Purpose

This study aims to address how educational board games, based on neuroplasticity, can enhance the cognitive and socioemotional skills of future leaders. It explores their impact on intrinsic motivation, which is crucial for knowledge management as it drives engagement and long-term learning, and the mediating role of socioemotional skills in this process. Integrating these games into the curriculum of economics and business professionals-in-the-making improves the competencies necessary for business leadership, complementing traditional academic training while fostering decision-making and leadership abilities. Educational board games can strengthen the skills required for leadership in business.

Design/methodology/approach

In this cross-sectional survey-based study, data was analysed from 71 volunteers to assess their levels of various soft skills. A descriptive analysis of the sample variables was conducted, followed by a mediation analysis to ascertain the impact of the predictor variables on the outcome variables – motivation, learning strategies and knowledge management and sharing.

Findings

In the pre-test assessment model, the predictor variables do not exhibit a significant direct influence on the objective variable, motivation, although both direct and indirect effects were measured. Conversely, in the post-test assessment model, conducted after the training, the predictor variables demonstrate a significant impact on motivation, leading to increased levels of the target variable in response to increases in the predictor variables.

Research limitations/implications

Given the population to which the study has been directed, namely future workers in the short term, some of the questions in the questionnaires, as well as the dynamics of the games, have not been fully understood, despite the information available on the objectives of the study, their interest and motivation to participate. These factors suggests that part of the currently validated psychometric tests should be reformulated to adapt them to the language of the new generations.

Practical implications

The authors can conclude that when soft skills are well acquired and internalized, individuals can better manage their emotions, establish positive and healthy intra- and interpersonal relationships, take decisions and resolve conflicts in the company more effectively. Although the skills considered in this research are associated with cognitive and socioemotional competencies associated with the personality traits of individuals, they are essential to provide synergies in the company’s knowledge management.

Social implications

The proper management of the measures considered here helps to promote cooperation and conflict resolution, emotional self-regulation and the resilience of workers in the face of stressful situations, improve the leadership capacity of work teams and collaboration and contribute to making balanced decisions linked to the company’s objectives. If, in addition, they consider that these skills have positive and direct effects on the motivation of workers, resulting in higher levels of certain skills, one of the strategic lines of companies should be to promote these skills and, therefore, positively affect their motivation.

Originality/value

This study is groundbreaking as it establishes a relationship between individuals’ cognitive and socioemotional skills and their motivation within the context of business administration. By using a mediation model, it aims to enhance knowledge management and its transfer in fragile and non-linear environments.

In the first quarter of the 20-first century, businesses are facing new challenges, such as technological and digital transformation (Khanom, 2023; Zapata-Cantu et al., 2023), sustainability (Kaftan et al., 2023; Dasgupta, 2023; Suriyankietkaew, 2023), corporate social responsibility (Taylor, 2023), innovation (Bortoló et al., 2023; Liu et al., 2024), internationalization (Feliciano-Cestero et al., 2023; Samant et al., 2023) and the new skills required of employees (Muzam, 2023; Purwanto et al., 2023; Thornhill-Miller et al., 2023; White et al., 2023). At the same time businesses are operating in uncertain environments, such as BANI (Brittle, Anxious, Non-linear and Incomprehensible environments) (Dias et al., 2024; Nataliia and Olena, 2023). In the aftermath of the COVID19 pandemic, these challenges have become more acute and accelerated (Hite and McDonald, 2020). These continuous changes create challenges not just for organizations, but also for individuals to integrate into the labor market (Vyas, 2022). Companies require employees to execute certain tasks, but also to be able to design and plan actions to be executed in changing environments through an early warning stimulus system for scenario-based planning (Vyas, 2022; Seeve and Vilkkumaa, 2022). Improving employees’ cognitive and socioemotional skills to make decisions in this environment will result in an increase in the ability to communicate effectively, increasing positive relationships, essential for the exchange of information and collaboration within the organization, which translates into progress in knowledge management (KM), mainly in small and mid-sized firms (Blázquez Puerta et al., 2022; Alfazzi, 2022; Su and Daspit, 2022).

The World Economic Forum states that, in the next 10 years, many jobs will be taken over by artificial intelligence [World Economic Forum (WEF), 2020], but that there will be opportunities with the creation of new ones. That report states that a 94% of business leaders expect employees to pick up new skills on the job. According to Seetha (2014) the soft skills gap caused the high unemployment rates of graduates in previous years. Moreover, this report details what skills employees must acquire to be successful in organizations, and ensure effective knowledge sharing (Poláková et al., 2023; Avença et al., 2023). Therefore, soft skills are important for improving work performance and then, knowledge management in the company (Rezaei et al., 2021; binti Ab Aziz and Balraj, 2022). However, currently, there is a mismatch between the training received in higher education institutions and the skills required by employers (Succi, 2019). There are numerous studies that list different groups of socioemotional skills necessary to be able to assume the reins of organizations in terms of task performance and knowledge management based on the different level of expertise of the workers (Almeida and Morais, 2023; Muzam, 2023; Ogutu et al., 2023). Succi and Canovi (2019) drew up a list of the soft skills that future employees of organizations should have. Among them, we can highlight:

  • Self-concept: refers to knowing oneself, one’s virtues and weaknesses (Van Doorn and Raz, 2023).

  • Empathy: refers to the ability to understand the emotions and feelings of others (Muzam, 2023).

  • Compassion: refers to the ability to care for others, to recognize people who suffer without getting involved in their pain (Abid et al., 2023).

In addition, other cognitive skills, such as strategic vision, have proven to be decisive in training professionals in the business world, providing them with the capability le of managing knowledge, enhancing communication skills and the ability to plan different scenarios in an environment of continuous change (Zaragoza-Sáez et al., 2023). Assuming that cognitive and socioemotional skills are transferable and trainable, and considering that some of them are not captured by artificial intelligence (Almeida and Morais, 2023), it is possible to design a training program for socioemotional competencies. Training programs have been developed that encompass different methodologies and techniques, such as coaching (Friedman et al., 2024), gamification (Altomari et al., 2023), augmented reality (Martins et al., 2023) and serious board games (Villena-Martínez et al., 2023; Sutil-Martín and Otamendi, 2021). In this research, the training of cognitive and socioemotional skills has been addressed from the latter technique.

Taking into consideration that an improvement in the levels of cognitive and socioemotional skills has a positive and direct effect on the motivation of business administration students and future leaders of organizations (Feraco et al., 2023, 2022), a model of mediation of variables associated with cognitive and socioemotional skills has been proposed in this research, in which the effects of each of these abilities on the intrinsic motivation of individuals are estimated and measured. An individual’s motivation, according to Ryan and Deci’s (2022) theory of self-determination, can be classified as extrinsic or intrinsic. Various studies have found that the improvement of extrinsic motivation, through active teaching-learning methodologies, such as gamification (Partovi, and Razavi, 2019), service-learning or experiential learning (Lo et al., 2022) or reward games (Tyni et al., 2022), can increase the performance of students and future workers. However, intrinsic motivation is not well trained in university classrooms, nor in the training of active workers, but it can be improved through the training of these cognitive and socioemotional skills, such as self-knowledge (Feraco et al., 2022, 2023).

Although the training of intrinsic motivation has been progressively introduced in the classrooms, including in the itineraries of university degrees, motivational strategies have been oriented toward teaching-learning methodologies, such as gamification (Módné Takács et al., 2021), autonomous learning, group work and improvement in communication skills. However, an aspect that has not been addressed is self-concept, which helps to know oneself and one’s potential and which captures: work on open-mindedness and openness to experience, that helps entrepreneurship; dominance that prepares the individual to have confidence in themselves and their assertiveness; and the dynamism to be an active, altruistic, understanding and tolerant person. In addition, self-concept prepares individuals to be warm, affectionate and confident, with great empathy. Emotional stability and impulse control are skills that are not usually studied in the classroom yet show great importance as mediating variables in their effect on intrinsic motivation. Studies, such as Augustyniak et al (2016), highlight this lack of training on students in socioemotional and cognitive skills that promote intrinsic motivation. Many other studies have addressed the effect of worker motivation on knowledge management and transfer in organizations (Nezafati et al., 2023; Joo et al., 2023; Zhao et al., 2023), so it is not a priority consideration for this work.

This research focuses primarily on examining the impact of serious board games on the cognitive and socioemotional development of students, based on the principle of brain neuroplasticity, which suggests that the brain can be trained and developed like any other part of the body (Bavelier et al., 2012). Specifically, the study explores how training with these games not only improves cognitive and socioemotional skills but also enhances intrinsic motivation, both directly and through the mediation of socioemotional skills. Additionally, while previous studies have highlighted the importance of intrinsic motivation in knowledge management (Sander and Riedl, 2020), the primary focus of this research is not on exploring that relationship, but rather on testing tools aimed at improving intrinsic motivation. Studies have shown that intrinsic motivation plays a crucial role in fostering knowledge sharing and collaboration, as motivated individuals are more likely to engage in behaviors that support knowledge transfer and organizational learning (Deci and Ryan, 2000; Sander and Riedl, 2020). Enhancing cognitive and socioemotional skills is increasingly recognized as a key factor in boosting students’ competitiveness in the labor market, giving them a comparative advantage, as highlighted in the World Economic Forum (WEF) (2020) report, which emphasizes the growing demand for soft skills and cognitive flexibility in the workforce. Integrating serious board games into economics and business programs could significantly improve students’ competencies, both qualitatively and quantitatively, better preparing them for leadership roles in business. This approach aligns with recent findings on experiential learning, which suggest that serious-game-based learning environments, including board games, foster critical thinking, decision-making and teamwork, essential skills for effective business leadership (Arnab et al., 2019; Steinkuehler and Duncan, 2008).

