Socio-economic determinants of the value attributed to human capital in the labour market from the employee's perspective

Ferreiro-Seoane, Francisco J.; del Campo-Villares, Manuel Octavio; Abad-Itoiz, Nerea; Jardón Ferreiro, Eladio Article Socio-economic determinants of the value attributed to human capital in the labour market from the employee's perspective Administrative Sciences Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Ferreiro-Seoane, Francisco J.; del Campo-Villares, Manuel Octavio; Abad-Itoiz, Nerea; Jardón Ferreiro, Eladio (2024) : Socio-economic determinants of the value attributed to human capital in the labour market from the employee's perspective, Administrative Sciences, ISSN 2076-3387, MDPI, Basel, Vol. 14, Iss. 7, pp. 1-19, https://doi.org/10.3390/admsci14070154 This Version is available at: https://hdl.handle.net/10419/320968 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Citation: Ferreiro-Seoane, Francisco-Jesús, Manuel Octavio del Campo Villares, Nerea Abad-Itoiz, and Eladio Jardón Ferreiro. 2024. Socio-Economic Determinants of the Value Attributed to Human Capital in the Labour Market from the Employee’s Perspective. Administrative Sciences 14: 154. https://doi.org/10.3390/ admsci14070154 Received: 9 May 2024 Revised: 27 June 2024 Accepted: 10 July 2024 Published: 17 July 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). administrative sciences Article Socio-Economic Determinants of the Value Attributed to Human Capital in the Labour Market from the Employee’s Perspective Francisco-Jesús Ferreiro-Seoane 1,* , Manuel Octavio del Campo Villares 2, Nerea Abad-Itoiz 3 and Eladio Jardón Ferreiro 4 1 Department of Applied Economics. Faculties of Law and Political Science and Administration, University of de Santiago de Compostela, 15782 Santiago de Compostela, Spain 2Department of Applied Economics, Faculty of Economics, University of da Coruña, 15071 A Coruña, Spain; [email protected] 3Organisation Studies Department, Faculty of Economics and Business Administration, National Distance Education University (UNED), 28015 Madrid, Spain; [email protected] 4International Institute of Marketing and Communication, 08012 Barcelona, Spain; eladio.jar[email protected] *Correspondence: franciscojesus.ferreir[email protected] Abstract: The aim of this study is to analyse the variables related to knowledge (Talent Management and Training) as a source of human capital in the companies listed in the ranking of the most attractive organisations within the Spanish labour market, published annually by the journal Actualidad Económica (period 2016–2022). We seek to determine the socio-economic variables impacting this assessment, while also exploring the sustainability of the companies in the ranking. Ten hypotheses are thus examined by descriptive statistics, ANOVA, and multiple linear regression models. The results show a significant relationship between permanence, nationality, professional–scientific– technical sector, size, stock market listing, and both variables representative of knowledge in the ranked companies. Focusing on Talent Management, it is noted that permanence in the ranking (>4 years), Anglo-Saxon nationality, being active in the professional, scientific, and technical sector, and being listed on the stock market all play a part. However, Training differs in that size is a factor that positively influences valuation, whereas the international area is irrelevant. Our findings are a key contribution, as there are no previous applied studies that correlate knowledge in the business environment, the valuation of organisations from the employee’s perspective, and several socio-economic variables. Keywords: knowledge; training; talent; labour; human capital; ranking 1. Introduction Today’s society is immersed in a process of constant transformation, fundamentally motivated by the accelerated technological advances that have occurred in recent decades. Although there are many benefits and applications derived from this digital evolution, it is necessary to consider one of the greatest challenges that this reality entails: the growing need for human beings to adapt to a highly changing context, which leads to increasingly interconnected and globalised scenarios, where innovation is key. In this regard, considerable efforts have been made to face the challenge that the socalled digital era represents for society in general. This is demonstrated by the widespread familiarisation with the use of technology and the universalisation of the access and treatment of large amounts of data in real time, differential factors of the current information society. The irruption of this expression took place in 1962, by Fritz Machlup, although it could be because of publications by authors such as Daniel Bell (1973) or Yoneji Masuda (1981) that it would reach greater popularity according to Alfonso-Sánchez (2016). Adm. Sci. 2024,14, 154. https://doi.org/10.3390/admsci14070154 https://www.mdpi.com/journal/admsci Adm. Sci. 2024,14, 154 2 of 19 With the arrival of the new century, a