Today, with the deepening of economic globalization and the further intensification of competition among enterprises, the role and functions of human resource managers of enterprises have undergone major changes. Their main responsibilities have shifted from administrative and logistical type work to becoming strategic partners in businesses. Therefore, competency-based human resource management is gradually attracting the attention of enterprises, which use the competency model to effectively identify the potential characteristics of individuals, thus dynamically realizing the consistency of human-work-organizational strategic goals. Based on the social reality of profound changes in human resource management in the era of big data, this study uses natural language processing and machine learning methods to dynamically mine 165,811 enterprise recruitment data for feature analysis and model construction of human resource management job capabilities. To capture the job profile data of job seekers, experts are required to observe and score the career profile according to the competency model, obtain training data, and then study the application of the competency model in resume screening, and build the competency matching model. The results show that among the variables extracted from resumes, whether they majored in human resources, internship experience, student officer experience, the size of the company they work for, their level and their highest level of education has a significant impact on overall ability characteristics, while working hours, reward experience, and education have no significant effect on ability characteristics. The accuracy, recall rate, accuracy, and Fl score of the ability prediction model built by machine learning are all above 70%, indicating that the model can effectively imitate the behavior of experts, evaluate the competency according to the resume, and realize the ability matching in the resume screening from small-scale manual operation to big data machine calculation.

1. Introduction

In recent years, the role and functions of HR managers in enterprises have changed significantly. Their main responsibilities have changed from being engaged in administrative and logistical work to being a strategic partner of the enterprise (HRBUSINESS PARTNER) [1]. It can be seen that HR managers play an important role in organizational change, culture construction, performance improvement, etc. [2]. Their role has changed from passive executor to proactive strategic leader. In view of the special role of HRM positions, it is especially important to accurately locate excellent HR managers in the recruitment of today’s enterprises. This leads to a series of questions: what qualities should an excellent HR manager possess? How to accurately locate the excellent HR managers and make an effective ability-position matching in the resume screening process?

Competency model provides effective guidance to solve these problems. Competent model (competence model) is a competency structure combined for excellent performance in specific positions, which is an important basis for a series of human resource management and development practices (such as job analysis, recruitment, selection, training and development, and performance management). It can effectively identify the potential characteristics of individuals and distinguish those with excellent performance from those with mediocre performance, so as to achieve a reasonable match between positions and employees, i.e., a person-job match [3]. As a result, researchers have conducted research on the competency of human resource managers and have achieved certain results [4]. However, most of the existing studies use field interviews and questionnaires to obtain data, which have small sample sizes and are easily affected by the knowledge level of respondents, and cannot truly reflect the needs of enterprises. Meanwhile, competency is a dynamic concept, not only related to specific work situations but also enriched with the changing characteristics of the times [5]. The advent of the big data era and the development of online recruitment will inevitably put forward new requirements for human resource managers, and the competency model already constructed cannot fully adapt to the current characteristics of the times [6].

On the other hand, due to its important role in dynamically aligning people-job-organization strategic goals, the competency model has been widely used in recruitment, employee performance, and career management. In recruitment, through longitudinal observation, the competency model is mostly used in the interview process [7]. In the preliminary resume screening process, most of the search matches are based on keywords such as school and education, which improves the efficiency but does not effectively combine with the competency model, making it difficult to realize the real job matching. This often leads to many talents with high competency passing by the right position and becoming a pearl in the ocean. In view of the important strategic role of human resource managers, the omission of highly competent talents in the recruitment of this position will undoubtedly bring more losses to the enterprise [8]. By manually screening the candidates’ resumes, we can effectively determine their competency characteristics and realize the ability to match the posts with the experience of experts. However, this kind of manual operation with small samples is inefficient and not enough to cope with the huge amount of candidate resumes in the era of big data [9].

Based on the above problems, this study focuses on the construction of HRM job competency model and the accurate positioning of high-quality talents in the era of big data, aiming to reconstruct the HRM job competency model by dynamic mining of recruitment texts for enterprise needs [10]. At the same time, the competency model is combined with resume screening through machine learning methods to build a competent job matching model, so as to realize the competent job matching of resume screening from small-scale manual operation to big data machine calculation.

