Occupational Mobility Driving Paths of College Graduates with Specialties in the Energy Industry —Com-B model-based configuration analysis

Occupational Mobility Driving Paths of College Graduates with Specialties in the Energy Industry —Com-B model-based configuration analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Occupational Mobility Driving Paths of College Graduates with Specialties in the Energy Industry —Com-B model-based configuration analysis Shuhua XIE, Lifeng XU This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6620518/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The career mobility of graduates from universities characteristic of the energy industry directly affects the supply of talents in the industry, the development of regional industry and economy, and relates to the benign interaction between higher education and the labor market. The study constructed a driver model of high occupational mobility effects of graduates based on the Competence, Opportunity, Motivation, and Behavior model (COM-B model), and explored the group effects of eight antecedent variables on occupational mobility under three dimensions of schooling, employment opportunities, and individual motivation using a group approach. The study found that the high occupational mobility effect is the result of multiple antecedent conditions acting in concert; the economic level of the workplace has strong explanatory power for the occupational mobility effect; the groupings that promote high occupational mobility effects include geographic advantage-development opportunity duality-driven, geographic salary attraction, and family expectation-influenced, suggesting that the occupational mobility of graduates from industry-characterized colleges and universities is inextricably linked to regional economic conditions, industry distribution, and personal motivation; accordingly, three driving paths are proposed: opportunity-driven, geographically-driven and energy-driven. The study clarifies the multiple choice paths of occupational mobility from a group perspective, suggests career mobility for graduates of industry characteristic universities, and expands the use of the COM-B model. Physical sciences/Energy science and technology Physical sciences/Engineering universities with industry characteristics graduate employment occupational mobility configuration COM-B model Figures Figure 1 Introduction Industry characteristic universities are an important part of China's higher education structure, closely linked to the national economy and social development, and play an important role in the process of promoting the economic development of local and industry[ 1 ]. Its graduates have the advantages of strong specialization and industry recognition in their career development, but also face the dilemma of relatively narrow career development options. Characteristic universities in the energy industry shoulder the important responsibility of delivering adaptable talents for the energy industry, and the occupational mobility of their graduates is not only related to the realization of the value of individual human capital, but also has a direct impact on the technological innovation in the field of energy and the coordinated development of the regional economy. In defining the definition of occupational mobility, this study refers to the definition method of Chen Yao[ 2 ] and others, Cai He, and Zhang Dong[ 3 ], which defines occupational mobility as including initial mobility and re-mobility, which refers to the change of work between different work units of a worker due to objective and subjective reasons, and is considered to be occupational mobility if the worker undergoes a change of work unit. If a worker only undergoes a change of position within the same organization, it is not included in the definition of occupational mobility in this paper. In defining the concept of occupational mobility, our researchers also measure whether there is a change in the type of occupation, which also implies that occupational mobility has taken place. Therefore, combining previous studies, occupational mobility in this paper is mainly judged based on two dimensions: change of work unit and change of occupation type. Existing literature on occupational mobility mostly focuses on the following perspectives: in terms of the content of mobility, including the direction of mobility[ 4 ], the region of mobility [ 5 ], the influencing factors[ 6 ], and the influencing effects[ 7 ]; in terms of the subject of mobility, the focus is on the more mobile groups such as migrant workers[ 8 ], educational and scientific researchers[ 9 , 10 ], professional women[ 11 ], and industrial workers[ 12 ]; in terms of mobility trends, the focus is on exploring intergenerational mobility[ 13 , 14 ]; In terms of the causes of mobility, existing studies have analyzed the effects of educationalization[ 15 , 16 ], urbanization[ 17 ], digitalization[ 18 ], and socialization[ 19 ] (the shadow of social capital on the multidimensional poverty of farm households) on occupational mobility, respectively, while the phenomenon of graduates' mobility with respect to the characteristics of different industries has seldom been explored in depth. Accompanying occupational mobility are changes in the economic level of the labor force, the nature of work, job satisfaction, etc[ 20 , 21 ]. This paper conducts a seminar for a large group of socially mobile graduates, on the basis of which it investigates the effects and driving paths of occupational mobility, which complements the existing literature in studying the main body and content of occupational mobility, and helps to understand the occupational choices, mobility, and development of graduates in a specific industry . This study introduces the COM-B model and uses groupthink to try to solve two questions: what are the factors affecting occupational mobility of graduates from universities with characteristics in the energy industry and how do different factors affect each other to produce a higher occupational mobility effect? How do the different influencing factors work together to produce a higher occupational mobility effect? In order to provide reference for the occupational mobility of graduates from universities with industry characteristics. Relevance analysis of the COM-B model The COM-B (Capability, Opportunity, Motivation-Behaviour) model is a theoretical framework for behavioral change proposed by British psychologist Susan Mitchell et al. in 2011, which is mainly used to explain the occurrence of individual behavior and its changes. The theory suggests that behavior arises as a result of the interaction of three elements: Capability, Opportunity and Motivation. The individual's capability determines whether he or she possesses the basic conditions to accomplish a behavior, opportunity factors relate to whether the external environment supports the occurrence of the behavior, and motivation reflects an individual's subjective will and drive. Individuals are likely to act only when capability, opportunity, and motivation are simultaneously available and the motivation is strong enough to enable the behavior to occur. The COM-B model has good appropriateness in explaining the occupational mobility of graduates from universities characterized by the energy industry. The element of competence is given by schooling and covers the graduate's personal qualifications, professional knowledge and skills and personal employment expectations; the opportunity element mainly refers to employment opportunities, which involves the opportunities for occupational mobility provided to graduates by the external environment, including the economic level of the place of work, the market demand of the industry, the salary and benefits of the employing organization and the development opportunities; the motivational element, on the other hand, is individual motivation, that is, the intrinsic motivation that pushes graduates to engage in occupational mobility, including individual motivation and family employment expectations; Behavior refers to the occupational mobility behavior of college graduates characteristic of the energy industry, is the result of the interaction of ability, opportunity, and motivation. Occupational mobility behavior generates occupational mobility effects, and this paper examines occupational mobility effects from a three-dimensional perspective of salary income, job stability, and job satisfaction to ultimately understand which factors or combinations of factors contribute to high occupational mobility effects. The visualization of the COM-B model's explanatory framework for occupational mobility is shown in Fig. 1 . Research design 2.1 Research method This paper adopts a group approach to analyze the group effects arising from the interdependence and interaction between the antecedent conditions of occupational mobility influencing factors, and to reveal the synergistic and interactive mechanisms between different factors. This paper utilizes fsQCA for the following considerations: First, fSQCA allows the feature encoding to have any continuous value between 0 and 1, the data processing in this paper meets the encoding requirements; second, from the previous elaboration, it is clear that occupational mobility behavior cannot develop without the interaction of three major internal and external factors: school education, employment opportunities and employment motivation of graduates of industry characteristic majors. Therefore, the fSQCA method was selected to identify the multiple causes of enhancing occupational mobility effects and their specific combination forms and mechanisms of action, and to further reveal the interactive effects of different factors of school, society and individuals on occupational mobility effects. 2.2 Questionnaire design and collection The data for this study is sourced from the research group's questionnaire "Survey on Occupational Mobility of Graduates from Energy Industry Characteristic Universities", and combined with online research on the annual employment quality reports of national energy industry characteristic universities over the years. The energy industry universities involved in this study are the industry characteristic type of colleges and universities with geomining and petroleum as the main or core, and the object of the research is the graduates of the main majors of the industry characteristic colleges and universities, that is, the graduates of energy industry colleges and universities with characteristic majors. The graduates participating in the study include China University of Mining and Technology, China University of Mining and Technology (Beijing), Liaoning University of Engineering and Technology, Shandong University of Science and Technology, China University of Petroleum (Beijing) and other universities. Considering that students who have just graduated have not yet carried out occupational mobility, the team distributed a total of 2,070 questionnaires using questionnaire star from December 2024 to February 2025 to students who have graduated from the characteristic majors of the national energy industry for more than 5 years, and 1,742 valid questionnaires were returned, with a sample validity rate of 84.15%. The questionnaire covers data on four aspects: basic individual information, occupational mobility, strength of motivation for occupational mobility, and factors influencing occupational mobility. Individual basic information covers the graduate's gender, age, field of study, and graduation institution. A series of scale questions allow graduates to self-assess their communication skills, teamwork skills, problem solving skills, creativity, and clarity of career planning; occupational mobility focuses on understanding respondents' work experience, with questions including the type of occupation, entry time, and job match for each job, using single choice and fill-in-the-blanks to facilitate statistics and analysis; corresponding questions on influencing factors were designed based on the COM-B model dimensions, such as “How much does the economic development of your region influence your career choice?”, individual motivational intensity sets individual employment expectation matching status, attention to other employment recruitment information, and so on. The impact factors were all rated on a scale of 1–5, with 1 being very low impact and 5 being very high impact. 