change in criteria took place, placing less importance on the fact of accessing information and greater emphasis on the generation of knowledge from that information (Pocol et al. 2022). This approach is shared by Castells ([1997] 2005), who considers that the central axis of the society of the new millennium is based on the development of knowledge, which is integrated into a process of feedback, where innovation is the main driver. As a result, the distinctive feature of human relations is increasingly dependent on knowledge, which is becoming a fundamental added value in any learning process (Alfonso-Sánchez 2016). The derived consequence of this development is the evolution towards a new concept, the knowledge society. Frequently used interchangeably in the literature, it is necessary to clarify the differences between the information society and the knowledge society. For UNESCO (2005), the pluralistic concept of the knowledge society is broader than the information society, as it encompasses economic, social, and cultural transformations that enable autonomy and lead to human development. Thus, the knowledge society is supposed to inherit a series of contributions derived from the information society. Information per se does not mean knowledge; it requires prior experience and learning, factors that, combined, create the desired knowledge. Similarly, the transmission of information should not be considered as a guarantee of knowledge generation (Burch 2005;Casas-Pérez 2010). The transition process from information to knowledge is characterised by complexity, as it requires time, specific skills, and diverse competences (López et al. 2018). This entails a paradigm shift and an evolution towards a more learning-oriented society, with a culture aimed at education, training, and innovation (Hsieh et al. 2019). This reality had already been predicted by the European Commission (1995) in the White Paper on Education and Training, which forecasted a future knowledge society, where education and training would be the two major players. Indeed, the dynamics of today’s knowledge society must necessarily be reflected in the field of education. In this sense, a context has been configured in which the role of education is becoming increasingly relevant. Today, education is the main driving force of innovation and has therefore been challenged by several important issues. Among the main goals to be considered is the fact of learning to learn, which is considered a key principle in all stages of a person’s life. Thus, current education is closely related to the concept of lifelong learning, which considers the continuous training of individuals as a social requirement demanded by the modern times, since it improves their employability and contributes to their personal development (Belando-Montoro 2017). Ensuring successful lifelong learning is essential, but this can only be achieved by broadening the focus of education beyond the traditional view of classrooms in schools, colleges, or universities (Maruyama [2019] 2019). Although education is directly related to these spaces, it must also be considered in many other areas, as it constitutes an intrinsic aspect of everyone’s life (Bagirathi and Magesh 2019). Therefore, one of the scenarios where the concept of education is particularly relevant is in the professional life of individuals, a reality that is highly linked to business contexts. In this regard, companies, as the most active agent of economic globalisation, are aware that technology, innovation, and knowledge are key factors for competitiveness (LladósMasllorens 2019;Azar et al. 2022;Tavakoli et al. 2022;Minardi et al. 2023). Therefore, access to knowledge and its development throughout the professional life of their employees becomes crucial. Consequently, organisations are committed to the development of organisational capabilities that translate this knowledge into effective actions which increase productivity (Lladós-Masllorens 2019). Indeed, Shultz (1963) already highlighted the role of education and any other investment in human capital to explain the improvement of labour productivity. And similarly, Drucker (1993) pointed out the need to place knowledge at the centre of wealth generation, considering it to be the most important input for productivity. In contemporary societies, the value and importance of knowledge is increasingly linked to Adm. Sci. 2024,14, 154 3 of 19 its ability to generate profits (Heinsch and Cribb 2019). In fact, there is a growing trend that recognises knowledge as a key production factor in a business environment that is becoming more and more competitive and intelligent (Dobrica 2021). In an organisation, human resource knowledge quality plays a key role in the effective performance of the organisation. In this regard, Iqbal et al. (2023) suggest that reciprocity, recruitment and selection, and performance appraisals have a significant relationship with knowledge sharing behaviour. Furthermore, recent studies demonstrate that knowledge in firms is an issue