2. State of the Art

In order to build a competency model for the needs of enterprises, it is necessary to study its development and current status, adopt its application technology, master its theoretical research, and effectively build a competency model.

2.1. Construction of Competency Model for Human Resource Managers

Based on the important role of human resource managers for companies, a series of studies on their competency models have been conducted by domestic and international scholars in recent years, and important results have been achieved. Ulrich et al. [11] collected data from 12,689 human resource professionals in 109 tourist attractions and conducted an extensive evaluation of their competency models. The results indicated that HR professionals were perceived by their colleagues to be more effective when they demonstrated competencies in business knowledge, HR delivery, and change management. Ulrich et al. [12] proposed a generic HR competency model based on in-depth interviews with 10 senior HR managers, including key competencies involving different industries, as well as specific competencies that are differentiated across roles. Zhu et al. conducted a questionnaire survey of human resource officers and managers from multiple organizations in the public and private sectors in South Africa and obtained a three-factor model of HR manager competencies through empirical analysis: professional behavior and leadership (including leadership and personal credibility, solution generation, interpersonal communication and innovation), service orientation and execution (consisting of factors such as talent management, HR risk, HR metrics, and HR service delivery), and business intelligence (including strategic contribution, HR business knowledge, HR business intelligence, and HR technology) [13]. Hamid surveyed 380 HR practitioners in manufacturing and service industries in Malaysia and the empirical results indicated that business competencies are not essential for HRM practitioners, while entrepreneurship and business acumen (including entrepreneurial skills, ICT, knowledge management) and basic performance advancement (including creativity, problem solving, decision-making) are important for HRM practitioners. Ulrich et al. [11] used the metaphor of food collocation. By examining multiple metaphors using food collocation, and integrating the competency models of human resource management practitioners in multiple literatures, he proposed that just as people in different countries and regions have different preferences for food, different regions or industries have different combinations of ability and quality requirements for human resource practitioners. Bommer et al explored the differences in the competency qualities of strategic and functional HR personnel through a conceptual mapping approach [14]. The results show that both require similar levels of competency qualities such as leadership and relationship building, strategic focus and drive, and engagement and support, with strategic HR requiring more business awareness and self-belief and social roles being more important among functional HR practitioners. In China, Wang et al. [15] constructed a competency model for HR managers in Chinese companies with four dimensions, including functional management competency, change management competency, employee management competency, and strategic management competency, with the help of a questionnaire. Ngugi et al. [16]. proposed a three-factor competency model for HRM managers, which consists of managing change competence, strategic contribution, and business knowledge, respectively. Chetty [17] explored the HR manager competency model through open-ended questionnaires, interviews, and 360-degree feedback assessment, and outlined the competency of HR managers as a structural model consisting of 14 competency factors in four dimensions: personal traits, HR management skills, strategic contribution, and business knowledge. At the same time, the multiple regression analysis proved that the competency model is an effective predictor of HR managers’ performance. And Heinrich et al. empirical study concluded that the HRM professionals’ competency model is a one-factor model including 11 indicators such as decision-making ability, emotional intelligence, self-efficacy, and achievement motivation [18].

Through the above discussion, it can be found that behavioral event interviews and questionnaires are commonly used in the construction of HRM job competency models, but these methods are often costly, can lead to narrow coverage, cannot really reflect the needs of the enterprise, and can be influenced by the respondents’ cognitive level at that time and have a large subjective factor [19]. As can be seen, in the above-mentioned studies, scholars have a certain consistency in their research on HR managers’ competency due to the difference in survey subjects and sample size, but there are also contradictions among them, and in short, a unified view has not been formed. With the advent of the big data era and the development of online recruitment, the rapid development of the Internet in the new era has brought a lot of derivatives in the past to build a capability model that may not fully meet the requirements of the new era.