In this way, the training of cognitive skills such as perseverance and tenacity in carrying out tasks, within the soft skill “strategic vision” is expected to have a direct effect on the motivation of students after training in serious board games. In addition, socioemotional skills training has a significant mediating effect, amplifying and increasing the direct effect of cognitive skills. In this context, cognitive skills associated with strategic vision have a significant direct effect on motivation, which can be enhanced through simultaneously working on socioemotional skills.

The general objective of this research is to determine the structure that relates to the different levels of cognitive and socio-emotional skills, associated with self-concept and strategic vision, measured through the Big Five psychometric test, on the intrinsic motivation of university students in the field of Economics and Business, at two different moments, before and after training.

The specific objectives of this research are:

  • To verify whether there are statistically significant differences between the levels of the skills studied before and after training.

  • To determine the structure of the different dimensions of cognitive and socioemotional skills, taking a role of mediation and/or moderation of the latter, on motivation in the two phases of the study.

  • To determine the degree of significance of the predictive and mediating variables on motivation at two moments in time.

This research work is organized as follows: Section 2 proposes a theoretical framework for the elaboration of a relational model of mediation variables, and reviews the literature, on socioemotional skills and motivation and its effect on knowledge transfer and management. Section 3 describes the methodology and data sample. Section 4 performs the data analysis. Section 5 discusses the results and their theoretical and social implications, along with the limitations and suggestions for future lines of research. Finally, Section 6 presents the main conclusions of the research.

Theory of self-determination (Deci and Ryan, 1985; Ryan and Deci, 2022) has become the most influential theory on human motivation and well-being, which has given rise to a vast scientific body of work on the subject. Self-determination provides the foundations of motivation through the individual’s personality and social behavior, psychological well-being and quality of life. It introduces the concept of autonomous and controlled motivation. Being autonomous implies acting with criteria and the ability to choose in one’s own way. However, being controlled implies feeling pressured externally in exchange for some reward (Ryan and Deci, 2017). As stated above, individuals with high self-motivation (intrinsic) have more creativity, more perseverance and tenacity, perform better on tasks and are more achievement-oriented, which provides better general well-being. The applications of the theory of self-determination are extensive, including in education, the labor market and in companies.

Individuals motivation is essential in the work environment and in organizations. It has a positive effect on worker productivity (Uka and Prendi, 2021; Johari and Jha, 2020) and on their commitment to the company (AlKahtani et al., 2021; Praditya, 2020; Mwesigwa et al., 2020). In addition, individuals with high levels of motivation tend to be more creative and have a greater capacity for innovation, which has a direct effect on knowledge management (Saputra, 2021; Afsar and Umrani, 2020). Finally, motivation has a positive and direct effect on individuals’ levels of satisfaction at work (Ali and Anwar, 2021; Riyanto et al., 2021). In this way, the hypothesis of the research can be formulated, in the same way, at two different times, before and after training, as follows:

H1.

The cognitive skills associated with strategic vision, such as perseverance and tenacity, have a direct effect on individuals’ motivation and learning strategies.

As described in the previous section, the improvement of intrinsic motivation can be approached from many different angles. However, one of those that has been most strongly manifested in recent research is related to socioemotional skills, measured through certain psychometric tests (Villena-Martínez et al., 2023; Feraco et al., 2022, 2023). Based on the work of Succi and Canovi (2019), in this research, the soft skill of self-concept (Kulakow, 2020) and the cognitive skill of strategic vision (Ali and Anwar, 2021) have been considered essential to achieve high levels of motivation. In Figure 1, the following research model is carried out, at two different moments of time, in a pre-test before training, and in a post-test after training using serious board games (Sutil-Martín and Otamendi, 2021), where socioemotional skills take a mediating role in the relationship.

Figure 1

Research model

Source: Authors’ own work

Figure 1

Research model

Source: Authors’ own work

Close modal

The complementary development of soft and hard skills is the guarantee of success in organizations (Wats and Wats, 2009), understanding success as the improvement of management capabilities and the transfer of knowledge within the business. However, an essential issue, from the point of view of organizational psychology, is to delimit and define the concept of socioemotional skills, due to its different interpretation depending on the scope of application (Lane and Smith, 2021).

Socioemotional skills are the set of learned behaviors that arise from intrapersonal and interpersonal relationships, and which determine how we communicate emotions and feelings. They help individuals identify and manage their own emotions and understand those of other people, facilitating self-knowledge, self-management or the ability to work in a team (Rezaei et al., 2022):

  • Self-knowledge: it is a socioemotional skill that allows individuals to know themselves in depth. It enables understanding of one’s emotions, analyzing them, identifying the reason for one’s existence and examining strengths and weaknesses (Wehrle and Fasbender, 2019).

  • Self-regulation: it is the socioemotional skill that allows individuals to identify and use emotions that are appropriate for achieving goals and promoting psychological well-being, while also fostering the integrity of others. It involves managing emotions in a way that facilitates achievements without interference (Inzlicht et al., 2021).

Considering that there are more than 20 socioemotional skills, this research has addressed the study of those that are not typically worked on as a cross-cutting competence in university training programs, and that cannot be assumed by artificial intelligence. Therefore, it focuses on emotional self-regulation and self-knowledge. In this sense, some studies have observed that there may be statistically significant differences in these skills between the levels before their training, and the levels after instruction by using serious board games (Sutil-Martín and Otamendi, 2021). So, some hypothesis of the research can be formulated, in the same way, at two different times, before and after training, as follows:

H2a.

The socioemotional skills associated with individuals’ self-concept mediate the relationship between cognitive skills, such as perseverance and tenacity, and their impact on intrinsic motivation.

Consequently, soft skills are recognized as critical to success and are valued in education, universities and human resource departments. They help organizations gain new customers, increase revenue and reduce employee turnover, which is why employers are placing an increasing emphasis on these skills (Altomari et al., 2023). Considering the new paradigm of organizational neuroscience, and advances in the knowledge of how the brain works, as well as the theory of brain neuroplasticity, cognitive skills and socioemotional skills can be trained to obtain better levels in educational and work competencies (Cavalcante et al., 2021). In this context, there are different methodologies for training cognitive and socioemotional skills, mainly those that are not acceptable to artificial intelligence and are not currently trained in university classrooms, and among which are serious board games (Sutil-Martín and Otamendi, 2021).

The development and training of socioemotional skills in organizations has been addressed in the literature from different perspectives. One of them has been through digital learning environments, as an active learning methodology (Saienko et al., 2022). However, digital serious games were designed as a complement to training, but not to acquire competencies or skills in workers (Romero et al., 2015).

Games are a systematic playful activity that conforms to a set of rules, challenges and goals, created to ensure entertainment (Cheng et al., 2015). However, gamification, as an active learning methodology, is defined as the use of playful elements in contexts outside of the game (Deterding, 2019). Serious games are defined as games that “have an explicit and carefully thought-out educational purpose and are not intended to be played primarily for amusement” (Abt, 1987). The main difference between gamification and serious games is that serious games are complete and maintain all the functions of the game itself, while gamification consists of applying game elements to real areas that are not games (Deterding, 2019). Presently, serious games have spread in many areas of knowledge, such as in the business world (Ypsilanti et al., 2014). Despite the growing interest in the use of serious games in the training of workers, there is a paucity of research on their effectiveness in training socioemotional skills (Altomari et al., 2023). Muhanna and Saleh (2023) conduct research on extrinsic factors that, through serious games, improve extrinsic factors of individuals; However, unlike other studies that focus on extrinsic factors, this research delves into intrinsic factors. The main difference between serious board games and those in digital format is that the former delves into intra and interpersonal contact and relationships (Sutil-Martín and Otamendi, 2021). In this context, serious board games provide a real training option for the socioemotional skills that derive from interpersonal competencies (Yanes et al., 2023). Radzi et al (2020) use serious board games to work on skills such as communication, teamwork, critical thinking and problem-solving. They establish that there is empirical evidence to affirm that serious board games provide significant potential to work on soft skills.

Another skill that is essential, both for the completion of studies, entrepreneurship and knowledge management is motivation. The concept of motivation has been approached from many theories, such as the theory of attribution, the value of expectations, achievement goals, social cognitive theory and self-determination, the latter being the one that provides a more general theoretical framework (Anderman, 2020). Within this framework, and as mentioned above, two types of factors can be distinguished, extrinsic and intrinsic. Extrinsic factors refer to all those that do not explicitly depend on the individual (Gunawan and Haryadi, 2022; Van den Broeck et al., 2021). However, intrinsic factors are more related to the individual’s cognitive structure and their socioemotional and affective structure (Van den Broeck et al., 2021). Many studies have shown that motivation is a key factor in improving the performance of employees in organizations (Susanto et al., 2023). In this context, it is not only important to have an appropriate technical education to promote task performance, but also to have creativity and innovative skills in uncertain environments (Aldabbas et al., 2023). Farooq (2023) carries out a bibliometric analysis confirming a significant increase in interest in the search for relationships between knowledge management and business performance, concluding that an adequate integration between both concepts can help companies improve their competitive advantage.

To understand the relationship between motivation and knowledge management and transfer, we must first define these concepts and their relevance in the management of organizations. Knowledge management aims to implement routines to identify, create, represent, distribute and enable the adoption of experience and knowledge in a company (Jashapara, 2004). Proper knowledge management in the company has decisive effects on the work environment of employees, such as innovation and competitiveness (Zhou et al., 2023), efficiency in processes (Gupta et al., 2023), decision-making (Melović et al., 2021) and in the improvement of organizational development (Rezaei et al., 2021). Effective knowledge management therefore has a real and direct impact on the development and sustainability of organizations (Abbas, 2020).

