, 1997) [ 2 ]. In 2005, Savickas further revised and improved the theoretical construction of career adaptability and proposed a more complete construction model. Savickas believed that the development of individual career adaptability develops along four dimensions or stages, which are career concern, career control, career curiosity, and career confidence (Savickas, 2005) [ 3 ]. In the theory of career adaptability, career concern is regarded as the first and most important dimension, which addresses the question “do I have a future?” It means that an individual can pay attention to his/her own future career (Savickas, 2005) [ 3 ]. Career control is the second important dimension of career adaptability. It addresses the question of “who owns my future?” that is, the belief that individuals are self-determined and responsible for building their own careers (Savickas, 2005) [ 3 ]. Career curiosity reflects the individual’s curiosity attitude, which motivates individuals to explore more careers, and enables teenagers to more realistically explore education and career choices, and then achieve future goals. The basic function of career curiosity in career construction is the same as the function of self-exploration and career exploration in career development theory, which means that individuals are willing to actively try to explore themselves and the work world (Savickas, 2005) [ 3 ]. Career self-confidence refers to individuals’ confidence in their problem-solving abilities and self-efficacy beliefs, which can help the individual to build a perfect future and overcome difficulties (Savickas, 2005) [ 3 ].

4.2. Scale Compilation

In the field of research on career adaptability, the career adaptability scale is undoubtedly an important research topic. Scholars in various countries have also localized the scales of Super and Savickas in light of their own cultural background and actual situation, and with further research in this field, more and more kinds of scales have been developed.

In the process of compiling various career adaptability scales, this study found that most of the subjects of the scales are adults. For example, the career adaptability scales of Savicks, Hirschi, and Rottinghaus. However, there are not many scales specifically designed for high school students and below, such as the Savickas and Porfeli Career Maturity Scale (Savickas & Porfeli, 2011) [ 51 ]. In China, secondary vocational students enter jobs after their secondary vocational studies are completed. As a special group, is the adult career adaptation strength chart applicable and is it also applicable to the measurement of the career adaptability of secondary vocational student groups? This needs to be verified by further research.

4.3. Influence Factors

When reviewing the literature of this period again, this study found that the factors affecting career adaptability were mainly concentrated in two aspects: one was the variables related to the individual, such as gender, grade (age), personality traits, etc. In terms of gender, Rottinghaus found that there was no significant gender difference in career adaptability among college students (Rottinghaus et al., 2005) [ 57 ], and Hirschi also believed that gender did not affect the development of career adaptability among middle school students (Hirschi, 2009) [ 29 ]. In terms of age level, most studies believed that grade (age) level was a good predictor of career development, but Hirschi found that grade does not affect the development of career adaptability (Hirschi, 2009) [ 29 ]. Therefore, the influence of grade (age) on career adaptability remains to be further studied. As for personality traits, Hirschi focused on the Big Five personality traits and the proactive personality. The second was the variables related to the environment, such as family, social support, and so on. In terms of family factors, the impact of family socioeconomic status and parents on career adaptability was mainly explored. Many scholars believe that family socio-economic status is positively related to the development of career adaptability, and that parenting styles also have an impact on youth career development (Chen et al., 2020; Dietrich & Kracek, 2009) [ 19 , 85 ]. In terms of social support, the social support of students was mainly reflected in family, school, and peers. While the social support of the adult group mainly came from the unit, supervisor, or related policies and measures.

5. Conclusions

Based on the scientometrics methodology, the thesis on career adaptability in the Web of Science database was systematized by using CiteSpace, and the science knowledge mapping of career adaptability and its evolution law were described. The results of this research are as follows:

• (1) In the field of career adaptability research, authors such as Savickas, Hirschi, Guan, Koen, Zacher, and Rudolph ranked high in the citation index. Among them, Savickas made an important contribution to the theory construction and scale compilation of career adaptability. Other scholars have also made contributions to this field in their research direction.
• (2) There are five hot topics in career adaptation research, namely: the boundaryless mindset, career construction, the proactive personality, career adaptability scale, and life design.
• (3) The research hotspot evolution in career adaptability is as follows: the keywords of the research hotspots before 2016 were relatively isolated, and research in 2010 focused on the predictive role of variables related to career adaptability with “predictor” as the hotspot. From 2011 to 2013, research was mainly focused on the revision, localization, and verification of the career adaptability scale. A study in 2014 focused on research related to career adaptability in different occupational groups. A study in 2015 mainly discussed the impact of the proactive personality on career adaptability. The keywords of research hotspots after 2016 tended to be diversified, which was reflected in the diversity of influencing factors related to the research object, research area, and career adaptability.