2.3 Data measurement and calibration In order to ensure the validity of data collection, the study was based on the results of the exploratory analysis of the pre-survey, and the reliability optimization and adjustment of the measurement items were implemented to reconstruct the questionnaire structure in line with the cognitive logic of the respondents. 2.3.1 Outcome measurement 2.3.1 Outcome measurement In terms of occupational mobility, 73.20% of graduates did not mobilize, 22.86% mobilized once, and only 3.94% mobilized two or more times, reflecting the high stability of the industry. In this paper, salary income, job stability and job satisfaction are used as indicators to evaluate occupational mobility effects. In this case, the annual income of salary up to 100,000 and above is assigned a value of 1, while others are assigned a value of 0. Signing an employment contract and having labor insurance are assigned a value of 1, while others are assigned a value of 0. Assign a value of 1 if you are satisfied with the employment organization and 0 if you are not satisfied. The final score of more than or equal to 2 (≥ 2) for the three indicators was defined as a high occupational mobility effect and assigned a value of 1, while a score below 2 was defined as a low occupational mobility effect and assigned a value of 0 [ 22 ]. 2.3.2 Antecedent condition measurement Although fsQCA can analyze the group effects of multiple antecedent conditions, in practice it is inevitably not possible to cover all antecedent conditions and only the more important influences can be selected. The antecedent condition framework system in this paper includes a total of 8 conditions in 3 dimensions and 5 level 1 indicators, which are measured as follows: school education is calculated by the degree of influence of the individual's educational status and the individual's employment expectations, which are assigned a value of 1–5 according to very little influence, little influence, average influence, great influence, and great influence, respectively; employment opportunities are assigned by the degree of influence of the job market demand, employment unit salary and benefits, employment unit development opportunities and the economic level of the place of work, with very small influence, small influence, average influence, large influence, and very large influence assigned a value of 1–5, respectively; individual motivation is assigned by the level of agreement with the statement “How much do you agree with the following viewpoints - I often pay attention to new job openings and make continuous efforts to find a new job” and the influence of family's employment expectations, which are assigned a value from 1 to 5 for strongly disagree, disagree very much, agree generally, agree somewhat, and agree very much, respectively. 2.3.3 Variable calibration Table 1 QCA Data Matrix of Career Mobility for Graduates from Majors Specializing in the Energy Industry Questionnaire number Occupational mobility effect Individual qualifications Employment market demand Individual employment expectations Family employment expectations Salary and welfare Development chance Economic level of workplace individual motivation 1 0.501 0.67 1 0.67 0.67 1 1 1 1 2 1 0.67 0.67 1 1 0.67 0.501 0.67 1 3 0.501 0.501 0.67 1 1 0.501 0.501 0.501 0.501 4 0.501 1 0.33 1 1 0.501 0.501 0.501 0.501 5 0.501 0.501 0.501 0.501 0.501 0.67 0.501 0.501 0 6 0.501 0.501 0.501 0.501 0.501 0.501 0.501 0.501 0.33 7 1 0.501 0.501 0.501 0.501 0.501 0.501 0.501 0.501 8 1 1 0.501 0.33 0.33 0.501 0.501 0.501 0.33 9 0.67 0.501 0.33 0.501 0.501 0.501 0.501 0.501 0.501 10 1 0.67 0.33 0 0.33 0.67 0.67 0.67 0.501 11 0.67 1 0.67 1 1 1 1 0.67 0.501 12 0.501 0 0 0.33 0.33 0 0 0 1 In this study, the eight antecedent variables were assigned logically and consistently, specifically, by scoring each sample's representation on each variable one by one, i.e., 0 (not at all affiliated), 0.33 (partially affiliated and biased toward not at all affiliated), 0.67 (partially affiliated and biased toward fully affiliated), 0.501 (maximum fuzzy point treatment), and 1 (fully affiliated), in order to accurately perceive the variability in occupational mobility effects for each variable. Combining the assignment of condition variables and outcome variables, the QCA data matrix of occupational mobility of graduates of characteristic majors in the energy industry can be obtained, as shown in Table 1 , which meets the fsQCA criteria. Empirical analysis 3.1 Necessity analysis Table 2 Necessity Analysis Antecedent variable High occupational mobility effect Consistency Coverage Employment market demand 0.783535 0.756014 ~Employment market demand 0.63574 0.885765 Individual education 0.836514 0.726752 ~Individual education 0.521415 0.864555 Individual employment expectation 0.805582 0.724322 ~Individual employment expectation 0.551853 0.859656 Family employment expectation 0.780869 0.750494 ~Family employment expectation 0.600984 0.842115 Salary and welfare 0.851018 0.719612 ~Salary and welfare 0.520999 0.911588 Career development opportunity 0.855863 0.724456 ~Career development opportunity 0.527013 0.92015 Level of economic development at workplace 0.829653 0.752263 ~Level of economic development at workplace 0.588371 0.903439 Individual motivation 0.806703 0.799578 ~Individual motivation 0.631725 0.847699 In this paper, the consistency threshold of the necessary conditions is set to 0.9 according to the criteria of Schneider et al. In the qualitative comparative analysis, when the degree of consistency was greater than 0.9 and the coverage was higher than 0.5, it indicated that this condition was a necessary condition for the results. As can be seen in Table 2 , in the high outcome group, all the antecedent variables have consistency levels less than 0.9, which does not constitute a necessary condition for the outcome variable; therefore, this paper includes these eight conditional variables in the sufficiency analyses to explore the group equivalence of the high occupational mobility effect. 3.2 Sufficiency analysis In this study, the case frequency threshold was set to 2, the raw consistency threshold was set to the system default threshold of 0.8, and the PRI consistency threshold was selected as 0.75. A total of 5 grouping paths were found for the high result group, and the specific results are shown in Table 3 . The consistency values for each grouping are above 0.9, which is above the minimum acceptable standard, and the overall consistency is strong, with a value of 0.936, indicating that all five grouping paths can be considered as sufficient conditions to produce a high outcome grouping and explain about 62.7% of the cases. Table 3 Configuration analysis Antecedent variable Configuration 1 Configuration 2 Configuration 3 Configuration 4 Configuration 5 Industry Market Demand ⊗ ⊗ ● ⊗ ⊗ Individual Education ● ● ● Individual employment expectation ⊗ ● ● ● ● Family Expectation ⊗ ● ● ● Salary and welfare ● ● ● ● Career development opportunity ● ● ● ● ● Economic level of workplace ● ● ⊗ ● ● Individual motivation ● ● ● ● Consistency 0.938 0.946 0.948 0.952 0.95 Original coverage 0.344 0.526 0.465 0.512 0.519 Unique coverage 0.011 0.016 0.039 0 0.003 Total coverage 0.627 Overall consistency 0.936 注: ●indicates that the core condition exists;●indicates that an edge condition exists; ⊗indicates that core conditions are missing; Uindicates that the edge condition is missing; blank cells indicate that conditions are optional By combing and summarizing the results of the condition grouping analysis, it is found that some of the paths show significant commonality in the distribution of the core conditions, on this basis, combined with the theoretical framework of this paper and the actual situation, the grouping H1-H5, H2-H4, H3 were named as “geographic advantage-development opportunities dual-driven”, “geographic salary attraction”, “family expectations influence type”, thus summarizing the three grouping paths that produce high career mobility effects. “Geographical Salary Attraction” and ‘Family Expectation Influence’, thus summarizing the three types of group paths of high occupational mobility effect. 3.2.1 Geographic advantages - development opportunities dual-driven In this category, occupational development opportunities and the economic level of the workplace are the core conditions of existence, and it includes configuration 1 and configuration 5, so it is named “Geographical advantage - development opportunities dual-driven type”. Salary and welfare and individual motivation are in marginal presence in configuration 1, individual education is non-essential (conditional gap), and individual employment expectation and family expectation are in marginal absence. Individual education, employment expectations, salary and welfare, and individual motivation are marginal states of existence in configuration 5, and family expectations are non-essential. In this type of grouping, even if the employment situation in the industry market is poor and the demand from employers is insufficient, graduates can help to realize high occupational mobility effects if they can quickly adjust their occupational strategies and actively pursue their careers in economically developed regions or in units with more room for growth. Even if individual employment expectations, family expectations, and so on, are not decisive, graduates are likely to have a high occupational mobility effect by virtue of two core conditions: career opportunities and the level of economic development of the place of work. Career development opportunities provide graduates with room for growth and advancement to see future potential, and jobs with stable room for growth are preferred when there is a lack of demand in the job market. The combination of a higher economic level in the workplace, which implies higher salaries, abundant resources and more social activities that motivate job search, makes graduates more satisfied with the employment effect. Even if the employment market is highly competitive, entry into local firms with good occupational prospects can lead to graduates' satisfaction with occupational mobility. This leads to proposition 1: In the absence of industry market demand and individual employment expectations, the level of the economy of the place of work and the opportunities for occupational pay and career development are crucial for the realization of a higher occupational mobility effect. Proposition 2: The level of the economy at workplace and the opportunities for career advancement and individual employment expectations are crucial for the realization of higher occupational mobility effects in case of insufficient market demand in the industry. 