Knowledge sharing at a practical level is key to knowledge management, including through methods such as training (Nguyen et al., 2023), mentoring (Hammouri and Altaher, 2020), creating communities of experience (Saffar and Obeidat, 2020) and information technologies (Goswami and Agrawal, 2023).

High levels of cognitive and socioemotional competencies improve knowledge management and transfer because they foster effective communication and positive inter- and intrapersonal relationships, which are vital for the exchange of information and collaboration of individuals within an organization. Improving intrinsic motivation drives employees to actively participate in knowledge management, as they find personal satisfaction in learning and contributing to collective knowledge (Sander and Riedl, 2020). The integration of socioemotional competencies, intrinsic motivation and knowledge management creates an environment conducive to innovation and organizational development and enhances the organization’s capacity to generate, share and apply knowledge effectively (Putra et al., 2020).

2.5.1 Intrinsic motivation as facilitating knowledge sharing and collaboration.

Extensive research has established that employees with high levels of intrinsic motivation are more likely to share their knowledge, an essential component of effective KM. Intrinsic motivation, being independent of external rewards, fosters a genuine spirit of collaboration, wherein individuals are inclined to share ideas, experiences and knowledge without the expectation of immediate or direct benefits (Cabrera and Cabrera, 2002). Such a willingness to engage in collaborative efforts is crucial for the success of KM initiatives, which require a culture that is conducive to knowledge exchange. By fostering an environment that promotes intrinsic motivation, organizations can encourage teamwork and the establishment of communities of practice, where employees learn from each other and contribute to the enrichment of the organization’s intellectual capital (Hislop, 2013).

2.5.2 Intrinsic motivation as enhancing organizational innovation.

Intrinsic motivation is also closely correlated with the capacity for innovation within organizations, a key dimension of knowledge management. Employees who are intrinsically motivated are more likely to take creative risks and explore novel solutions to problems, thereby facilitating the generation of new knowledge (Amabile et al., 1996). Intrinsic motivation not only enhances individual performance on specific tasks but also promotes collaborative innovation within the organization. Innovation is crucial for the continuous creation of organizational knowledge, as it enables firms to adapt to dynamic market conditions and improve their products, services and internal processes (Shin et al., 2017). In this regard, intrinsic motivation acts as a catalyst for innovation, which in turn perpetuates the ongoing cycle of knowledge creation and application.

2.5.3 Intrinsic motivation as promoting organizational learning.

A further dimension of the relationship between intrinsic motivation and knowledge management is its influence on organizational learning. When individuals are intrinsically motivated, they are more inclined to engage in learning activities, such as continuous professional development and knowledge updates, which contribute to the accumulation of intellectual capital within the organization (Deci and Ryan, 2000). Organizational learning, which involves the ongoing accumulation and application of knowledge, is heavily dependent on the motivation of employees to engage in learning processes (Bock et al., 2005). Furthermore, intrinsically motivated employees are more likely to actively participate in knowledge transfer processes, as they possess a heightened sense of responsibility and commitment to the continuous improvement of the organization (Becerra-Fernandez et al., 2004). This form of learning is indispensable for ensuring that organizations remain competitive and can retain and apply knowledge effectively.

2.5.4 Intrinsic motivation as sustaining the knowledge management system.

The long-term sustainability of a knowledge management system relies on the continual and active participation of employees in the creation, sharing and application of knowledge. Intrinsic motivation plays a vital role in ensuring this sustainability, as it, unlike extrinsic rewards, maintains employee commitment over time (Hansen et al., 1999). KM systems that are underpinned by intrinsic motivation are more robust and durable, as employees who are intrinsically motivated are not only willing to access knowledge but are also motivated to contribute to its creation and dissemination (Sander and Riedl, 2020).

Consequently, and summarizing, the relationship between intrinsic motivation and knowledge management is both clear and crucial. Intrinsic motivation facilitates the sharing of knowledge, the creation of new knowledge, organizational innovation and continuous learning. It serves as a driving force for collaboration and active engagement in KM processes, thereby strengthening the competitiveness and sustainability of organizations. Therefore, it is imperative for organizations to recognize the significance of cultivating an environment that fosters intrinsic motivation, as this approach can substantially enhance the effectiveness of their knowledge management initiatives and their ability to innovate and adapt to changing market conditions.Several researches have shown that cognitive and socioemotional competencies not only improve academic and professional performance but also prepare individuals for a more effective and fulfilling working life (Adhvaryu et al., 2023). The integration of these competencies in the educational and professional environment helps to improve social and emotional skills, attitude toward oneself and others and social interactions. In addition, these competencies are associated with significant improvements in academic and professional performance (Putra et al., 2020). Organizations that promote the development of socioemotional competencies among their employees often observe an increase in collaboration and innovation (Fikri et al., 2020). The combination of high intrinsic motivation and socioemotional skills creates an environment where knowledge is shared more openly and effectively, thereby improving the organization’s ability to adapt and thrive in a competitive and continuously changing environment (Succi and Canovi, 2020). Then, finally, a new hypothesis can be established, as follows, at two different times, before and after training:

H2b.

The socioemotional skills associated with self-concept have a direct effect on the motivation and learning strategies of individuals.

As already justified in the literature review, there is a causal relationship between motivation and knowledge management and transfer. In addition, other research has verified the relationship between socioemotional and cognitive skills on motivation. This study aims to combine, in a single relational model, the effects that the levels of these skills have on motivation, considering them before and after training through serious board games (Feraco et al., 2022, 2023). The choice of the selected skills is due to the fact that they are those collected by the psychometric test that currently has the greatest impact in the business world (Alderotti et al., 2023).

To respond to the objectives of this research, a pilot sample has been carried out, through the selection of a group of university Economics and Business students, studying Marketing, through a simple two-stage conglomerate and random sampling process (León-Novelo and Savitsky, 2023). According to the competencies and skills described in Marketing studies, the student, upon completion of the studies, will be able to work in an interdisciplinary team, in an international and global context and in diverse and multicultural environments. In addition, students will be trained to acquire the ability to be leaders, creative and with an entrepreneurial spirit. Marketing studies is aimed at training professionals to support companies to lead projects, analyze market opportunities, analyze and extract information from the external environment facing the firm, as well as formulating and exploring new business opportunities through entrepreneurship. Although students of Business Administration and Management were also invited to participate in the study, a higher response rate was obtained from students in the Marketing degree. To control extraneous variables, such as the degree of studies, it was determined that the experiment would be carried out just with Marketing students. The chosen sample was composed of 71 individuals. The research approach applied has been mixed, being qualitative and quantitative, considering group dynamics, to elaborate the hypotheses and describe and quantify the effects.

The research has been carried out in different stages:

  • Stage 1. Selection of individuals from the sample through their voluntary participation, after approval by the university’s Research Ethics Committee. An initial assessment of the levels of the skills described was carried out.

  • Stage 2. Eight training sessions were carried out, with small groups of students, using group dynamics techniques and serious board games.

  • Stage 3. A final assessment of the skill levels described in this research was conducted.

A experimental mixed design (Tabachnick and Fidell, 2013) is carried out with information collected in pre-test and post-test states (Wan, 2021; Alessandri et al., 2017; Sutil-Martín and Rienda-Gómez, 2020) to evaluate the effect of training cognitive and socioemotional skills through serious board games (Villena-Martínez et al., 2023). This experimental design involves the collection of data before and after the intervention in each of the groups. As summarized in Table 1, individuals were randomly assigned to each group to ensure high internal validity, reducing the impact of extraneous variables. In any case, the format of the chosen design, with the rotations in the games and individuals who study the same degree, is aimed at controlling and minimizing the impact of extraneous variables. A high external validity is guaranteed as the sample has been selected by means of probabilistic sampling, which guarantees the representativeness of the sample. All the measurement instruments are validated by the scientific community, which provides validity and reliability of these, ensuring consistency. Consistency has been carried out uniformly in all groups, minimizing variations. For the internal consistency of the measuring instruments, Cronbach’s alpha (Cronbach, 1951) has been used.

Table 1

Experiment design

PhasesSessionsObjetiveSubjetcs
1.-Informative1 (2 h)–Acceptance of voluntary participation –Informed consent form101
2.-Pre-test measurement1 (2 h)Testing: –Big five test –Break (30 min) –Motivation questionnaire71
3.-TrainingSession 0 (2 h)–Random group configuration –Group dynamics to get to know each other6 groups of 10 1 group of 11
Session 1 and Session 5 (2 h)–Game 1 group assignment (G1-G3) –Game 2 group assignment (G4-G7) –Game 1 group assignment (G4-G7) –Game 2 group assignment (G1-G3)G1-G6: 10 G7: 11
Session 2 and Session 6 (2 h)–Game 1 group assignment (G4-G7) –Game 2 group assignment (G1-G3) –Game 1 group assignment (G1-G3) –Game 2 group assignment (G4-G7)G1-G6: 10 G7: 11
Session 3 and Session 7 (2 h)–Game 3 group assignment (G1-G3) –Game 4 group assignment (G4-G7) –Game 4 group assignment (G4-G7) –Game 3 group assignment (G1-G3)G1-G6: 10 G7: 11
Session 4 and Session 8 (2 h)–Game 1 group assignment (G4-G7) –Game 2 group assignment (G1-G3) –Game 1 group assignment (G1-G3) –Game 2 group assignment (G4-G7)G1-G6: 10 G7: 11
4.-Post-test measurementSession 9 (2 h)Performance of testing –Big five test –Break (30 min) –Motivation questionnaire
Source(s): Authors’ own work

The participants who collaborated in this study belong to the degree in Marketing of the Faculty of Economics and Business Sciences of the Rey Juan Carlos University, a class that is part of an Educational Innovation Project.

