Socio-Economic Determinants of the Value Attributed to Human Capital in the Labour Market from the Employee’s Perspective

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.

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 so-called 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).

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 (Lladós-Masllorens 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 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.

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).

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?

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 3 presents 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.

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. Ferreiro-Seoane 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; Vilčiauskaitė 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 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 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

illustrates 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.

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 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

reveals 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.

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. 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
China	2	0.3%	175.00	130.00	1.23
South Korea	5	0.7%	189.00	186.00	0.93
Japan	8	1.2%	180.63	159.13	1.04
Total	694	100.0%	176.52	173.97	0.93

).

Regarding the assessment of Talent Management by companies, it can be observed that Anglo-Saxon and Asian companies hold a higher assessment than European companies. However, in Training, the result differs, with Anglo-Saxon and European companies scoring very similar values, while Asian companies have worse results. The only region that has a higher average relative rating of its Talent Management potential points in relation to Training is Asia.

Table 4. Statistical analysis of Talent Management and Training, according to international areas.

Variable	Geograf_Area	N	Average Value	F.	Sig.	Levene Test	Sig. (bilateral)
Talent	Anglo-Saxon	193	181.290	0.096	0.328	Equal variances	0.025
	Central Europe	122	175.442
Training	Anglo-Saxon	193	174.963	6.505	0.011	Not equal variances	0.980
	Central Europe	122	174.893

reveals that Anglo-Saxon companies perform significantly better in Talent Management (F) than European and Mediterranean companies. On the other hand, regarding Training, there are no relevant differences; the origin is not a determining factor here with a bilateral sig. 0.980 and F 6.505. These results are consistent with those obtained in the regression models of

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)
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.

and

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)
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.

.

3.3. Results for H5 and H6: Economic Sectors

Table 5. Number of companies and assessment of Talent Management and Training by economic sector.

Sector	Number of Companies	%	Talent Value	Training Value	Talent/Training
Administrative and auxiliary services	44	6.34%	181.5	173.6	0.96
Arts, recreation, and entertainment	3	0.43%	160.0	146.7	1.00
Wholesale and retail trade	97	13.98%	175.1	170.4	0.94
Construction	16	2.31%	185.0	180.6	0.94
Education	4	0.58%	126.3	147.5	0.78
Finance and insurance	170	24.50%	176.2	176.9	0.91
Hospitality	14	2.02%	170.0	170.4	0.91
Manufacturing industry	75	10.81%	170.5	171.1	0.91
Information and communication	78	11.24%	179.1	167.4	0.98
Real estate	13	1.87%	179.2	178.8	0.92
Professional–scientific–technical	119	17.15%	182.5	177.9	0.94
Health and social services	10	1.44%	168.0	170.8	0.90
Energy supply	31	4.47%	179.9	185.3	0.89
Transport and storage	14	2.02%	160.0	172.8	0.85
Total	694	100.00%	176.5	174.0	0.93

shows that, in Talent Management, the most highly rated sectors are professional–scientific activities (182.5) and information–communication (179.1). It is remarkable that the education and health sectors receive low ratings, a possible explanation being that many professionals in these activities work in the public sector, where professional careers are regulated by law.

When referring to Training, similar results are obtained, although it is worth mentioning the drop in the information–communication activity with respect to other sectors (from 6th to 13th place), whose effect is reflected in the fact that the Talent/Training ratio (0.98) is the highest among the sectors with the most companies represented (>10).

Table 6. Statistical analysis of Talent Management and Training, by economic activities.

Variable	Sector	N	Average	F.	Sig.	Levene Test	Sig. (bilateral)
Talent	Professional–scientific	119	182.538	0.129	0.720	Equal variances	0.302
	Information–Communication	78	179.103
Talent	Professional–scientific	119	182.538	3.255	0.720	Equal variances	0.012
	Finance–insurance	170	176.182
Talent	Professional–scientific	119	182.538	0.473	0.492	Equal variances	0.019
	Wholesale and retail trade	97	175.052
Training	Professional–scientific	119	177.899	1.466	0.227	Equal variances	0.005
	Information-Communication	78	167.410
Training	Professional–scientific	119	177.899	3.906	0.049	Not equal variances	0.747
	Finance–insurance	170	176.924
Training	Professional–scientific	119	177.899	0.631	0.428	Not equal variances	0.046
	Wholesale and retail trade	97	170.360

highlights how the professional–scientific–technical sector scores significantly better in Talent Management than the other sectors, except when compared to information and communication activities (bilateral sig. 0.302). The regression models (Appendix A) reveal that this is more relevant in the professional–scientific–technical sector. The significance in several years (2018 and 2022) of information and communication and, to a minor extent, of Finance and Insurance can also be seen. Both ANOVA analyses and multiple regressions converge in the same direction.

