A BIBLIOMETRIC OVERVIEW OF COMPETENCY AND CAPABILITY MODELING: RESEARCH CONTRIBUTIONS AND TRENDS (2000–2024)
In almost half a century, competency modeling has become an important movement in Human Resource and talent management. This study aims to identify the characteristics of the research published on competency/capability modeling using bibliometric analysis. To answer the research questions, 367 journal articles were analyzed. The results showed an increasing number of publications in the most recent two decades, and there is a worldwide collaboration in advancing this field while countries such as the United States and Australia are leading. Moreover, there are two competency/capability camps. One camp focuses on individual job performance, while the other focuses on organizational core competencies and capabilities. Capabilities often co-occur with the keywords related to competitive advantage and innovation. Behavioral event interviews and Delphi are among the common methods used to design competency models. Researchers and practitioners can benefit from the findings by gaining insights into the latest trends in this field.
Though competency measures previous performance, capability focuses on the undetermined future, change, and unknown circumstances.
There are really two competency/capability “camps.” One camp focuses attention on ORGANIZATIONAL “core competencies” and “capabilities,” and another “camp” focuses attention on INDIVIDUAL job performance.
Leadership and management competency models are among the most popular applications of competency modeling. Behavioral event interviews and Delphi are among the common methods that can be utilized to design competency models.
INTRODUCTION
The rapid growth of service activities during recent decades indicates that intangibles are a critical issue for organization management. In recent years, various approaches have been developed, focusing on the company's intangible resources, competencies, and capabilities as the key lever for creating a competitive advantage (Bounfour, 2002). Before the 1960s, organizational success often depended on management practices or traditional Tayloristic techniques. However, in the following years, the need for managerial competencies prompted organizations to start valuing these competencies (Naquin & Holton, 2006). Therefore, it can be said that today, most of the value of companies is related to intangible assets, among which talent is considered the most important asset of the 21st century (Rana et al., 2013). Talent is considered a very valuable asset that requires new management approaches in today’s organizations, so competency-based management has become a strategic approach to human resource management and organizational change (Rejas-Muslera et al., 2012). Talent management means providing the right individuals for the right job at the right time as a critical activity that significantly contributes to the current and future performance of a company. Talent management in all human resource (HR) functions, especially recruitment and selection, compensation and benefits, training and development, and performance management, requires effective planning and coordination from the HR department (Escolar-Jimenez et al., 2019). This means that if someone knows what separates the best from the rest, this information can be used in selection and recruitment, performance evaluation, development planning, and succession planning (Lucia & Lepsinger, 2013).
Competency-based human resource management was raised after Lawler’s (1994) article titled “From job‐based to competency‐based organizations.” Although job analysis concentrates on effective performance, competency modeling can focus on outstanding performance (Mansfield, 2000). Interest in utilizing competencies as a basis for human resource management programs originates from continuous downsizing in organizations, increasing market volatility in many industries, decreasing profit margins, and rising acceptance of behaviorally-based research (Rothwell & Lindholm, 1999). Today, in the recruitment systems of most large organizations, competency models have taken a pivotal position. Based on a report in 2013, 70–80% of Fortune 500 companies rely on the competency model as a foundation for their talent management systems (Campion et al., 2020).
Competency and Capability Definition
The concept of competency has been a topic for research in various fields, such as psychology, education, organizational management, human resources, and information systems. There is much discussion about this concept. However, it can be said that there is no common definition or understanding regarding it (Prifti, 2019). Simply stated, words or phrases like competency, capability, or competency model often mean whatever definers want them to mean (Rothwell & Lindholm, 1999).
Based on the definition that many researchers and experts in this field have agreed upon, which was first proposed by Boyatzis (1991) and also utilized by Spencer & Spencer (1993) in their book, “Competence at Work,” competencies refer to the individuals’ underlying characteristics that are causally related to criterion-referenced effective and/or superior performance in a job. This definition consists of three key terms: underlying characteristics, causal relationship, and criterion-referenced superior performance. Accordingly, underlying characteristics refer to the potential and stable internal characteristics of an individual, including motivation, attitudes, and values. The causal relationship means these underlying characteristics can consistently predict a person’s behavior and performance in complex work environments. Finally, the criterion-referenced superior performance indicates that a feature can be called a competency if it leads to the distinction between superior performance from other performances. These measures are usually some key performance indicators to identify individual differences (Kou et al., 2013).
On the other hand, Hase and Davis (1999) suggested that though competency measures previous performance, capability focuses on the undetermined future, change, and unknown circumstances (Gardner et al., 2008). Capability has diversely been described as having the potential to become competent, as being similar to competence but less prescriptive, as being practically synonymous with an extensive version of competency, and as including competence but going beyond it in several means (Lester, 2014). Capability is also used in the literature in the context of core capability as “a set of business processes strategically understood” and representing “technological and production expertise at specific points along the value chain.” So, capability can be defined as the organizational ability to perform activities repeatedly, predictably, and efficiently (Smith, 2008).
Competency Modeling Process
A competency model can usually be defined as a narrative description of job competencies for a distinguishable group, including a job category, a department, or a profession. It can determine key characteristics differentiating exemplary performers from fully successful performers (Rothwell & Lindholm, 1999). Competencies are most often identified by a combination of methods and models. Methods can be focus groups, interviews, observations, and surveys (Langdon & Marrelli, 2002). Models include products or processes, job responsibilities, or distinctions between superior and other performers (Langdon & Marrelli, 2002). Moreover, behavioral event interviews (BEI) can be used. In BEIs, the performer’s competencies are collected through an intensive face-to-face interview that involves questioning about critical incidents from performers and writing down what the performers are thinking, feeling, and doing during the event (Rothwell & Lindholm, 1999). The Delphi method has also been used in developing competency models (Penciner et al., 2011; Rothwell & Gerity, 2000).