2.2. Theoretical Basis
2.2.1. The Theory of Energy and Job Matching

Xia proposed a trade-off matrix model of ability-position matching, as Figure 1. As shown, the model forms four types of competency matching according to the high and low degree matching of employee orientation and job tendency: moderate, selecting people by post, determining posts by people, and conflict type, and each type has its own characteristics, and the simple post priority or personnel priority is impractical in the development process of the organization [20]. Therefore, it is especially important to develop an effective competency model for the position to achieve a dynamic balance between employee orientation and position tendency, so as to achieve the ability to match the position. The details are shown in Figure 1.

2.2.2. Trait Factor Theory

Trait factor theory means that on the basis of a clear understanding of the subjective conditions of the individual and the conditions of the demand for social occupations, the objective and subjective conditions are compared and matched with the social occupations, and finally an occupation is selected that matches the individual. It can be said that the theory of trait factors for career guidance is based on the basic premise of the assessment of human characteristics [21]. It puts forward the idea of human-job matching in career decision-making, and most of the theories on talent assessment widely used in enterprises today are developed on the basis of this theory. In particular, the recruitment and selection of talents based on job competency in enterprises strives to achieve the requirements of human knowledge, skills, personality, values, etc., and competent positions, which is also a reflection of the requirements of trait factor theory.

2.2.3. Competency Model–Related Theories

The significance of the competency model is to identify the potential characteristics of individuals, so that recruitment and selection, employee training, performance management, and other work can be carried out efficiently, so that individuals can effectively exert their self-worth, so as to dynamically achieve the consistency of people-work-organization strategic goals, and create a lasting competitive advantage for enterprises.

Venn et al. at home and abroad have studied the important role of competency model in job allocation. They believe that the competency model enables enterprises to clarify the qualities required for excellent performers in each position and provides specific directions for the implementation of competency matching. Among the competency model theories, the iceberg model and the onion model are two internationally recognized theories for constructing competency models, and the following is a brief introduction of these two models [22], as is shown in Figure 2.

(1) Iceberg model. The model was developed from the psychoanalytic school’s iceberg theory, which was proposed by psychologist McClelland in 1973. The Spencers improved on it by arguing that competence consists of knowledge and skills, social roles, self-image, personality, and motivation. These components, like icebergs, are distributed above and below the surface of the water. The part below the surface consists of social roles, self-image, personality, and motivation, which are called implicit competencies. Explicit competencies are relatively easy to observe and measure and can be acquired through training, but they are not a key factor in differentiating performance; implicit competencies are not easy to observe and measure, and are difficult to acquire through acquired training, but they are a key factor in differentiating the excellent group from the average group Yu [23].

(2) Onion model. In 1981, Richard Boyatzis, based on McClelland’s research, proposed the Onion Model, which is similar to the Iceberg Model, in his books “The Competent Manager: A Model of Effective Performance,” and “Competent Managers: A Model of Effective Performance.” He proposed the “Onion Model,” which is similar to the “Iceberg Model” and discussed in detail the method and process of its construction (Li et al. [24]). The “onion model” summarizes the competency qualities from the inside out as core motivation, wrapped in a structure of personality, self-image and values, social roles, attitudes, knowledge, and skills in the outward direction. The outer layer of competencies, such as knowledge and skills, can be acquired later in life and are equivalent to the upper half of the Iceberg Model, while the more inward, the more competencies, such as motivation and personality, which are difficult to acquire later in life, and are equivalent to the lower half of the Iceberg Model. The lower half of the iceberg model is the key factor that differentiates performance. The onion model and the iceberg model are similar in that both emphasize the core qualities that can predict a person’s long-term performance, i.e., they share the same essence Li et al. [24], as is shown in Figure 3.

3. Methodology

3.1. Research Methodology

Specifically, the research methods used in this study are the following:

3.1.1. NLP Method

Since the information in the recruitment information of enterprises and the resumes of job seekers are unstructured, it is necessary to extract the information from them and perform certain preprocessing using the relevant methods of natural language processing, so as to turn the unstructured information into structured information for further analysis. The main methods involved are: extraction of valid fields using regular expressions, word separation processing, TF-IDF (term frequency-inverse document frequency) calculation, keyword extraction, word vectorization, etc.