This study consists of 4 phases:

  • Phase 1: Informative: Explanatory session where volunteers were informed of the aspects of their participation. An informed consent was signed only by those who agreed to their participation and were assigned a code to maintain their anonymity.

  • Phase 2: Pre-test Measurement: A 2-hour session where students completed the tests, in a common room, with individual answers and rest between the tests.

  • Phase 3: Training. It has 9 sessions: with an approximate duration of 2 hours except the first which was 60 minutes.

    • Session 0: Distribution of the subjects to be raised into groups, with the result of 6 groups of 10 members and 1 of 11. Random assignment of the groups to their monitor, a group dynamic was carried out to establish trust among the students of each group, by using “Social Skills Game”.

    • Session 1 and 5: Duration of two hours each, in which two games of one hour duration were played with a change to the other game in the 2nd hour. The games were Game 1 “Personality Poker” (1st hour: G1 - G3) and Game 2 “Growing in Mindfulness” (1st hour G4 - G7). The procedure was repeated in session 5.

    • Session 2 and 6: Duration 2 hours: 1st Hour: Game 1 for groups G4-G7 and Game 2 for G1-G3. The game played was swapped in the second hour.

    • Session 3 and 7: Duration 2 hours. Groups G1-G3 played Game 3 “Bingo of Emotions” for 1 hour, groups G4-G7 played Game 4 “Neuroparty.” In the next hour they exchanged games in the same order.

    • Session 4 and 8: Duration 2 hours. The G4-G7 groups played for 1 hour Game 3 “Bingo of Emotions,” the G1-G3 groups played Game 4 “Neuroparty.” In the next hour they exchanged games in the same order.

  • Phase 4: Post-Test Measurement. After Phase 3 of training, a measurement was carried out by completing the same test, with a rest time between each test. Duration 2 hours.

The research obtained the favorable consent of the Research Ethics Committee of the Rey Juan Carlos University, with internal reference 2704202213422. All participating students signed an informed consent form and were instructed on the procedure to withdraw consent from their participation, as well as the deletion of their responses in the study. Each student was provided with a nine-digit code, randomly generated using a number simulator, delivered in a sealed envelope to maintain the anonymity of the student. This is the code with which all the questionnaires taken by each student have been coded.

As mentioned above, the choice of cognitive and socioemotional skills is determined by those that, in literature, have been shown to be necessary in the labor market (Alderotti et al., 2023). The psychometric tests analyzed are: Big Five Scale (Mammadov, 2022) and MSLQ-SF adapted (Villena-Martínez et al., 2024). Table 2 shows the soft skills and their components.

Table 2

Description of the components of soft skills

Soft skillSoft skill dimensionSoft skill ComponentsTrained bySoft skill measurement
Emotional self-management1. Energy: inherent in a confident and enthusiastic view of multiple aspects of life, mainly of an interpersonal nature1.1. Dominance: aspects related to the ability to impose oneself, to excel, to assert one’s own influence over others 1.2. Dynamism: energetic behaviors, ease of speech, enthusiasmPersonality pokerBig five questionnaire (BFQ)
2. Affability: altruistic concern and emotional support for others2.1. Cooperation: ability to understand and work with the problems of others 2.2. Cordiality: affability, trust, and openness to othersSocial skill game
3. Emotional Stability: the ability to cope with the negative effects of anxiety, depression, irritability or frustration3.1. Control of emotions: aspects related to the control of states of tension associated with emotional experience 3.2. Impulse control: aspects related to the ability to maintain control of one’s own behavior even in situations of discomfort, conflict, and dangerGrowing up in mindfulness Bingo of emotions
Personal and/or professional responsibility4. Strategic vision and open-mindedness: ability to predict different possible scenarios and generate solutions for each of them. Intellectual nature in the face of new ideas, values, feelings and interests4.1. Openness to experience: aspects of open-mindedness favorable to novelty, to the ability to consider everything from different perspectives, and to favorable openness to different values, styles, ways of life, and cultures 4.2. Openness to culture: interested in staying informed and knowledgePersonality poker
5. Thoroughness: cognitive ability that measures meticulous attitude, attention to detail, and reliability5.1. Conscientiousness: aspects related to reliability, meticulousness, and love of order 5.2. Perseverance: persistence and tenacity with which tasks undertaken are carried out and not failing to keep promisesNeuroparty
Motivation6. Intrinsic Motivation: Behavior driven by internal rewards, which arises from the subject themselves and not from external factors6.1. Intrinsic motivation: conducting an activity not for a separate result, but for its inherent satisfaction 6.2. Learning strategies: processes of acquiring and storing information and course concepts are optimized by students’ operations and actionsMLSQ-SF- adapted
Source(s): Authors’ own work based on Villena-Martínez et al. (2023) 

To carry out the training in these skills, the following materials and games were available:

  • Focus groups (Henriques and O’Neill, 2023): these are joint training activities that promote the integration of team members. They enable group cohesion by making the whole greater than the sum of the parts, developing new perspectives and skills and enhancing creativity and productivity.

  • NeuroParty (Vázquez-Sánchez, 2019): It is a game for psychostimulation intervention in adults, favoring the training of different cognitive areas. It provides spaces for stimulation and cognitive maintenance in adults in a playful way. The different tasks of the game allow the training of different cognitive areas to promote attention, memory, reasoning, calculation, praxis, language and executive functions. An adaptation of the game’s cards was made to Gen Z players.

  • Growing up in Mindfulness (Delgado-Ríos et al., 2019): a game that shows mindfulness, self-knowledge and emotional self-regulation.

  • Bingo of emotions (Mitlin, 2008): a game that allows you to discuss feelings and understand your own and others’ emotions.

  • Personality poker (Hugentobler et al., 2009): a game that allows you to get to know yourself through self-perception and other people’s perceptions of yourself. It encourages self-concept and emotional self-regulation.

  • Social Skills Game (Berg and Madden, 2010): a game based on the principles of modeling, reinforcement, feedback and practice. It teaches six specific cognitive skills, self-reinforcement, causal attribution, coping with anxiety, coping with errors, efficacy expectations and outcome expectations.

The participation of the students in the experiment was distributed as follows, 28% of men and 72% of women. This distribution of the demographic variable “sex” corresponds to the distribution of the population in the areas of Economics and Business. To estimate the internal consistency of the proposed instruments, Cronbach’s alpha (Cronbach, 1951) has been obtained, with a value of 0.78, which is considered a good consistency. To respond to the objectives of this research, a descriptive study of the variables of interest has been carried out, together with an analysis of differences in mean values between the different skills before and after training. The variable coding is shown in the following table (Table 3).

Table 3

Variable coding

TestVariablesDimensions
BIG FIVEB_CP_ACOOPERATION PRE-TEST
B_Di_ADYNAMISM PRE-TEST
B_Do_ADOMINANCE PRE-TEST
B_Co_ACORDIALITY PRE-TEST
B_Es_ASCRUPULOUSNESS PRE-TEST
B_Pe_APERSEVERANCE PRE-TEST
B_Ce_AEMOTION CONTROL PRE-TEST
B_Ci_AIMPULSE CONTROL PRE-TEST
B_Ac_AOPENNESS TO CULTURE PRE-TEST
B_Ae_AOPENNESS TO EXPERIENCES PRE-TEST
B_CP_DCOOPERATION POST-TEST
B_Di_DDYNAMISM POST-TEST
B_Do_DDOMINANCE POST-TEST
B_Co_DCORDIALITY POST-TEST
B_Es_DSCRUPULOUSNESS POST-TEST
B_Pe_DPERSEVERANCE POST-TEST
B_Ce_DEMOTION CONTROL POST-TEST
B_Ci_DIMPULSE CONTROL POST-TEST
B_Ac_DOPENNESS TO CULTURE POST-TEST
B_Ae_DOPENNESS TO EXPERIENCES POST-TEST
MSLQ-SF adaptedM_AMOTIVATION PRE-TEST
M_DMOTIVATION POST-TEST
Source(s): Authors’ own work based on Villena-Martínez et al. (2023) 

To respond to the objectives and verify whether the training modifies or sustains the cognitive skills associated with strategic vision, with the mediating effect of socioemotional skills, on motivation, a structural model of mediation variables is developed (Agler and De Boeck, 2017), where the cognitive skills variables are the predictor variables, and the variables of socioemotional skills are the mediation variables. In the structure of the model, two analyses are carried out for the pre-training and post-training variables, taking into consideration the structural model that obtains better fit indices.

In  Appendix 1Tables 1 and 2, results of the mean derived typical scores for all the items of the research are presented. Most of the values of the scores are around the median value, which would be 50 points, with a standard deviation of about 10 points, except in dimension B_Ce_A, which is the one that reaches the greatest dispersion. In addition, the B_Ce_D variable is also the one that is furthest from the median values, due to the lower quartiles.

If we perform an analysis of paired samples to determine if the change in the levels of the dependent, predictor and mediating variables, before and after training, has been statistically significant, we can observe that, in most cases, the value of the estimated difference is negative, so there is an increase in post-training levels in most of the variables. However, the relationship is only significant in those variables related to motivation ( Appendix 1), and in the opposite direction for the variable B_Ce, Control of emotions, where post-training levels are lower than pre-training. This is as expected, because high levels of the variable indicate less control. For the rest of the variables, the changes are not significant.

Table 4 shows the parameter estimates of the variables predicting their direct effect on motivation at pre-workout levels. According to the results, the variables “openness to culture” and “openness to experience” have a statistically significant direct and positive effect on motivation, at a significance level of 10%. The variables “conscientiousness” and “perseverance” have a non-significant effect.