A competency model would have three elements: competency labels, competency definitions, and behavioral illustrations for each competency. The process should be validated and accurately monitored against objective performance assessments and business results (Alldredge & Nilan, 2000). Moreover, traditional competency modeling methodologies can be categorized into three main approaches: the borrowed approach, the borrowed-and-tailored approach, and the tailored approach, which in turn can include the process-driven, the output-driven, the invented, the trends-driven, and the work responsibilities-driven approaches (Rothwell & Lindholm, 1999).
In recent years, technologies, especially disruptive ones such as artificial intelligence, have been used in various areas of human resource management, including talent management. In talent analytics, artificial intelligence helps the human resources department obtain the best talents needed by the organization. It also supports focusing on candidates based on competencies to place the right talent in the right job (Geetha & Bhanu, 2018). Therefore, artificial intelligence allows human resource management to optimize various processes, especially the selection process. In this regard, it uses expert systems, data mining algorithms, fuzzy methods, and artificial neural networks (Chen et al., 2022). In addition to reducing time and cost and eliminating human bias, these applications achieve the best match by predicting the probability of success in the desired job using personality analysis based on participation in the web (social networks, articles, etc.), eye tracking and analysis of facial expression, voice analysis, and behavior analysis during a game or online activity (Achchab & Temsamani, 2021).
Outcomes of Competency Modeling
The early years of competency modeling were dominated by the McBer-trained consultants’ approach. This approach involved a precise research methodology, which included identifying standard samples of superior and average performers, BEIs, thematic analysis of half interview samples’ transcripts, and cross-validation by coding and statistical analysis of the other half of the interviews. During this time interval, competency models were often used to guide employee selection and professional development. Meanwhile, in the more recent years, organizations have begun to utilize competency models in new ways. The organizations that have restructured their work processes and jobs have developed competency models for newly designed jobs for which there are few or no experienced job occupants. These new competency models may describe emerging and expected skill requirements rather than skills that have been effective in the past. Competency models also work well as drivers for organizational change (Mansfield, 2000). On the other hand, organizational capabilities refer to a company’s ability to utilize its tangible and intangible resources effectively in accomplishing tasks or activities to enhance performance. These current and prospective capabilities play a crucial role in shaping strategic decisions and serve as the foundation for developing core competencies, ultimately leading to a competitive advantage over other firms (O’Regan & Ghobadian, 2004). So, the literature emphasizes that it is the capabilities through which organizations develop strategies and acquire and utilize the necessary resources for executing those strategies that account for differences in performance among firms. (Spyropoulou et al., 2018).
Also, it can be said that a well-designed competency model not only consists of behavior anchors that lead to superior performance on the job but also includes those that support the organization's strategic direction and develop and maintain the culture necessary to achieve business results (Ashkezari & Aeen, 2012). In general, the literature on competency and competency models focuses on four aspects: the clarity of the competency concept, using the competency concept in different areas, theoretical frameworks for competency development, and the strategic relevance of competency-based human resource management (Salman et al., 2020). A wide range of social organizational theories (such as institutional theory, practice theory, structuration, and sensemaking) can be used to examine competency modeling approaches in organizations. In addition, research that examines the connections between the content of competency models and their use with other practices, such as organizational learning and knowledge management, can help advance the understanding of key organizational processes and their associated outcomes (Goldman & Scott, 2016).
Despite a history spanning over 50 years, the concept of competency and competency modeling is receiving more attention daily, and the relationship between competency and performance has been investigated in various studies. According to Vroom’s expectancy theory, the factors that affect job performance are motivation, skills and abilities, role perception, and opportunities, which indicate the relationship between competencies and job performance (Chen et al., 2022).
In light of the extensive literature in the competency and capability modeling field, studies have been presented to review the literature on this topic. Chouhan & Srivastava (2014) presented a critical literature review for understanding competency and competency modeling. They pointed out that developing and utilizing competencies is a complex endeavor. Competency modeling as a prevalent alternative to job analysis emerged, which offers several benefits to organizational decision-makers and human resource development professionals also serves as the basis on which training and development programs can be created to promote superior performance while maintaining a robust link to the overall strategy and direction of the organization. Competency modeling has typically been plagued with issues of conceptual ambiguity, lack of methodological precision, and questionable psychometric quality. Another literature review conducted by Stevens (2013), though, led to a clearer conceptualization of competencies and competency modeling, suggests that there remains the potential to further refine these concepts (Stevens, 2013).
Bibliometric analysis studies have been conducted in various fields such as management (Merigó & Yang, 2017; Nobanee et al., 2021), technology (Ardito et al., 2019; Tandon et al., 2021), education (Huang et al., 2020; Li & Wong, 2022), and healthcare (de Las Heras-Rosas et al., 2021; Zhu et al., 2021). Nonetheless, to find out whether there exist any bibliometric studies conducted on the topic of competency and capability modeling, during January 2024, a search query with the same keywords and the term “bibliometric” was utilized in the Scopus database, which led to no result. So, due to the huge amount of research in this field, this study, as a bibliometric analysis, aims to analyze research on competency and capability modeling to identify trends, key themes, and the evolution of research in this field from 2000 to 2024. In this regard, various analyses and also network analysis were conducted to answer the following research questions:
What is the pattern of publications and document citations of the articles on competency modeling and capability modeling from 2000 to 2024?
What is the pattern of contribution and collaboration of various countries to the publication of competency modeling and capability modeling?
Who are the influential scholars and what is the author co-citation network on the topic of competency modeling and capability modeling?