directly linked to human capital and that it has a significant and positive correlation with organisational performance (Aman-Ullah et al. 2022). Therefore, human capital is a fundamental element, whose importance has already been highlighted by Eide and Showalter (2010), who defined this term as an active element related to the intrinsic productive capabilities of human beings, which can be enhanced through investment in education and job training. In fact, human capital is considered an important resource in any organisation. However, most companies are concerned with external customer satisfaction, without devoting much attention to the satisfaction of their employees. Indisputably, employee satisfaction is fundamental and determines the success or failure of what the customer experiences (Fernandes et al. 2023). Human capital is also an important driver of innovation and competitiveness, as it will shape the uniqueness of the company as well as the process to obtain skills, capabilities, knowledge, and expertise (Costa et al. 2023). Figure 1shows the relationship between learning, knowledge and innovation in companies and how innovation generates positive benefits and eternalities for society, among others. Adm. Sci. 2024, 14, x FOR PEER REVIEW 3 of 19 and any other investment in human capital to explain the improvement of labour productivity. And similarly, Drucker (1993) pointed out the need to place knowledge at the centre of wealth generation, considering it to be the most important input for productivity. In contemporary societies, the value and importance of knowledge is increasingly linked to its ability to generate profits (Heinsch and Cribb 2019). In fact, there is a growing trend that recognises knowledge as a key production factor in a business environment that is becoming more and more competitive and intelligent (Dobrica 2021). In an organisation, human resource knowledge quality plays a key role in the effective performance of the organisation. In this regard, Iqbal et al. (2023) suggest that reciprocity, recruitment and selection, and performance appraisals have a significant relationship with knowledge sharing behaviour. Furthermore, recent studies demonstrate that knowledge in firms is an issue directly linked to human capital and that it has a significant and positive correlation with organisational performance (Aman-Ullah et al. 2022). Therefore, human capital is a fundamental element, whose importance has already been highlighted by Eide and Showalter (2010), who defined this term as an active element related to the intrinsic productive capabilities of human beings, which can be enhanced through investment in education and job training. In fact, human capital is considered an important resource in any organisation. However, most companies are concerned with external customer satisfaction, without devoting much attention to the satisfaction of their employees. Indisputably, employee satisfaction is fundamental and determines the success or failure of what the customer experiences (Fernandes et al. 2023). Human capital is also an important driver of innovation and competitiveness, as it will shape the uniqueness of the company as well as the process to obtain skills, capabilities, knowledge, and expertise (Costa et al. 2023). Figure 1 shows the relationship between learning, knowledge and innovation in companies and how innovation generates positive benefits and eternalities for society, among others. Figure 1. Professional lifecycle in the 21st century. Skills and training are both key determinants of human capital (Pansuwong et al. 2023). Companies need to be competitive in an increasingly global world, which entails a constant need to train their employees in order to innovate and improve their use of information and communication technologies (Rueda 2019). This training is generally more important and effective in the case of young workers (Choi et al. 2020). Empirical evidence supports the positive relationship between training employees and job satisfaction (Cerdin et al. 2020). The adaptation and regeneration of knowledge assets in the business environment is achieved through appropriate talent management. More specifically, it is through employee training and empowerment plans that true lifelong learning is promoted. By proximity, these two variables can be considered the most representative of knowledge in the Figure 1. Professional lifecycle in the 21st century. Skills and training are both key determinants of human capital (Pansuwong et al. 2023). Companies need to be competitive in an increasingly global world, which entails a constant need to train their employees in order to innovate and improve their use of information and communication technologies (Rueda 2019). This training is generally more important and effective in the case of young workers (Choi et al. 2020). Empirical evidence supports the positive relationship between training employees and job satisfaction (Cerdin et al. 2020). The adaptation and regeneration of knowledge assets in the business