Teaching or coaching of social skills is a part of organizational processes based on experiential learning theory suggested by Jones et al. (2017). Experiential learning theory was given by Kolb (1984) providing experience obtained through coaching that leads to learning and the development. Experiential learning, as the title indicates, is based on personal experience. David Kolb, a psychotherapist, created the idea after being inspired by the research of many other theorists such as Dewey, Lewin, and Piaget. Another sort of learning, according to Kolb, is “the process by which knowledge is formed via the transformative learning.” The process of comprehending and changing an experience yields knowledge. According to Kolb, a variety of circumstances can impact preferred learning methods. Adaptive abilities, career choice, present job function, educational specialty, and personality type are some of the elements he has discovered (Kolb, 1984). Some of the researchers suggested the theory as basis for devising program curriculum of social skill learning in the context of youth development programs (YDPs) through sports (Jones et al., 2017). School developed a three-phase procedure to assist experiential learning: briefing to define the experience, action to enable the expertise, and reflection to learning from past on the expertise (Lower-Hoppe et al., 2020). This theory allowed us to utilize coaching social skill in career and academic development by mediating organizational climate.

The social cognitive theory outlines the method of learning through observation and imitation. The theory refers to a psychological model of activity based on Bandura’s research. It must have been created with such an emphasis on the acquisition of new behaviors in mind and it continues to emphasize that learning takes place in a social setting, with most of what is acquired coming through observation. Because it focuses on how mental cognitive aspects are engaged in learning, social cognitive theory is sometimes referred to as a bridge between classical learning theory, such as behaviorism and the mental process (Bandura, 1977). As per Bandura, most social learning is based on imitation, a process known as observational learning and the best approach to understanding how learning happens is to study behavior modification.

Because learners must pay attention, develop and store mental representations of what they see, recover these representations from memory later, and use them to guide action, observational learning is considered a cognitive kind of learning (Sigelman and Rider, 2009). According to Bandura, observational learning may be summed up in two statements: most of the human learning is based on seeing and copying the actions of others or symbolic models such as fictitious characters in television shows and literature. Imitative behavior can be regarded an operant from a Skinnerian perspective, which is a response that is not generated by any known or evident stimuli. In basic words, the person choose whether or not to respond. Imitative activities become more likely when they result in positive consequences or eliminate or prevent negative contingencies. In operant conditioning terms, a reinforcer enhances the likelihood that a response (i.e., behavior) will occur (Sigelman and Rider, 2009). The application of social cognitive theory as basis of sports coaching has been practiced in past (Jones et al., 2017).

According to organizational behavior theory, an organization’s procedures have an indirect influence on performance through organizational climate, but there is no direct effect on performance as a result of the indirect effect (Lower-Hoppe et al., 2020). Organizational behavior theory is the basis for studying situations in youth sport programs that may have an impact on young people’s development. Youth accomplishments are seen as a key indication of organizational effectiveness among the community youth sports groups (Kaplan, 2001; Anderson-Butcher et al., 2014). This objective is comparable to how most non-profit sports organizations evaluate their effectiveness by looking at their members. More specifically, when members of an organization engage in targeted behaviors, such as organizational citizenship or demonstrate achievement of intended outcomes, such as learning and development that are aligned with the organization’s mission and goals, the organization has evidence of its performance (Ehrhart and Raver, 2014).

Organizational performance has been postulated due to organizational environment and process in organizational behavior theory. Youth outcomes, such as organizational performance, are impacted by youth participants’ impressions of the program environment, such as organizational climate, in the setting of a youth athletic program. The project’s policies and procedures or organizational process shapes the project’s climate. As a result, a community youth sports organization’s climate, policies, and practices should potentially impact juvenile outcomes (Ehrhart and Raver, 2014). A few researchers assessed the organizational climate under the lenses of organizational behavior theory (Wheeler, 2000; Daily et al., 2013; Berberoglu, 2018). So, based on these situations of organizational behavior in managing the organizational climate for academic and career development, this research was formulated.

There are several reasons to believe that sports and physical activities provide a good environment in which to develop these abilities. Numerous youngsters already engage in sports activities, whether individually or in groups and structured or unstructured, but these settings provide numerous chances to grow essential skills such as teamwork as well as collaboration, compassion and prosocial behavior, making plans, and solving problems. According to the Sports and Fitness Industry Association, which counts participation in 120 sports, recreation, and fitness activities in the United States, more than 70% of children aged 6–12 years participate in particular team sports at least 1 day each year (Hurd and Deutsch, 2017). Academic development in school necessitates mastery of certain abilities such as social and academic work.