3.1.2. Machine Learning Methods

Machine learning methods are needed to train classification and regression algorithms when combining job applicant resumes with competency models to build prediction models. Specifically, classification algorithms mainly involve plain Bayes, logistic regression, K-nearest neighbor, decision tree, support vector machine, etc. Regression algorithms mainly involve linear regression, support vector machine, decision tree, etc. The appropriate algorithms are selected according to the research needs so as to achieve the optimal prediction effect.

3.1.3. Statistical Analysis Methods

After using relevant technologies to structure and vectorize the recruitment information of enterprises and the resumes of job seekers, basic descriptive statistics of variables are needed to grasp the overall demand of enterprises and the distribution of individual characteristics of job seekers. After structuring the resume data, we need to further construct regression models to investigate the variables in the resumes that have a significant impact on the overall competency of HR managers, so as to provide a reference for the subsequent construction of a machine learning model for job matching. The specific process is shown in Figure 4.

3.2. Analysis of Competency Characteristics and Model Construction Based on Job Requirements

Competency traits are “the underlying characteristics of an individual who distinguishes outstanding achievers from those who perform eloquently in a job (or organization or culture) and can be motivations, traits, self-image, attitudes or values, domain knowledge, cognitive or behavioral skills—the characteristics of any individual who can be reliably measured or counted and who can significantly distinguish between excellence and general performance. ” The specific workflow for building the model is shown in Figure 5.

3.2.1. Job Demand Degree Analysis

For the competency characteristics extracted from 165,811 job descriptions above, it is necessary to further explore their degree of demand in human resource management positions, i.e., which competency characteristics are in greater demand in human resource positions. Generally speaking, the job descriptions in the job advertisements of enterprises can effectively reflect their needs, and the competency qualities that are in greater demand in enterprises may be repeatedly mentioned in the job descriptions. Therefore, this study uses the TF-IDF (word frequency-inverse document frequency) of each competency characteristic as a weight to be used as a job demand degree indicator by calculating it.

TF-IDF (term frequency-inverse document frequency) is mainly used to measure the importance of a word in one document of a corpus. The main idea is that the importance of a word is proportional to the number of times it appears in that document and inversely proportional to the number of documents containing the word in the whole corpus. Thus, it is expressed as the product of term frequency (TF) and inverse document frequency (IDF). To eliminate the influence of different document lengths, it is often normalized in the calculation by formula (1), where the numerator indicates the number of times the word appears in the document mountain, and the denominator indicates the total number of words in the document. The formula for calculating the inverse document frequency is Eq. (2), where |D| denotes the total number of documents in the corpus and the denominator is the number of documents containing the word, and the addition of 1 is to eliminate the effect of the calculation caused by the number of documents containing the word being 0. Therefore, the TF-IDF value of the word is specifically expressed as Eq..

In order to calculate the demand degree of each competency characteristic in human resource management jobs, the data of job postings for human resource category were screened from 165,811 job advertisements, totaling 10,000. According to the above formula, each job description is taken as a document, and a total of 10,000 job descriptions are taken as the total corpus. The more frequently a competency feature appears in the job descriptions and the fewer job descriptions containing the competency feature in the total corpus, the greater the job demand for the competency feature. The TF-IDF value of each competency characteristic in the HR category is calculated as the weight, and the result is ranked, and the higher the ranking indicates that the competency is in higher demand in the HR management position. Using python programming language, we calculate the TF-IDF value of each competency in 10,000 HRM job descriptions, and select the top 10 competencies for visual display, as shown in Figure 6. Professional skills such as performance management, labor relations, and employee training have a high demand degree in this position. In addition, because the work of HRM involves labor and the same, employee relations, etc., therefore, knowledge of laws and regulations and certain practical experience also have a high demand in this position. At the same time, as the strategic position of HRM personnel has increased, the demand of enterprises for their qualities in decision-making, motivation, and strategic planning has also increased.