environment is achieved through appropriate talent management. More specifically, it is through employee training and empowerment plans that true lifelong learning is promoted. By proximity, these two variables can be considered the most representative of knowledge in the workplace. They are also variables that determine the attractiveness of a company from the perspective of the job seeker (Vinichenko et al. 2019), who is increasingly valuing issues related to career management, job satisfaction, and training offered by the company (Cerdin et al. 2020;Lima and Gaspar 2021). Adm. Sci. 2024,14, 154 4 of 19 New research highlights that most organisations are keen to provide an optimal location for their optimal workers (Senarathne 2020). This is because today’s dynamic and competitive business environment perceives talent as a key factor for successful business performance and for achieving a competitive advantage in the marketplace (Alparslan and Saner 2020;Carvalho and Areal 2016). Redefining the talent management process is a major challenge for organisations as they seek to correlate existing tools and to manage employees’ lifecycle (Martínez-Morán et al. 2021). Successful talent management is a prerequisite for attracting, empowering, and retaining talented employees (Coculova et al. 2020). Finding the right strategy for this purpose requires investments in employer branding policies (Monteiro et al. 2020). Although this concept has been used for more than two decades, it has not been fully analysed yet ( Gregorka et al. 2020 ). By contrast, there is evidence to suggest that managers should redesign employer branding activities and apply employer branding principles to better reflect the sense of employment and thus build employee appreciation towards the brand, which can enhance and increase the value of the organisation (Kim and Legendre 2023). While employees are a target market for internal employer branding, they can also contribute to the co-creation of the employer’s value proposition. Hence, employees act as brand members, representatives, advocates, and influencers, who increase the awareness of the organisation, both internally and externally (Naeppae et al. 2023). Findings from a study conducted in the UK, US, Greece, and Australia suggest that the main strategies for attracting and retaining talent include a friendly and open access culture, teamwork, compensation, training and development, as well as the involvement of staff (Marinakou 2019). Another relevant aspect is employee job state effect, which has been found to have differential impacts on employee behavioural outcomes including creativity, job satisfaction, and performance (Egan and Zigarmi 2023). Motivation plays an increasingly important role in this field, as it can be a management tool in organisations and a competitive differentiator in companies. Motivated employees are more productive, perform better, and contribute to the success of the organisation (Sousa et al. 2023). Thus, to what extent is the employer’s good work, in terms of training and talent management, a conditioning factor of the company’s reputation, attractiveness, or popularity in the eyes of employees, both current and potential? The answer to this question, far from being simple, requires in-depth analysis. Nowadays, there are many rankings that try to identify the most valued companies for work performance, and among the main variables analysed, aspects such as talent management and training are increasingly common (Carvalho and Areal 2016). Fortune100 in the USA, Great Place to Work in Europe, or Merco in Spain and Latin America are some outstanding examples of rankings. Undoubtedly, understanding how employees and potential applicants value employer brand features can help organisations develop truly effective strategies to attract and retain top talent (Ronda et al. 2018). Several studies have been published about the importance of rankings in terms of employer branding and about the variables that could explain their values. For instance, it has been analysed which companies were the most attractive to work for during the COVID-19 pandemic, as well as the international dimension of these organisations and the most characteristic economic sectors of these kinds of organisations (Ferreiro-Seoane et al. 2021b,2021a,2023a; or Miguéns-Refojo et al. 2021), but there are no previous and recent studies that have investigated the factors that may influence the knowledge-related variables (Talent Management and Training) of the most attractive companies to work for, which are recognised through a ranking that increases the employer brand. Thus, all of the above is related to the socio-occupational structure, which is the framework in which this research is located. Hence, it addresses the problem of the valuation of knowledge as a potential source of economic output and added value. Therefore, we are facing one of the biggest debates in economic thought: what quantity of human capital employed is the most convenient for the successful performance of a productive activity? Adm. Sci. 2024,14, 154 5 of 19 This paper is structured as follows. The next section describes the study sample, the explanatory variables of knowledge, and the techniques employed in the analysis. Section 3presents the results. Finally, the last section provides discussion, conclusions, and some recommendations. 