Table 4

Direct effects

95% Confidence interval
 Direct effectsEstimateStd. Errorz-valuepLowerUpper
B_Es_A → M_A−0.0220.048−0.4610.645−0.1160.072
B_Pe_A → M_A0.0290.0500.5790.563−0.0700.128
B_Ac_A → M_A−0.123*0.066−1.8580.063−0.2520.007
B_Ae_A → M_A0.041**0.0162.5560.0110.0090.072
Note(s):

*p < 0.1; **p < 0.05; ***p < 0.01

Source(s): Authors’ own work

If we look at Table 5, which shows the indirect effects of the predictor variables, cognitive skills associated with strategic vision, through the mediation variables, socioemotional skills, we can see that, before training through serious board games, none of the variables has a statistically significant indirect effect.

Table 5

Indirect effects

95% Confidence interval
Indirect effectsEstimateStd. errorz-valuepLowerUpper
B_Es_A → B_Ce_A → M_A−0.0020.005−0.3290.742−0.0110.008
B_Es_A → B_Co_A → M_A0.0040.0080.5300.596−0.0110.019
B_Es_A → B_DI_A → M_A0.0040.0080.5130.608−0.0110.019
B_Es_A → B_Do_A → M_A−6.215 × 10–40.003−0.2130.831−0.0060.005
B_Es_A → B_CP_A → M_A0.0110.0120.9380.348−0.0120.034
B_Es_A → B_Ci_A → M_A−0.0060.010−0.6190.536−0.0250.013
B_Pe_A → B_Ce_A → M_A0.0020.0050.3640.716−0.0080.011
B_Pe_A → B_Co_A → M_A−0.0060.011−0.5790.562−0.0280.015
B_Pe_A → B_DI_A → M_A−0.0060.012−0.5510.581−0.0290.016
B_Pe_A → B_Do_A → M_A0.0040.0100.4020.688−0.0160.024
B_Pe_A → B_CP_A → M_A−0.0150.015−1.0620.288−0.0440.013
B_Pe_A → B_Ci_A → M_A0.0080.0100.7970.426−0.0120.029
B_Ac_A → B_Ce_A → M_A−0.0020.003−0.6090.542−0.0060.003
B_Ac_A → B_Co_A → M_A0.0020.0030.5730.567−0.0040.008
B_Ac_A → B_DI_A → M_A5.426 × 10–40.0010.3660.714−0.0020.003
B_Ac_A → B_Do_A → M_A4.673 × 10–40.0010.3400.734−0.0020.003
B_Ac_A → B_CP_A → M_A−0.0010.003−0.4540.650−0.0060.004
B_Ac_A → B_Ci_A → M_A0.0980.0651.5040.133−0.0300.226
B_Ae_A → B_Ce_A → M_A−3.393 × 10–40.001−0.2610.794−0.0030.002
B_Ae_A → B_Co_A → M_A−0.0040.006−0.6100.542−0.0160.009
B_Ae_A → B_DI_A → M_A−0.0040.006−0.5740.566−0.0160.009
B_Ae_A → B_Do_A → M_A6.464 × 10–40.0020.3750.708−0.0030.004
B_Ae_A → B_CP_A → M_A−0.0070.006−1.1820.237−0.0200.005
B_Ae_A → B_Ci_A → M_A0.0030.0030.9530.340−0.0030.010
Source(s): Authors’ own work

In Table 6, we can evaluate the total combined direct and indirect effect of the predictor variables on motivation. In this case, it is observed that the only variable that has a significant total effect is “openness to experience,” at a significance level of 5%.

Table 6

Total effects

95% Confidence interval
Total effectsEstimateStd. errorz-valuepLowerUpper
B_Es_A → M_A−0.0110.049−0.2270.821−0.1070.0l85
B_Pe_A → M_A0.0150.0500.2990.765−0.0830.113
B_Ac_A → M_A−0.0250.015−1.5880.112−0.0550.006
B_Ae_A → M_A0.029**0.0152.0080.0456.965 × 10–40.058
Note(s):

*p < 0.1; **p < 0.05; ***p < 0.01

Source(s): Authors’ own work

Considering the total flow diagram of the overall design of the mediation model, we can see in  Appendix 2 that the predictor variable with a statistically significant direct and positive effect on the motivation-dependent variable is “openness to experience.” In addition, the variables “cordiality,” “dynamism” and “cooperation” are partial mediating variables of the variable “openness to experience” on motivation. However, the variable “impulse control” is a confounding variable of the predictive variable “openness to culture” over the motivation-dependent variable. Figure 2 shows a path plot representing the mediation model before training.

Figure 2

Path plot before training

Source: Authors’ own work

Figure 2

Path plot before training

Source: Authors’ own work

Close modal

If we focus on the mediation model that is carried out after training, we can see that the initial structure has been modified. In Table 7 we can see which variables now have a statistically significant direct and positive effect on final motivation levels. In this case, the predictor variables associated with “perseverance,” such as “conscientiousness” or “thoroughness” in the task, have assumed the relevant role in explaining the different levels of motivation after training. However, “openness to experience” and “openness to culture” are no longer significant in terms of direct effect on motivation.

Table 7

Direct effects

95% Confidence interval
Direct effectsEstimateStd. errorz-valuepLowerUpper
B_Es_D → M_D−0.1010.051**−1.9950.046−0.200−0.002
B_Pe_D → M_D0.1370.053***2.5830.0100.0330.241
B_Ac_D → M_D0.0610.0431.4100.159−0.0240.146
B_Ae_D → M_D−0.0130.017−0.7900.430−0.0470.020
Note(s):

*p < 0.1; **p < 0.05; ***p < 0.01

Source(s): Authors’ own work

If we focus on the indirect effects (Table 8), on their own, we can see that all estimates of the parameters are statistically non-significant, which indicates that isolated indirect effects are not statistically significant, so it can be considered the mediation model is only partially fulfilled.

Table 8

Indirect effects

95% Confidence interval
Indirect effectsEstimateStd. Errorz-valuepLowerUpper
B_Es_D → B_Ce_D → M_D−0.0100.013−0.7350.463−0.0360.016
B_Es_D → B_DI_D → M_D0.0030.0080.3270.743−0.0130.018
B_Es_D → B_CP_D → M_D−0.0030.008−0.4470.655−0.0180.012
B_Es_D → B_Ci_D → M_D0.0070.0120.5880.557−0.0170.032
B_Es_D → B_Do_D → M_D9.834 × 10–40.0120.0820.935−0.0230.025
B_Es_D → B_Co_D → M_D0.0070.0110.6380.523−0.0150.029
B_Pe_D → B_Ce_D → M_D0.0060.0130.4310.666−0.0200.031
B_Pe_D → B_DI_D → M_D0.0020.0080.2580.796−0.0130.017
B_Pe_D → B_CP_D → M_D0.0060.0130.4720.637−0.0190.031
B_Pe_D → B_Ci_D → M_D−0.0080.013−0.5890.556−0.0330.018
B_Pe_D → B_Do_D → M_D−0.0110.013−0.8140.416−0.0370.015
B_Pe_D → B_Co_D → M_D−0.0050.011−0.4960.620−0.0260.016
B_Ac_D → B_Ce_D → M_D−0.0070.005−1.4190.156−0.0170.003
B_Ac_D → B_DI_D → M_D0.0030.0040.7450.456−0.0050.011
B_Ac_D → B_CP_D → M_D3.366 × 10–40.0010.2910.771−0.0020.003
B_Ac_D → B_Ci_D → M_D−0.0250.042−0.6110.541−0.1070.056
B_Ac_D → B_Do_D → M_D−0.0060.005−1.2030.229−0.0150.004
B_Ac_D → B_Co_D → M_D−0.0030.004−0.8880.374−0.0110.004
B_Ae_D → B_Ce_D → M_D−0.0020.004−0.5760.564−0.0100.006
B_Ae_D → B_DI_D → M_D0.0050.0060.8360.403−0.0060.016
B_Ae_D → B_CP_D → M_D0.0020.0050.4780.633−0.0070.012
B_Ae_D → B_Ci_D → M_D−1.80 × 10–40.001−0.1650.869−0.0020.002
B_Ae_D → B_Do_D → M_D7.325 × 10–50.0040.0200.984−0.0070.007
B_Ae_D → B_Co_D → M_D0.0110.0081.3590.174−0.0050.027
Note(s):

*p < 0.1; **p < 0.05; ***p < 0.01

Source(s): Authors’ own work

Table 9 shows the estimation of the parameters for the total effect of the predictor variables on the dependent variable, motivation. In this case, the variables associated with tenacity, within the soft skill “strategic vision,” are those that have a statistically significant positive and total effect on the dependent variable.

Table 9

Total effects

95% Confidence interval
Total effectsEstimateStd. Errorz-valuepLowerUpper
B_Es_D → M_D−0.0960.053*−1.8200.069−0.2000.007
B_Pe_D → M_D0.1270.054**2.3500.0190.0210.233
B_Ac_D → M_D0.0220.0151.4480.148−0.0080.053
B_Ae_D → M_D0.0020.0160.1470.883−0.0290.034
Note(s):

*p < 0.1; **p < 0.05; ***p < 0.01

Source(s): Authors’ own work

Considering the estimation of the post-training mediation model ( Appendix 3), we observe that the structure has been modified from the initial pre-training estimate. Regarding the predictor variables, direct and significant effects on conscientiousness and perseverance are estimated. For the mediating variables, and their direct effect on motivation, we observed that they have statistically significant direct and negative effects on “emotion control” and “dominance.” Regarding the indirect effects of the predictor variables on motivation, through the socioemotional skills associated with self-concept, we find the following statistically significant effects. First, the effect of “openness to experience” on dynamism, which is a confounding variable on motivation. Second, “cooperation” appears as a confounding variable for “perseverance” and “openness to experience” over motivation. Third, “impulse control” takes on the role of a confounding variable for “openness to culture,” “conscientiousness” and “perseverance.” In addition, “cordiality” also limits the effect of the variable “openness to experience.” In Figure 3 we can see the estimated mediation model with the predictor variables, the mediating variables and the dependent variable after training.

