The impact of social capital, human capital and knowledge sharing activity on school performance indicators: case of Saint-Petersburg
Social and human capital as a performance impactful factor. Measurement of school performance. The definition of concepts and measurement levels. Significance of educational organization type, correlation of the factors, compilation of the matrix.
| Рубрика | Менеджмент и трудовые отношения |
| Вид | дипломная работа |
| Язык | английский |
| Дата добавления | 01.12.2019 |
| Размер файла | 822,7 K |
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3.2 Sample
In order to estimate the impact social capital, human capital and knowledge sharing possess on the performance of educational institutions, St. Petersburg schools were selected as research subjects. The total population consists of 678 educational organizations among which there are schools, gymnasiums, lyceums and schools with advanced subject studying (advanced schools) (Government of St. Petersburg Education Committee, 2019).
Due to the fact, that research observes factors of knowledge sharing and social capital, the population representatives taken as sample objects should demonstrate the usage of these factors, otherwise the evaluation of influence SC along with KS possess on EO performance is irrelevant. Therefore, in order to design sample framework there were taken 11 main educational events of the city level (notion: district activities were not involved) taken place in Saint-Petersburg in 2018, such as EdExpo, Saint-Petersburg International Educational Forum and etc. The list of events sorted in chronological order contains:
a) Russian Pedagogical Forum «EdExpo2018» (January 22-23, 2018);
b) Conference «Distance Learning: Realities and Prospects» (February 15, 2018);
c) XIV city festival «The use of information technology in educational activities» (February-March 2018);
d) Saint-Petersburg International Educational Forum 2018 (March 29, 2018);
e) IX conference «Information technologies for the New School 2018» (March 28-30, 2018);
f) XXI International Scientific and Practical Conference «Personality. Society. Education» (March 29-30, 2018);
g) IV Congress of teachers of social disciplines: «Social disciplines at school: what is considered success?» (March 30, 2018);
h) XXIII scientific-practical conference «Cervantes Readings» (April 19, 2018);
i) The lessons' contest «Learn to see 2018» (September 2018);
j) Scientific-practical conference «School of joy: yesterday, today, tomorrow» (October 2-3, 2018);
k) Conference «The Introduction of Secondary Professional Education (SPE) in Educational Organizations: Problems and Prospects for Implementing SPE in Educational Organizations in St. Petersburg 2018» (November 1, 2018).
Educational organization who were not only listeners but active participants (provided a platform for event activities, conducted seminars and master classes, presented reports, provided members of the jury, etc.) represent the research sample. The size of research sample equals 301 EO of different forms (schools, advanced schools, gymnasium and lyceum) from each district of Saint-Petersburg (18 districts) what makes the sample a representative base for research conduction. Thorough descriptive statistics would be provided in following Results section. Nevertheless, usage of constructed convenience based purposive sample instead of entire population imposes certain limitations as well as develop areas for future researches discussed further.
3.3 Data collection and pre-processing
Data collection has been partially considered as creating the list of objects for sample represents the initial stage of data gathering. Other data gathering stages include collection of accompanying general (district, type, code) and specific (records for indicated variables) information for each EO from the sample; acquiring measurement evidences of school efficiency from publicly available sources; extracting data from interim research results.
The data source for sample construction were the web pages and programs of events, meanwhile the list of events was based on Saint-Petersburg education committee event calendar of 2018. The information upon majority of variables measuring factors was kindly granted by the state budgetary institution of supplementary vocational education «St. Petersburg Center for Education Quality Assessment and Information Technologies» and included more than 200 variables used for independent assessment of education quality. The rest of the required variables were retrieved from the EO self-assessment reports of 2018 (example of publication from one of sample agents: State Budgetary Educational Institution of School №93 (2018)). Regarding performance measurement variables records, data has been partially included in handed dataset as well as additionally both semi-automatically and manually scraped from the state website publishing information about the state (municipal) institutions (https://bus.gov.ru) and publications of the St. Petersburg Center for Education Quality Assessment and Information Technologies» (Center): ratings of educational organizations in St. Petersburg that implement educational programs of secondary education (https://monitoring.rcokoit.ru). In parallel to data approval from the Center and scrapping, there has been built a network, whose measurement attributes became the final variables to include in the data set. The final step of data collection implies a single dataset construction from data obtained on previous stages including all the mentioned characteristics for further convenient use. In its final version, dataset contained observations for 301 EO upon 54 variables. The structure of the final dataset is given in Appendix 2.
3.4 Analysis
The entire process of analysis includes several milestones as one may note from the overall research description reviewed before. The very first stage of the analysis represents an exploratory analysis of dataset with all obtained information in a form of descriptive statistics. On this stage not only the distribution of variables, its key measures such as (number of observations, average, standard deviation, etc.) but the correlation matrix based on Spearman rank correlated coefficient ought to be conducted, which is essential for both getting a primary understanding of the research subjects' population and identifying features of dataset that may possess influence on following research steps. Spearman's correlation coefficient is used due to the fact that part of the data is abnormally distributed (Spearman, 1910).