3.2.2 Geographic salary attraction This category includes two configurations, configuration 2 and configuration 4, in which wages and benefits and the economic level of the workplace are the core conditions of existence, and is therefore named “geographic salary attraction”. In configuration 2, individual education, individual employment expectation, family expectation, and career development opportunity are marginal presence states, employment market demand is a core missing state, and individual motivation is not a necessary condition, suggesting that individual motivation to be willing to engage in occupational mobility is not always necessary to achieve a high-quality employment effect. This configuration suggests that higher salary and welfare, as well as a favorable economic environment at the place of work, are crucial for graduates to achieve a high occupational mobility effect in the face of insufficient demand in the employment market. While factors such as career development opportunities did not play a central role, salary and welfare are directly related to the quality of life of graduates, and the economic level of the place of work brings additional amenities and resources, which are sufficient to attract graduates and lead to a high level of satisfaction with employment. For example, some graduates choose to work in more economically developed regions in order to obtain better living conditions and salary income, and although there is little room for career advancement, the material rewards allow them to recognize the employment situation. This leads to proposition 2: With higher economic levels and occupational salaries in the place of work, even if there is a lack of market demand in the industry, a higher occupational mobility effect can be realized due to the synergistic effect of career development opportunities, family expectations and personal qualifications. In configuration 4, individual employment expectation, family expectation, individual motivation and career development opportunities are marginal existence states, and individual education is not a necessary condition, which has an approximate coverage with configuration 2, indicating that the path coverage of the two is more universal. In addition, individual education in configuration 2 and individual motivation in configuration 4 are both borderline conditions that can serve as a replacement relationship. This leads to Proposition 4: with higher economic levels and occupational salaries in the workplace, even if there is a lack of market demand in the industry, a higher occupational mobility effect can be realized due to the synergistic effect of career development opportunities, family expectations and personal motivation. 3.2.3 Family expectation influenced In configuration 3, only the family expectation is the core existence condition, the employment market demand, individual education, individual employment expectation, individual motivation and career development opportunities are the marginal existence condition, and the economic level of the workplace is the core missing condition, so it is named “family expectations influence type”. This configuration shows that, under certain conditions, family expectation becomes a central factor influencing the high occupational mobility effect of graduates. When individual employment expectation is influenced by family expectation, graduates may be more motivated to choose a job that matches their family's wishes. Even if the economy of the workplace is poorer and the salary and welfare may not be satisfactory, the proximity to parents or a more stable career can reduce the uncertainty of future life from the perspective of family expectations. Retrospective analysis of these students found that graduates with higher family expectations would weigh the job market demand, their own education and future development opportunities, and choose a stable career path through continuous practice, from which they would then derive a strong sense of fulfillment, and the positive emotional feedback further strengthened their sense of identity and satisfaction with their career choices. This leads to Proposition 3: Family expectation, individual employment expectation, industry market demand, occupational development opportunities and are critical for realizing higher occupational mobility effects in the context of lower economic levels at the workplace. For ease of understanding and comparison, Table 4 summarizes and presents the five occupational mobility configurations, configuration views, and research propositions that promote graduates to achieve high occupational mobility effects. Table 4 Promoting High Career Mobility Effects among Graduates 5 Configurations、Configurational View、and Research Propositions Note: A shaded ellipse in the Configuration view indicates that the core condition is present and a white ellipse indicates that the core condition is absent; a shaded rectangle indicates that the edge condition is present and a white rectangle indicates that the edge condition is absent. As Table 4 illustrates, in the high - career - mobility - effect group, irrespective of whether career mobility is more influenced by economic factors or development opportunities, when there is no perceived market demand, better pay, benefits, and a stronger motivation to seek employment can lead to a higher career mobility effect (Combination 1). Moreover, when there is no perceived market demand, lower pay and benefits but better career development opportunities, along with higher personal and family expectations, can also result in a higher career mobility effect (Combination 2). Furthermore, strong family expectations, regardless of external support, when combined with resource support and individual initiative, can lead to better career mobility effects (Combination 3). 3.3 Robustness test To enhance the robustness of the results, the grouping results are evaluated by modifying the original consistency threshold and frequency value, as demonstrated in Table 5 . Initially, this study maintains the case frequency value and the PRI threshold at their original levels and adjusts the original consistency threshold to 0.85. Subsequently, it keeps the original consistency threshold and PRI threshold constant and raises the case frequency value to 3. The findings reveal that the new grouping paths, overall coverage, and consistency in the first scenario remained unchanged. In contrast, the number of groupings in the second scenario decreased by one, and the grouping variables' states closely resembled the grouping, suggesting improved robustness of the results. Table 5 Robustness Test of High Career Mobility Effect for Graduates from Industry-Specific Majors Antecedent variable Adjust the consistency level to 0.85 Adjust frequency to 3 Configuration 1 Configuration2 Configuration3 Configuration4 Configuration5 Configuration1 Configuration2 Configuration3 Configuration4 Industry market demand ⊗ ⊗ ● ⊗ ⊗ ⊗ ⊗ ⊗ ⊗ Individual qualifications ● ● ● ● ● ● Individual Employment expectations ⊗ ● ● ● ● ⊗ ● ● ● Family expectations ⊗ ● ● ● ⊗ ● ● Salary and benefits ● ● ● ● ● ● ● ● Career development opportunities ● ● ● ● ● ● ● ● ● Economic level of the workplace ● ● ⊗ ● ● ● ● ● ● Individual motivation ● ● ● ● ● ● Consistency 0.938 0.946 0.948 0.952 0.95 0.938 0.946 0.946 0.95 Original coverage 0.344 0.526 0.465 0.512 0.519 0.344 0.526 0.526 0.519 Unique coverage 0.011 0.016 0.039 0 0.003 0.011 0.016 0.016 0.003 Overall coverage 0.627 0.615 Overall consistency 0.936 0.935 Note:●indicates that the core condition exists;●indicates that the marginal condition exists; ⊗ indicates that the core condition is missing;⊗ indicates that the marginal condition is missing; a blank cell indicates that the condition is optional. Career mobility-driven paths for graduates of energy industry-specific colleges and universities A cross-sectional comparative analysis of factor combinations influencing high career mobility among graduates of energy industry specialties reveals that, first, the economic level of the workplace, salary, benefits, and career development opportunities frequently emerge as core conditions in these five combinations. The impact of economic factors and career development opportunities on graduates' career mobility is not solely dependent on individual, familial, or institutional differences. In fact, there is a direct correlation with the external environment, including the industry's prosperity and differences among employers. This implies that enhancing high career mobility among graduates of industry-specific specialties requires greater attention to industry resource availability and institutional arrangements. Secondly, the presence of individual employment expectations, family expectations, and personal motivation as auxiliary factors significantly contributes to the high career mobility effect among graduates of industry-specific specialties and should not be overlooked. Thirdly, talent cultivation by schools and graduates' personal qualifications are crucial factors influencing career mobility. The alignment between practitioners' specialties and the industry also yields varying outcomes in career mobility. These factors often co-occur, and their relative contributions differ across combinations. This study identifies three occupational mobility-driven pathways for graduates of energy industry-specific higher education institutions (Table 6 ). The first type is opportunity-driven (institutional education). This type emphasizes how schooling influences occupational mobility for graduates of industry-specific institutions. Industry-specific institutions align their programs with sector needs, ensuring strong professional-industry fit. Graduates from high-reputation schools are particularly favored by employers, gaining more job opportunities and career advancement prospects. This facilitates the conversion of academic training into industry-ready human capital. When employers implement formal systems (e.g., laws, industry standards) and informal practices that enhance career development—along with supportive economic and non-economic resources—graduates benefit from improved mobility. A strong regional economy and practitioner mobility further amplify these effects. The second type is region-driven (socio-industrial). Regional economic development fosters industrial growth, resulting in higher salaries and better benefits for graduates. This establishes a supportive external environment for career mobility among industry-specific graduates. Coupled with institutional career training and employment services, this further strengthens career mobility outcomes.Thus, socio-economic and industrial factors create macro-level conditions and opportunities for occupational mobility. Additionally, university career services provide decision-making support and resources, aiding graduates in managing transition challenges. Specifically, for energy sector graduates, resource-based support is the key driver of industry-specific career mobility. Table 6 Driving Pathways and Characteristics of High Career Mobility Effect for Graduates from Industry-Specific Majors Drive Path Driving Paths and Their Characteristics of High Career Mobility Effects of Graduates from Industry-Specific Colleges and Universities Development opportunity-driven (tertiary education) The school-to-work process centers schooling around the needs of the industry, and graduates are provided with many employment and career development opportunities, which, coupled with the level of economic development at the place of work, will result in a better career mobility effect for graduates. Geographically driven (social sector) The higher level of economic development and salary and benefit levels in the region, the external conditions that meet both the family expectations of graduates and their personal employment expectations, complemented by a certain degree of career development opportunities, can produce a better career mobility effect. Energetically driven (families and individuals) Graduates' family expectations play an important role in facilitating graduates to synthesize issues such as salary and opportunities, and to make changes accordingly in the real world of work, thus building their personal career development. The third type is individual-driven (family and personal). This refers to the significant influence of family on the career mobility of graduates from specialized disciplines. Throughout this process, individuals tend to make relatively clear career decisions, actively engage in skill-enhancing practical activities, and exercise rational agency through reflective actions. Driven by family expectations, their educational background, and career aspirations, they adapt their career paths, seeking roles that better suit their circumstances. Conclusions and Revelations 5.1 Conclusion This study surveyed graduates from energy industry-specialized institutions through questionnaire research. Cluster analysis revealed that enhanced occupational mobility depends on multiple synergistic factors. While individual conditions (e.g., education, employment opportunities, personal motivation) are insufficient alone, workplace economic conditions, compensation packages, and career development opportunities demonstrate significant explanatory power. The study identifies three core grouping paths:“geographic advantage-development opportunities dual-driven”, “geographic salary attraction” and “family expectations influence type”, and then summarizes three driving paths: development opportunity-driven and family expectation influence, indicating that career mobility can be realized through the differentiated combination of resources, environment and individual initiative. Then, three driving paths are summarized: development opportunity-driven, geographic salary-attractive, and family expectation-influenced, indicating that career mobility can be realized through a differentiated combination of resources, environment, and individual mobility, which can lead to “different paths to the same destination”. 