What are the major topics emerge from keyword co-occurrence analysis that have been explored in the field of competency and capability modeling?
The paper is organized as follows: The Method Section describes the research method. The Results Section reports the findings for the research questions. As a consequence, the Discussion Section discusses the research contributions and how the results were utilized to answer the research questions. Finally, Implications for Practice and Conclusion sum up the implications and findings.
METHOD
The growth of publications in a specific field leads to the rising need for comprehensive reports and analytical data. The notion of systematic review is closely associated with conducting bibliometric analyses, viewed by many scientists as an essential step and the outset of the scientific process (Ellegaard, 2018). This study used bibliometric analysis to evaluate the research contributions and trends in competency and capability modeling. Bibliometric analysis stands as a widely adopted and rigorous approach for investigating and analyzing extensive sets of scientific information. It allows for the detailed examination of the evolution within a particular field while also illuminating the emerging focal points within it. Researchers utilize bibliometric analysis for diverse purposes, including uncovering emerging patterns in articles, collaboration patterns, co-citation analysis, and research themes, as well as delving into the intellectual framework of a specific area within existing literature (Donthu et al., 2021).
This analysis can be defined as the “quantitative study of physical published units, or of bibliographic units, or of the surrogates for either” (Broadus, 1987). In other words, the bibliometric methodology summarizes the application of quantitative to bibliometric data (Donthu et al., 2021). Utilizing statistical analyses on the growing volume of accessible data enables the creation of dependable mappings of scientific progress, collaboration networks, and rankings (Ellegaard, 2018).
Search Strategy, Inclusion and Exclusion Criteria
In this research, the retrieval date of the data was 25 January 2024, using the Scopus database, which is one of the most widely utilized academic databases. According to Elsevier, which is its developer, Scopus “is the largest abstract and citation database of peer-reviewed literature: scientific journals, books, and conference proceedings.” So, it can offer good coverage of scientific literature. The initial keyword search included “competency model*” OR “capability model*” OR “competency vs. capability” OR “capability versus competency” OR “capability model versus competency model” OR “capability model vs. competency model,” which were searched in the titles resulting in a total 863 documents. The inclusion criteria involve only the journal articles, written in English and published from 2000 to 2024, resulting in 391 articles. The documents that fall outside of these inclusion criteria were excluded from consideration. Therefore, by applying time frame, language, and source type criteria, 472 articles were eliminated since many articles were published before 2000, with the first one in 1977. These criteria were established in order to focus on more recent as well as peer-reviewed articles.
A more thorough selection process was implemented to assess the appropriateness of each article. This evaluation encompassed an accurate content review to ensure alignment with the defined research objectives. Therefore, the titles and abstracts of the articles were reviewed by two experts in this field to confirm relevance to the research scope and adherence to specific inclusion criteria for the study. So, 24 articles were removed in this step. Figure 1 briefly shows the search strategy, filtering criteria, and the final number of articles prepared for the following steps. The search process template was adopted from Zakaria et al. (2021).
Citation: Performance Improvement Quarterly 37, 2; 10.56811/PIQ-24-0022
Data Extraction and Cleansing
The information for the documents that meet the requirements contained year of publication, journal, title, author, affiliation, keywords, document type, abstract, and counts of citations were exported into comma-separated values (CSV) format. Moreover, to conduct the various analyses accurately, data cleansing is necessary. Data cleansing can be performed by identifying wrongly entered entries, missing values, or similar words with different spellings that should be merged. In this regard, the fields, such as author names and keywords, were refined using the OpenRefine data-cleansing tool (Verborgh & De Wilde, 2013). By utilizing clustering in this tool, various forms of similar words can be clustered into one, enhancing the accuracy of different network analysis results.
Data Analysis
In order to answer the research questions, co-occurrence analysis of keywords extracted from the articles that can reveal clusters of articles, co-authorship analysis, and co-citation analysis were conducted by using the VOSviewer software tool (version 1.6.20). The “total link strength attribute” was applied to compare various items in these analyses. Total link strength is one of the standard weight attributes. It signifies the total strength of the links of an item with other items. For instance, in the context of co-authorship analysis, it represents the links of a given researcher with other researchers (Van Eck & Waltman, 2011).
Moreover, for further citation network analysis, which indicates the correlations between publications, Gephi software was used. Gephi was chosen because of its integrated network analysis tool and appropriate functional features (Bilan et al., 2022).
RESULTS
This section presents the results of the analyses using graphs, tables, and visualization of bibliometric networks. It is followed by the Discussion Section, which consists of a discussion of each analysis and answers the research questions accordingly.
The Pattern of Publications and Citations
In general, all of the publications were obtained based on the search strategy presented in Figure 1 (n = 367), which related to different subject areas. Figure 2 presents an analysis of search results according to the proportions of the scientific area.
Citation: Performance Improvement Quarterly 37, 2; 10.56811/PIQ-24-0022
As Figure 2 shows, the largest number of publications (18%) were related to “Business, Management, and Accounting.” Social Sciences followed with 17% of publications. Other subject areas, such as Engineering, Computer Science, Medicine, and Psychology, are placed in the next ranks. This information gives us an indication of the major concentration of the topic being examined.
To examine the publication and citation patterns of the articles on competency and capability models, Figure 3 illustrates the cumulative papers published from 2000 to 2024. It should be noted that since the date of data extraction was 25 January 2024, only one article from this year was investigated in this study.
Citation: Performance Improvement Quarterly 37, 2; 10.56811/PIQ-24-0022
The rising number of publications in this field indicates that competency and capability modeling is a salient research topic, and a considerable amount of research has been conducted in this area in recent years.