Subsequently, a knowledge sharing network is built based on the event participants data and a corresponding social network and knowledge network analysis, who share pert of methods, is carried out. Particularly, this phase of analysis aims to extract knowledge sharing activity indicators as well as individual (organizational) characteristic of schools' social interaction and positioning such as interaction groups via walktrap and fast greedy clustering algorithms, betweenness centrality, in/out degree centrality, etc. Walktrap algorithm based on the idea of random walks that supposed to be more frequent within subgraph with dense connection than outside the community (Pons & Latapy, 2005). Fast Greedy Algorithm is a hierarchical agglomerative clustering of rare hierarchically organized networks (Clauset, Newman & Moore, 2004). Even though both are based on modularity maximization, fast greedy algorithm allows to identify the list of relatively autonomous communities (who rarely interact with other clusters and has few links with them), while the walktrap highlights communities of most cohesive subgraphs, nodes in which are tightly connected (closeness of interaction), both allowing to evaluate social capital schools possess. The dominant utilities enabling both network construction and following analysis are ORA LITE, a tool for dynamic network assessment and analysis, and RStudio, integrated development environment for conducting all sorts of analysis via its packages such as igraph, corrplot, partykit, etc.
Finally, regarding the assessment of influence possessed by social capital, human capital and knowledge sharing characteristics, analysis of the impact will be conducted in 3 main phases. Firstly, Principal Component Analysis with varimax rotation is used in order to reduce dimensions and check whether obtained measurement fit into required factors (human capital, social capital, knowledge sharing). The factors then checked with Cronbach's alpha coefficients to test their reliability. Secondly, regression analysis, where the dependent variable is one of the listed measurements of the educational institution performance, while independent variables are either separate characteristics of human capital, social capital, knowledge sharing or factors acquired from Factor Analysis, is performed. In order to reach the most accurate inferential model, several methods will be tested as part of the regression analysis, including linear for scores in the same ratings or in the Independent Assessment of the Quality of Education and non-linear regression (e.g. logistic regression for binary school performance measurement variables such as presence of EO in top-100 ratings in any of direction). The quality of models is checked with ANOVA counted significance level and R2 parameters, that allows to determine whether the models are relevant and which part of the observations it explains. Finally, at the third stages to prove the results obtained and confirm the significance of the revealed factors, the non-parametric criteria from Mann - Whitney U-test and Kruskal - Wallis test are used.
Worth mentioning, proceeding from the theoretical foundation, this paper assumes that human and social capital have both independent and mutual influence, which is reflected in the research hypotheses. In order to investigate the influence exerted by each of the forms of capital separately, as well as in conjunction, the regression analysis will be conducted in following directions:
a) assessment of the impact of human capital characteristics on organizations' performance;
b) evaluation of the influence of social capital features on schools' achievements;
c) appraisal of effect knowledge sharing activity on institutions' success:
d) estimation of the cumulative effect of parameters on results of educational institution performance.
Above forms of analysis requires statistical packages of R programming language, SPSS Statistics Software and Microsoft Excel, possessing a variety of statistical algorithms suitable for the research tasks.
3.5 Limitations
The study has a number of essential limitations to be settled. One of the main limitations, as already noted, might be associated with usage of an incomplete entire population, which negatively reflects on the representativeness and reliability of the study. The sample represents 44.4% of all educational institutions of St. Petersburg and 46.7% of the statistical population, which includes only those organizations that provide both primary and secondary education, as indicated in Research statement section. The distribution of organizations by type is similar to the distribution of entire population (Figure 1), and the average deviation is 6%, which makes the sample representative. Despite this and the fact, that the findings are statistically tested, there is still a possibility for a minor bias in results if compared to entire population, which is hardly reachable.
Figure 1. Population and sample distribution of educational institutions of types
Another limitation is also related to the objects selected for the study. Considering the fact that research is conducted on the basis of St. Petersburg educational organizations, a significant issue associated with results generalization arises. Despite the fact that in Russia the education system is the same for the entire country, the author does not exclude that the geographical position of research subjects and the related features may affect the factors under consideration and, as a consequence, their influence on the school performance. As for foreign countries, generalization of the obtained findings is impossible due to severe differences in education systems of the countries.
Unavailability of data upon State Unified Exam results represents another limitation of the study, as it imposes boundaries on the schools' performance evaluation versatility. The research, however, partially solves this issue as it implies state ranking of high students' achievements, whose calculation includes evaluation of high State Unified Exam results, which however present only few variables for rankings.
Current research applies Spearman's correlation coefficient to all the variables for correlation analysis and factor analysis. Nevertheless, in terms of social network characteristics a more appropriate method would be the Quadratic Assignment Procedure (QAP) (Downey, 2018), which uses a permutation test. The limitation in this context is associated with the fact that despite a wide range of used utilities' capabilities, none of the programs allows to perform QAP without creating obstacles for further analysis.