2. Methodology 2.1. Objective Based on the framework presented in the previous section, the main objective of our study is to determine which socio-economic variables have the capacity to impact talent management practices and the content of training in the organisations analysed, both of which delimit the knowledge employed by the human capital of these organisations. To this end, this study is based on information from the ranking published annually by the Economic News Journal (Revista de Actualidad Económica, RAE) of the 100 most attractive companies to work for in Spain in the period 2016–2022. This paper analyses those factors that can influence the knowledge employed by any productive organisation, with Talent Management and Training being the two variables of the ranking that measure the human capital recruited by that organisation and on which a series of external (out of the ranking) socio-economic variables are applied (see Graphical Abstract). The relevance of this research lies in the absence of previous studies that address the determinants of human capital in business organisations from the employee’s perspective. 2.2. Variables Two categories of variables are distinguished: those used by the RAE in the ranking and those aggregated to this study, which identify characteristics of the companies included in the list and which will allow investigating possible relationships between both types of variables. The RAE uses six variables to elaborate the ranking, where it establishes a maximum score that each company can obtain in each variable, out of a total of 1000 points. The ranking is based on the responses of more than 500 companies to a hundred questions grouped into six pillars of a questionnaire carried out by experts in the field of staff recruitment. The enterprises that aspire to be in the ranking of the 100 best companies to work for in Spain must satisfy the criteria of conducting business in Spain for more than five years and employing more than 100 people. This article focuses on two of these six variables, Talent Management and Training, given the relationship between knowledge and productivity in the business world. The remaining variables are linked to other aspects of work: remuneration, working environment, CSR policy, or the employees’ perception and valuation of their organisation. In addition, the two selected variables are, together with Remuneration, the most highly valued by the RAE, with a weight of 24% and 22%, respectively, of the total score. The five control variables added to the study are the following: permanence in the ranking; nationality (which will allow an analysis by country and geo-cultural area); economic sector described by the Economic Activity Code (National Classification of Economic Activities, CNAE), and other dimension variables such as size or stock market listing, all of them influencing the key variables investigated. 2.3. Sample and Techniques Included The RAE publishes an annual ranking of the 100 most attractive companies to work for. The results of this ranking in the 2016–2022 timeframe constitute the sample used in this research. This implies a total of 694 entries (the 2017 ranking had just 94 entries), which correspond to 272 enterprises. Only 18 of these 272 companies have managed to remain in the ranking for seven years (6.6%). Indeed, a cut-off at the median of 4 years reveals that 81.6% of the companies have been present in the ranking for less than four years, which demonstrates the difficulty of remaining in this ranking. Adm. Sci. 2024,14, 154 6 of 19 Once the companies in the ranking were identified, as well as the values of the various published items, the independent (control) variables are included (e.g., permanence in the ranking, nationality, economic sector, size or stock market listing). All the hypotheses are initially analysed with descriptive statistics. In addition, for each hypothesis, we examine the averages of different groups (e.g., average of variables in the Anglo-Saxon region versus the Central European region). For this purpose, the Analysis of Variance (ANOVA) technique, which is used in the most recent studies, is chosen because of its utility in comparing averages between groups. The inference analysis is completed by the OLS estimation of 7 models (one per year) that regress Talent Management based on the control variables and of other 7 models that examine Training based on the control variables. Both inference analyses are supported by the pertinent statistics and robustness tests. 