A contrast is drawn in behavior between the ideas of social skills and social competency, which are defined differently in the literature. Social awareness involves the cognitive knowledge of social skills and how to apply them in interactions with other students, whereas social skills are the actual behaviors that students accomplish in specific social contexts (Gresham et al., 2010; Wentzel, 2015). Student’s social skills were defined in this study as a significant collection of acquired behaviors that facilitate pleasant interactions with people in their surroundings. Social skills are demonstrated by behavior and it is typical to categorize teacher-rated social skills into three categories: collaboration, self-control, and assertiveness (Gustavsen, 2017). There are several forms of social skills deficiencies, which are defined as difficulties in learning or performing social behaviors. When acquiring deficits are caused by a student’s lack of specific social skills, it means that the student does not know how to perform the targeted social skill and, thus, cannot solve problems. When performance deficits are caused by a student knowing how to perform, but not displaying it appropriately, the student cannot solve problems (Gresham et al., 2010).

Student’s behavior or interactions with classmates and teachers are the sole ways for researchers to investigate social skills in schools. Student’s social skills may be influenced by their educational environment, but they may also choose whether to use their social skills positively or negatively. Individual and interpersonal results are a product of student’s personal efforts as well as interactions with classmates and teachers (Wentzel, 2015). Longitudinal studies of the influence of social skills on academic success have yielded varied results. Some studies have identified a substantial link between social skills and subsequent academic success (Caprara et al., 2000), whereas others have indicated that social skills are not a major predictor of later academic achievement (Claessens et al., 2009). A meta-analysis of a broad sample of kids from kindergarten through high school found that universal socioemotional learning programs in schools resulted in an increased prosocial behavior, social skills, and success (Claessens et al., 2009). This could lead to a linear relationship between teaching social skills in sports context with academic development and the organizational climate being a mediator between the both could be developed (Lower-Hoppe et al., 2020). So, the following hypothesis were developed.

Sport coaches are trusted by parents, athletes, and athletic organizations to help athletes improve both on and off the field. Coaches are critical in supporting good youth development via sport, since they impact the sports atmosphere. Positive youth development is more likely when sport coaches intentionally establish mastery-oriented environments that strive to develop players beyond athletic skills and strategies (Feltz et al., 1999; Cope et al., 2013; Camiré, 2015; Strachan et al., 2016; Holt et al., 2017). Coaches must stress the purpose of sport as learning from mistakes while appreciating working hard for personal growth in order to create a mastery-oriented atmosphere favorable to good young development.

Coaches should concentrate on their athletes growth rather than their achievement and players should be judged against self rather than others. Coaches must ensure that all the athletes are valued and appreciated. Coaches may help athletes grow even more by introducing intentional lessons into their programs that teach life skills such as emotional control, cultural competency, personal responsibility, and interpersonal skills. Finally, coaching techniques such as role modeling, creating solid connections, employing compassionate conversation, and positive thinking can help child athletes develop positively (Gould and Carson, 2008; Light, 2010; Camiré et al., 2012; Falcão et al., 2012). Unfortunately, athlete growth via athletics is not always favorable. Coaches impact performance-oriented athletes in the same way as they influence mastery-oriented surroundings, presumably owing to societal standards of winning at all the costs (Shields and Bredemeier, 2010; Neely and Holt, 2011). Impact of sports coaching could be further analyzed for organizational climate as suggested by Lower-Hoppe et al. (2020) and career development of students after completing the education so, we devised the following hypothesis.

A display of the culture, a collection of attitudes, sentiments, and actions that characterize corporate life and an organizational reality with an objective idea are all examples of organizational climate. The organizational climate refers to the core organizational components such as attitudes and understandings among the organization’s members. Because the climate is dependent on individual viewpoints, it varies rapidly and influences the behavior of the organization’s members. The environment in which individuals work may impact their ability to be creative and innovative. Having a creative climate that is in sync with the organization’s culture creates an atmosphere that boosts organizational power (West and Farr, 1989; Ekvall, 1996; Nasurdin et al., 2006). Because they come from separate theoretical traditions, the phrases “organizational climate” and “organizational culture” are frequently viewed as independent ideas. However, they contain many overlapping components. Climate, according to Stringer, is a subset of the corporate culture. Culture refers to all the businesses deeply ingrained value systems, which are difficult to modify. The context and milieu in which a work group performs are influenced by organizational strategy, the macroenvironment, organizational configurations, and historical influences. Such “cultural” impacts emerge from outside the work group and outside the manager’s direct control (Denison, 1996). The environment of a work group may be comparable to or distinct from that of the organization as a whole. High-performing work groups can occasionally be found in companies that are suffering from dwindling budgets or a lack of senior leadership. Even if an organization’s atmosphere is less than ideal, a manager’s leadership and management skills may produce a favorable work climate and great results inside a work group. Regardless of a manager’s level, efforts to improve the workgroup’s atmosphere can lead to great employee performance and outcomes (Perry et al., 2005).