Finally, this study considers the static situation of 2018, which also limits the level of generalization to the particular year as in the dynamics the conclusion may take a different form. The author also does not deny the possibility that at other time periods both future and past the situation may differ.
Overall, based on the established research question, hypotheses and theoretical foundation, the corresponding methodology of data collection and following analysis with inevitable limitations was designed. Described research design allows to fill in the gap in existing scientific knowledge through evaluating the impact social capital and human capital characteristics possess on educational organizations, as well as exploring the network partnership of schools arising from the knowledge sharing activities.
4. Results
4.1 Descriptive analysis
To give an introduction to data structure the results of a descriptive analysis of the dataset are presented. A brief description of the data, its internal patterns and characteristics that can affect the results of the analysis and their interpretation is crucial for understanding the findings of the research and its limitations.
The final database used for the research consisted of records on 45 variables (57 after adding network characteristics) for 301 educational organizations of different types (schools, advanced schools, gymnasium, lyceum) from 18 districts of St. Petersburg. The distribution of educational institutions by their type is illustrated in the Figure 2, from which it is clear that the majority (more than 48%) are representatives of general education schools, while a minority are lyceums (less than 10%). The distribution of records by school's city district is also uneven, as shown in Figure 3: while the Nevsky district is represented by 29 schools as well as Kalininsky district, Kronstadtsky region is represented by only 2 schools, which imposes certain limitations on the study results. These characteristics were taken into account at the subsequent stages of the analysis and checked for statistical significance discussed further.
Given the subsequent regression analysis, an important role among dataset's features was played by the distribution of school performance measurement values. The distribution of the total score in the independent assessment of the quality of education (IAQE), according to the Kolmogorov-Smirnov test, is normal with an average value of 120 points (Figure 4), while the representation of educational institutions in the examined rankings is non-normally distributed (Figure 5). The latter is characteristic of most other variables used to measure school performance as well as social capital, human capital and knowledge sharing. The uneven distribution of the majority of dependent variables (Appendix 3) led to the use of the Spearman's correlation coefficient at the stage of building the correlation matrix, as mentioned in Methodology.
Figure 2. Distribution of educational organizations by type within the sample
Figure 3. Distribution of educational organizations by city district within the sample
Figure 4. Kolmogorov-Smirnov test for IAQE score distribution
Figure 5. Kolmogorov-Smirnov test for IAQE score distribution
4.2 Social Network
To extract the measurements of schools' social capital and knowledge sharing, a network of educational organizations' interaction within city-level events of 2018 was constructed (Figure 6). Built network is non-directional and contains 301 nodes, which are encoded educational organization with list of attributes (type, district), and 60,514 links formed by active participation in joint activities. The graph demonstrates that there are communities of various sizes within the network connected by a number of large events, e.g. central cluster, as well as individual pairs or groups of schools on the periphery of the network.
Figure 6. Network of educational organizations' interaction within 2018 city-level events
In order to details and simultaneously simplify the interaction network, solitary connections between the nodes, caused by a single joint participation, were removed from the network as well as resulted isolated nodes (Figure 7). As an outcome of the network reduction, the network cohesive groups of interaction got highlighted more significantly, as well as individual nodes, like node number 135, acting as a binders.
Figure 7. Event participation network without solitary links
Further, this network interaction was considered from the point of belonging of a school to certain educational organization types (Figure 8) and city districts (Figure 9). Coloring nodes in accordance with the type of educationa organization revealed that most frequently connecting nodes are educational institutions of the gymnasium and secondary educational schools with advanced study of the subject (advanced schools) types (Figure 8).
Figure 8. Event participation network with highlighted educational organization types
The coloration of nodes in accordance with the city district of a school also indicated some network characteristics worth attention (Figure 9). Firstly, in some clusters a certain district tends to dominate, however a more common situation for communities is to contain representatives of different city areas. Moreover, binding nodes frequently link communities with each other through connection with representatives of the same district, which indicates the importance of geographical location.
Figure 9. Event participation network with highlighted city districts
Obtained networks were also subjected to clustering by walktrap and fast greedy methods of community detection. Walktrap method failed to cope with a large volume of single bonds, resulting in identification of 10 clusters, among which were solitary clusters containing 1 or 2 nodes. The hierarchical approach of fast greedy, in contrast, highlighted only 3 clusters (Figure 10) with a final modularity
Figure 10. Event participation network with highlighted fast greedy clusters
On the basis of the constructed networks, in/out degree centrality, betweenness centrality, transitivity, closeness, hub/authorities, pagerank, Clique Count, Triad Count were extracted along with belonging to the fast greedy community and the walktrap community.