Figure 3

Path plot post-training

Source: Authors’ own work

Figure 3

Path plot post-training

Source: Authors’ own work

Close modal

The results of the study align with the established body of research on the role of cognitive and socioemotional skills in motivation and their impact on individual performance, as well as organizational behavior. This research investigates the effects of a training intervention through serious board games on cognitive and socioemotional skills, and their subsequent impact on intrinsic motivation. The findings are significant, particularly considering the theoretical frameworks provided by Deci and Ryan’s (1985, 2022) self-determination theory and other studies on motivation and socioemotional competencies (Feraco et al., 2022, 2023; Succi and Canovi, 2019). The model structure relating the predictor variables (strategic vision, self-concept) to the dependent variable (intrinsic motivation), through the mediating variables (socioemotional skills), was found to differ statistically when comparing pre-training and post-training levels.

First, it is worth noting that the overall structure of the model was statistically different when comparing the pre- and post-training results, confirming previous research that indicates the dynamic and context-dependent nature of motivation (Feraco et al., 2022, 2023). The significant improvement in motivation observed post-training can be attributed to the specific intervention of serious board games, a method which sets itself apart from other digital or gamification strategies. Unlike digital games that often cater to extrinsic motivation through rewards (Romero et al., 2015), serious board games, as highlighted by Sutil-Martín and Otamendi (2021), offer a more holistic approach by focusing on interpersonal skills and emotional regulation, crucial aspects for enhancing intrinsic motivation. This finding supports the idea that intrinsic motivation, when nurtured through proper training, can positively influence cognitive and socioemotional skills (Gunawan and Haryadi, 2022; Van den Broeck et al., 2021).

The hypothesis H1, which posited that cognitive skills, such as perseverance and tenacity, directly affect intrinsic motivation, was found to require further nuance. Initially, it was believed that a direct relationship would exist between these cognitive skills and intrinsic motivation. However, the results indicate that such effects are contingent upon other mediating factors, particularly socioemotional competencies. This reinforces the importance of integrating both cognitive and socioemotional development in training programs, as suggested by Ali and Anwar (2021) and Rezaei et al. (2022). When examining strategic vision as a cognitive skill, we observed that despite an increase in individual levels, the effects were not homogeneous across participants. Some individuals displayed higher adaptability and curiosity, reflecting a stronger openness to experience, which positively impacted their motivation. This finding aligns with prior research by Succi and Canovi (2019) and Feraco et al. (2022), suggesting that personal attributes such as openness and perseverance may be more salient in motivating individuals when coupled with emotional regulation and self-awareness.

In terms of the socioemotional skills, the variable “self-concept” showed significant positive effects on intrinsic motivation, echoing the findings of Succi and Canovi (2019) who emphasized the importance of self-awareness in driving motivation. Self-concept, as a socioemotional skill, facilitates greater self-knowledge, which is essential for regulating emotions and promoting autonomy in decision-making (Wehrle and Fasbender, 2019). Moreover, this research confirmed that, after the serious board games intervention, improvements in self-concept contributed to increased intrinsic motivation, supporting the hypotheses H2a and H2b. These results underscore the mediating role of self-concept in enhancing motivation, which further corroborates the conclusions of Van den Broeck et al. (2021) regarding the essential role of socioemotional skills in fostering intrinsic motivation.

The training intervention also revealed interesting interactions between socioemotional and cognitive skills. High levels of socioemotional skills like “openness to experience” were found to interact with cognitive skills such as “perseverance” and “tenacity,” thereby enhancing intrinsic motivation. However, some socioemotional skills, such as “impulse control,” were found to dampen this effect. This result reflects the nuanced nature of motivation, where certain characteristics can either enhance or detract from an individual’s ability to stay engaged and intrinsically motivated. The findings are consistent with the research of Inzlicht et al. (2021), who discussed the role of emotion regulation in achieving long-term goals. In this case, while “openness to experience” and “strategic vision” improved motivation, “impulse control” acted as a confounding variable, showing that emotional self-regulation must be balanced for optimal motivational outcomes.

Notably, the improvement in motivation post-training suggests that individuals’ intrinsic motivation can indeed be cultivated through targeted interventions, particularly those involving interactive and cooperative experiences such as serious board games (Sutil-Martín and Otamendi, 2021). These findings suggest that the soft skills, particularly self-regulation and self-concept, should be considered vital components in organizational training programs, as they facilitate not only personal development but also workplace integration and productivity (Fikri et al., 2020; Gunawan and Haryadi, 2022; Farooq, 2023).

The comparison of pre- and post-training results also reveals that, while socioemotional skills such as cooperation and “openness to experience” were positively associated with motivation, other skills like “dominance” and “emotional instability” were found to nullify these effects. These findings align with those of Putra et al. (2020), who emphasize the crucial role of emotional stability in fostering an environment conducive to learning and collaboration. As noted by Bock et al. (2005), the sustainable development of an organization depends on fostering intrinsic motivation through the continuous development of socioemotional competencies.

In conclusion, the results highlight the intricate relationship between cognitive skills, socioemotional competencies and intrinsic motivation. While cognitive skills like perseverance are important, the mediating role of socioemotional skills such as self-concept is indispensable in promoting sustained motivation. The application of serious board games has proven effective in improving these competencies, with particular emphasis on socioemotional skills as key drivers of intrinsic motivation. This research offers valuable insights for organizations looking to integrate these findings into training programs to foster a more motivated and engaged workforce, ultimately enhancing organizational performance and innovation (Cavalcante et al., 2021). Future studies should continue to explore the interaction between these variables and the long-term effects of such interventions in various organizational contexts, particularly in terms of knowledge management and organizational learning.

The development of socio-emotional and cognitive competencies directly influences employees’ intrinsic motivation. According to self-determination theory (Deci and Ryan, 2000), employees with higher socio-emotional and cognitive skills, such as emotional self-regulation and critical thinking, are better equipped to meet their needs for competence, autonomy and relatedness, which in turn strengthens intrinsic motivation and improves job satisfaction and performance (Vansteenkiste et al., 2020).

As for theoretical implication in knowledge management, the improvement of socio-emotional and cognitive competencies among employees plays a critical role in knowledge management within organizations. A work environment that fosters intrinsic motivation and promotes skills such as empathy and collaboration results in greater willingness among employees to share and use knowledge (Kankanhalli et al., 2021). Knowledge management benefits from intrinsically motivated employees because they are more willing to actively engage in idea exchange and continuous learning (Choi et al., 2020).

Regarding implications for organizational behavior, socio-emotional and cognitive competencies also have direct implications for interpersonal relationships in the workplace, and thus, for organizational behavior. Emotional skills such as empathy and self-regulation help improve group cohesion and reduce conflicts, fostering a collaborative and efficient environment (Fletcher and Baldry, 2021). Moreover, employees with higher emotional intelligence tend to perform better in dynamic and challenging work settings (Côté & Miners, 2020). For organizational innovation, recent studies suggest that employees with critical and creative thinking skills, along with socio-emotional competencies, are more likely to generate new ideas and actively engage in innovation processes (Salovey and Mayer, 2021).

Concerning organizational development and leadership, the development of cognitive and socio-emotional competencies among employees also enhances organizational learning and adaptability to change. Organizations that promote these competencies are better positioned to face transformations and maintain a continuous learning process, which is essential for long-term success (Argyris and Schön, 2021). For effective leadership, emotional intelligence, as highlighted by various studies, plays a key role in leaders’ ability to manage teams and promote knowledge sharing and can positively influence employee motivation and performance, creating an environment conducive to knowledge management (Goleman et al., 2019).

Therefore, as we have observed, the relationship between different individual models that associate improvements in motivation through cognitive and socio-emotional skills and their effects on knowledge management is well-documented in the literature, although independently of one another and not in a combined manner (Nisula and Olander, 2023; Berlamont et al., 2023; Kotera et al., 2023; Iqbal et al., 2023; Aldabbas et al., 2023). That is why, in this research, the study of the relationships of cognitive skills associated with strategic vision and open-mindedness, relevant in the company, with the mediation of socioemotional skills, on the motivation of students in the field of Economics and Business, which is an area not previously explored in the literature, is addressed. The theoretical model formulated in the introduction of this work is established to determine if there are differentiating effects between the skills sets represented on the dependent variable before and after the training of these skills, in an inferential and correlational analysis. Given the enormous literature that addresses the significant relationships between motivation and knowledge sharing and management, the search for them in this research has not been established as a priority, focusing on the relationships previously described, although it could be proposed as a future line of research.

The findings found in this research are aligned, although individually and not simultaneously, with other research (Feraco et al., 2022, 2023), although the means of training have been different. The theoretical model described shows us the relevance of cognitive competencies associated with tenacity on motivation, and the levels of other competencies that can act in the opposite direction, which would cancel out the main effect. In addition, it has been verified that the training carried out, with its limitations, has made it possible to modify the structure of the impact of the predictor variables on the dependent variable, which enhances the use of serious board games, in business environments, to improve the skills of workers, positioning itself as a tool for the stimulation of work teams (Sutil-Martín and Otamendi, 2021).