6. Limitation

This research has some limitations. Firstly, the types of data that can be processed by the software were limited. This study only selected literature from the Web of Science (WoS) database as the data source. Future research can further enrich the data source. Secondly, this study only discussed the trends and characteristics of international literature research from 2010 to 2020. Future research can also be combined with domestic research for further analysis and comparison. Thirdly, due to the limitation of language, it was impossible for this study to cover all the studies. Finally, when combing the literature, it was found that culture also had an impact on individuals’ career adaptability. Is there any difference in the adaptability of different cultural groups? Researchers in the field of careers can strengthen international cooperation to explore the differences of career adaptability among different cultural groups.

7. Future Trends

In a word, according to the above data analysis results, it is not difficult to find that in the past ten years, more and more studies have focused on the practice of career adaptability, and have studied and explored the impact of career adaptability on work and learning even in other areas. Therefore, the main points that this research found, which need to be paid attention to in future research, are summarized as follows.

• (1) The diversity of research objects. In the early research of career adaptability, the main focus was on individual working and learning. However, from 2016 to 2020, the research subjects in this field were diverse, including research on different groups of students, refugees, enterprise employees, and migrant groups. Therefore, future research may pay more attention to the research groups that have not received much attention, such as the adaptation of female workers to the workplace, and the maladjustment of diverse populations caused by the current epidemic situation.
• (2) The diversity of research tools. The development and use of the career adaptation scale has become mature. However, with the rapid development of society, many factors that affect career adaptability cannot be found only by scales. In the choice of future research tools, qualitative research should be done, such as some field research or role experience. Researchers should research the environment in more depth, and experience the impact of various factors on the research subjects.
• (3) The diversity of research relationships. In the current relationship research, career adaptability has been verified in many dimensions, but with more focus on the psychological level. Therefore, future research should not only focus on the relationship between these dimensions and career adaptability, but also pay more attention to the mediating role of career adaptability. In addition, more influencing factors in observational research should also be found. Starting from the data analysis, the relationship between the two and whether they have a positive effect or a negative effect should be explored.

Acknowledgments

The authors would like to thank Lingling (working at Beijing Union University) for her great support in data analysis and Hongling Yin (working at Nanjing Normal University) for her great writing and grammar revision. At the same time, we also thank the reviewers for their valuable comments on the manuscript.

Author Contributions

H.C. was PI for the project. T.F., L.P., and F.L. collected the data. H.C. developed the analytical plan and did the analyses. H.C., T.F., L.P., F.L., and X.G. wrote the paper, H.C., Y.W., and S.C. revised the paper and checked the grammar. All authors have read and agreed to the published version of the manuscript.

This research was funded by a Jiangsu Graduate Innovation Project, grant number “KYCX20_1145”, Jiangsu Province Basic Education Prospective Teaching Reform Experiment Project, China National Fund for Study Abroad, and Jiangsu Province University’s Advantageous Discipline Construction Project, grant number “PAPD”.

Conflicts of Interest

The authors declare no conflict of interest.

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Career maturity.

In specifying the sets of tasks, Super also thought about how progress in these tasks might be measured so that individuals could be compared both ipsatively and with others at the same developmental stage and therefore theoretically undertaking the same tasks. Almost all instrument development and much of the research related to the CM construct has addressed the latter kind of comparison. A number of measures of CM, most often closely aligned with Super’s theories, have been developed for use with adolescents and adults.

One of the largest areas of research into CM has been in relation to the constructs with which it is related. Research into the CM construct and its correlates is now well into its fifth decade. Correlates that have received recurring attention include age, level of education, gender, socioeconomic status, and a wide spectrum of career-related variables, such as vocational identity, career decision, career indecision, and work role salience. Findings of large bodies of international research in relation to age and gender have produced equivocal findings, although in general, career maturity scores in adolescents increase with age, and overall, the development of CM seems to differ for females and males. While socioeconomic status has been theorized as likely to be an important determinant of career behavior, even if acting largely through moderator variables, most studies have found only a minor or no correlation between CM and socioeconomic status in school-age adolescents and in university students. Several studies have reported that students who were more mature were also more career decided. Similarly, studies have reported that career indecision was the single most important predictor variable of career-immature young people.