4.3 Correlation matrix
To determine the relationships between variables within the dataset, which can also have an impact on future inferential models, a pair correlation matrix was created using the Spearman's correlation coefficients with a two-sided criterion (Appendix 4). Analysis of matrix allowed to identify several groups of co-dependent variables: those describing the size of the school (positive correlation between area, number of classes, students, teachers and staff rates), informatization (positive correlation between information technologies, infrastructure, computers, etc.), network characteristics (besides closeness, all characteristics demonstrated a significant relationship). At the same time, variables from the group associated with organization's size also indirectly affected the number of points for qualifying categories of personnel and awards, which in turn correlated with scores for high results in various assessment procedures for students (Unified State Exam, State Final Certification, Russian School Olympiad).
4.4 Factor analysis
To reduce the data size and test the selection of variables, factor analysis in form of Principle component analysis was performed. All numerical, binary and nominal independent variables except for the school's type, city district and walktrpap community were used. The rotation converged in 7 iterations and made it possible to isolate 9 factors (for more details, the table of factor loads is given in Appendix 5):
a) Factor 1 unites Triad Count with factor load 0.996, Degree centrality (0.995), Hub centrality (0.994), Closeness centrality (0.989), Total Degree centrality (0.980), Page Rank (0.956), Fast Greedy Community (-0.471), which together characterize the closest social capital of the node (primarily the neighbours of first order) and, thus, called as Network indicators of Neighbourhood.
b) Factor 2 brings together number of classes (0.947), number of teachers' salary packages (0.934), number of students (0.933), number of teacher (0.909), teachers' qualification category score (0.711), total school's building area (0.657), amount of Supporting Staff's Salary Packages (0.552), number of interactive whiteboards (0.470) and computers (0.411), which all are indicators of the educational organization size, thus, giving a name to factor as Scale of educational institutions:
c) Factor 3 combines Clique Count (0.914), number of events (0.872), Betweenness Centrality (0.846), Transitivity (-0.867), Fast Greedy Community (0.701). These variables in total measure the position of node in the network along with its significance and mediation degree, thus the factor is called Network indicators of node as mediator.
d) Factor 4 includes variables devoted to the extent of school's informatization such as provision of informatization tools (0.904), the number of students' (0.764) and teachers (0.696) computers, interactive whiteboards (0.670) and projectors (0.547) leading to defining a factor as Level of informatization.
e) Factor 5 possesses administrative awards (0.781), teachers' awards (0.773), teacher's qualification category score (0.443), which are the characteristics of staff skill and qualification level, thus attributing to a factor the name of Staff qualification.
f) Factor 6 integrates a weighted assessment of achievements in pedagogical competitions (0.677), the methodist's salary packages (0.557), whether the school is an event platform (0.530), a weighted assessment of the administrative achievements (0.483), the presence of the regional stage winners of Russian School Olimpiad (0.475). These variables depict the degree of participation of school together with staff professional events and contests, which thus can be considered as characteristics of Knowledge sharing factor.
g) Factor 7 is devoted to infrastructure features such as weighted assessment of provision of infrastructure (0.845) and halls for various purposes (0.900), the total school's building area (0.412), thus the factor is called Infrastructure.
h) Factor 8 combines the number of refresher courses completed by both the administration (0.860) and teachers (0.850) in 2018, therefore, indicating the growth of staff's skill and qualification, naming the factor Professional development.
i) Factor 9 concerns Innovations, which became a name for the factor, as it unites whether school is noted in competitions for the introduction of innovations in the educational process (0.518), number of interactive consoles (0.807), projectors (0.446).
Interestingly, factor analysis revealed all the examined factors (human capital, social capital, knowledge sharing), though subdivided some of them into two separate factors as in the case of network interaction and human capital factors.
4.5 Regression analysis
Further, obtained factors were used to construct inferential regression models for several school performance measurements, acting as dependent variables.
Linear regression on the total score of IAQE (independent assessment of quality of education) score. The model explained 32% of the variance and was found to be statistically significant as a result of the ANOVA test. Factors Network indicators of node as mediator (social capital), Informatization, Staff's qualification (human capital), Knowledge sharing turned out to be significant (in terms of significance level), while all factors with no exception are suitable for the model (according to the Variance inflation factor), the model coefficients are presented in Table 3. Even though the model overall highlighted examined factors (social capital, human capital, knowledge sharing), high level of significance was given only to part of factors describing human capital and social capital.