As for the estimated model, both before training and after, we have not considered, in terms of interpretation, the quantification of the result of the estimation of the parameters, but its statistical significance and the sign in which the effect occurs, to interpret whether this effect increases or decreases, both directly and indirectly. Given that the indirect effects have not been statistically significant, although it has been possible to estimate mediation and confounding effects of some of the variables, a future line of research may be to improve the specification of the theoretical model.

5.2.1 Managerial implications.

As already seen, developing socio-emotional and cognitive competencies in employees plays a crucial role in fostering intrinsic motivation, which, in turn, influences behaviors essential to effective knowledge management, such as knowledge sharing and innovation. Employees who are intrinsically motivated are more likely to engage in continuous learning, idea exchange and problem-solving, all of which are key drivers of organizational success. Managers should therefore prioritize the creation of environments that promote emotional intelligence, cognitive flexibility and a culture of collaboration. Some recommendations for managers can be drawn:

  • Invest in employee development: managers should place a high priority on continuous professional development programs aimed at enhancing both cognitive and socio-emotional competencies. This includes developing critical thinking, emotional regulation and interpersonal communication skills.

  • Promote collaboration: fostering an environment that encourages teamwork, open dialogue and knowledge sharing is crucial for creating a culture conducive to effective knowledge management.

  • Support autonomy and recognize contributions: providing employees with autonomy in their roles and recognizing their achievements can help strengthen intrinsic motivation, leading to enhanced performance in tasks related to knowledge management.

5.2.2 Societal implications.

From a societal perspective, the improvement of cognitive and socio-emotional skills has the potential to create a more adaptable and resilient workforce. Societies that emphasize these competencies are better positioned to cultivate a workforce that is not only innovative but also collaborative and capable of addressing complex challenges. By prioritizing these competencies within educational systems and workplace practices, societies can foster a culture of lifelong learning and adaptability, key traits for navigating the demands of a rapidly evolving world. Some recommendations for policymakers and governments can be drawn:

  • Integrate socio-emotional and cognitive skills into Education systems: policymakers should advocate for educational curricula that emphasize the development of both cognitive and socio-emotional competencies from early education onward. These competencies are critical for preparing the workforce of the future.

  • Raise public awareness: governments should lead efforts to raise public awareness regarding the importance of socio-emotional and cognitive skills, not only for individual development but also for organizational and societal progress.

  • Encourage workplace learning initiatives: governments can incentivize organizations to invest in programs that improve employees’ socio-emotional and cognitive competencies, especially in industries where effective knowledge management is vital to success.

5.2.3 General implications.

Consequently, the general implications that can be deduced from our study are that, when soft skills are well acquired and internalized, individuals can better manage their emotions, establish positive and healthy intra and interpersonal relationships, take decisions and resolve conflicts in the company more effectively. Although the skills considered in this research are associated with cognitive and socioemotional competencies associated with the personality traits of individuals, they are essential to provide synergies in the company’s knowledge management. Specifically, the proper management of the measures considered here helps to promote cooperation and conflict resolution, emotional self-regulation and the resilience of workers in the face of stressful situations, improve the leadership capacity of work teams and collaboration and contribute to making balanced decisions linked to the company’s objectives. If, in addition, we consider that these skills have positive and direct effects on the motivation of workers, resulting in higher levels of certain skills, one of the strategic lines of companies should be to promote these skills and, therefore, positively affect their motivation. The motivation of workers in the company should not only be extrinsic, through recognition in job categories or salary, but intrinsic, which is born from individuals themselves, since, in this way, their commitment and dedication in the company will be greater, and the individual will see the benefits and results of the company as its own. Motivated individuals, with high levels of non-technical cognitive and socioemotional skills bring greater commitment and productivity. They are individuals whose levels of creativity and innovation are above average, which makes them good team leaders, able to provide creative solutions to problems and add insightfulness to decision-making, given their high level of strategic vision. In general, the results provided in this research offer evidence on which competencies are desirable in employees in the second quarter of the 20-first century. If these competencies are trainable, and for the moment not assumable by artificial intelligence, the challenge is to look for evidence of how they can be enhanced, to improve the initial starting values. In this work, a training mechanism has been proposed, through serious board games, as group dynamics, for the improvement of them, which is an efficient and effective tool to achieve the objective.

As is the case with other studies of a similar nature, the results of this study should be interpreted within the context in which the data have been collected and analyzed, seeking a more longitudinal study to be able to extrapolate the results to the population level. Although the study sample was drawn from students enrolled in Economics and Business degree programs, they are not yet active members of the workforce. In addition, the group dynamics were carried out with a slightly higher number than recommended including the indications of the serious board games used, although this fact produced an enrichment in some of them due to the cooperative work dynamics that resulted. The training sessions were eight. In this regard, a new study should be carried out with a control group to determine the most appropriate number of training sessions, as well as the optimal duration of the sessions, to assess the peak where workers lose interest in participation. The study has been carried out in groups of students who are studying related to the Economics and Business, but it has not worked with students from other fields of knowledge, such as experimental sciences and engineering, whose structure of skill levels may be different. Finally, and given the population to which the study has been directed, namely future workers in the short term, some of the questions in the questionnaires, as well as the dynamics of the games, have not been fully understood, despite the information available on the objectives of the study, their interest and motivation to participate. These factors suggests that part of the currently validated psychometric tests should be reformulated to adapt them to the language of the new generations, as well as the dynamics of the games used. The experiment has been carried out in a group of students from a Spanish university, so the results would not be extrapolated to another region or country with a different culture and population structure.

Regarding future lines of research, several of them are aligned with the improvement of the procedure detected in the previous limitations. The aim is to extrapolate this research to other areas and fields of knowledge, to determine whether the training environment influences the structure of the estimated effects. In addition, a control group will be developed, with alternative dynamics and means, to verify the real effect of improving the levels of the skills considered. In a previous study already published, the authors evidenced an asymmetrical behavior due to the sex of the individuals, which suggests that training and group dynamics should be carried out with a gender perspective, for an optimal improvement of results. This future research is already underway, and the first data sets are being collected. A new study is currently being conducted, highlighting improved control over certain extraneous variables that may partially influence individual motivation. These include measuring the appropriate number of training sessions, the number of sessions with each game and organizing workgroups with limited training on specific games to assess the isolated effect of each game on overall outcomes. Furthermore, based on recent research, there is a growing consideration to measure resilience as a strategic soft skill in highly dynamic and uncertain environments characterized by elevated levels of work-related stress, as well as its impact on employees’ intrinsic motivation.

As part of an educational innovation project, a longitudinal study is planned with the current participants to evaluate their cognitive and socioemotional competencies over a series of time periods. Concurrently, a training program is being implemented for students and faculty at the university center affiliated with the authors, as part of the university’s competency-based training initiative. Finally, a training program is being developed for employees and mid-level managers at a major national financial institution, scheduled to be launched next year, with its results to be analyzed and published subsequently.

Since the COVID-19 pandemic, the labor market and business management are suffering turbulent times, with tensions in certain areas of organizations. The events of the pandemic have shown companies the vulnerability posed by fragile and brittle environments, which can lead to their disappearance. Certain studies carried out subsequently have placed part of the responsibility for this vulnerability on the lack of acquisition of certain non-technical cognitive and socio.emotional skills of company employees. It is at this point that this research work aims to provide relevant insight. Several WEF reports have stated that the requirements of workers in this new period are significantly different from what future employees are being trained for and taught in universities. Where the technical skills acquired by students in any field are not aligned with the requirements of companies, the challenges facing businesses and their vulnerability continue to increase.

This research work addresses the initial assessment of certain non-technical cognitive competencies, together with other socioemotional ones, aligned with the WEF recommendations, to determine and quantify their effect on the motivation of individuals, the central axis of academic and professional achievement. In addition, a training program is carried out, through serious board games, since these skills are trainable and transferable, to assess if there is significant improvement, consequently assessing their effect on motivation. Given that there is a large literature on the effect of high levels of worker motivation on the management and transfer of knowledge in the company, this work has not addressed this topic, implying that, if it is possible to improve the motivation of future employees of organizations, the direct and significant effect of motivation on the transfer of knowledge in the company will be maintained, given the enriching nature provided by the skills studied on inter and intrapersonal relationships, responsibility, creativity, empathy, cooperation and management of stressful situations at work.

Two models of mediation are proposed, where the predictor variables, collected as non-technical cognitive variables, such as open-mindedness and strategic vision, influence the motivation of individuals, through the mediation of variables associated with socioemotional skills related to personal self-concept. Each of the models is estimated at two different moments of time, before and after training through serious board games, and whose results are very revealing. While, before training, it is found that the influencing variable on motivation is openness to experience, within the cognitive ability mental openness, both in its direct and indirect effect, after training, the influencing variables are linked to tenacity, such as perseverance and conscientiousness in the task, as well as the determination of those variables that are confounding and reduce or mitigate the effect of the predictive variables on the dependent variables. These findings represent a very useful tool to determine the best candidates in the company, as well as the training and training programs within it.

The authors would like to thank the Rey Juan Carlos University for their support and funding to be able to carry out this research, without which it would not have been possible. In addition, the authors would like to thank the colleagues who have helped us with the dissemination for data collection.

Funding: This research work represents the partial study of a competitive EIP from the Universidad Rey Juan Carlos, PIE23_78.

Ethics approval and consent to participate: The research work obtained the favorable consent of the Research Ethics Committee of the Rey Juan Carlos University. All participants were informed of the objective of the study, its scope and marked a consent on the form itself about their intention to participate.

The data controller, the consent and purpose of the study, the communication of the data, the conservation of the data and the exercise of their rights of access, rectification, deletion, limitation of processing, opposition and others recognized by the General Data Protection Regulation, as well as by Organic Law 2/2018, were informed of Personal Data Protection and guarantee of Digital Rights. The personal data collected will not be transferred without the express consent of the participants except in cases where there is a legal obligation to do so. Personal data will be kept only for the duration of the project.