5.2 Revelations Energy industry characteristics of college graduates is to support the development needs of the industry and promote the transformation and upgrading of the industry is an important force of talent, the reasonable distribution of its employment and orderly flow for the efficient allocation of human capital in China is of great significance. Occupational mobility of graduates of industry-specific specialties will be affected by individual factors, school factors, family factors and social factors, and the size and direction of the influence of different factors on the effect of occupational mobility are also different. In order to efficiently utilize the human resources of industry-specific universities, the society, the industry, the universities students should adopt the district linkage strategy to promote the reasonable occupational mobility of graduates of industry-specific universities and to promote the industry and the regional economy. Coordinated development of industry and regional economy. References Fang, P. E. N. G., Shaonan, Z. H. E. N. G. & Nuosi, W. A. N. G. Employability Evaluation of Students in Colleges with Industry Characteristics: Take Maritime Graduates as Examples[J]. J. Northeastern Univ. (Social Science) 2022 , 24 (04): 145–150 . Yao, C. H. E. N. & Kai-li, P. E. N. G. Chong-guang. The influence of job mobility on employed migrant workers’ employment quality: Evidence[J]. Res. Agricultural Modernization . 43 (02), 273–284 (2022). CAIHe, Z. H. A. N. 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LIANG Haiyan.Research on the Chinese Floating Population Employment Quality and Influencing Factor-Based on the Year. of 2016 National Floating Population Dynamic Monitoring Survey[J]. Popul. Dev. , 25 (04): 44–52. (2019). Chen Weimin, H. The impact of Internet use on the occupational mobility of rural labor[J]. Economic Jingwei . 40 (03), 45–54 (2023). Chen, C. Academic career mobility of postdocs and its implicit influence path[J]. Res. Educational Dev. 43 (Z1), 107–116 (2023). Huang & Haigang Lian Jie. Does career mobility enhance scientists' research productivity? [J]. Tsinghua J. Educ. 41 (05), 127–135 (2020). Jin Leijing, W., Yiwei, L., Chengtao, H. & Dexin Academic career destinations and levels of female engineering PhDs - An analysis based on PhD graduates from Tsinghua University from 2005 to 2014[J]. J. Graduate Educ. , (03): 1–7. (2018). Xuexin, Y. A. N., Jie, Z. H. O. U., Fubin, S. H. E. N. G. & Qiang, N. I. U. Differential Influences of Industry Type on Employment Population Mobility in Inner Suburbs of Big Cities: Evidence from Wuhan[J]. Econ. Geogr. 43 (10), 63–74 (2023). Zhu Honggen, S. Chengshu. Measurement of Intergenerational Mobility of Rural Family Labor Occupation and Analysis of Driving Factors: An Empirical Study Based on Family Farms[J]. Reform , (11): 141–155. (2021). Ye-qin, Z. H. A. O. GU Hong-huan. Occupational Stability and Intergenerational Differences:A Retrospective Survey Based on the Occupation Experience of Shanghai Residents[J]. Popul. Dev. 29 (06), 111–121 (2023). Ding Shulei, W. & Jiaping, L. Cuihua. The impact of educational mismatch on youth employment quality: theory and empirical evidence[J]. China Youth Res. , (09): 101–110. (2024). Li & Wenhua Li Guirong. Research on the employment quality effect of education matching[J]. J. Capital Univ. Econ. Bus. 24 (04), 69–80 (2022). Wang Yihang, X. Local urbanization and intergenerational mobility in education[J]. Res. Economic Manage. 45 (09), 100–125 (2024). Zhongchao, Y. & Siyu, Y. Characteristics and Influencing Factors of College Graduates' Employment in Digital Economy Industry–Taking Beijing Colleges and Universities as an Example[J]. J. Chin. Youth Social Sci. 43 (02), 98–110 (2024). Heng, W. A. N. G. & Yuchun, Z. H. U. Impact of social capital on multi-dimensional poverty of farmers:Analysis of intermediary effect based on labor mobility[J]. J. China Agricultural Univ. 26 (04), 240–254 (2021). Zhang & Haidong Yang Chengchen. Institutional compartmentalization, occupational mobility and job satisfaction–Another discussion on the characteristics of cross-institutional mobility of new social classes[J]. Social Sci. J. , (06): 91–101. (2018). Deng Rui. Assessment of the employment effect of migrant workers' social capital under the perspective of multidimensional employment quality–evidence from China's labor force dynamics survey[J]. Zhejiang Social Sci. , (12): 47–56. (2019). Wang Qiong, L., Ant & Sha Yingjie. Research on the influencing factors of promoting college students to realize high-quality employment under the group perspective[J] 65–71 (China University Science and Technology, 2023). 12. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6620518","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":463812373,"identity":"33fa5d66-f252-4554-91ba-8330440b1318","order_by":0,"name":"Shuhua XIE","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYDACZjA6ACT5Hz6QqJCQkydeC3sPs4HFGQtjwwbiLAJq4TnDJlHZVpEIYuMFBseZD38ubLuT2D8j94DEzXkSCYwNzA8f3cCjRbKZLU16ZtuzxBk38hIMZ26TyGNnYDM2zsGjhZ+Zx4yZt+1wYsONBINkyW0SxYwNPGzS+LSwMfN//gzSMh+o5fDfORKJDQcIaAHawiAN0rLhzBnDBskGIrQA/WImzXPusPHG423JDBLHJIwNmwn4xeD84cefecoOy847zHz8h0RNnZw8e/PDx/i0YAHMpCkfBaNgFIyCUYAFAACbyU2TCWxF6gAAAABJRU5ErkJggg==","orcid":"","institution":"Liaoning Technical University","correspondingAuthor":true,"prefix":"","firstName":"Shuhua","middleName":"","lastName":"XIE","suffix":""},{"id":463812374,"identity":"27d1d0fa-bce0-4536-ab37-3e85c8aae040","order_by":1,"name":"Lifeng XU","email":"","orcid":"","institution":"Liaoning Provincial Party School of the Communist Party of China","correspondingAuthor":false,"prefix":"","firstName":"Lifeng","middleName":"","lastName":"XU","suffix":""}],"badges":[],"createdAt":"2025-05-08 12:08:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6620518/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6620518/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83662889,"identity":"d38fabfe-b14a-4ac1-b69c-5a5ffd13584f","added_by":"auto","created_at":"2025-05-30 10:36:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":36225,"visible":true,"origin":"","legend":"\u003cp\u003eDriving Mechanisms of High Career Mobility Effects in Graduates from Energy Sector-Focused Majors\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6620518/v1/3623f0b508c098cb9b511842.png"},{"id":86154516,"identity":"98e0d9f0-6dfb-401c-b1f9-ba45bab1b25b","added_by":"auto","created_at":"2025-07-07 10:47:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1353619,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6620518/v1/cb3863a8-05a3-44f1-8928-2208934c9ecd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Occupational Mobility Driving Paths of College Graduates with Specialties in the Energy Industry —Com-B model-based configuration analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIndustry characteristic universities are an important part of China's higher education structure, closely linked to the national economy and social development, and play an important role in the process of promoting the economic development of local and industry[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Its graduates have the advantages of strong specialization and industry recognition in their career development, but also face the dilemma of relatively narrow career development options. Characteristic universities in the energy industry shoulder the important responsibility of delivering adaptable talents for the energy industry, and the occupational mobility of their graduates is not only related to the realization of the value of individual human capital, but also has a direct impact on the technological innovation in the field of energy and the coordinated development of the regional economy.\u003c/p\u003e \u003cp\u003eIn defining the definition of occupational mobility, this study refers to the definition method of Chen Yao[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and others, Cai He, and Zhang Dong[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], which defines occupational mobility as including initial mobility and re-mobility, which refers to the change of work between different work units of a worker due to objective and subjective reasons, and is considered to be occupational mobility if the worker undergoes a change of work unit. If a worker only undergoes a change of position within the same organization, it is not included in the definition of occupational mobility in this paper. In defining the concept of occupational mobility, our researchers also measure whether there is a change in the type of occupation, which also implies that occupational mobility has taken place. Therefore, combining previous studies, occupational mobility in this paper is mainly judged based on two dimensions: change of work unit and change of occupation type.\u003c/p\u003e \u003cp\u003eExisting literature on occupational mobility mostly focuses on the following perspectives: in terms of the content of mobility, including the direction of mobility[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], the region of mobility [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], the influencing factors[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and the influencing effects[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]; in terms of the subject of mobility, the focus is on the more mobile groups such as migrant workers[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], educational and scientific researchers[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], professional women[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and industrial workers[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]; in terms of mobility trends, the focus is on exploring intergenerational mobility[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]; In terms of the causes of mobility, existing studies have analyzed the effects of educationalization[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], urbanization[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], digitalization[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and socialization[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] (the shadow of social capital on the multidimensional poverty of farm households) on occupational mobility, respectively, while the phenomenon of graduates' mobility with respect to the characteristics of different industries has seldom been explored in depth. Accompanying occupational mobility are changes in the economic level of the labor force, the nature of work, job satisfaction, etc[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This paper conducts a seminar for a large group of socially mobile graduates, on the basis of which it investigates the effects and driving paths of occupational mobility, which complements the existing literature in studying the main body and content of occupational mobility, and helps to understand the occupational choices, mobility, and development of graduates in a specific industry .