Table 3/ Regression model* coefficients
|
Model |
Unstandardized Coefficients |
Standardized Coefficients |
Collinearity Statistics |
||||
|
B |
Std. Error |
Beta |
Tolerance |
VIF |
|||
|
1 |
(Constant) |
120,064 |
705 |
||||
|
Network indicators of Neighbourhood |
-1,055 |
706 |
-, 083 |
1,000 |
1,000 |
||
|
Scale of educational institutions |
702 |
706 |
055 |
1,000 |
1,000 |
||
|
Network indicators of node as mediator |
1,547 |
706 |
122*** |
1,000 |
1,000 |
||
|
Informatization |
1,673 |
706 |
132*** |
1,000 |
1,000 |
||
|
Staff's qualification |
1,647 |
706 |
130*** |
1,000 |
1,000 |
||
|
Knowledge sharing |
2,090 |
706 |
165*** |
1,000 |
1,000 |
||
|
Infrastructure |
267 |
706 |
021 |
1,000 |
1,000 |
||
|
Professional development |
597 |
706 |
047 |
1,000 |
1,000 |
||
|
Innovation |
1,342 |
706 |
106* |
1,000 |
1,000 |
||
|
Model Summary |
R: |
R Square |
Adjusted R Square |
F |
Sig. |
||
|
316a |
100 |
072 |
3,587 |
000b |
*Dependent Variable: IAQE total score
Linear regression on the representation of a school in the ratings. The model explained 76% of the variance and also turned out to be significant, according to the ANOVA test. All factors except for Infrastructure are considered as significant and appropriate. At the same time, factor of network indicators characterizing the neighbourhood of the schools possesses a decreasing coefficient (-0.096) and thus is negatively related to the school's representation in the ratings, which was also presented in the previous model, where the IOQE score was a dependent variable, with a low level of significance (Table 4).
Logistic regression models. For the school's performance measurements presented in the form of binary variables and reflecting the presence of school in the top-100 educational institutions in various areas, logistic regression was applied. All models possessed a fairly high level of accuracy (average percentage of correct predictions on models: 86.3%). Short summary for performed models are presented in the Table 5, while full summary of the models is presented in Appendix 6. The models highlighted the examined factors such as human factors (current staff qualifications, staff development), knowledge sharing and social capital (networking indicators are significant in 4 out of 5 models).
Table 4/ Regression model* coefficients
|
Model |
Unstandardized Coefficients |
Standardized Coefficients |
Collinearity Statistics |
||||
|
B |
Std. Error |
Beta |
Tolerance |
VIF |
|||
|
1 |
(Constant) |
1,176 |
049 |
||||
|
Network indicators of Neighbourhood |
-, 096 |
049 |
-, 076** |
1,000 |
1,000 |
||
|
Scale of educational institutions |
152 |
049 |
120*** |
1,000 |
1,000 |
||
|
Network indicators of node as mediator |
199 |
049 |
157*** |
1,000 |
1,000 |
||
|
Informatization |
293 |
049 |
230*** |
1,000 |
1,000 |
||
|
Staff's qualification |
555 |
049 |
436*** |
1,000 |
1,000 |
||
|
Knowledge sharing |
588 |
049 |
462*** |
1,000 |
1,000 |
||
|
Infrastructure |
-, 042 |
049 |
-, 033 |
1,000 |
1,000 |
||
|
Professional development |
244 |
049 |
192*** |
1,000 |
1,000 |
||
|
Innovation |
243 |
049 |
191*** |
1,000 |
1,000 |
||
|
Model Summary |
R: |
R Square |
Adjusted R Square |
F |
Sig. |
||
|
316a |
100 |
072 |
3,587 |
000b |
|||
|
* Dependent Variable: Representation of school in ratings |
Table 5. Logistic regression models summary
|
Dependent Variable |
Total Percentage of Correct Predictions |
Significant factors |
|
|
The presence of educational organization in the top-100 rating by mass education |
79.1% |
Network indicators of Neighbourhood, Staff's qualification, Knowledge sharing, Infrastructure, Innovations |
|
|
The presence of educational organization in the top-100 rating by high students' achievements |
84,4% |
Scale of educational institutions, Network indicators of node as mediator, Staff's qualification, Knowledge sharing, Infrastructure, Innovations |
|
|
The presence of educational organization in the top-100 rating by provided conditions |
92,0% |
Informatization, Infrastructure, Professional development, Knowledge sharing, Network indicators of Neighbourhood, Network indicators of node as mediator |
|
|
The presence of educational organization in the top-100 rating by staffing |
88,0% |
Staff's qualification, Knowledge sharing, Innovations, Professional development |
|
|
The presence of educational organization in the top-100 rating by management quality |
88,0% |
Staff's qualification, Knowledge sharing, Professional development, Network indicators of node as mediator |
4.6 Significance of educational organization type
The significance of schools' type as a moderating variable and as a factor of influence was evaluated by non-parametric criteria for independent samples. First of all, its significance as a controlling variable was considered in the context of the distribution of factor values. Significant differences were found in the factors of networking indicators describing neighbourhood, personnel qualifications and knowledge sharing (Appendix 7). The significance of schools' type as an indicator capable of influencing school performance was tested on a number of indicators: the total IOQE score, the scores in the five rating directions and the overall representation in the ratings (Appendix 8). The type of educational organization has proven to be a significant factor in the distribution of all indicators, except for the total IOQE score.
4.7 Correlation of the factors
To test the fourth hypothesis of mutual influence among the factors, a correlation matrix was calculated, and several models were constructed. The correlation matrix found no significant interrelationships (Appendix 9), nor did the models, none of which was significant.