Along with these contributions of organizational climate in different organizational processes, the organizational climate had been studied in mediating and moderating the relationships of different organizational processes (Imran and Anis-ul-Haque, 2011; Al Ghazo et al., 2019; Mehralian et al., 2020). Moreover, direct impacts were studied by Ehrhart and Raver (2014) and Lower-Hoppe et al. (2020) suggested indirect role of organizational climate for future studies. Hence, we developed the following hypothesis for testing.

A following conceptual model (Figure 1) has been formed based on the above literature and hypothesis.

The questionnaire was used as the survey instrument for this study. The questionnaire included items for each variable. This study consisted of five variables, social skill coaching and sports coaching; two dependent variables, namely, academic development and career development; and one mediating variable, namely, organizational climate. This study adopted a scale from the past studies conducted in a similar context. A six-item scale for social skill coaching and a five-item scale for sports coaching were also adopted from a study by Lower-Hoppe et al. (2020). A four-item scale for academic development and a three-item scale for career development were adopted from a study by Lee (2020). A 14-item scale for the organizational climate was adopted from a study by Perry et al. (2005). A five-point Likert scale (from 1 being strongly disagreed to 5 being strongly agreed) was used to collect the responses from the study participants.

Out of the sports federation members (overall 308), there were 59.09% males and 40.09% females. The participants below 20 years were 36.36, 31.49% were in the age bracket of 21–25 years, 23.38% were between the age of 23.38%, and the participants who were 31 years were above 8.77%. The participants with Bachelor’s degrees were 45.80%, Master’s degree holders were 37.09%, and the respondents with Ph.D. or other degrees were 17.09%. Table 1 below shows the demographics details.

The measurement model algorithm of this study is given in Figure 2, explaining how much the study’s independent variables contributed toward dependent variables.

Table 2 shows each item’s factor loadings and variance inflation facto (VIF) values. Factor loadings are used to explain the contribution made by each item of the variable and use multiple items to measure a required and desirable variable (Lambert et al., 1991). According to Lambert et al. (1991), factor loading values have been categorized into three different classes, i.e., undesirable (value < 0.3), fair (value > 0.5), and highly desirable (value > 0.7). The benchmark values for factor loadings were 0.5 and above for this study. For this study, all the factor loading values are above the set benchmark of 0.5. Hence, a significant contribution was made by each item toward this study.

Table 2 shows the alpha reliabilities and AVE values. Alpha reliabilities include the Cronbach’s alpha, which should be higher than 0.7 (Dar et al., 2022). The Cronbach’s alpha values for each variable are above the threshold (ranging from 0.823 to 0.954), indicating that the variables were highly reliable. Moreover, unlike the Cronbach’s alpha, equal loading for a construct is not included in composite reliability. The value of composite reliability ranges from 0 to 1 and it is also divided into three categories: fair reliability (value of 0.6), satisfactory reliability (value from 0.6 to 0.7), and highly satisfactory reliability (value from 0.7 to 0.9). Table 2 depicts that the value of composite reliability for every construct is more than 0.6; therefore, the composite reliability is considered to be satisfactory. Furthermore, AVE examines the existence of convergent validity and its value must be greater than 0.5 (Avotra et al., 2021). Table 2 below shows that the value of AVE for every construct is more than 0.5, indicating the presence of convergent validity.

The HTMT ratio and Fornell and Larcker criterion are two tests that are commonly used to examine the discriminant validity of the variables under study. Discriminant validity explains whether one construct is different from the other construct or not. Considering the HTMT ratio for determining the discriminant validity between variables, the value of the HTMT ratio must be below 0.9 (Franke and Sarstedt, 2019). Table 3 shows that the value of the HTMT ratio of every construct is below 0.90; thus, discriminant validity exists between the variables. Similarly, to determine the discriminant validity of the construct using Fornell and Larcker criterion, the square root of every variable’s AVE must be more than the highest correlation of the construct with the other latent variable. The result of Fornell and Larcker criterion has been shown in Table 4. Table 4 shows that the value on top of each column is higher than the following values indicating that discriminant validity exists between the variables.