\u003c/p\u003e \u003cp\u003eThis study introduces the COM-B model and uses groupthink to try to solve two questions: what are the factors affecting occupational mobility of graduates from universities with characteristics in the energy industry and how do different factors affect each other to produce a higher occupational mobility effect? How do the different influencing factors work together to produce a higher occupational mobility effect? In order to provide reference for the occupational mobility of graduates from universities with industry characteristics.\u003c/p\u003e"},{"header":"Relevance analysis of the COM-B model","content":"\u003cp\u003eThe COM-B (Capability, Opportunity, Motivation-Behaviour) model is a theoretical framework for behavioral change proposed by British psychologist Susan Mitchell et al. in 2011, which is mainly used to explain the occurrence of individual behavior and its changes. The theory suggests that behavior arises as a result of the interaction of three elements: Capability, Opportunity and Motivation. The individual's capability determines whether he or she possesses the basic conditions to accomplish a behavior, opportunity factors relate to whether the external environment supports the occurrence of the behavior, and motivation reflects an individual's subjective will and drive. Individuals are likely to act only when capability, opportunity, and motivation are simultaneously available and the motivation is strong enough to enable the behavior to occur.\u003c/p\u003e \u003cp\u003eThe COM-B model has good appropriateness in explaining the occupational mobility of graduates from universities characterized by the energy industry. The element of competence is given by schooling and covers the graduate's personal qualifications, professional knowledge and skills and personal employment expectations; the opportunity element mainly refers to employment opportunities, which involves the opportunities for occupational mobility provided to graduates by the external environment, including the economic level of the place of work, the market demand of the industry, the salary and benefits of the employing organization and the development opportunities; the motivational element, on the other hand, is individual motivation, that is, the intrinsic motivation that pushes graduates to engage in occupational mobility, including individual motivation and family employment expectations; Behavior refers to the occupational mobility behavior of college graduates characteristic of the energy industry, is the result of the interaction of ability, opportunity, and motivation. Occupational mobility behavior generates occupational mobility effects, and this paper examines occupational mobility effects from a three-dimensional perspective of salary income, job stability, and job satisfaction to ultimately understand which factors or combinations of factors contribute to high occupational mobility effects. The visualization of the COM-B model's explanatory framework for occupational mobility is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e"},{"header":"Research design","content":"\u003ch2\u003e2.1 Research method\u003c/h2\u003e\u003cp\u003eThis paper adopts a group approach to analyze the group effects arising from the interdependence and interaction between the antecedent conditions of occupational mobility influencing factors, and to reveal the synergistic and interactive mechanisms between different factors. This paper utilizes fsQCA for the following considerations: First, fSQCA allows the feature encoding to have any continuous value between 0 and 1, the data processing in this paper meets the encoding requirements; second, from the previous elaboration, it is clear that occupational mobility behavior cannot develop without the interaction of three major internal and external factors: school education, employment opportunities and employment motivation of graduates of industry characteristic majors. Therefore, the fSQCA method was selected to identify the multiple causes of enhancing occupational mobility effects and their specific combination forms and mechanisms of action, and to further reveal the interactive effects of different factors of school, society and individuals on occupational mobility effects.\u003c/p\u003e\n\u003ch3\u003e2.2 Questionnaire design and collection\u003c/h3\u003e\n\u003cp\u003eThe data for this study is sourced from the research group's questionnaire \"Survey on Occupational Mobility of Graduates from Energy Industry Characteristic Universities\", and combined with online research on the annual employment quality reports of national energy industry characteristic universities over the years. The energy industry universities involved in this study are the industry characteristic type of colleges and universities with geomining and petroleum as the main or core, and the object of the research is the graduates of the main majors of the industry characteristic colleges and universities, that is, the graduates of energy industry colleges and universities with characteristic majors. The graduates participating in the study include China University of Mining and Technology, China University of Mining and Technology (Beijing), Liaoning University of Engineering and Technology, Shandong University of Science and Technology, China University of Petroleum (Beijing) and other universities. Considering that students who have just graduated have not yet carried out occupational mobility, the team distributed a total of 2,070 questionnaires using questionnaire star from December 2024 to February 2025 to students who have graduated from the characteristic majors of the national energy industry for more than 5 years, and 1,742 valid questionnaires were returned, with a sample validity rate of 84.15%.\u003c/p\u003e \u003cp\u003eThe questionnaire covers data on four aspects: basic individual information, occupational mobility, strength of motivation for occupational mobility, and factors influencing occupational mobility. Individual basic information covers the graduate's gender, age, field of study, and graduation institution. A series of scale questions allow graduates to self-assess their communication skills, teamwork skills, problem solving skills, creativity, and clarity of career planning; occupational mobility focuses on understanding respondents' work experience, with questions including the type of occupation, entry time, and job match for each job, using single choice and fill-in-the-blanks to facilitate statistics and analysis; corresponding questions on influencing factors were designed based on the COM-B model dimensions, such as \u0026ldquo;How much does the economic development of your region influence your career choice?\u0026rdquo;, individual motivational intensity sets individual employment expectation matching status, attention to other employment recruitment information, and so on. The impact factors were all rated on a scale of 1\u0026ndash;5, with 1 being very low impact and 5 being very high impact.\u003c/p\u003e\n\u003ch3\u003e2.3 Data measurement and calibration\u003c/h3\u003e\n\u003cp\u003eIn order to ensure the validity of data collection, the study was based on the results of the exploratory analysis of the pre-survey, and the reliability optimization and adjustment of the measurement items were implemented to reconstruct the questionnaire structure in line with the cognitive logic of the respondents.\u003c/p\u003e\n\u003ch3\u003e2.3.1 Outcome measurement\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003e2.3.1 Outcome measurement\u003c/div\u003e \u003cp\u003eIn terms of occupational mobility, 73.20% of graduates did not mobilize, 22.86% mobilized once, and only 3.94% mobilized two or more times, reflecting the high stability of the industry. In this paper, salary income, job stability and job satisfaction are used as indicators to evaluate occupational mobility effects. In this case, the annual income of salary up to 100,000 and above is assigned a value of 1, while others are assigned a value of 0. Signing an employment contract and having labor insurance are assigned a value of 1, while others are assigned a value of 0. Assign a value of 1 if you are satisfied with the employment organization and 0 if you are not satisfied. The final score of more than or equal to 2 (\u0026ge;\u0026thinsp;2) for the three indicators was defined as a high occupational mobility effect and assigned a value of 1, while a score below 2 was defined as a low occupational mobility effect and assigned a value of 0 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3.2 Antecedent condition measurement\u003c/h2\u003e \u003cp\u003eAlthough fsQCA can analyze the group effects of multiple antecedent conditions, in practice it is inevitably not possible to cover all antecedent conditions and only the more important influences can be selected. The antecedent condition framework system in this paper includes a total of 8 conditions in 3 dimensions and 5 level 1 indicators, which are measured as follows: school education is calculated by the degree of influence of the individual's educational status and the individual's employment expectations, which are assigned a value of 1\u0026ndash;5 according to very little influence, little influence, average influence, great influence, and great influence, respectively; employment opportunities are assigned by the degree of influence of the job market demand, employment unit salary and benefits, employment unit development opportunities and the economic level of the place of work, with very small influence, small influence, average influence, large influence, and very large influence assigned a value of 1\u0026ndash;5, respectively; individual motivation is assigned by the level of agreement with the statement \u0026ldquo;How much do you agree with the following viewpoints - I often pay attention to new job openings and make continuous efforts to find a new job\u0026rdquo; and the influence of family's employment expectations, which are assigned a value from 1 to 5 for strongly disagree, disagree very much, agree generally, agree somewhat, and agree very much, respectively.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.3.3 Variable calibration\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQCA Data Matrix of Career Mobility for Graduates from Majors Specializing in the Energy Industry\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuestionnaire number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOccupational mobility effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndividual qualifications\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEmployment market demand\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndividual employment expectations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFamily employment expectations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSalary and welfare\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDevelopment chance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEconomic level of workplace\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eindividual motivation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn this study, the eight antecedent variables were assigned logically and consistently, specifically, by scoring each sample's representation on each variable one by one, i.e., 0 (not at all affiliated), 0.33 (partially affiliated and biased toward not at all affiliated), 0.67 (partially affiliated and biased toward fully affiliated), 0.501 (maximum fuzzy point treatment), and 1 (fully affiliated), in order to accurately perceive the variability in occupational mobility effects for each variable. Combining the assignment of condition variables and outcome variables, the QCA data matrix of occupational mobility of graduates of characteristic majors in the energy industry can be obtained, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which meets the fsQCA criteria.