5. Discussion
In this section, the results are considered in the context of the established hypotheses and research question, and compared to the previous study findings discussed in Theoretical background section.
First of all, it is worth summing up the results hypothesises testing. Hypotheses 1, 2 and 3, dedicated to the presence of an impact on the educational organization performance imposed by human capital, social capital and knowledge sharing, were confirmed, which coincides with the results of previous researches considering the same topic. (Hitt et al., 2001; Finkelstein et al., 2009; Beattie & Smith, 2010; Yang, 2017; Dufur et al., 2013). The fact that the influence possessed by factor has been confirmed allows not only to derive conclusions regarding the hypotheses, but also to answer the research question: human capital, social capital and knowledge sharing impact the educational organization indicators. Moreover, highlighting several factors characterizing human capital and social capital, factor analysis confirmed the assumptions about the versatility of social and human capital, driven from the literature review (Burt, 1997; Coleman, 1988b; Lil & Smith, 2001; Schultz, 1961; Becker, 1964). Hence, according to current research findings, the social capital of the school should be assessed in at least two dimensions: social capital as the nearest environment consisting of the closest neighbours and social capital as the position of the school within the network and its level of mediation. Human capital should also be considered in 2 dimensions: the current level of human resources development and the efforts applied to their development. Nevertheless, the study does not exclude that these factors can be further subdivided into smaller features, and also be supplemented by additional factors.
During the performed analysis and hypotheses testing, there was revealed an influence exerted by the social capital can have both positive and negative interactions depending on the characteristics considered. A similar phenomenon has been mentioned in the theoretical background part: Vlasov and Andreeva (2015) revealed that structural embeddedness has a negative relationship with the creation of new knowledge, while junctional embeddedness, on the contrary, had a positive effect. In this research, the factor devoted to the network characteristics of the neighbourhood, similar in its essence to structural embeddedness, has also received a negative coefficient in the inferential regression models. In contrast, Network indicators of node as mediator, synonymous to junctional embeddedness, had a positive coefficient. Vlasov and Andreeva (2015) attributed this effect to the fact that an organization is not able to process the amount of information provided by surrounding companies, which leads to fact that the knowledge creation does not increase with the level of professional events participation. Another reasons might be organization's costs required to maintain connections, that is why some of them get lost (Vlasov & Andreeva, 2015). Indeed, a huge amount of relatively weak ties leads to increase of so-called nominal social capital - a large number of neighbours, relations with which are not strong and cannot be profitably used. Moreover, a plenty of activities increases the workload and creates a dispersive allocation of human resources to a greater number of tasks, which can also cause a negative effect on the network characteristics of the neighbours. It is also worth taking into account the fact that the network characteristics representing the social capital factor were identified as significant in majority of logistic regression models, but not all of them. For example, the representation of an educational institution in the state-owned ranking of top-100 in terms of staff provision turned out to be related to the knowledge sharing, while social capital did not reveal its significance. This serves as an additional basis for rejecting the fourth hypothesis, which will be discussed next.
As for Hypothesis 4 considering mutual influence between factors of social capital, human capital and knowledge sharing, the results of the correlation matrix for both individual variables and extracted factors at this stage did not provide grounds for confirming the hypothesis. That, however, may change if a deeper study of correlations is conducted with the use of methods inaccessible for this study (for example, QAP that is more appropriate for network variables' correlations).
Regarding Hypothesis 5, it has, in turn, been confirmed: the type of educational institution was not only a significant variable in the distribution of school performance indicators, but also a moderating variable for factors, which values were unevenly distributed across different types of educational organizations. Among the factors with non-normal distribution by type of educational institution were networking characteristics of neighbourhood, staff qualifications and knowledge sharing; while among school performance indicators, a significant deviation in the distribution from the normal was identified across all factors except for the total IAQE score. Interestingly, the distribution of factors' values for the listed indicators in schools is slightly lower than in other types of educational institutions, the same applies to a row of performance indicators (management quality scores, mass education scores, etc.). This might happen due to higher standards to both staff and students in gymnasiums, lyceums and advanced schools whose programs go beyond state educational programs. Moreover, the status of institutions associated with the attributed type requires maintenance and that might be a reason for more active participation in professional events, higher requirements for both pupils and teacher qualifications. Thus, for schools of general education, the development of social capital, human capital and knowledge sharing presents an opportunity to catch up with the level of other organizations. Another unexpected result is the fact that total IAQE does not reveal a significant difference, which may indicate both the objectivity of the assessment and, vice versa, its inconsistency.
In addition to the factors considered with the hypotheses, the level of informatization and innovation have also been highlighted as significant, positively determining both the score IOQE and representation in the ratings, and a number of binary of indicators (presence in the top-100 rating for mass education, high achievements, etc.). The infrastructure also influenced the representation in the ratings on mass education, high results and the quality of the educational process conditions. Thus, the informatization, innovation and infrastructure of the institution represent additional significant factors that educational institutions should take into account.