A number of studies have investigated whether the amount of part-time work in high school is able to predict CM. Findings have been mixed, with some studies demonstrating no relationship between individuals who did a large amount of part-time work and levels of CM and other studies reporting higher mean scores on CM for students with work experience than for students without. It has been suggested that young people will benefit from part-time employment only when the job area is congruent with their career aspirations.

While research has been conducted on the relevance of CM in ethnic groups within the United States, very little work has been conducted outside the United States to explore its relevance across cultures. Although studies have been conducted in Nigeria, Israel, Lebanon, India, Canada, and Australia, there have been few studies of comparison across cultures. However, a considerable body of work from South Africa has consistently shown that Black high school and university students are less career mature than their White counterparts. Researchers have suggested that measures of CM may be oriented to Western White values and need to focus more broadly on life role salience.

Super envisioned that the relative importance assigned by individuals to roles at different stages of their lives would govern their commitment to and involvement in tasks associated with the roles, as well as the rewards they expected to experience in the roles. Very few studies have examined the relationship between the salience of student or worker roles to aspects of CM. Only a few studies of relationships between these constructs have been reported, with those studies reporting the relationships expected between student and work role salience and aspects of CM.

Recent Conceptualizations of Career Maturity

With the acknowledgment of the notion that careers evolve in a social context, the 1980s saw a concentrated move to extend theory to situate careers in social contexts and define their relationships with historical eras, geographical locations, race, and culture. A number of authors emphasized the importance of social ecology in careers and stressed the reciprocity of individual and societal development, meaning that the social context is important in the individual’s development and the individual is important in the development of social contexts. In addition, the individual and the context may change. The timing of the interaction between the organism and the environment, not sufficiently captured in the portrayal of CM as based on time- and age-based stage models, is an important underpinning of a number of recent theoretical formulations. Research has also demonstrated that CM is influenced by differences in social and political systems.

Constructivists have encouraged career theorists to assist individuals in consciously working to construct their own development. Specifying a career choice involves more than just the cognitive activity of aligning self- and occupational information. This activity takes place within a larger story that engages the sociocultural context of the individual. The individual’s career story is the collection of images of the way the individual sees himself or herself in the world. Whereas the informational aspects of the self (e.g., interest, abilities) and of the world of work constitute the content of the story, the individual’s constructions of these and the positioning of them within the story—the individual’s narrative about self— provide its uniqueness for each individual. And it is the individual’s understanding of his or her role in the construction of the story that is a signal point for the traditional construct of CM.

As shown in this review, the construct of CM has received considerable theoretical, conceptual, and research attention, including suggestions for ways in which it can be enhanced to make it more appropriate in times of changing career patterns and more applicable to a wider range of societal groups. The importance of taking contextual factors into account was part of Super’s original formulation of the construct, as was the idea of adjustment as a component of mature behavior. In his later writings, Super himself proposed a change in terminology from “career maturity” to “career adaptability” to better convey the range of career-related attitudes, knowledge, and skills at the various stages and transition points in career development.

The amount of research that continues to be reported on CM, almost 50 years since the construct was first proposed, is indicative of its importance in our understanding of career behavior. The construct has matured to a point where it may change in name or form to better reflect the changing world of work in the twenty-first century. However, it is likely that Super’s principles will remain central to any reformulation.

• Career salience
• Continuing professional education
• Erikson’s theory of development
• Super’s career development theory

References:

• Niles, S. G. 1998. “Special Section Introduction: Time and Timing in Career Development.” Career Development Quarterly 47:4-5.
• Patton, W. and Lokan, J. 2001. “Perspectives on Donald Super’s Construct of Career Maturity.” International Journal for Educational and Vocational Guidance 1(1-2): 31-48.
• Raskin, P. M. 1998. “Career Maturity: The Construct’s Validity, Vitality, and Viability.” Career Development Quarterly 47:32-35.
• Savickas, M. L. 2002. “Career Construction: A Developmental Theory of Vocational Behavior.” Pp. 149-205 in Career Choice and Development, 4th ed., edited by D. Brown and Associates. San Francisco, CA: Jossey-Bass.
• Super, D. E., Savickas, M. L. and Super, C. M. 1996. “The Life-span Life-space Approach to Careers.” Pp. 121-178 in Career Choice and Development, 3d ed., edited by D. Brown, L. Brooks and Associates. San Francisco, CA: Jossey-Bass.