International Contribution and Collaboration
Affiliation analysis was conducted to determine the regional distribution and number of highly cited publications by country. The related information was extracted from the affiliation field exported from the Scopus database. Table 1 provides the regional distribution of the competency and capability modeling publications.
From Table 1, the Conclusion can be obtained that the USA dominates worldwide with the highest quantity of affiliation, followed by China, the United Kingdom, Australia, Germany, Malaysia, Spain, Iran, Canada, and India as the top ten affiliations of the publications on competency and capability modeling. Overall, America (39%) and then Asia (30%) had the most contributions in this field, followed by Europe (24.5%).
Further analysis of patterns of contribution by country was conducted based on the most-cited publications. Figure 4 presents the number of most-cited (top 10%) publications by country. It can be observed that the publications with United States, United Kingdom, and New Zealand affiliations have attributed the most citations to themselves.
Citation: Performance Improvement Quarterly 37, 2; 10.56811/PIQ-24-0022
To investigate the collaboration among countries, co-authorship country analysis was conducted by using VOSviewer, as explained in the previous section. Therefore, countries can be grouped into clusters based on collaboration in publications. Table 2 and Figure 5 present the co-authorship country analysis. To obtain the results, the type of analysis, unit of analysis, counting method, and maximum number of authors per document were set to “co-authorship,” “countries,” “full counting,” and “25”, respectively. Moreover, the minimum number of documents of a country was assumed to be 3 in order to achieve an acceptable number of countries that meet the thresholds, which led to 36 countries. Table 2 shows that the United States, Australia, United Kingdom, China, Germany, France, Ireland, Japan, Denmark, and Finland are the top ten countries sorted by total link strength attribute, which have the most collaboration in the publications on competency and capability modeling.
Citation: Performance Improvement Quarterly 37, 2; 10.56811/PIQ-24-0022
As displayed in Figure 5, nine clusters can be recognized in the network. The first cluster, highlighted in red, shows collaboration among Canada, the Netherlands, the Russian Federation, Saudi Arabia, South Africa, South Korea, and Ukraine. Cluster 2, represented in green, consists of Brazil, Finland, Ireland, Lithuania, and Portugal. The third cluster, in blue, indicates the collaboration among China, France, Italy, and Taiwan. The subsequent yellow cluster involves collaboration among Denmark, Germany, Japan, and Thailand. Bahrain, Egypt, New Zealand, and the United Kingdom create the fifth cluster, highlighted in purple. Australia, Iran, and the United Arab Emirates are grouped in one cluster represented in Cyan. Cluster 7 includes India, Norway, and Poland. Belgium, Spain, and the United States constitute the eighth cluster, and finally, Cluster 9 involves Indonesia and Malaysia.
Another analysis was conducted with the aim of recognizing the collaboration between authors in the extracted publications on competency and capability modeling. So, Table 3 and Figure 6 present co-authorship author analysis. Before using VOSviewer, data cleansing was applied to merge the author names detected as belonging to the same author. Table 3 sorts the top seven authors based on total link strength, and Figure 6 shows the co-authorship author network.
Citation: Performance Improvement Quarterly 37, 2; 10.56811/PIQ-24-0022
Key Concepts
For discovering the main themes of knowledge that have been explored on the topic of competency and capability modeling, co-occurrence network analysis of the most frequently used author keywords was performed by employing VOSviewer. In this regard, the minimum number of keywords is set to 3. Data cleansing in this analysis is very important since several words can be merged with each other. For example, the singular and plural forms of the keywords merged into the singular forms. Moreover, some keywords, such as the names of countries, were eliminated. Table 4 provides the most frequent keywords sorted by total link strength in the network.
As illustrated in Figure 7, the keyword co-occurrence analysis identified 13 clusters indicating the themes. Two of these clusters consist of only one keyword. Table 5 also provides the keywords in each cluster for further investigation.
Citation: Performance Improvement Quarterly 37, 2; 10.56811/PIQ-24-0022
Table 5 also provides the keywords in each cluster for further investigation.
Co-citation analysis is another important analysis conducted in this study to discover the relationships between publications. This analysis specified the authors’ co-citation network, the most cited research in the investigated publications, and the clusters of publications based on citations. These kinds of analyses can be utilized to better detect not-previously-recognized inclinations in the development of the research topic and the factors affecting scientific interest (Bilan et al., 2022).
Table 6 presents the top ten authors sorted based on total link strength in the author co-citations network. The minimum number of citations of an author is set to 10.
Figure 8 depicts the authors’ co-citations network. Based on this network, which was obtained by VOSviewer, 6 clusters can be detected. For this analysis, the type of analysis, unit of analysis, and counting method were set to “co-citation,” “cited-authors,” and “full counting,” respectively. Moreover, the minimum number of citations of an author was assumed to be 10 to achieve an acceptable number of authors who meet the threshold, which led to 150 authors. The cluster highlighted in blue consists of 26 authors, among them “McClelland,” “Boyatzis,” “Hamel,” “Prahalad,” “Ulrich,” “Spencer,” and “Spencer,” which are recognized with the highest total link strength. The green cluster, which includes 32 authors, comprised “Sanchez,” “Carr,” “Campion,” “Levine,” “Eyde,” “Ash,” and “Hesketh” as some authors with higher total link strength. The cluster in red consists of 39 authors, such as “Teece,” “Podsakoff,” “Schroeder,” “Narasimhan,” “Schoenherr,” “Eisenhardt,” and “Mackenzie.” Yellow cluster with 26 authors consists of authors such as “Lawler,” “Soderquist,” “Wang,” “Rothwell,” “Miller,” “Mcguire,” and “Mclagan.” The fifth cluster in purple with 15 authors includes “Kaslow,” “Rousseau,” “Rodolfa,” “Bebeau,” “Lichtenberg,” “Calhoun,” and “Eisman.” The last cluster, highlighted in Cyan, has seven authors such as “Rodriguez,” “Patel,” “Bright,” “Gregory,” “Gowing,” and “Hayton.” A more complete list of the authors in each cluster is presented in the Discussion Section.