2.4. Hypotheses H1. The permanence in the ranking positively influences the assessment obtained in Talent Management. H2. The permanence in the ranking positively influences the assessment obtained in Training. Being in the ranking certifies the perception about the desirability of the company, as such a presence would act as a class mark of “attractive company”, i.e., a filter of company quality valued by the worker (Ferreiro-Seoane et al. 2021b). If staying in the ranking is difficult, it makes sense that those that have been in the ranking for longer obtain better values in both variables. Thus, the permanence of these companies in the ranking is an indicator of socio-economic sustainability, measurable through employment creation, wage payments, territorial development, and tax revenues collected by public administrations. H3. The assessment of Talent Management does not depend on the geo-cultural area to which they belong. H4. The assessment of Training does not depend on the geo-cultural area to which they belong. Although Ibrahim and Shah (2013) found no impact of state of origin on the HR policies of Malaysian corporations, Ferner (1997) conducted an analysis of consistent disparities in human resource management in multinational companies based in the home country. Liu (2004) and Guthrie et al. (2008) documented discoveries in the same direction with emerging evidence-based research. Miguéns-Refojo et al. (2021) studied the international profile of the most valued companies to work for, and their findings indicated that the origin of the companies did not seem to be relevant in the valuation of these organisations. FerreiroSeoane et al. (2023b) concluded that Training is a significant factor influencing companies according to their origin, but there are no opposing studies. Similar is the case for Talent Management. Numerous authors highlight Talent Management in organisations as an attractive factor for professionals and job seekers (Alparslan and Saner 2020;Vilˇciauskait ˙ e et al. 2020). Other authors highlight the positive relationship between Talent Management and job satisfaction (Victor and Hoole 2017). But there are no studies suggesting that Talent Management depends on the geo-cultural area to which companies belong. H5. Talent Management assessment does depend on the business activity conducted. H6. Training assessment does depend on the activity performed. Jackson and Schuler (1995) included, among the factors influencing human resource practices, the features of the area of activity. According to Conway et al. (2008), these can be categorised in various ways: services, industry, construction, etc. Analysing the economic sectors, it is reasonable to think that activities such as education, health, energy supply, finance, IT, or professional–scientific should have better values in Talent Management and Adm. Sci. 2024,14, 154 7 of 19 Training than other less qualified sectors such as hospitality, construction, administrative activities, or agriculture. However, there is no scientific evidence that companies linked to specific sectors are associated with a higher value of knowledge (Talent Management and Training). H7. The assessment of Talent Management does not depend on the size (workforce) of the company. H8. The valuation of training does depend on the size (workforce) of the company. Size is a potentially influential factor in human resource practices (Fields et al. 2002), although there is no consensus on the sign of its effects. Kortekaas (2007) found a significant positive sign impact of small/medium size on employee behaviours (engagement or job satisfaction) and a negative sign impact of the same size on an operational performance indicator (absenteeism and sick leave). Morgan (2014) suggested that small firms have several advantages such as exchanging positions, close employer–employee relationship. In contrast, Ibrahim and Shah (2013) argued that small firms lack the means to adopt advanced human resource management practices. H9. The valuation of Talent Management is not higher in companies listed on the stock market. H10. The valuation of training is not higher in companies listed on the stock market. Conway et al. (2008) concluded that, despite the logic that publicly listed enterprises are the most controlled, observed, and better structured in terms of division of labour, this characterisation does not always prove to be decisive in terms of the use made of human capital by these firms. In Ferreiro-Seoane et al. (2023a), it was concluded that being listed on the stock market is not a significant factor influencing the value of the most attractive companies to work for. Therefore, there is no empirical evidence to suggest that being listed on the stock market can influence knowledge-related attributes (Talent Management and Training). 