A similar phenomenon occurs in Training, with the professional–scientific–technical sector obtaining significantly better ratios than the other sectors, apart from the Financial–Insurance sector (bilateral sig., 0.747). However, the multiple regression models that relate economic sectors with Training indicate that professional–scientific–technical has a significance, although weaker than Talent Management (Appendix B).

3.4. Results for H7 and H8: Company Size

The organisations in the reference ranking employ an average workforce of 4228.9 professionals. To analyse the influence of size, the companies are divided according to their median size, around 1000 employees. It can be appreciated that there are no differences in Talent Management (Bilateral sig. 0.725), but there are significant differences in Training (

Table 7. Statistical analysis of Talent Management and Training, by company size.

Variable	Size	N	%	Average	F.	Sig.	Levene Test	Sig. (bilateral)
Talent	<1000 professionals	328	47.3%	176.201	0.407	0.523	Equal variances assumed	0.725
	=>1000 professionals	366	52.7%	176.811
Training	<1000 professionals	328	47.3%	169.353	1.626	0.203	Equal variances assumed	0.000
	=>1000 professionals	366	52.7%	178.109

). See also the regression models in Table A1 and Table A2.

3.5. Results for H9 and H10

Table 8. Statistical analysis of Talent Management and Training, according to stock index.

Variable	Stock Market	N	%	Average	F.	Sig.	Levene Test	Sig. (bilateral)
Talent	Unlisted	245	35.3%	172.975	5.546	0.019	Equal variances not assumed	0.002
	Listed	449	64.7%	178.458
Training	Unlisted	245	35.3%	167.951	9.834	0.002	Equal variances not assumed	0.000
	Listed	449	64.7%	177.256

demonstrates that 64.7% of the enterprises in this ranking are publicly listed companies and that the valuations in both Talent Management and Training are significantly higher in the listed companies (F statistic and a quasi-zero bilateral sig.). The regression models demonstrate that the presence of a company in a stock market index is one of the most relevant variables related to knowledge outcomes.

As a complement to the previous univariate analysis, a multiple linear regression model using ordinary least squares was estimated for each of the seven years of the study and for each of the two variables investigated, resulting in 14 models that share the same independent variables (presence, nationality, sector of activity, size, stock market listing).

The main results were in the case of Talent Management (Table A1); the variable that most often reaches a high significance is the stock market listing, and the permanence in the ranking is also significant, although not in a regular way. On the other hand, size shows significance in several years, although in the opposite way; the other variables show their significance in a milder way. Meanwhile, in the case of Training (Table A2), the share price is also the most relevant variable, followed by the size of the company, which grows in influence compared to the case of Talent Management.

4. Discussion and Conclusions

Gregorka et al. (2020), highlighted the need to analyse the advantages of employer branding for companies, one of them being to be included in a ranking that represents a company’s attractiveness. For this reason, being listed in a ranking of the most attractive companies in the labour market strengthens the employer brand and justifies the efforts in this domain, as noted by Monteiro et al. (2020), since only 272 organisations have been ranked in the last seven years out of the 3,363,197 companies (2020) that exist in Spain.

Regarding the human resource management variables related to knowledge, the highest values for Talent Management and Training have significantly appreciated from the fourth year onwards, thus confirming hypotheses 1 and 2. Therefore, permanence in the ranking influences lifelong learning, giving a value not only to the worker, as Belando-Montoro (2017) concluded, but also to the organisations that appear in the rankings (employer branding). According to Vinichenko et al. (2019), professionals are increasingly valuing issues related to career management and training. This means that the most attractive companies to work for are those that stay in the ranking for the longest time. The fulfilment of hypothesis 1 and hypothesis 2 supports the conclusion of Ferreiro-Seoane et al. (2021b). Thus, the persistence in a ranking like the RAE is related to a higher valuation but is also evidenced by the variables associated with knowledge, Talent Management, and Training. Additionally, the presence of companies in the ranking may be linked to an optimal management of human resources and knowledge, which contributes to triple—organisational, economic, and social—sustainability.