Citation: Performance Improvement Quarterly 37, 2; 10.56811/PIQ-24-0022
Investigating the articles by these authors indicates the major subjects in this field since the most prominent researchers and scholars have worked on them.
Table 7 provides the top ten most-cited publications on competency and capability modeling in the investigated period (2000–2024).
As mentioned, Gephi was also used for network analysis. For utilizing this software for network analysis, the CSV file was converted to “.GEXF” format by using Table2net. In this format, the citation network can be created by specifying the nodes and the links. So, article titles were defined as the nodes and references of the articles were defined as the links. In the Gephi software, the Fruchterman Reingold layout is adjusted for better visualization so that the nodes are arranged in a way that clusters become apparent. The degree range was filtered to be more than 7 to achieve an acceptable number of nodes. The modularity algorithm introduced by Blondel et al. (2008) was used to define the publication classes. By using this approach, the connections between research in the extracted articles from Scopus can be examined by linking the articles and those cited to them. Figure 9 displays the result obtained in Gephi by adjusting the partition to the modularity class. It can be seen that 7 clusters are recognized among the publications. Each cluster consists of titles or codes of references which can be used to recognize the focus of articles in each cluster.
Citation: Performance Improvement Quarterly 37, 2; 10.56811/PIQ-24-0022
DISCUSSION
Based on the results section, our findings are now presented to answer the research questions.
RQ1: What is the Pattern of Publications and Document Citations of the Articles on Competency Modeling and Capability Modeling from 2000 to 2024?
The first research question aims to investigate the publication and citations of articles on competency and capability modeling. As presented in Figure 3 in the Results Section, the number of publications on this subject increased during the last two decades, and the trend of citations demonstrates an upward course, with nearly 900 citations reached in 2023.
So, this trend highlights the growing interest of researchers in the field of competency modeling. This mounting interest can be related to two paradigm shifts in human resource management. The first change relates to the concept of talent acquisition. Human resources and business managers have moved their traditional focus beyond the exclusive services of human resources and moved towards the decision-making science based on which decisions regarding human resources are made wherever needed. One of the effects of this change is that talent segmentation has become as important as customer segmentation. The second change is related to HR and business managers increasingly defining organizational effectiveness beyond traditional financial and stakeholder results and considering the concept of sustainability that includes today's success without compromising future needs. Leading organizations use talent acquisition and segmentation frameworks to improve the achievement of financial goals (Boudreau & Ramstad, 2005).
In this regard, talent is considered a valuable asset that requires new management approaches in today’s organizations, and competency-based management has become a strategic approach for managing human resources and organizational changes (Rejas-Muslera et al., 2012). This approach is emphasized by the limitations of traditional job analysis. For example, traditional job analysis in today’s dynamic environment is becoming obsolete because job activities change rapidly. Moreover, traditional job analysis has a retrospective analysis approach that focuses on the typical level of performance. In contrast, the goal of the competency model is to influence behavior. It sees the job as a role to be created and focuses on the organization. This approach focuses on the future and seeks superior performance. Accordingly, the traditional methods of developing job selection strategies are less flexible than the approaches based on the competency model. Another strength of competency models is that they are often associated with the business goals and strategies of the organization. These differences show that to solve the problems of job analysis in organizations with strategic approaches, appropriate competency models should be used to adapt to the dynamic environment and fit with organizational strategies (Mahbanooee et al., 2016).
RQ2: What is the Pattern of Contribution and Collaboration of Various Countries to the publication of Competency Modeling and Capability Modeling?
The first part of the second research question examines patterns of various countries' contributions to the research about competency and capability modeling. In this regard, affiliation analysis was conducted and the results presented in Table 1 and Figure 4 in the Results Section highlight countries' contribution in this field. From Table 1, it is revealed that more than 60 countries around the world have contributed to this field in the last two decades led by the United States followed by China, the United Kingdom, Australia, Germany, Malaysia, Spain, Iran, Canada, and India as top ten affiliations of the publications on competency and capability modeling. Overall, America (39%) and then Asia (30%) had the most contribution in this field, followed by Europe (24.5%). Historically, the concept of competency originates from the United States through the work of David McClelland (1973), a psychology professor at Harvard University and the pioneer of the modern competence movement. They are then extended to business and management through the works of Richard Boyatzis, Lyle, and Signe Spencer, among others. Following the United States, this concept expanded to other parts of the world, such as the United Kingdom, France, Germany, and Australia, and numerous researchers have made significant contributions, thereby fostering its development and expansion (Salman et al., 2020). The significant contributions from diverse countries underscore the importance of this field, highlighting its global relevance and impact.
Moreover, Figure 4 shows the contribution of countries based on having the most-cited publications in the last two decades. Although the United States and the United Kingdom are leading, the emergence of other countries from Europe, Oceania, and Asia in the list is a promising observation. This necessitates knowledge exchange and collaboration in this critical area of human resource management and talent management.