3. Results 3.1. Results for H1 and H2: Permanence in the Ranking Table 1illustrates how the firms that have remained in the ranking for the longest period of time obtain better values in Talent Management, although two groups can be distinguished, those that have been in the ranking for four or more years with an average of 183.1 points and those that have been ranked for a maximum of three years, with an average of 168.2 points. The results are similar in the Training section. Table 1. Valuation of Talent Management and Training, according to permanence in the ranking. Permanence (Years) Number of Companies %/Total Average Talent Average Training Talent/Training 1 122 44.9% 163.5 157.7 0.951 2 50 18.4% 171.8 166.2 0.947 3 28 10.3% 169.3 166.5 0.932 4 22 8.1% 180.3 177.5 0.931 5 18 6.6% 184.7 184.3 0.918 6 14 5.1% 181.5 179.7 0.926 7 18 6.6% 186.0 187.2 0.911 Total 272 100.0% 170.8 166.6 0.930 Adm. Sci. 2024,14, 154 8 of 19 It can also be observed that Training is proportionally more highly valued than Talent Management. To this end, the relationship between both variables is established, with respect to the maximum number of points that can be obtained by both variables; a coefficient <1 indicates that Training is highly valued and vice versa. In this case, it can be appreciated that companies with more years in the ranking score better in Training than in Talent Management compared to those with lower permanence in the ranking. Table 2reveals a large difference in terms of significance (F) after the fourth year in the ranking. When comparing both Talent Management and Training between companies listed up to a maximum of three years and those listed for more than four years, the differences are statistically significant. Table 2. Statistical analysis of Talent Management and Training, according to permanence in the ranking. Variable Perm_Rank N Average F. Sig. Levene Test Sig. (bilateral) Talent >4 years 88 180.250 0.028 0.867 Equal variances 0.001 ≤3 years 84 169.286 Talent >4 years 389 183.820 18.143 0.000 Equal variances 0.000 ≤3 years 305 167.216 Training >4 years 88 177.546 0.480 0.489 Equal variances 0.001 ≤3 years 84 166.500 Training >4 years 389 181.439 20.227 0.000 Not equal variances 0.000 ≤3 years 305 164.445 3.2. Results for H3 and H4: Companies’ Nationality The companies come predominantly from Mediterranean Europe, with Spain (40.3%) and France (8.5%) standing out. However, the presence of Anglo-Saxon companies is notable: USA (16.6%), UK (10.1%), and Germany (5.9%) (Table 3). Table 3. Assessment of Talent Management and Training by country and international areas. Areas/Countries Number of Entries % Talent Value Training Value Talent/Training Anglo-Saxon 193 27.8% 181.29 174.96 0.95 USA 115 16.6% 182.28 178.03 0.94 Ireland 7 1.0% 165.00 172.86 0.88 UK 70 10.1% 181.60 170.93 0.97 Mediterranean Europe 364 52.4% 174.10 173.54 0.92 Spain 280 40.3% 173.48 171.74 0.93 France 59 8.5% 178.81 180.17 0.91 Italy 17 2.4% 175.88 175.00 0.92 Portugal 7 1.0% 157.86 188.57 0.77 North-Central Europe 122 17.6% 175.44 174.89 0.92 Germany 41 5.9% 173.15 170.85 0.93 Denmark 2 0.3% 120.00 137.50 0.80 Finland 4 0.6% 190.00 171.25 1.02 Luxembourg 2 0.3% 162.50 160.00 0.93 Sweden 17 2.4% 171.47 176.47 0.89 Switzerland 21 3.0% 183.10 184.86 0.91 Netherlands 34 4.9% 178.09 176.62 0.92 Asian 15 2.2% 182.67 164.20 1.02 Adm. Sci. 2024,14, 154 15 of 19 Another point suggested by the results obtained is that the presence in the ranking should be seen by both the employer and the employee as a credential with added socioeconomic value. The distinctive "presence in the ranking" is a label that may be used by the organisation as a measure of its quality towards the market and by the employee as a distinctive sign that he or she is part of an entity that meets the requirements of the effective management of human capital. Finally, it is worth noting that the presence and continuity in the ranking indicates that the human capital management practices of such organisations are in compliance and align, at least in general terms, with the basic principles of Corporate Social Responsibility. Consequently, the companies included in the ranking are qualified as “socially responsible”. Further research might contrast the results of this study by incorporating new years, as well as new control variables. It would also be interesting to extend the results to other countries, which would facilitate international comparisons. Author Contributions: Conceptualization, F.-J.F.-S., M.O.d.C.V. and N.A.-I.; Methodology, F.-J.F.-S. and N.A.-I.; Validation, M.O.d.C.V. and E.J.F.; Formal analysis, F.-J.F.-S.; Investigation, F.-J.F.-S., M.O.d.C.V. and N.A.-I.; Resources, F.-J.F.-S. and N.A.-I.; Writing—original draft, F.-J.F.-S.; Visualization, M.O.d.C.V. and E.J.F.; Supervision, F.-J.F.-S. and E.J.F.; Project administration, F.-J.F.-S. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data are contained within the article. Conflicts of Interest: The authors declare no conflicts of interest. Appendix A Table A1. Annual multiple linear regression model Talent Management, 2016–2022. 