Consent for publication: The authors authorize the publisher to proceed with the publication of the research work if it deems it necessary and passes the manuscript review phases.

Availability of data and materials: The data collected will not be transferred without the express consent of the participants except in cases where there is a legal obligation to do so. Personal data will be kept only for the duration of the project. Raw data that support the findings of this study are available upon reasonable request from the corresponding author, JJRG. The data are not publicly available due to restrictions from the Research Ethics Committee because it contains information that compromises the privacy of the participants in the research.

Competing interests: Authors have no competing interests as defined by JKM, or other interests that might be perceived to influence the interpretation of the article.

Authors’ contributions: JJRG: Theoretical framework, Methodology, Analysis and Results, Model Validation and Discussion. EIVM: Theoretical framework, Methodology, Data curation and evaluation, Discussion and conclusions. DLSM: Introduction, Theoretical Framework, Instrument Design and Grading. FEGM: Methodology, Analysis and Results, Discussion and Conclusions. All authors read and approved the final manuscript.

Authors’ information: Authors presented this research at BENI Conference 2024 Business, Entrepreneurship & Innovation held online, 27th and 28th June 2024.

Table A1

Descriptive statistics

95% Confidence interval mean
VariablesValidMissingMeanUpperLowerSDIQR
M_A71049.97252.36247.58210.09812.500
B_DI_A71040.05642.52537.58810.42914.000
B_Do_A71050.59252.90148.2839.75514.500
B_CP_A71049.94452.41447.47410.43514.500
B_Co_A71053.08555.43450.7359.92814.000
B_Es_A71055.73257.86853.5979.02217.000
B_Pe_A71046.95849.06244.8538.89112.500
B_Ce_A71055.38059.81450.94718.73136.000
B_Ci_A71050.07052.25647.8849.23614.000
B_Ac_A71042.84544.86740.8238.5428.500
B_Ae_A71050.28252.40748.1578.97812.000
M_D63853.34955.83650.8629.87511.500
B_DI_D65640.09242.20037.9848.50714.000
B_Do_D65653.24655.68550.8079.84413.000
B_CP_D65651.63154.01349.2499.61415.000
B_Co_D65655.21557.77752.65410.33913.000
B_Es_D65657.66259.76955.5548.50410.000
B_Pe_D65648.55450.64346.4658.43012.000
B_Ce_D65633.92335.67832.1697.08113.000
B_Ci_D65650.61552.70048.5318.41314.000
B_Ac_D65643.72345.83941.6078.5397.000
B_Ae_D65651.50853.77049.2469.12912.000
Source(s): Authors’ own work
Table A2

Paired samples T-Test

Measure 1Measure 2tdfp
M_AM_D−4.140620.001
B_DI_AB_DI_D0.480640.633
B_Do_AB_Do_D−1.565640.122
B_CP_AB_CP_D−0.556640.580
B_Co_AB_Co_D−0.906640.368
B_Es_AB_Es_D−1.679640.098
B_Pe_AB_Pe_D−1.402640.166
B_Ce_AB_Ce_D8.615640.001
B_Ci_AB_Ci_D−0.640640.525
B_Ac_AB_Ac_D−0.926640.358
B_Ae_AB_Ae_D−1.036640.304
Note(s):

Student’s t-test; *p < 0.1; **p < 0.05; ***p < 0.01

Source(s): Authors’ own work
Table A3

Path coefficient model before training

95% Confidence interval
Path coefficientEstimateStd. errorz-valuepLowerUpper
B_Ce_A → M_A−0.0820.116−0.7080.479−0.3080.145
B_Co_A → M_A−0.0880.141−0.6210.535−0.3650.189
B_DI_A → M_A−0.0830.143−0.5820.561−0.3640.197
B_Do_A → M_A0.0590.1420.4150.678−0.2200.338
B_CP_A → M_A−0.1820.143−1.2780.201−0.4620.097
B_Ci_A → M_A0.8740.5801.5060.132−0.2642.011
B_Es_A → M_A−0.0220.048−0.4610.645−0.1160.072
B_Pe_A → M_A0.0290.0500.5790.563−0.0700.128
B_Ac_A → M_A−0.1230.066−1.8580.063−0.2520.007
B_Ae_A → M_A0.041**0.0162.5560.0110.0090.072
B_Es_A → B_Ce_A0.0190.0500.3720.710−0.0790.116
B_Pe_A → B_Ce_A−0.0220.051−0.4240.671−0.1210.078
B_Ac_A → B_Ce_A0.0190.0161.1970.231−0.0120.050
B_Ae_A → B_Ce_A0.0040.0150.2810.779−0.0250.033
B_Es_A → B_Co_A−0.0460.045−1.0160.309−0.1340.043
B_Pe_A → B_Co_A0.0740.0461.6040.109−0.0160.164
B_Ac_A → B_Co_A−0.0210.014−1.4770.140−0.0490.007
B_Ae_A → B_Co_A0.044**0.0133.300< 0.0010.0180.070
B_Es_A → B_DI_A−0.0480.044−1.0860.278−0.1340.038
B_Pe_A → B_DI_A0.077*0.0451.7230.085−0.0110.165
B_Ac_A → B_DI_A−0.0070.014−0.4720.637−0.0340.021
B_Ae_A → B_DI_A0.044**0.0133.396< 0.0010.0190.070
B_Es_A → B_Do_A−0.0110.042−0.2480.804−0.0930.072
B_Pe_A → B_Do_A0.0680.0431.5710.116−0.0170.152
B_Ac_A → B_Do_A0.0080.0130.5940.553−0.0180.034
B_Ae_A → B_Do_A0.0110.0130.8720.383−0.0140.035
B_Es_A → B_CP_A−0.0600.044−1.3810.167−0.1460.025
B_Pe_A → B_CP_A0.0850.0441.9100.056−0.0020.172
B_Ac_A → B_CP_A0.0070.0140.4850.628−0.0200.034
B_Ae_A → B_CP_A0.040**0.0133.1210.0020.0150.066
B_Es_A → B_Ci_A−0.0070.010−0.6790.497−0.0260.013
B_Pe_A → B_Ci_A0.0100.0100.9390.348−0.0100.029
B_Ac_A → B_Ci_A0.112**0.00335.897< 0.0010.1060.118
B_Ae_A → B_Ci_A0.0040.0031.2320.218−0.0020.009
Note(s):

*p < 0.1; **p < 0.05; ***p < 0.01

Source(s): Authors’ own work
Table A4

Path coeffcient model after training

95% Confidence interval
Path coefficientsEstimateStd. errorz-valuepLowerUpper
B_Ce_D → M_D−0.260**0.121−2.1590.031−0.497−0.024
B_DI_D → M_D0.1610.1770.9070.364−0.1870.509
B_CP_D → M_D0.0700.1440.4860.627−0.2120.351
B_Ci_D → M_D−0.2280.373−0.6110.541−0.9590.503
B_Do_D → M_D−0.254*0.132−1.9190.055−0.5130.005
B_Co_D → M_D0.2160.1481.4670.142−0.0730.506
B_Es_D → M_D−0.101**0.051−1.9950.046−0.200−0.002
B_Pe_D → M_D0.137***0.0532.5830.0100.0330.241
B_Ac_D → M_D0.0610.0431.4100.159−0.0240.146
B_Ae_D → M_D−0.0130.017−0.7900.430−0.0470.020
B_Es_D → B_Ce_D0.0380.0490.7810.435−0.0570.133
B_Pe_D → B_Ce_D−0.0220.049−0.4400.660−0.1170.074
B_Ac_D → B_Ce_D0.028*0.0151.8830.060−0.0010.057
B_Ae_D → B_Ce_D0.0090.0150.5980.550−0.0200.038
B_Es_D → B_DI_D0.0160.0450.3510.725−0.0720.104
B_Pe_D → B_DI_D0.0120.0450.2690.788−0.0770.101
B_Ac_D → B_DI_D0.0180.0141.3090.191−0.0090.045
B_Ae_D → B_DI_D0.030**0.0142.1490.0320.0030.057
B_Es_D → B_CP_D−0.0490.043−1.1410.254−0.1340.035
B_Pe_D → B_CP_D0.086**0.0431.9840.0470.0010.171
B_Ac_D → B_CP_D0.0050.0130.3640.716−0.0210.031
B_Ae_D → B_CP_D0.034***0.0132.5660.0100.0080.060
B_Es_D → B_Ci_D−0.032**0.015−2.1390.032−0.061−0.003
B_Pe_D → B_Ci_D0.034**0.0152.2220.0260.0040.063
B_Ac_D → B_Ci_D0.111***0.00524.213< 0.0010.1020.120
B_Ae_D → B_Ci_D7.905 × 10–40.0050.1720.864−0.0080.010
B_Es_D → B_Do_D−0.0040.047−0.0820.935−0.0970.089
B_Pe_D → B_Do_D0.0430.0480.8980.369−0.0510.136
B_Ac_D → B_Do_D0.0220.0151.5430.123−0.0060.051
B_Ae_D → B_Do_D−2.883 × 10–40.015−0.0200.984−0.0290.028
B_Es_D → B_Co_D0.0330.0460.7090.478−0.0580.123
B_Pe_D → B_Co_D−0.0240.046−0.5270.598−0.1160.067
B_Ac_D → B_Co_D−0.0160.014−1.1160.264−0.0440.012
B_Ae_D → B_Co_D0.051***0.0143.614< 0.0010.0230.079
Note(s):

*p < 0.1; **p < 0.05; ***p < 0.01

Source(s): Authors’ own work
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