The second part of this research question investigates the collaboration among countries in publications related to competency and capability modeling. The results obtained by employing VOSviewer were presented in Table 2 and Figure 5 in the Results Section. As Table 2 provides, the most collaboration between countries based on total link strength includes the United States (40), Australia (20), United Kingdom (20), China (14), Germany (13), France (9), Ireland (7), Japan (7), Denmark (6), and Finland (6). The network of countries collaboration, which is gained by co-authorship country analysis, Figure 5, indicates nine clusters formed by various countries. The United States, as the pioneer in this field, has collaborated with most of the countries and formed a cluster consisting of Belgium and Spain. Another cluster (cluster 1) that can be detected includes authors from all over the world, including Canada, Netherlands, Russia, Saudi Arabia, South Africa, South Korea, and Ukraine. This cluster exemplifies extensive international collaboration within the realm of this field, signifying a concerted effort by researchers across all continents to advance scholarly research on competency and capability modeling. Other clusters also highlight the collaboration between Asia and Europe (cluster 3, 4, and 7), America and Europe (cluster 2), Asia, Europe, Africa, and Oceania (cluster 5), and Asia and Oceania (cluster 6).
Co-authorship author analysis examines the collaboration in this field from another dimension and reveals other details about collaboration on competency and capability modeling. Table 3 and Figure 6 present the results related to this analysis. Based on Table 3, Jill Rye (21), Stephanie Vaughn (21), Elizabeth Ablah (20), Audrey Gotsch (20), and Juan Sanchez (16) are the top five authors with higher total link strength in the co-authorship author network related to the competency modeling publications in the last two decades. In Figure 6, four clusters of authors' co-authorship can be recognized with Sanchez (cluster 1), Campion and Carr (cluster 2), Pearlman (cluster 3), and Kehoe, Battista, Prien, Eyde, Ash, and Hesketh (cluster 4) have higher total link strength in each cluster respectively. This result again emphasizes that while numerous countries worldwide have contributed to and collaborated within the field of competency modeling, a notable network of robust collaboration emerges primarily among countries such as the United States, Australia, and the United Kingdom with other countries, indicating a concentrated and influential alliance within this academic and practical domain.
RQ3: Who are the Influential Scholars and What is the Author Co-citation Network on the Topic of Competency Modeling and Capability Modeling?
The goal of the third research question is to find out about the relationship between publications and the most influential scholars in this field based on the citations to their articles and their co-citation network. To achieve this goal, various analyses were conducted the results of which were presented in Tables 6 and 7 and Figures 8 and 9.
First of all, based on Table 6, J. I. Sanchez (1754), L. Carr (1319), D. C. McClelland (1280), M.A. Campion (1179), and R.E. Boyatzis (1033) are the top five authors with higher total link strength in the author co-citation network. Moreover, the author co-citation network comprises 6 clusters, each of which consists of prominent and the most influential scholars and researchers in the field of competency and capability modeling. The results of this clustering were provided in the Results Section based on Figure 8. “McClelland,” “Boyatzis,” “Hamel,” “Prahalad,” “Ulrich,” “Spencer,” and “Spencer” formed one of the major clusters in this field. Table 8 presents some of the prominent scholars and researchers in each cluster based on their total link strength.
By investigating the most prominent researchers in each cluster, some of the most focused subjects in this field can be revealed. Cluster 1 highlights the studies on leadership, strategic management, and organizational capabilities and development, particularly the concept of core competencies. Reviewing the research conducted by the researchers in cluster 2, competency framework development and methodologies for competency modeling, assessment, and their applications in different organizational settings, which are required for successful job performance, can be distinguished. Cluster 3 indicates contributions to strategic management and organizational theories by focusing on competitive advantage, resource-based view theory, and dynamic capabilities. Cluster 4 highlights studies about organization development and innovation management by studies focused on leadership development, performance improvement, succession planning, and change management. Clinical psychology, organizational behavior, and human resource management are the subjects in the studies by researchers in clusters 5 and 6, respectively. Therefore, it can be observed that there are really two competency/capability “camps.” One camp focuses attention on ORGANIZATIONAL “core competencies” and “capabilities” (such as some of the articles by authors in clusters 1 and 3) (Boyatzis, 2013; Helfat, 2022; Helfat & Peteraf, 2009; Prahalad, 1993; Prahalad & Hamel, 2009; Teece, 2007) and another “camp” focuses attention on INDIVIDUAL job performance (such as some of the articles by authors in clusters 1, 2, and 4) (Campion et al., 2020; Dubois & Rothwell, 2004; Lucia & Lepsinger, 2013; Ryan et al., 2009; Sanchez & Levine, 2009; Shippmann et al., 2000).
Moreover, as presented in Table 7, the most-cited publications in the duration under investigation belong to Shippmann et al. (2000) and Campion et al. (2011), with 411 and 340 citations, respectively.
Then, to obtain better insights into the contents of the cited publications, the citation analysis was conducted at Gephi with the clusters depicted in Figure 9. Reviewing the articles in each of the 7 clusters, achieved by the modularity algorithm, indicates the focus of each cluster. These clusters concentrate on “leadership competency model and learning capability model,” “clinical competency models,” “capability model of intelligent enterprises,” “data competencies,” “competency model for professional rehabilitation nursing,” “competency models and job performance,” and “competency models for competitive advantage and business capability models.”
RQ4: What are the Major Topics Emerge from Keyword Co-Occurrence Analysis that have been Explored in the Field of Competency and Capability Modeling?
The fourth research question seeks to delve into the prevalent key concepts, particularly the authors’ keywords, in the competency and capability modeling research. Recognizing frequently-examined concepts offers an alternative viewpoint on the conceptual framework of the knowledge base within a given data boundary (Mohamed et al., 2020). The co-occurrence network can then be interpreted to understand recent topics in this field. Table 4 in the Results Section provided the most frequently used author keywords in the co-occurrence network. In addition to common words that are repeated in all of the articles due to the search query such as “competency” and “competency modeling,” other words such as “training,” “skill,” “development,” “leadership,” “competency mapping,” “talent management,” and “assessment” are among the top ten keywords in the co-occurrence network based on their total link strength.