\u003c/p\u003e"},{"header":"Empirical analysis","content":"\u003ch2\u003e3.1 Necessity analysis\u003c/h2\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNecessity Analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAntecedent variable\u003c/p\u003e\n \u003c/th\u003e\u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eHigh occupational mobility effect\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eConsistency\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eCoverage\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployment market demand\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.783535\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.756014\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e~Employment market demand\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63574\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.885765\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eIndividual education\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.836514\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.726752\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e~Individual education\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.521415\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.864555\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eIndividual employment expectation\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.805582\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.724322\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e~Individual employment expectation\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.551853\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.859656\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eFamily employment expectation\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.780869\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.750494\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e~Family employment expectation\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.600984\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.842115\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSalary and welfare\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.851018\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.719612\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e~Salary and welfare\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.520999\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.911588\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCareer development opportunity\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.855863\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.724456\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e~Career development opportunity\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.527013\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92015\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLevel of economic development at workplace\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.829653\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.752263\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e~Level of economic development at workplace\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.588371\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.903439\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eIndividual motivation\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.806703\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.799578\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e~Individual motivation\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.631725\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.847699\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn this paper, the consistency threshold of the necessary conditions is set to 0.9 according to the criteria of Schneider et al. In the qualitative comparative analysis, when the degree of consistency was greater than 0.9 and the coverage was higher than 0.5, it indicated that this condition was a necessary condition for the results. As can be seen in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, in the high outcome group, all the antecedent variables have consistency levels less than 0.9, which does not constitute a necessary condition for the outcome variable; therefore, this paper includes these eight conditional variables in the sufficiency analyses to explore the group equivalence of the high occupational mobility effect.\u003c/p\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Sufficiency analysis\u003c/h2\u003e\n \u003cp\u003eIn this study, the case frequency threshold was set to 2, the raw consistency threshold was set to the system default threshold of 0.8, and the PRI consistency threshold was selected as 0.75. A total of 5 grouping paths were found for the high result group, and the specific results are shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The consistency values for each grouping are above 0.9, which is above the minimum acceptable standard, and the overall consistency is strong, with a value of 0.936, indicating that all five grouping paths can be considered as sufficient conditions to produce a high outcome grouping and explain about 62.7% of the cases.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eConfiguration analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr style=\"height: 35px;\"\u003e\u003cth align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003eAntecedent variable\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003eConfiguration 1\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003eConfiguration 2\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003eConfiguration 3\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003eConfiguration 4\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003eConfiguration 5\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr style=\"height: 36px;\"\u003e\u003ctd align=\"left\" style=\"height: 36px;\"\u003e\n \u003cp\u003eIndustry Market Demand\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 36px;\"\u003e\n \u003cp\u003e⊗\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 36px;\"\u003e\n \u003cp\u003e⊗\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 36px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 36px;\"\u003e\n \u003cp\u003e⊗\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 36px;\"\u003e\n \u003cp\u003e⊗\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr style=\"height: 35px;\"\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003eIndividual Education\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr style=\"height: 36px;\"\u003e\u003ctd align=\"left\" style=\"height: 36px;\"\u003e\n \u003cp\u003eIndividual employment expectation\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 36px;\"\u003e\n \u003cp\u003e⊗\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 36px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 36px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 36px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 36px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr style=\"height: 36px;\"\u003e\u003ctd align=\"left\" style=\"height: 36px;\"\u003e\n \u003cp\u003eFamily Expectation\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 36px;\"\u003e\n \u003cp\u003e⊗\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 36px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 36px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 36px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 36px;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr style=\"height: 35px;\"\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003eSalary and welfare\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr style=\"height: 35px;\"\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003eCareer development opportunity\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr style=\"height: 36px;\"\u003e\u003ctd align=\"left\" style=\"height: 36px;\"\u003e\n \u003cp\u003eEconomic level of workplace\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 36px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 36px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 36px;\"\u003e\n \u003cp\u003e⊗\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 36px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 36px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr style=\"height: 35px;\"\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003eIndividual motivation\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr style=\"height: 35px;\"\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003eConsistency\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr style=\"height: 35px;\"\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003eOriginal coverage\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e0.526\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr style=\"height: 35px;\"\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003eUnique coverage\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr style=\"height: 35px;\"\u003e\u003ctd align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003eTotal coverage\u003c/p\u003e\n \u003c/td\u003e\u003ctd colspan=\"5\" align=\"left\" style=\"height: 35px;\"\u003e\n \u003cp\u003e0.627\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr style=\"height: 35.4006px;\"\u003e\u003ctd align=\"left\" style=\"height: 35.4006px;\"\u003e\n \u003cp\u003eOverall consistency\u003c/p\u003e\n \u003c/td\u003e\u003ctd colspan=\"5\" align=\"left\" style=\"height: 35.4006px;\"\u003e\n \u003cp\u003e0.936\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr style=\"height: 25px;\"\u003e\u003ctd colspan=\"6\" style=\"height: 25px;\"\u003e注: ●indicates that the core condition exists;●indicates that an edge condition exists;\u0026nbsp;⊗indicates that core conditions are missing;\u0026nbsp;Uindicates that the edge condition is missing;\u0026nbsp;blank cells indicate that conditions are optional\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eBy combing and summarizing the results of the condition grouping analysis, it is found that some of the paths show significant commonality in the distribution of the core conditions, on this basis, combined with the theoretical framework of this paper and the actual situation, the grouping H1-H5, H2-H4, H3 were named as “geographic advantage-development opportunities dual-driven”, “geographic salary attraction”, “family expectations influence type”, thus summarizing the three grouping paths that produce high career mobility effects. “Geographical Salary Attraction” and ‘Family Expectation Influence’, thus summarizing the three types of group paths of high occupational mobility effect.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2.1 Geographic advantages - development opportunities dual-driven\u003c/h2\u003e\n \u003cp\u003eIn this category, occupational development opportunities and the economic level of the workplace are the core conditions of existence, and it includes configuration 1 and configuration 5, so it is named “Geographical advantage - development opportunities dual-driven type”. Salary and welfare and individual motivation are in marginal presence in configuration 1, individual education is non-essential (conditional gap), and individual employment expectation and family expectation are in marginal absence. Individual education, employment expectations, salary and welfare, and individual motivation are marginal states of existence in configuration 5, and family expectations are non-essential.