Conclusion
Considering the mission of educational organizations, the examined factors of social capital, human capital and knowledge sharing present crucial value ??for improving the performance of a school and its goal accomplishment. The analysis allowed to confirm this idea and prove that the factors of social capital, human capital and knowledge sharing are significant in the context of general educational organization performance. Even though the factors were considered by all models as significant, they were mostly supplemented with additional factors such as innovation, informatization, etc. However, in terms of management quality score, social capital, human capital and knowledge sharing were the most impactful factors for an indicator, which may indicate the most subjected to the factors' influence sphere. Moreover, the factors possess both positive and negative effect. Thus, educational institutions should concentrate on strengthening their positioning within the network of interaction between educational organizations, without scattering on increasing the number of «unfamiliar» institutions, which are only nominal representatives of its social capital, as such a scattering decrease performance indicators values. Educational institutions should also pay attention to the current state of human resources and invest in the professional development of the employees, which have a positive effect both on a total IAQE score and on the presence in the state-owned ratings. Stimulation of knowledge sharing also plays a crucial role as this factor has been considered as significant in all models built and positively correlated with all the performance indicators. In this term, the knowledge sharing should occur consciously and substantively, otherwise it leads to an increase in undesired network characteristics such as extensive number of neighbours. Thus, it is necessary to take into account the versatility of the studied factors, which are not limited to the measurements and dimensions given in this study.
Social capital, human capital, knowledge sharing are important sources of improving performance indicators, especially for general education schools, whose performance rates in the rankings for personnel, managerial and other criteria are lower than among representatives of lyceum gymnasiums and schools with advanced study of the subject types of educational organizations (advanced schools). Moreover, it is also worth paying attention to the infrastructure of the educational institution, its level of innovation and informatization. Given the widespread adoption and use of information technology, these factors also possess a positive impact on performance indicators, including total IOQE score. Therefore, this study not only answers the research question about the presence of the impact social capital, human capital and knowledge sharing impose on school performance indicators, but also complements it with a number of factors that are also of notional value for educational organizations.
The findings of the study, however, should be considered with a number of accompanying limitations. Firstly, the initial limitation concerns the fact that the paper had an intention to confirm the significance of the examined factors, but not to determine absolute values the educational institution should reach in order to obtain the highest performance indicators. Some of the restrictions are related to the objects of the research that possess specific characteristics, such as the type of educational organization (which has been a criterion for the sample), geographical location, static situation of 2018. In geographic and temporal change, the results may take a different form. Moreover, hypothesis devoted to mutual effect of the factors requires a deeper detailed analysis which will take into account the specifics of variables, as is the case with network indicators and correlation within and outside social capital factor. Finally, as measurements of school performance the study applied representation in state-owned ratings, as well as the total IOQE score, which also limits the generalization of conclusions. Given the fact that research's models with various dependent variables demonstrated a different set of factors as significant, using another measurement of school performance can redistribute the significance of factors and reveal new patterns.
All this represents areas for possible future researches. A full confirmation of the results obtained in the current paper is possible through expanding the scope of the study and involving the entire general population of educational institutions of general education, including the minority types of educational organizations such as academies, private educational institutions, etc. Moreover, agents that are not schools and represent other types of educational institutions (such as Education Centers, Centers of children and youth creativity, etc.) can be incorporated in interaction network, which will allow to assess the characteristics of social networks of the St. Petersburg educational system and the role of government agents within. Comparison of the results of this work with previous and subsequent time periods may also make it possible to estimate the dynamics of changes in the factors of social capital, human capital and knowledge sharing over time, which may, in turn, lead to new significant variables not defined during static analysis. Finally, given the similar goal setting and missions of educational institutions of different levels (for example, preschool or secondary professional education), the study of social capital, human capital and knowledge sharing in the context of these organizations also represents a significant gap in the academic field.
Reference list
1. Adler, P.S., & Seok-Woo Kwon. (2002). Social capital: Prospects for a new concept. Academy of Management.the Academy of Management Review, 27 (1), 17-40. Retrieved from https://proxylibrary.hse.ru:2078/docview/210979816? accountid=45451
2. Ahmad, F. (2017). Knowledge-sharing networks: Language diversity, its causes, and consequences. Knowledge and Process Management, 24 (2), 139-151. doi:http://proxylibrary.hse.ru:2112/10.1002/kpm.1539
3. Balatsky, E.V., & Ekimova, N.A. (2014). Academic performance of higher economic schools in Russia. Terra economicus, 12 (1).