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Theories of Career Development: An analysis

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Career Maturity: A Review of Four Decades of Research

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International Journal of Evaluation and Research in Education

Mar'atus Sholikah

This study aimed to analyze students&#39; readiness for careers out of their professions by considering the effect of locus of control and socio-economic status. Career maturity acts as the variable intervening. The quantitative method was chosen to test the hypothesis. A total of 80 students were selected to be respondents based on purposive sampling. Model testing was done by using PLS-SEM. The findings of this study found that the locus of control influenced student career readiness. Conversely, socioeconomic status had no significant effect on their career readiness. On the other hand, this study&#39;s career maturity partially mediates between locus of control and career readiness. Regarding the effect of socioeconomic status on career readiness, career maturity in this study was known had a role as an explanatory or predictor. This study concluded that if students want to improve their career readiness, they should pay attention to the locus of control and achieve career succe...

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Career maturity is defined as a person&#39;s ability to identify and prepare for a career choice. Career maturity will be necessary for students because as the level of career maturity increases, the achievement of their work will also increase. This study aims to determine the factors that influence student career maturity. The type of research used is correlational research with quantitative methods. Data were collected using a questionnaire method and data analysis techniques using multiple linear regression. The results of the regression analysis showed that gender variables influenced career maturity, while academic achievement and socioeconomic status do not have a significant influence on student career maturity.

Suzanne King

The purpose of this study is to identify the level of career maturity among students in three different types of schools which are SBPI Gombak (full boarding school), SMKA Maahad Hamidiah (religious school) and SMK Sri Serdang (daily school) in Lembah Klang, Selangor. This study is an ex-post facto research. The instrument used to examine career maturity is Career Maturity Inventory (CMI) introduced by Crites (1978) which was later translated by Tan Wat Jin (2005). A total of 182 students made up the study by using stratified sampling proportion and sample size calculated by G*Power software. Spearman’s Rho Correlation was used to analyse the relationship of the variables in this study while t test and one-way ANOVA was used to determine significant difference at .05 between the variables. The findings showed that there was no relation between socio-economic status and parents’ academic achievement to students’ career maturity level. In addition, it was found that there was no diffe...

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Frequently asked questions about NIA’s training and career development programs July 31, 2024

Applying for a grant can be a challenging process, particularly for early-career investigators who are navigating the variety of fellowship and career development programs offered by NIA. To help, we’ve collected answers to some of the most common applicant questions.

Q: Which career stages are supported by NIA’s training and career development awards?

A: NIA is proud to offer career development opportunities for the full spectrum of trainees, from K-12 students to established investigators. Our training and career development landscape graphic breaks down the available programs at each career stage. These awards include fellowships (F), career development (K) awards, and research (R) grants for individual investigators as well as support for institutions to develop training and research education programs. Check out our recent blog post on developing the aging research workforce from the earliest career stages for more details.

Q: Does NIA support initiatives to enhance research workforce diversity?

A: Yes. Members of groups underrepresented in the biomedical and behavioral sciences are encouraged to apply for any of NIA’s grant programs. NIA participates in NIH-wide initiatives aimed at diversifying the research workforce, including an F31 for predoctoral students and diversity and other supplements . In addition, NIA has created our own suite of programs at different career stages, including an aging research dissertation award as well as predoctoral, postdoctoral, and career transition awards focused on Alzheimer’s disease and related dementias translational research.

We also support institutions seeking to diversify the aging research workforce via R25 research education programs in aging research for undergraduates and for summer and postbaccalaureate experiences in Alzheimer’s and related dementias research . Moreover, NIA’s Butler-Williams Scholars Program provides opportunities for early-stage investigators who are faculty or conducting research in a lab to establish an independent career in aging research.

Q: Are international students or postdocs pursuing research in the United States eligible to apply for NIA training and career development programs?

A: Yes. International graduate students who plan to pursue both their graduate degree and their postdoctoral research in the U.S. can consider the F99/K00 predoctoral to postdoctoral transition award . Similarly, international postdocs working in the U.S. who are interested in a future tenure-track position in the U.S. can consider the K99/R00 postdoctoral to independent research transition award . An applicant for these awards may be a U.S. citizen or a U.S. non-citizen national who has been lawfully admitted for permanent residence (i.e., possesses a currently valid Permanent Resident Card USCIS Form I-551 or other legal verification of such status), or be a non-U.S. citizen with a valid U.S. visa. There is no U.S. citizenship requirement for most R grants; international scientists can apply for research project grants such as the R01, R03, or R21, if their institutions are willing to support them.