Non-compliance with hypothesis 3 suggests that Anglo-Saxon firms have significantly higher value in Talent Management (Table 3 and Table 4), which is a contribution to the work of Ibrahim and Shah (2013), who found no effects of the country of origin on HR practices. While Ferner (1997), Liu (2004), and Guthrie et al. (2008) evidenced systematic differences in HRM in multinationals by country, this paper further concludes that the specific origin of Anglo-Saxon companies has an influence on the Talent Management of the organisations ranked as the most attractive companies to work for. This non-compliance is a finding with regard to the work of Ferreiro-Seoane et al. (2021b), as there were no indications in the conceptual framework to suggest that Anglo-Saxon companies would have a significantly higher valuation than those from other geo-cultural areas. Moreover, it also represents a discovery with respect to the work of Ferreiro-Seoane et al. (2023b), which revealed the influence of Talent Management as an independent variable. Our results show that this variable depends on the origin, proving that diversity exists according to the organisational culture.

The fulfilment of hypothesis 4 suggests that, in a globalised and extremely competitive world, knowledge is essential for these companies. In this respect, knowledge goes beyond the country of origin and the organisational culture.

In line with the results obtained, a future research question arises: why do companies of Anglo-Saxon origin influence the result of the Talent Management value, and why is it not significant for Training? There are signs that this may be related to the activity of the company, as Anglo-Saxon companies are concentrated in the professional–scientific–technical sector, and these require excellent Talent Management. However, it is recommended to study this issue in future papers.

The non-fulfilment of hypotheses 5 and 6 reveals that, in contrast to the contributions of Jackson and Schuler (1995) and Conway et al. (2008), companies with professional–scientific–technical activities receive significantly higher ratings in the analysed variables, (Table 5 and Table 6). Thus, the activity sector has an influence on these ratings. For Talent Management, the information–communication sector also stands out, and for Training, finance–insurance. This finding is aligned with the work of Ferreiro-Seoane et al. (2021a), which highlighted the energy and finance–insurance sectors. Another relevant fact is that 14 of the 21 economic sectors corresponding to the first digit of the CNAE code are present in this ranking (Table 5).

Additionally, Talent Management is not found to be dependent on firm size, fulfilling hypothesis 7, which is aligned with the work of Fields et al. (2002), who stated that, although size is a potentially influential factor in HR practices, it is not possible to determine in advance whether the sign will be positive or negative. This information suggests that smaller companies can be equally effective as large corporations in terms of human capital development.

By contrast, size and Training are positively correlated; therefore, hypothesis 8 is not satisfied, such that the more people employed per firm, the higher the rating on Training (Table 7 and Table 8). This is in line with Morgan (2014) and Ibrahim and Shah (2013). It further demonstrates that large companies have more resources to create training plans, which, in turn, influences the development of human capital.

The research questions formulated through hypotheses 9 and 10 address the influence of the stock market listing on the scores of the target variables. Whilst Conway et al. (2008) had not come to clear conclusions in this respect, in this paper, such variables are higher in the organisations listed on the stock market. Hence, both hypotheses are rejected. This represents a new finding, as it is evidenced for the first time that being listed on the stock market influences the variables related to knowledge of the most attractive companies to work for. Such a finding is also significant because it is associated with the concept of organisational development. In this respect, a listed company is supposed to be highly efficient in organisational terms. Thus, an optimal organisational structure may be related to the development of human capital.

Shultz (1963) already emphasised the importance of investments in human capital to explain improvements in labour productivity (Human Capital Theory). This explains the value that companies attribute to the variables of Talent Management and Training in terms of business attraction. This line is also shared by Drucker (1993), who stressed the need to place knowledge at the centre of wealth generation. Another remarkable point is that professionals are increasingly appreciating the training actions that a particular company may offer to them (Cerdin et al. 2020; Lima and Gaspar 2021), which explains why the firms preferred by the workforce have good ratings in Training and Talent Management and are included in rankings that improve their employer branding (Credentialism Theory).

This discussion section provides additional information on the profile of the most attractive companies to work for, according to the analysed variables (

Table 9. Summary of the variables that influence Talent Management and Training.