Figure 7 and Table 5 present the clusters detected in the co-occurrence network. Hence, there are 13 identifiable clusters, the keywords of which were reported in Table 5. Some of these clusters are being investigated further in this section.
Important keywords in cluster 1 are “case study,” “competitive advantage,” “cumulative capabilities,” “dynamic capabilities,” and “innovation.” Examining the keywords in this cluster indicates that it represents the theme of the necessity of utilizing dynamic capabilities within organizations to achieve innovation and competitive advantage, a topic addressed in various articles in this field (Bitencourt et al., 2020; Steen et al., 2021).
Cluster 2 consists of keywords such as “behavioral event interview,” “competency mapping,” “competency model,” “core competency,” “Delphi,” “leadership development,” and “organizational change.” Behavioral event interviews are offered as an approach with maximum flexibility to discover differences between two categories of job incumbents, which are outstanding and typical, nominated by knowledgeable judges (McClelland, 1998). This approach has been used by several recent research studies for competency modeling (Jais et al., 2023; Zhou, 2022). Delphi is also a popular method in developing competency models used in various studies (von Treuer & Reynolds, 2017; Wei et al., 2021; Yoon et al., 2019). The Delphi technique entails a structured approach to achieving consensus among a diverse panel of experts (Wei et al., 2021). Leadership competencies and development are also detected in this cluster. Since an important threat facing the world today is the lack of effective leadership, many organizations try to find competent and effective leaders (Çitaku & Ramadani, 2020). So, these keywords once again underscore the importance of the leadership competency model as a topic of many publications in this field. The co-citation network also extracts this theme in the findings related to the previous research question. A profound investigation of the articles indicates that this cluster is recognized in the co-occurrence network since several articles utilized BEI and Delphi techniques for leadership competency model development (Beram et al., 2023; Choi et al., 2012; Li & Wivatvanit, 2016; Özgen et al., 2013; Sebelski et al., 2020).
Distinguished keywords in Cluster 4 are “career development” and “management skills.” The theme associated with this cluster also emphasizes the importance of utilizing competency models, particularly at managerial levels. “Exploratory factor analysis,” “leadership competency,” “managerial competency,” and “Nurse manager” are the major keywords in Cluster 5. Exploratory factor analysis (EFA) is employed in some publications as a method to initially identify the underlying dimensions of the competency model by reducing a large number of variables to a smaller number (Koenigsfeld et al., 2012).
In cluster 7, some keywords such as “competency-based education,” “curriculum,” and “nursing” can be recognized. The field of healthcare encompasses competency models related to nursing, general practitioners, healthcare professionals, and clinical settings, which are among the most common research themes in this domain, and several publications have addressed this topic (Ma et al., 2023; Mahbanooee et al., 2016; Supamanee et al., 2011; Vaughn et al., 2016). So, the keywords related to this topic are observed in this cluster as well as in clusters 5 and 6.
“Education,” “knowledge,” and “training” formed cluster 8. This cluster can represent some components of competencies, including knowledge and education and can be developed by training. Cluster 12 consists of “structural equation model.” Structural Equation Modeling (SEM) comprises a range of statistical methods enabling the examination of relations between one or more independent variables and one or more dependent variables (Ullman & Bentler, 2012). This approach was also utilized by some studies as a method for measuring and developing competency models (Chen et al., 2020; Kin et al., 2014). “Project manager” can be observed in cluster 13 as one of the frequent jobs addressed to design competency models.
Therefore, from investigating the recognized themes of publications, it can be concluded that both competency and capability models are the topics of several studies. However, capabilities, especially in the forms of dynamic and cumulative capabilities, often co-occur with the keywords related to competitive advantage and innovation, which are organizational concepts. Leadership and management competency models are among the most popular applications of competency modeling. Behavioral event interviews, Delphi, factor analysis, and structural equation models are among the common data collection or analysis methods used in designing competency models. Moreover, adopting competency models in the healthcare industry has garnered a considerable volume of published articles in this field. Finally, Competency components such as skills, knowledge, and education appeared to be the subject of some research in the last two decades.
Implications for Practice
From the practical point of view, the findings of this study have several implications for researchers, policymakers, professionals, and practitioners. Bibliometric analysis can help practitioners by guiding them to the most often-cited research on the topic of competency/capability. Identifying influential scholars and topics extracted from co-occurrence of keywords can guide future research directions. Researchers can utilize this information to identify gaps in the current literature and concentrate on unexplored areas or where there is emerging interest. Moreover, policymakers can gain insights about which countries are leading in this field and can help them allocate resources and develop strategies to foster collaboration. Professionals and practitioners can also benefit from this study by obtaining insights into the latest trends in competency and capability models and staying updated with the current situation in this field. The analysis of collaboration among countries and researchers can encourage international collaboration in the field of competency and capability modeling by identifying potential collaborators and understanding the strengths of different research groups, which can be an opportunity to leverage various expertise and resources to tackle complex challenges more effectively. Finally, the extraction of themes based on keywords can help practitioners develop frameworks and methodologies that are more aligned with current best practices and theoretical advancements.
CONCLUSION
The concepts of competency and capability modeling, with a history of over half a century, have garnered significant attention in both research and industry. Today, competency-based human resource management has gained a prominent position in various HR functions, from recruitment to succession planning (Dubois & Rothwell, 2004; Kandula, 2013; Ratnawat, 2018; Rothwell, 2012; Shermon, 2004). However, numerous challenges surround these concepts and their implementation, including clarity in defining competency, methods for designing competency models, competency assessment, and competency model development for various professions, especially leadership and management competency models. Consequently, numerous articles and research studies have been conducted in this field, resulting in a substantial body of literature. This bibliometric analysis addresses four main questions regarding literature in this field and provides valuable insights into competency and capability modeling.