\u003c/p\u003e\n \u003cp\u003eIn this type of grouping, even if the employment situation in the industry market is poor and the demand from employers is insufficient, graduates can help to realize high occupational mobility effects if they can quickly adjust their occupational strategies and actively pursue their careers in economically developed regions or in units with more room for growth. Even if individual employment expectations, family expectations, and so on, are not decisive, graduates are likely to have a high occupational mobility effect by virtue of two core conditions: career opportunities and the level of economic development of the place of work. Career development opportunities provide graduates with room for growth and advancement to see future potential, and jobs with stable room for growth are preferred when there is a lack of demand in the job market. The combination of a higher economic level in the workplace, which implies higher salaries, abundant resources and more social activities that motivate job search, makes graduates more satisfied with the employment effect. Even if the employment market is highly competitive, entry into local firms with good occupational prospects can lead to graduates' satisfaction with occupational mobility. This leads to proposition 1: In the absence of industry market demand and individual employment expectations, the level of the economy of the place of work and the opportunities for occupational pay and career development are crucial for the realization of a higher occupational mobility effect. Proposition 2: The level of the economy at workplace and the opportunities for career advancement and individual employment expectations are crucial for the realization of higher occupational mobility effects in case of insufficient market demand in the industry.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2.2 Geographic salary attraction\u003c/h2\u003e\n \u003cp\u003eThis category includes two configurations, configuration 2 and configuration 4, in which wages and benefits and the economic level of the workplace are the core conditions of existence, and is therefore named “geographic salary attraction”.\u003c/p\u003e\n \u003cp\u003eIn configuration 2, individual education, individual employment expectation, family expectation, and career development opportunity are marginal presence states, employment market demand is a core missing state, and individual motivation is not a necessary condition, suggesting that individual motivation to be willing to engage in occupational mobility is not always necessary to achieve a high-quality employment effect. This configuration suggests that higher salary and welfare, as well as a favorable economic environment at the place of work, are crucial for graduates to achieve a high occupational mobility effect in the face of insufficient demand in the employment market. While factors such as career development opportunities did not play a central role, salary and welfare are directly related to the quality of life of graduates, and the economic level of the place of work brings additional amenities and resources, which are sufficient to attract graduates and lead to a high level of satisfaction with employment. For example, some graduates choose to work in more economically developed regions in order to obtain better living conditions and salary income, and although there is little room for career advancement, the material rewards allow them to recognize the employment situation. This leads to proposition 2: With higher economic levels and occupational salaries in the place of work, even if there is a lack of market demand in the industry, a higher occupational mobility effect can be realized due to the synergistic effect of career development opportunities, family expectations and personal qualifications.\u003c/p\u003e\n \u003cp\u003eIn configuration 4, individual employment expectation, family expectation, individual motivation and career development opportunities are marginal existence states, and individual education is not a necessary condition, which has an approximate coverage with configuration 2, indicating that the path coverage of the two is more universal. In addition, individual education in configuration 2 and individual motivation in configuration 4 are both borderline conditions that can serve as a replacement relationship. This leads to Proposition 4: with higher economic levels and occupational salaries in the workplace, even if there is a lack of market demand in the industry, a higher occupational mobility effect can be realized due to the synergistic effect of career development opportunities, family expectations and personal motivation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2.3 Family expectation influenced\u003c/h2\u003e\n \u003cp\u003eIn configuration 3, only the family expectation is the core existence condition, the employment market demand, individual education, individual employment expectation, individual motivation and career development opportunities are the marginal existence condition, and the economic level of the workplace is the core missing condition, so it is named “family expectations influence type”. This configuration shows that, under certain conditions, family expectation becomes a central factor influencing the high occupational mobility effect of graduates. When individual employment expectation is influenced by family expectation, graduates may be more motivated to choose a job that matches their family's wishes. Even if the economy of the workplace is poorer and the salary and welfare may not be satisfactory, the proximity to parents or a more stable career can reduce the uncertainty of future life from the perspective of family expectations. Retrospective analysis of these students found that graduates with higher family expectations would weigh the job market demand, their own education and future development opportunities, and choose a stable career path through continuous practice, from which they would then derive a strong sense of fulfillment, and the positive emotional feedback further strengthened their sense of identity and satisfaction with their career choices. This leads to Proposition 3: Family expectation, individual employment expectation, industry market demand, occupational development opportunities and are critical for realizing higher occupational mobility effects in the context of lower economic levels at the workplace.\u003c/p\u003e\n \u003cp\u003eFor ease of understanding and comparison, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes and presents the five occupational mobility configurations, configuration views, and research propositions that promote graduates to achieve high occupational mobility effects.\u003c/p\u003e\n \u003cp\u003eTable 4 Promoting High Career Mobility Effects among Graduates 5 Configurations、Configurational View、and Research Propositions\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eA shaded ellipse in the Configuration view indicates that the core condition is present and a white ellipse indicates that the core condition is absent; a shaded rectangle indicates that the edge condition is present and a white rectangle indicates that the edge condition is absent.\u003c/div\u003e\n \u003cp\u003eAs Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates, in the high - career - mobility - effect group, irrespective of whether career mobility is more influenced by economic factors or development opportunities, when there is no perceived market demand, better pay, benefits, and a stronger motivation to seek employment can lead to a higher career mobility effect (Combination 1). Moreover, when there is no perceived market demand, lower pay and benefits but better career development opportunities, along with higher personal and family expectations, can also result in a higher career mobility effect (Combination 2). Furthermore, strong family expectations, regardless of external support, when combined with resource support and individual initiative, can lead to better career mobility effects (Combination 3).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Robustness test\u003c/h2\u003e\n \u003cp\u003eTo enhance the robustness of the results, the grouping results are evaluated by modifying the original consistency threshold and frequency value, as demonstrated in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Initially, this study maintains the case frequency value and the PRI threshold at their original levels and adjusts the original consistency threshold to 0.85. Subsequently, it keeps the original consistency threshold and PRI threshold constant and raises the case frequency value to 3. The findings reveal that the new grouping paths, overall coverage, and consistency in the first scenario remained unchanged. In contrast, the number of groupings in the second scenario decreased by one, and the grouping variables' states closely resembled the grouping, suggesting improved robustness of the results.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRobustness Test of High Career Mobility Effect for Graduates from Industry-Specific Majors\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAntecedent variable\u003c/p\u003e\n \u003c/th\u003e\u003cth colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003eAdjust the consistency level to 0.85\u003c/p\u003e\n \u003c/th\u003e\u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eAdjust frequency to 3\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eConfiguration 1\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eConfiguration2\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eConfiguration3\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eConfiguration4\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eConfiguration5\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eConfiguration1\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eConfiguration2\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eConfiguration3\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eConfiguration4\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustry market demand\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e⊗\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e⊗\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e⊗\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e⊗\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e⊗\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e⊗\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e⊗\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e⊗\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eIndividual qualifications\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eIndividual Employment expectations\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e⊗\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e⊗\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eFamily expectations\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e⊗\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e⊗\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSalary and benefits\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e●\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e●\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCareer development opportunities\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eEconomic level of the workplace\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e⊗\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e●\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e●\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eIndividual motivation\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eConsistency\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eOriginal coverage\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.526\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.526\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.526\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eUnique coverage\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eOverall coverage\u003c/p\u003e\n \u003c/td\u003e\u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e0.627\u003c/p\u003e\n \u003c/td\u003e\u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eOverall consistency\u003c/p\u003e\n \u003c/td\u003e\u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e0.936\u003c/p\u003e\n \u003c/td\u003e\u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote:●indicates that the core condition exists;●indicates that the marginal condition exists; ⊗ indicates that the core condition is missing;⊗ indicates that the marginal condition is missing; a blank cell indicates that the condition is optional.