2016 2017 2018 2019 2020 2021 2022 Mediterranean ACs −1.76 −2.38 2.00 0.88 −4.05 −16.27 * −7.66 (5.89) (6.26) (5.33) (7.61) (8.15) (9.18) (7.48) Northern ACs −3.74 4.45 20.90 * −7.65 11.95 −13.07 −5.81 (8.47) (11.98) (10.86) (11.76) (21.95) (11.34) (10.99) Rest of ACs −5.61 −11.78 −4.57 4.44 −3.40 10.50 (15.20) (10.62) (15.64) (27.27) (12.39) (11.67) Central and Northern Countries (Europe) 2.10 −7.13 −1.18 −5.22 −10.42 −7.13 0.05 (5.79) (6.46) (5.52) (8.05) (9.66) (9.38) (7.48) Mediterranean Countries (Europe) −12.66 * −12.63 −9.91 0.22 −11.67 6.34 5.33 (6.89) (7.67) (6.42) (9.57) (10.86) (9.56) (8.00) Spanish nationality 14.26 * −1.33 9.64 0.08 6.12 −11.99 0.62 (7.50) (8.11) (6.85) (8.76) (10.58) (9.65) (8.38) Size 16.17 ** −9.12 −5.62 −17.11 ** −4.79 9.34 1.35 (6.14) (7.16) (6.17) (8.40) (7.37) (6.65) (6.41) Stock market listing 11.35 ** −2.36 12.21 ** 8.82 1.64 −3.97 −12.25 ** (5.26) (5.70) (4.91) (6.01) (7.03) (6.81) (6.05) Energy supply −3.13 −4.19 11.57 3.67 42.74 23.11 36.14 (10.27) (27.19) (10.04) (15.72) (42.33) (29.07) (25.92) Construction 28.06 10.26 21.82 −16.28 43.89 8.79 51.34 * (20.19) (32.78) (13.82) (16.91) (41.60) (28.62) (27.14) Trading 14.59 * −13.17 6.15 −17.18 16.73 4.64 41.86 * (7.66) (26.07) (7.16) (11.29) (38.07) (26.64) (23.78) Transport and storage 1.80 −16.36 −11.51 33.56 * −26.49 36.65 51.40 (14.76) (27.12) (12.00) (19.63) (39.65) (32.03) (32.32) Adm. Sci. 2024,14, 154 16 of 19 Table A1. Cont. 2016 2017 2018 2019 2020 2021 2022 Hospitality 8.79 −9.24 17.91 −17.26 −15.49 −12.57 14.96 (15.75) (27.81) (12.18) (27.96) (42.72) (36.03) (28.60) Information and communication 8.20 −13.16 15.59 * −11.96 29.82 20.64 50.43 ** (7.69) (25.72) (8.26) (12.45) (39.52) (28.03) (24.37) Finance and insurance 13.81 * −9.90 8.67 −4.03 17.81 15.91 36.40 (6.94) (25.59) (6.76) (10.15) (39.03) (26.41) (23.24) Real estate 16.13 −3.29 4.19 −23.55 17.07 14.93 33.62 (20.14) (28.72) (13.74) (20.20) (46.58) (31.95) (26.18) Professional, scientific, technical 27.94 *** −3.28 26.51 *** 8.83 9.26 10.37 41.19 * (7.41) (24.78) (7.39) (10.64) (38.50) (27.26) (23.92) Administrative and auxiliary services 15.08 −4.04 22.35 ** −6.67 17.32 20.09 41.27 * (11.03) (26.41) (9.84) (12.76) (40.99) (27.64) (24.28) Education −71.21 ** −38.59 −35.28 (32.71) (25.76) (42.30) Health and social services −9.88 −14.82 11.29 −14.76 2.13 (19.47) (27.59) (13.67) (25.83) (40.98) R2 0.27 0.28 0.34 0.30 0.38 0.25 0.20 F Statistic 1.37 (df = 21; 78) 1.23 (df = 24; 75) 1.82 ** (df = 22; 77) 1.32 (df = 24; 75) 1.71 ** (df = 24; 67) 1.03 (df = 24; 75) 0.81 (df = 23; 76) Notes: *** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant at the 10 percent level. Appendix B Table A2. Annual multiple linear regression model Training, 2016–2022. 2016 2017 2018 2019 2020 2021 2022 Mediterranean ACs 2.03 −5.46 −2.34 6.66 3.10 1.55 10.44 (6.48) (5.99) (5.22) (7.70) (9.83) (10.67) (8.72) Northern ACs 4.26 −4.69 −3.53 −8.11 −4.32 5.11 −1.52 (9.31) (11.47) (10.63) (11.91) (26.49) (13.19) (12.81) Rest of ACs −3.42 16.28 −40.04 ** 12.65 −8.56 18.53 (14.56) (10.40) (15.83) (32.91) (14.41) (13.61) Central and Northern Countries (Europe) −1.13 2.10 −4.80 −7.74 −1.81 16.62 −0.01 (6.37) (6.18) (5.40) (8.15) (11.66) (10.90) (8.72) Mediterranean Countries (Europe) −10.00 0.71 7.90 2.22 −1.16 19.49 * −0.87 (7.58) (7.34) (6.29) (9.69) (13.10) (11.12) (9.33) Spanish nationality 14.84 * 3.38 −6.81 7.34 −4.56 −14.11 −8.52 (8.25) (7.77) (6.70) (8.87) (12.77) (11.22) (9.78) Size 6.22 6.34 3.77 2.60 5.41 5.63 11.04 (6.75) (6.86) (6.04) (8.50) (8.89) (7.73) (7.48) Stock market listing 2.63 10.17 * 7.63 10.61 * −13.36 −7.84 −4.33 (5.78) (5.46) (4.81) (6.09) (8.49) (7.92) (7.05) Energy supply −14.49 31.12 17.93 * 10.66 64.93 36.85 11.95 (11.28) (26.04) (9.83) (15.92) (51.09) (33.81) (30.22) Construction 34.23 42.47 10.25 −8.89 51.33 9.66 19.32 (22.19) (31.39) (13.53) (17.12) (50.21) (33.28) (31.65) Trading −10.77 16.57 1.98 −10.04 40.59 16.23 −2.81 (8.42) (24.96) (7.01) (11.43) (45.95) (30.99) (27.73) Transport and storage 21.96 39.05 −15.81 32.43 41.89 31.49 −0.22 (16.22) (25.97) (11.75) (19.87) (47.85) (37.26) (37.69) Hospitality 4.63 26.49 8.94 −3.54 −6.88 −4.39 −26.00 (17.32) (26.63) (11.92) (28.31) (51.56) (41.91) (33.35) Information and communication −9.22 25.06 −4.29 −15.66 38.27 6.85 −0.20 (8.46) (24.63) (8.08) (12.61) (47.70) (32.60) (28.42) Finance and insurance −3.96 34.83 6.45 −3.08 38.49 24.19 2.11 (7.63) (24.51) (6.61) (10.28) (47.10) (30.72) (27.10) Adm. Sci. 2024,14, 154 17 of 19 Table A2. Cont. 2016 2017 2018 2019 2020 2021 2022 Real estate −14.61 33.36 15.00 −16.67 58.54 61.82 −0.25 (22.14) (27.50) (13.45) (20.45) (56.22) (37.16) (30.53) Professional, scientific, technical 5.37 34.94 14.18 * 13.57 39.16 25.33 5.04 (8.14) (23.73) (7.23) (10.77) (46.46) (31.70) (27.89) Administrative and auxiliary services −13.57 13.36 21.96 ** 5.48 39.79 29.18 −5.69 (12.12) (25.29) (9.64) (12.92) (49.47) (32.15) (28.31) Education 65.08 ** −22.60 10.55 (31.32) (26.07) (51.05) Health and social services 12.04 43.48 11.56 3.57 13.98 (21.41) (26.42) (13.38) (26.15) (49.46) R2 0.20 0.29 0.29 0.35 0.36 0.27 0.20 F Statistic 0.96 (df = 21; 78) 1.25 (df = 24; 75) 1.43 (df = 22; 77) 1.70 ** (df = 24; 75) 1.55 * (df = 24; 67) 1.14 (df = 24; 75) 0.81 (df = 23; 76) Notes: ** Significant at the 5 percent level. * Significant at the 10 percent level. References Alfonso-Sánchez, Ileana R. 2016. 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