The examination of the pattern of published research in this field from 2000 to 2024 indicates a continuous growth in published articles and citations, underscoring the increasing importance of research in this area and the industry’s demand for the development of competency and capability models. Furthermore, the analysis of the pattern of contribution and collaboration among different countries in this field indicates the involvement of various countries worldwide in advancing research in this area. While countries such as the United States, Australia, and the United Kingdom have formed stronger international collaborations, many countries from all continents contribute to the progress and development of this field through collaboration, and that is promising.
Moreover, the analysis of co-citation networks provides insights into influential scholars and researchers in this field. Considering the articles written by each cluster of influential researchers revealed that the focus of their studies can be categorized into “leadership and strategic management,” “organizational capabilities and core competencies,” “competency framework development,” “competitive advantage, dynamic capabilities, and organizational theories,” “leadership development and performance improvement,” “clinical psychology,” and “organizational behavior.” Moreover, the examination of the content of clusters obtained from this co-citation network indicates the application of competency models in various domains, ranging from leadership competency models, learning capability models, clinical competency models, and competency models for professional rehabilitation nursing to newer domains such as data competencies and capability model for intelligent enterprises resulting from technological trends. Additionally, capability models for business and gaining competitive advantage are among the topics extracted from the co-citation network. These topics, emphasized by keyword co-occurrence analysis, include competency models for leadership and management and some professions, particularly in the healthcare industry. Some prevalent methods for data collection or analysis in designing competency models, such as the behavioral event interview and Delphi as qualitative approaches and exploratory factor analysis and structural equation model as statistical methods highlighted in the co-occurrence clusters. Also, increased application of capability models in organizational concepts such as innovation and achieving competitive advantage can be revealed. Finally, developing various competency components, such as skills and knowledge, is also a recognized theme. Hence, this study can serve as a valuable resource for researchers and industry professionals in this field, offering a comprehensive overview of the subject literature.
Nevertheless, this research also has limitations, including its limitation to the articles from a single database, searching only in the article titles, excluding articles not published in journals, and limiting the time range. Using authors’ keywords to find co-occurrence and related topics is another limitation. Therefore, it is suggested that future research utilize more extensive databases and reduce the mentioned limitations for a more comprehensive review and analysis by extending the time range and increasing the number of articles under consideration. Additionally, the use of other tools, in addition to VOSviewer and Gephi, such as text mining techniques to examine latent topics in abstracts, is proposed to provide a more precise understanding of underlying topics in the articles. From the key concepts analysis point of view, it can be observed that utilizing various methods and technologies in designing competency models to achieve a better relation between competencies, job performance, and organizational outcomes is an important trend. Meanwhile, leadership competency model development, due to its complexities, can be considered the most challenging subject to be studied. Finally, more work needs to be done to align job performance and/or organizational performance to competencies/capabilities. more work needs to be done to measure competencies/capabilities and to measure levels of competence.
Flow Diagram of the Search Strategy
Analysis of Publications according to Subject Area
Publications and Citations on Competency and Capability Modeling Per Year
Number of Most-Cited Publications by Country
Country Collaboration Network on Competency and Capability Model Publications
Author Collaboration Network on Competency and Capability Model Publications
Co-Occurrences of Author Keywords Network on Competency and Capability Model Publications
Author Co-Citation Network on Competency and Capability Model Publications
Clusters of Publications Based on the Citation Analysis Network
Contributor Notes
WILLIAM J. ROTHWELL, PhD, DBA, SPHR, SHRM-SCP, RODC, FLMI, CPTD Fellow is Distinguished Professor of Workforce Education and Development in the Department of Learning and Performance Systems, College of Education, on the University Park campus of The Pennsylvania State University. In that capacity he advises residential PhD students specializing in Organization Development as an emphasis in Workforce Education and co-directs an online graduate program in Organization Development and Change. He has authored, co-authored, edited, or co-edited 168 books on HR, OD, Training, and related topics and has delivered over 1,600 talks in 15 nations over a 30-year period. He owns three consulting companies (one in Korea), a motel, and a vacation rental home company. Before joining Penn State in 1993 he had nearly 20 years of full-time executive work experience in HR, OD, and training in government and in a Fortune 100 multinational business.
FATEMEH MOZAFFARI is a PhD candidate in Information Technology Management (Intelligent Business) and a lecturer at the College of Management, University of Tehran. She has taught courses such as Business Intelligence and Emerging Technologies in Business. She has an academic background in Information Technology Management and Telecommunications. She has more than seven years of experience working as a researcher and engineer at the Information and Communication Technology Research Institute and has been involved in various projects related to emerging technologies during her tenure. She has published several articles in the international journals on using data mining in various fields. In recent years, her work and research focus has been on utilizing AI in human resource management, particularly competency models, to enhance HRM processes.
AZZA AL HAJRI is a PhD student in Workforce Education and Development at Pennsylvania State University, USA. With 16 years of experience in human resource development within the healthcare sector, she spent nine years in training and development at the Ministry of Health in Oman. She has authored and co-authored several research papers published in international journals. She is a lifelong learner dedicated to continuous education and its role in driving personal and professional growth. She currently serves as a Graduate Assistant at Penn State University, where she contributes to academic research and teaches courses in her field. Her current research work explores leadership competency requirements for nurse managers in Oman’s healthcare system. Azza is also a Certified Career Services Provider, supporting new graduate nurses in their career development and transition into professional roles.