Q: Does NIA support career development programs for clinician-scientists?

A: Yes. NIA offers several mentored career development programs for clinician-scientists, including the K08 to provide training in basic aging research, the K23 to provide training in patient-oriented research, and the K76 Paul B. Beeson Emerging Leaders Career Development Award in Aging. The Beeson K76 is unique in that it supports early-stage clinician-investigators who have begun to establish research programs and demonstrate leadership in the field of aging research.

NIA also offers the K24 to support mid-career clinician-scientists as they mentor early-stage investigators and advance their own patient-oriented research efforts. Additionally, the Early Medical/Surgical Specialists’ Transition to Aging Research ( GEMSSTAR ) is NIA’s signature program supporting physicians who have recently completed clinical training and are interested in pursuing an aging research career related to their specialty.

Connect with us for help!

We hope this FAQ is helpful, but if you have additional questions, please visit the NIA Training and Career Development homepage to sign up for the latest updates, email [email protected] , or leave a comment below. If you have questions regarding the NIA Butler-Williams Scholars Program, please email the NIA Office of Special Populations at [email protected] .

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What Research Says About Being a Stay-at-Home Parent

Ask people what they think about  stay-at-home moms  (SAHMs) and stay-at-home parents in general, and you'll likely get a variety of answers. Some might say they've got it easy, or that life at home with the kids would be boring. Some might think they're lazy or not contributing much to society. Others contend that stay-at-home parents are making the best decision of their lives and that they're making a noble, worthwhile sacrifice to stay home and nurture their kids day in and day out.

If you're contemplating whether or not to be a stay-at-home parent, what matters most is what works best for your family. So, first and foremost, consider your personal beliefs, priorities, finances, and lifestyle. However, there is also a wealth of research on the subject that you can consult when making your decision. The findings on life as a stay-at-home parent may surprise you.

Brianna Gilmartin

Pros and Cons of Staying at Home

There are, of course, many personal reasons for or against staying home with your kids. Benefits may include more opportunities for quality time with your children and having more direction over their learning and development. You may not want to miss a minute of their childhoods. You also might not trust others to care for your little loves. Drawbacks include the big hit to your family's income and the trajectory of your career as well as the big change to your lifestyle.

While there is no right or wrong answer, this research may help inform your choice. Remember that each of these benefits and drawbacks may or may not apply to you. There are many different factors, such as budget, lifestyle, priorities, social support, relationship status, spousal involvement, and your kids' specific needs, to consider before making your final decision.

Evidence-Based Benefits of Being a Stay-At-Home-Parent

There are many reasons that parents choose to stay at home with their children. Studies have shown that many people think this is the best option for kids when financially plausible. According to a Pew Research Center study, about 18% of American parents stayed home with their children in 2021.

According to Pew Research Center's Social and Demographic Trends, 60% of Americans say a child is better off with at least one parent at home. While 35% of responders said that kids are just as well off with both parents working outside the home.

Benefits for Children of Stay-at-Home-Parents

A 2014 study found that the benefits of having a parent at home extend beyond the early years of a child's life. The study measured the educational performance of 68,000 children. Researchers found an increase in school performance to high school-aged children. However, the biggest educational impact was on kids ages 6 and 7.

Most  homeschooled students  also have an at-home parent instructing them. A compilation of studies provided by the National Home Education Research Institute supports the benefits of a parent at home for educational reasons. Some research has found homeschoolers generally score 15 to 30 percentile points above public school students on standardized tests and achieve above-average scores on the ACT and SATs.

Regardless of whether parents stay home or work outside the home, research shows that parent involvement in schools makes a difference in children's academic performance and how long they stay in school.  Some kids with learning differences and/or special needs may do better in a school (vs. homeschooling) to access any required services .

Decreased Stress and Aggression in Kids

Some studies link childcare with increased behavioral problems and suggest that being at home with your children offers benefits to their development compared with them being in  being in childcare  full-time.  This may be reassuring news for stay-at-home parents knee-deep in diapers and temper tantrums.

Studies have found that children who spend a large amount of their day in daycare experience high stress levels, particularly at times of transition, like drop-off and pick-up.