Hypothesis	Compliance	Significant Influence
		Talent Management	Training
H1, H2 Permanence in ranking influences	Yes, Yes	Yes, >4 years	Yes, >4 years
H3, H4 Geo-cultural area does not influence	No, Yes	Yes, Anglo-Saxon	No
H5, H6 Economic sectors do not influence	No, No	Yes, professional–scientific–technical	Yes, professional–scientific–technical
H7, H8 Size does not influence	Yes, No	No	Yes, >1000 professionals
H9, H10 Stock market does not influence	No, No	Yes, the ones that are listed	Yes, the ones that are listed

).

Being in the ranking is meritorious, as only 272 companies have achieved this in the last seven years (6.6% every year). The highest values in Talent Management and Training are observed from the fourth year onwards, with significant differences compared to companies that have been in the ranking for fewer years. The higher the company’s presence in the ranking, the higher the value of the knowledge variables. Upholding excellence requires an optimal development, both organisational and human, contributing to socio-economic sustainability.

The ranking reveals its international dimension, with 60% of foreign companies coming from 19 countries. Spanish (40.3%), American (16.6%), British (10.1%), and French (8.5%) companies predominate. Anglo-Saxon companies score higher on average in Talent Management. A possible reason for this is that the large consulting firms that operate at an international level are Anglo-Saxon in origin, and this is an activity with a high turnover of human resources, which requires excellent Talent Management. It can be concluded that Anglo-Saxon companies perform well in Talent Management, while there are no significant differences in the Training variable.

Companies are present in 14 of the 21 economic sectors (first digit of the CNAE code). The most represented sector is finance–insurance (24.5%), followed by professional–scientific–technical activities (17.5%), and commerce (13.9%). In terms of Talent Management, professional–scientific–technical activities stand out (182.5) with significant differences compared to the major sectors, followed by information–communication (179.1). Both activities are highly dependent on the quality of their workers, which makes an optimal management of talent essential to prevent talent drain and to attract the best professionals. In Training, the differences are smaller, although they remain significant and in favour of the professional–scientific sector (177.9) compared to most sectors, with only the financial–insurance sector coming close (176.9). The fact that economic sectors influence knowledge and, in particular, professional–scientific–technical activity highlights the importance of innovation and change management.

The RAE companies are large with an average of 4228.9 employees per firm. There are no differences in Talent Management, but there are significant differences in Training in favour of large organisations (>1000 professionals). This is explained by the fact that Talent Management is more dependent on the activity performed and the resulting competitiveness than on the size; furthermore, large companies can allocate more resources to training. Business size influences the development of organisations.

An amount of 64.7% of the companies are listed on the stock market, with higher and more significant values in both Talent Management and Training than those that are not listed. This responds to a double circumstance; on the one hand, listed companies are more observed and regulated, and on the other hand, communication, financial, insurance, and energy companies of the IBEX 35 are in the ranking, all of them being activities that are considered socially basic.

In addition, and as anticipated, the contrast of the hypotheses with the multiple linear regression models using ordinary least squares allows us to conclude that the growth over the study period reveals how the two dependent variables analysed and related to knowledge content—Talent Management and Training—increased their valuation steadily. A more pronounced business profile is perceived in Talent Management (Table 9): more than four years in the ranking, Anglo-Saxon, professional, scientific, and technical sector, and listed on the stock market. On the other hand, Training differs in that size is a factor that positively influences the assessment, and nationality is not statistically significant.

In summary, it can be concluded that being continuously included in the ranking of the best companies to work for demonstrates leadership in human resource management and the development of human capital that drives knowledge, innovation, and sustainable business development. In addition, as they are large listed companies, these firms become leaders in terms of job creation, thus contributing to sustainability, both socially and economically, in line with the Sustainable Development Goals.

The main limitations of the study are that it refers to a ranking prepared by an economic journal for a single country (Spain), and the data used correspond to a relatively short period of time (2016–2022).

The implications of this research include the fact that it provides a better understanding of labour market excellence. Thus, it explains through different dimensions (permanence in the ranking, geo-cultural origin, economic activity, size, and stock market listing) how these influence knowledge (Talent Management and Training) as components of human capital. This provides added value to the employer brand, which incentivises companies to develop policies that promote knowledge and talent attraction and retention. At the same time, it enables professionals to be aware of them for decision-making purposes.

Our research also highlights the importance of the companies analysed in terms of functional diversity, an aspect that demands a wide variety of recruitment profiles, which means that both talent management and investment in training become two aspects that are highly valued by employees.

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 socio-economic 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.