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Career mobility-driven paths for graduates of energy industry-specific colleges and universities","content":"\u003cp\u003eA cross-sectional comparative analysis of factor combinations influencing high career mobility among graduates of energy industry specialties reveals that, first, the economic level of the workplace, salary, benefits, and career development opportunities frequently emerge as core conditions in these five combinations. The impact of economic factors and career development opportunities on graduates' career mobility is not solely dependent on individual, familial, or institutional differences. In fact, there is a direct correlation with the external environment, including the industry's prosperity and differences among employers. This implies that enhancing high career mobility among graduates of industry-specific specialties requires greater attention to industry resource availability and institutional arrangements. Secondly, the presence of individual employment expectations, family expectations, and personal motivation as auxiliary factors significantly contributes to the high career mobility effect among graduates of industry-specific specialties and should not be overlooked. Thirdly, talent cultivation by schools and graduates' personal qualifications are crucial factors influencing career mobility. The alignment between practitioners' specialties and the industry also yields varying outcomes in career mobility. These factors often co-occur, and their relative contributions differ across combinations.\u003c/p\u003e\u003cp\u003eThis study identifies three occupational mobility-driven pathways for graduates of energy industry-specific higher education institutions (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The first type is opportunity-driven (institutional education). This type emphasizes how schooling influences occupational mobility for graduates of industry-specific institutions. Industry-specific institutions align their programs with sector needs, ensuring strong professional-industry fit. Graduates from high-reputation schools are particularly favored by employers, gaining more job opportunities and career advancement prospects. This facilitates the conversion of academic training into industry-ready human capital. When employers implement formal systems (e.g., laws, industry standards) and informal practices that enhance career development—along with supportive economic and non-economic resources—graduates benefit from improved mobility. A strong regional economy and practitioner mobility further amplify these effects.\u003c/p\u003e\u003cp\u003eThe second type is region-driven (socio-industrial). Regional economic development fosters industrial growth, resulting in higher salaries and better benefits for graduates. This establishes a supportive external environment for career mobility among industry-specific graduates. Coupled with institutional career training and employment services, this further strengthens career mobility outcomes.Thus, socio-economic and industrial factors create macro-level conditions and opportunities for occupational mobility. Additionally, university career services provide decision-making support and resources, aiding graduates in managing transition challenges. Specifically, for energy sector graduates, resource-based support is the key driver of industry-specific career mobility.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDriving Pathways and Characteristics of High Career Mobility Effect for Graduates from Industry-Specific Majors\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eDrive Path\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eDriving Paths and Their Characteristics of High Career Mobility Effects of Graduates from Industry-Specific Colleges and Universities\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eDevelopment opportunity-driven (tertiary education)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eThe school-to-work process centers schooling around the needs of the industry, and graduates are provided with many employment and career development opportunities, which, coupled with the level of economic development at the place of work, will result in a better career mobility effect for graduates.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eGeographically driven (social sector)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eThe higher level of economic development and salary and benefit levels in the region, the external conditions that meet both the family expectations of graduates and their personal employment expectations, complemented by a certain degree of career development opportunities, can produce a better career mobility effect.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eEnergetically driven (families and individuals)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eGraduates' family expectations play an important role in facilitating graduates to synthesize issues such as salary and opportunities, and to make changes accordingly in the real world of work, thus building their personal career development.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n \u003c/div\u003e\u003cp\u003eThe third type is individual-driven (family and personal). This refers to the significant influence of family on the career mobility of graduates from specialized disciplines. Throughout this process, individuals tend to make relatively clear career decisions, actively engage in skill-enhancing practical activities, and exercise rational agency through reflective actions. Driven by family expectations, their educational background, and career aspirations, they adapt their career paths, seeking roles that better suit their circumstances.\u003c/p\u003e"},{"header":"Conclusions and Revelations","content":"\u003ch2\u003e5.1 Conclusion\u003c/h2\u003e\u003cp\u003eThis study surveyed graduates from energy industry-specialized institutions through questionnaire research. Cluster analysis revealed that enhanced occupational mobility depends on multiple synergistic factors. While individual conditions (e.g., education, employment opportunities, personal motivation) are insufficient alone, workplace economic conditions, compensation packages, and career development opportunities demonstrate significant explanatory power. The study identifies three core grouping paths:“geographic advantage-development opportunities dual-driven”, “geographic salary attraction” and “family expectations influence type”, and then summarizes three driving paths: development opportunity-driven and family expectation influence, indicating that career mobility can be realized through the differentiated combination of resources, environment and individual initiative. Then, three driving paths are summarized: development opportunity-driven, geographic salary-attractive, and family expectation-influenced, indicating that career mobility can be realized through a differentiated combination of resources, environment, and individual mobility, which can lead to “different paths to the same destination”.\u003c/p\u003e\u003ch2\u003e5.2 Revelations\u003c/h2\u003e\u003cp\u003eEnergy industry characteristics of college graduates is to support the development needs of the industry and promote the transformation and upgrading of the industry is an important force of talent, the reasonable distribution of its employment and orderly flow for the efficient allocation of human capital in China is of great significance. Occupational mobility of graduates of industry-specific specialties will be affected by individual factors, school factors, family factors and social factors, and the size and direction of the influence of different factors on the effect of occupational mobility are also different. In order to efficiently utilize the human resources of industry-specific universities, the society, the industry, the universities students should adopt the district linkage strategy to promote the reasonable occupational mobility of graduates of industry-specific universities and to promote the industry and the regional economy. Coordinated development of industry and regional economy.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFang, P. E. N. G., Shaonan, Z. H. E. N. G. \u0026amp; Nuosi, W. A. N. G. Employability Evaluation of Students in Colleges with Industry Characteristics: Take Maritime Graduates as Examples[J]. \u003cem\u003eJ. Northeastern Univ. 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(2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Qiong, L., Ant \u0026amp; Sha Yingjie. \u003cem\u003eResearch on the influencing factors of promoting college students to realize high-quality employment under the group perspective[J]\u003c/em\u003e65\u0026ndash;71 (China University Science and Technology, 2023). 12.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"universities with industry characteristics, graduate employment, occupational mobility, configuration, COM-B model","lastPublishedDoi":"10.21203/rs.3.rs-6620518/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6620518/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe career mobility of graduates from universities characteristic of the energy industry directly affects the supply of talents in the industry, the development of regional industry and economy, and relates to the benign interaction between higher education and the labor market. The study constructed a driver model of high occupational mobility effects of graduates based on the Competence, Opportunity, Motivation, and Behavior model (COM-B model), and explored the group effects of eight antecedent variables on occupational mobility under three dimensions of schooling, employment opportunities, and individual motivation using a group approach. The study found that the high occupational mobility effect is the result of multiple antecedent conditions acting in concert; the economic level of the workplace has strong explanatory power for the occupational mobility effect; the groupings that promote high occupational mobility effects include geographic advantage-development opportunity duality-driven, geographic salary attraction, and family expectation-influenced, suggesting that the occupational mobility of graduates from industry-characterized colleges and universities is inextricably linked to regional economic conditions, industry distribution, and personal motivation; accordingly, three driving paths are proposed: opportunity-driven, geographically-driven and energy-driven. The study clarifies the multiple choice paths of occupational mobility from a group perspective, suggests career mobility for graduates of industry characteristic universities, and expands the use of the COM-B model.\u003c/p\u003e","manuscriptTitle":"Occupational Mobility Driving Paths of College Graduates with Specialties in the Energy Industry —Com-B model-based configuration analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-30 10:36:39","doi":"10.21203/rs.3.rs-6620518/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"50095722-ef4c-4c0a-85a9-979379d75ac9","owner":[],"postedDate":"May 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49250538,"name":"Physical sciences/Energy science and technology"},{"id":49250539,"name":"Physical sciences/Engineering"}],"tags":[],"updatedAt":"2025-07-07T10:38:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-30 10:36:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6620518","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6620518","identity":"rs-6620518","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}