The R² values for the dependent variables, i.e., academic development and career development, are 63.8 and 65.5%, respectively, and the R² value for the mediating variable, i.e., organizational climate, came out to be 55.2%.

As explained above, once the measurement model’s criteria are completed, the structural model is examined. The data analysis of this study requires a thorough examination of the structural model. This involves analyzing the direct relationship between the study variables through partial least squares (PLS)-SEM bootstrapping (see Figure 3). The model includes values of coefficients and p-values. A 95% bias-corrected bootstrap was adopted for this study to analyze the direct relationship between the variables (Yingfei et al., 2021). Additionally, this section also includes t-statistics, SE, and path coefficients to examine the study’s hypothesis (see Table 5).

Tables 5, 6 show the results of each hypothesis using PLS-SEM algorithms and PLS-SEM bootstrapping. The inner model has been analyzed with the help of indicators such as p-value, t-statistics, the original sample mean, and SD. Table 5 shows that all the outer loadings, except one, are significant, as the value of t-statistics came out to be more than 1.96.

The study’s first hypothesis (H1) states that social skill coaching impacts academic development. The result of this hypothesis shows that t-statistics = 12.07 with p-value = 0.000 (p < 0.05), thus this hypothesis has been accepted. The study’s second hypothesis (H2) states that social skill coaching impacts organizational climate. The result of this hypothesis shows that t-statistics = 6.34 with p-value = 0.000 (p < 0.05), thus this hypothesis has also been accepted. The study’s third hypothesis (H3) states that sports coaching impacts organizational climate. The result of this hypothesis shows that t-statistics = 11.15 with p-value = 0.000 (p < 0.05), thus this hypothesis has also been accepted. The study’s fourth hypothesis (H4) states that sports coaching impacts on career development. The result of this hypothesis shows that t-statistics = 1.61 with p-value = 0.108 (p < 0.05), thus this hypothesis has been rejected. The study’s fifth hypothesis (H5) states that organizational climate has an impact on academic development. The result of this hypothesis shows that t-statistics = 1.71 with p-value = 0.087 (p > 0.05), thus this hypothesis has also been rejected. The study’s sixth hypothesis (H6) states that organizational climate has an impact on career development. The result of this hypothesis shows that t-statistics = 16.32 with p-value = 0.000 (p < 0.05), thus this hypothesis has been accepted.

The study’s seventh hypothesis (H7) states that organizational climate mediates the relationship between social skill coaching and academic development. This hypothesis has been rejected as t-statistics = 1.57 with p-value = 0.116 (p > 0.05). The study’s last and eighth hypothesis (H8) states that organizational climate mediates the relationship between sports coaching and career development. This hypothesis has been accepted as t-statistics = 10.05 with p-value = 0.000 (p < 0.05).

This study inculcates some practical implications for universities around the world. First, the universities must hire a highly competent coach to provide training and coaching to the students to develop their social skills and sports orientation. The coach’s competency must be measured using effective methods such as physical tests, preassessments, and psychological tests. Second, the universities in China have to develop a culture of social skills and sports to improve academic and career development. Third, sports festivals must be made common in Chinese universities, so that the organizational climate can be improved and the students can have better career development. However, this study includes some limitations that can be considered for future studies. This study took data from only members of the sports federation enrolled in different programs in Chinese universities; therefore, future studies can examine other populations such as European countries. Moreover, the future studies can use other mediating variables such as personal growth or psychological well-being to examine its effect on the existing model.

By measuring the role of social skills coaching on career and academic development, this study has certain limitations. First, it has used the purposive sampling technique; however, stratified random sampling could better present the results according to each department. Furthermore, this study has been confined to the variables of interest; nonetheless, more objective variables can be included in the framework for future studies. For example, future studies can collect data from the staff or coaches to understand the study’s long-term results. Third, this study has used the adapted scales. However, future studies can develop their scales following the coaches or staff level sample accordingly. This study has focused on the sports-oriented students’ goals. Future studies may focus on the consequences of social skills and sports coaching to further understand these variables under sports leadership.