3.2.2. Job Importance Analysis

Through enterprise demand degree analysis, we can explore which competency qualities are in greater demand in HRM positions; however, there are some competency qualities that may be in higher demand in all positions, and these competency qualities do not represent HRM as a position well. Which specific competency qualities are important for corporate HRM personnel? Or which competency qualities are more important for HRM personnel than those in other positions? This is an important question that needs to be further explored in this study. In this paper, the cardinality value is used as an indicator of the importance of competency qualities in a position. The original assumption is that the characteristics and the category Cj are independent of each other, i.e., the characteristics are not representative for the category. Then, the original hypothesis is tested by calculating the deviation between the actual value and the theoretical value, and the larger the calculated chi-square value is, the greater the deviation between the theory and the actual, i.e., the more confidently the original hypothesis is rejected, and the feature considered are representative for this category. Its calculation formula is equation (4), where the numerator is the theoretical value and the denominator is the actual value.

The following is an example of the recruitment data of human resources category to illustrate the calculation process. The original assumption is that the competency characteristic “stable” and “human resource category” are independent of each other, i.e., the competency characteristic “stable” is not representative of “human resource category.” Now there are N job descriptions, of which M are for “human resources.” As shown in Table 1 , in the “human resources” job description, there are A articles containing the competent characteristic “stable” and C articles not containing it. In the non-HR job descriptions, there are B articles containing the competency characteristic “stable” and D articles not containing it. It is easy to see that the competent characteristic “stable” appears A + B times. And the previous has assumed that “stable” and “human resources class” are independent of each other; therefore, we can calculate the probability of the competent characteristic “stable” in the whole job description as shown in the following equation:

At the same time, it is easy to calculate the number of times the theoretical competency characteristic “stability” appears in the “human resources” category, as shown in the following equation:

In practice, the number of occurrences of “stable” in the “human resources” category is A. Based on this, the deviation of the theoretical value from the actual value can be calculated, as shown in the following equation:

Similarly, we can calculate the deviation between the theoretical and actual values of “Stability” for the “Human Resources” category without the competency characteristic D21, the deviation between the theoretical and actual values of “Stability” for the non-“Human Resources” category D12, and the deviation between the theoretical and actual values of “Stability” for the non-“Human Resources” category D22. The deviation between the theoretical and actual values of “Stability” in the category “Human resources” and “Stability” in the category “Non-human resources” is calculated as D12, and the deviation between the theoretical and actual values of “Stability” in the category “Human resources” is calculated as D22. The cardinality of the competency characteristic “stability” in the “human resources” category can be calculated as Eq. (8), which can be reduced to Eq (9).

According to the related principle of chi-square test, in order to calculate the chi-square value of human resource management job competency quality, other job data are also needed as category information. Therefore, from 165,811 job advertisements, we screened out 9 job descriptions by job name, including finance, real estate and property management, public service, computer and Internet, customer service, trade and sales, design, culture and education, and logistics and transportation, according to the latest national job classification standards. Using a program written in Python, each competency characteristic in each category was subjected to a chi-square test, and the calculated chi-square values were ranked, with larger values indicating that the competency characteristic was more representative of the category, i.e., the more important the competency characteristic was in the position. The cardinal values of the competency qualities of the HR category are visualized and compared with other job categories.

3.2.3. Job Centrality Analysis

(1) Centrality Analysis. In his book “Social Network Analysis: Methods and Applications,” Stanley Wasserman points out that centrality is a way to distinguish between important and nonimportant players in a social network. Centrality is a concept commonly used in social network analysis (SNA) to express a point in a social network or the degree to which a person is centered in the entire network. This degree, expressed numerically, is called centrality (that is, the concept of determining the importance of a node in the network by knowing the centrality of the node). The methods of determining centrality can be divided into degree centrality, near-centroidity (or closeness centrality), median centroidity (or spacing centrality, betweenness centrality), and so on.

The most commonly used centrality indicators are degree centrality, proximity centrality, and mediated centrality. Among them, degree centrality (CD) is determined by the number of direct relationships a node has in the network. The higher the degree of centrality, the more centrally located the competency is, and the formula is Eq. (10). d(ni) represents the number of relationships directly related to competency i, and is the size of the network.

Proximity to center.

Intermediary Center Degree.