Subsequent studies also showed higher levels of stress in children in childcare settings compared with those who are cared for at home. But that doesn't mean you have to keep your children with you every minute until they're ready to go to school. Look for a nanny or babysitting co-op that allows your kids to play with others while giving you some time alone.

Greater Control of Children's Upbringing

The ability to directly protect, spend time with, and nurture their children each day is often cited as a primary benefit of not working outside the home. Studies show that some parents stay home specifically to have greater first-hand control over the influences their child is exposed to. Others simply see it as their duty to be the one who provides daily care to their little ones.

More Parents Want to Stay Home

According to the Pew Research Center, more people are becoming stay-at-home parents—and 60% of Americans believe that choice is best for children. The number of stay-at-home parents jumped from a low of 23% in 1999 to 29% in less than 15 years. However, today's rates don't match those of the 1970s and earlier when around 50% of women (and very few men) were stay-at-home parents.

While the number of men taking on this role is far lower than that of women (around 210,000 compared with over 5.2 million), the rate of men becoming stay-at-home dads is on an upswing, too. Between 2010 and 2014, the prevalence of men choosing to stay home increased by 37%.

Downsides of Being a Stay-at-Home Parent

Regardless of the increasing numbers and some important benefits, a decision to quit your job to become a stay-at-home parent shouldn't be made out of guilt or peer pressure. While there are many great reasons to be a stay-at-home parent, it's not necessarily right or beneficial (or financially plausible) for everyone. For some families, the drawbacks significantly outweigh any positives.

Some People Miss Working

Research shows that many stay-at-home parents miss working outside the home and think about  going back to work  someday.  It can be tough to leave behind the tangible rewards and results of a job, especially one you enjoyed and were good at.

If you stay home when your kids are little but plan to return to the workforce, you can take some steps to bridge that employment gap, such as taking classes, earning licenses or certificates that enhance your resume, or even taking a part-time job.  You might also consider at-home business opportunities as well as  remote jobs  that let you stay home while also earning money and reclaiming some of what you missed about your career.

Costs to Your Career and Wallet

The decision to stay at home with your kids means giving up income. Research shows that stay-at-home parents must contend with lost wages now and decreased wages when returning to work. This "wage penalty" often amounts to 40% less in earned income over time.

There is also a big hit to the stay-at-home parent's career trajectory. Some parents can regain their previous work roles upon reentering the workforce, while others struggle to get a foothold professionally after taking time off.

The direct impact on your family's finances will depend on your personal earning potential, skills, and career choices—as well as the income of your partner if you have one. However, studies show that mothers who reenter work after having children experience between a 5% and 10% pay gap compared with their childless peers. This is in addition to the gender pay gap.

Adverse Impacts on Physical and Mental Health

Studies show that stay-at-home parents experience poorer physical and mental health compared with parents who work outside the home. Effects include higher rates of mental health conditions, such as depression and anxiety, as well as higher rates of chronic illness. A 2012 Gallup poll surveyed 60,000 women including women with no children, working moms, and stay-at-home moms who were or were not looking for work, and found more negative feelings among SAHMs. There are likely several reasons for this, including experiencing more parental and financial stress. Working parents tend to have access to more robust health insurance plans than stay-at-home parents. They also tend to benefit from greater self-worth, personal control over their life, economic security, and more dynamic socio-economic support.

However, it's worth noting that significant research shows that whether they work outside the home or not, parents generally are less happy than their childless counterparts.  Of course, the joy you get from parenting (and staying home with the kids) is likely to be highly individual.

More Social Isolation

A 2015 study found that many moms are spending lots of time with their kids, more so than in years past. Researchers believe this extra kid-focus results in a higher potential for social isolation. Interestingly, the research found no scientifically proven difference in outcomes for the children with this additional parental attention.

Some stay-at-home parents may feel isolated or undervalued by what some call the " mommy wars, " which pit parents against each other. This social dynamic can create perceived judgments or pressures that leave some stay-at-home parents feeling like they're not respected as worthy members of society. On the flip side, some working parents may feel criticized for not spending as much time with their children. Both groups can end up feeling socially isolated.

A 2021 study found that around a third of all parents experience loneliness. That's why it's so important for all parents (whether they stay at home or work outside the home) to find the right balance of social activities, exercise, sleep, hobbies, and self-care. Additionally, it's helpful to make the most of your family time, including  creating gadget-free zones  and planning fun activities you can all enjoy.