4. Results and Analysis

The top 10 competency qualities are selected for each indicator and the results are shown in Table 2. Obviously, these ten abilities include resource management, planning, coordination, communication, responsibility, laws and regulations, execution, motivation, affinity, and learning ability. Resource management ranks in the top ten core indicators, far more than other capabilities, indicating that resource management is the core of human resource management, which is the key link to ensure that all work can be completed efficiently. At the same time, every ability is indispensable. And, it has a clear strategic position. Therefore, these capabilities are named central competencies.

Competent qualities with rich structural holes in the top 10 were selected, and the results are shown in Table 3. Competent qualities, such as inquisitiveness, wisdom, good looks, reasoning ability, courageous innovation, willingness to share, fairness, judgment and analysis ability, self-awareness, and reliability, although they do not have a high degree of centrality and their central role is not obvious, they occupy rich structural holes and have an important supporting role. In the work of human resource management, these are the competency qualities that should not be neglected either. They play an important role as a bridge, can connect the first ten capabilities of centrality, to achieve the purpose of complementarity; this bridge is to reduce the degree of organizational redundancy and improve the efficiency of organizational operations, so they are named auxiliary capabilities.

Through the previous steps of data cleaning, word separation, word2vec generation of word vectors, and K-means clustering, accurate extraction of competency characteristics from 165,811 job descriptions was effectively achieved. However, as many as 600 kinds of competency features were extracted, and it was not possible to determine which competency quality was important for HRM personnel. Therefore, job demand degree, job importance degree, and job centrality degree were analyzed separately for HRM positions, and weights were calculated for each competency quality separately. These three weights were combined, and their products were used as the total weights. Referring to most of the literature, the top 30 competency qualities in terms of total weights were selected as the core competencies of HRM personnel, and the results were prepared for the subsequent construction of the competency model, as shown in Table 4.

Based on the challenges posed to HRM by the development of big data era and online recruitment, this study explores the required competency characteristics of HRM personnel and constructs a competency model by mining the job advertisement data of companies in online recruitment from the perspective of corporate needs. At the same time, we further explore the impact of variables in resumes on competency qualities by correlating resume data with competency models through expert observation scores, and summarize the clues in the career development path of HR managers. Finally, a competency prediction model is constructed using machine learning algorithms to realize the application of the competency model in the resume screening process, so as to help companies accurately locate high-quality talents and effectively realize the ability-job matching in the resume screening process.

At the same time, a series of conclusions are drawn from this study. In the dynamic mining of enterprise recruitment text, this study found that the data related to human resource management has high utilization value.

In the construction of the competency matching model, it was found that: (1) Factors that have a significant impact on the overall ability characteristics are: the professional requirements of human resources majors, the practical experience of internships is required, the experience of student leadership is required, and the size, level, and highest academic qualifications of the company personnel, while the working hours, reward experience, and education level have no significant impact on the ability characteristics. (2) The four indexes of accuracy, recall rate, precision rate, and Fl Score of the competency prediction model constructed by using machine learning are all above 70%, which indicates that the model can effectively imitate the behavior of experts to make competency evaluation based on resumes, thus realizing the automated operation of competency-job matching in resume screening, effectively improving the efficiency and providing reference for enterprises to accurately locate high-quality talents.

5. Conclusion

Based on the challenges brought about by the development of big data era and online recruitment to human resource management, this study explores the necessary ability characteristics of human resource management talents and constructs a competency model by mining the recruitment advertising data in online recruitment of enterprises from the perspective of enterprise demand. At the same time, through expert observation and scoring, the resume data are associated with the competency model, the impact of resume variables on the quality of competency is further explored, and the clues in the career development path of human resources managers are summarized. Finally, the machine learning algorithm is used to construct the competency prediction model, realize the application of the competency model in the resume screening process, help enterprises accurately locate high-quality talents, and effectively realize the ability and job matching in the resume screening process. It provides a reference for enterprises to accurately locate high-quality talents.

Data Availability

The labeled dataset used to support the findings of this study is available from the corresponding author upon request.

Conflicts of Interest

The authors declare that there are no conflicts of interest.


This work was supported by the Hebei Vocational University of Technology and Engineering.