It's also key to take care of your own emotional well-being and let your children spend some time away from you. Whether it's a date night with your spouse or scheduling a day off so you can have some alone time, you're not going to shortchange your child because you didn't spend every minute with them. Giving yourself parenting breaks and opportunities to socialize is important for your well-being, particularly during times of stress.

Parenthood and well-being: A decade in review .  J Marriage Fam .

Stay at home moms and dads account for about 1-in-5 U.S. parents . Pew Research Center. 

After decades of decline, a rise in stay-at-home mothers . Pew Research Center. 

Home with mom: The effects of stay-at-home parents on children’s long-run educational outcomes .  J Labor Econ. 

National Home Education Research Institute.  Research facts on homeschooling .

Effect of parental involvement on children’s academic achievement in chile .  Front Psychol.  

School performance among children and adolescents during COVID-19 pandemic: A systematic review .  Children (Basel) .

The NICHD study of early child care and youth development . U.S. Department of Health and Human Services. 

Toddlers’ stress during transition to childcare .  European Early Childhood Education Research Journal .

Examining change in cortisol patterns during the 10-week transition to a new child-care setting .  Child Dev .

7 key findings about stay-at-home moms . Pew Research Center. 

The mother's perspective: Factors considered when choosing to enter a stay-at-home father and working mother relationship .  Am J Mens Health .

The relationships between mothers' work pathways and physical and mental health .  J Health Soc Behav .

The motherhood penalty at midlife: Long-term effects of children on women's careers .  J Marriage Fam .

Parents' work schedules and time spent with children .  Community Work Fam .

Gallup.  Stay-at-home moms report more depression, sadness, anger .

Parenthood and happiness: Effects of work-family reconciliation policies in 22 OECD countries .  AJS .

Does the amount of time mothers spend with children or adolescents matter? .  J Marriage Fam.

Experiencing loneliness in parenthood: A scoping review .  Perspect Public Health .

  First things first: Parent psychological flexibility and self-compassion during COVID-19.   Behav Anal Pract .

Related Articles

Chefs’ Communities of Practice for Managing Innovations in the Tourism and Hospitality Sectors

• Published: 07 August 2024

Cite this article

• Paulo Sergio Gonçalves de Oliveira   ORCID: orcid.org/0000-0001-9122-4904 1 ,
• Luciano Ferreira da Silva 2 ,
• Sérgio Ignácio de Oliveira 3 ,
• Rodrigo Cunha da Silva 4 &
• Mauro de Mesquita Spinola 5  

This paper aims to verify how communities of practice influence the development of innovations in the gastronomy sector. Primary data were collected at the interval of 1 year and achieved an amount of ten interviews using an unstructured interview with an in-depth interview with renowned chefs with parallel academic careers. To collect the data, the researcher adopted a snowball method, where the interviewees were motivated to indicate another participant until the study achieves theoretical saturation. The data were analysed using grounded theory through the analysis of incidents in three cycles of coding (open, axial, and selective), which enabled the generation of data-driven and theory-driven categories. The findings showed that chefs use their communities of practice to obtain the tacit and explicit knowledge they need to innovate in their menus. This study provides as practical implications a description of how chefs deal with sharing knowledge when they are thinking about menu modifications and how they obtained the necessary tacit and explicit knowledge to innovate in their menus to keep them up to date to the competitive marketing. The study carried out presents the process of obtaining knowledge using the community of practice by chefs to innovate in their menus.

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Buhalis, D., & Sinarta, Y. (2019). Real-time co-creation and nowness service: Lessons from tourism and Hospitality. Journal of Travel & Tourism Marketing, 36 (5), 563–582.

Buhalis, D., Harwood, T., Bogicevic, V., Viglia, G., Beldona, S., & Hofacker, C. (2019). Technological disruptions in services: Lessons from tourism and Hospitality. Journal of Service Management .

Capdevila, I. (2017). The local and global knowledge dynamics through communities. The case of communities of makers and social entrepreneurs in Barcelona. Dinámicas de Conocimiento Locales Y Globales a Través de Comunidades. El Caso de Las Comunidades De "makers" y Emprendedores Sociales En Barcelona, 21 (3), 59–70.

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de Oliveira, P.S.G., da Silva, L.F., de Oliveira, S.I. et al. Chefs’ Communities of Practice for Managing Innovations in the Tourism and Hospitality Sectors. J Knowl Econ (2024). https://doi.org/10.1007/s13132-023-01616-y

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