4. Basov, V. (2014). Setevoye obrazovatel'noye partnerstvo i setevoye vzaimodeystviye kak usloviye razvitiya obrazovatel'nogo uchrezhdeniya [Network educational partnership and network interaction as a condition for the development of an educational institution]. Nauchno-metodicheskiy zhurnal Pedagogicheskiy poisk, (9), 24-29. https://proxylibrary.hse.ru:2283/download/elibrary_27641909_53744485.pdf
5. Bao, H., Zhu, X., Cen, Y., Peng, Y., & Xue, J. (2018). Effects of social network on human capital of land-lost farmers: A study in zhejiang province. Social Indicators Research, 137 (1), 167-187. doi:http://proxylibrary.hse.ru:2096/10.1007/s11205-017-1592-6
6. Beattie, V., & Smith, S.J. (2010). Human capital, value creation and disclosure. Journal of HRCA: Human Resource Costing & Accounting, 14 (4), 262-285. doi:http://proxylibrary.hse.ru:2096/10.1108/14013381011105957
7. Becker, Gary S. (1964). Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education. University of Illinois at Urbana-Champaign's Academy for Entrepreneurial Leadership Historical Research Reference in Entrepreneurship. Available at SSRN: https://ssrn.com/abstract=1496221
8. Becker, Gary S. (1975), «Investment in human capital: effects on earnings», in Becker, G.S. (Ed.), Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education, 2nd ed., National Bureau of Economic Research, New York, NY, pp. 1-270.
9. Bosua, R. (2015). Secure knowledge management: Influencing the development of human knowledge sharing networks. International Journal of Cyber Warfare and Terrorism, 5 (2), 1-n/a. doi:http://proxylibrary.hse.ru:2096/10.4018/IJCWT.2015040101
10. Burt, R.S. (1997). The contingent value of social capital. Administrative Science Quarterly 42 (2): 339 - 365.
11. Choi, K., Goldschmidt, P., & Yamashiro, K. (2005). Exploring models of school performance: From theory to practice. Yearbook of the National Society for the Study of Education, 104 (2), 119-146.
12. Chow, W.S., & Chan, L.S. (2008). Social network, social trust and shared goals in organizational knowledge sharing. Information & Management, 45 (7), 458. Retrieved from https://proxylibrary.hse.ru:2078/docview/237021036? accountid=45451
13. Chuang, M., Chen, C., & Lin, M.J. (2016). The impact of social capital on competitive advantage: The mediating effects of collective learning and absorptive capacity. Management Decision, 54 (6), 1443-1463. Retrieved from https://proxylibrary.hse.ru:2078/docview/1798750101? accountid=45451
14. Clauset, A., Newman, M.E., & Moore, C. (2004). Finding community structure in very large networks. Physical review E, 70 (6), 066111.
15. Coleman, J.S. (1988a, 04). «Social capital» and schools. The Education Digest, 53, 6. Retrieved from https://proxylibrary.hse.ru:2078/docview/218184579? accountid=45451
16. Coleman, J.S. (1988b). Social capital in the creation of human capital. The American Journal of Sociology, 94, 26. Retrieved from https://proxylibrary.hse.ru:2078/docview/195936734? accountid=45451
17. Demina, E.V., & Mikhailova, E.N. (2016). Kachestvo obrazovatel'nykh uslug kak pokazatel' rezul'tativnosti obrazovatel'noy deyatel'nosti sovremennoy shkoly. [The quality of educational services as an indicator of the effectiveness of educational activities of the modern school]. Vestnik of Tomsk State Pedagogical University, (8 (173)). Retrieved from https://vestnik.tspu.edu.ru/files/vestnik/PDF/articles/demina_e._v._48_52_8_173_2016.pdf
18. Downey, C. (2018). Utilising Social Network Analysis to Identify the Structural Features of Teachers' knowledge and Resourcesharing Networks Within Schools. Profesorado, Revista de Currнculum y Formaciуn del Profesorado, 22 (2), 133-159.
19. Dufur, M.J., Parcel, T.L., & Troutman, K.P. (2013). Does capital at home matter more than capital at school? Social capital effects on academic achievement. Research in Social Stratification and Mobility, 31, 1-21.
20. Fare, R., Grosskopf, S., & Weber, W.L. (1989). Measuring school district performance. Public Finance Quarterly, 17 (4), 409. Retrieved from https://proxylibrary.hse.ru:2190/docview/207632481? accountid=45451
21. Federal law of December 29, 2012 (ed. Of December 25, 2018) «Ob obrazovanii v Rossiyskoy Federatsii» [On education in the Russian Federation] 2018 (№273-FZ) (Rus.).
22. Felнcio, J.A., Couto, E., & Caiado, J. (2014). Human capital, social capital and organizational performance. Management Decision, 52 (2), 350-364. doi: http://proxylibrary.hse.ru:2112/10.1108/MD-04-2013-0260
23. Field, A. (2009). Discovering Statistics Using SPSS (3rd ed.). London, United Kingdom: SAGE Publications.
24. Finkelstein, S., Hambrick, D.C. and Canella, B. (2009) Strategic Leadership: Theory and Research on Executives, Top Management Teams, and Boards. Oxford: Oxford University Press.
25. Franck, E., Nьesch, S., & Pieper, J. (2011). Specific Human Capital as A Source of Superior Team Performance**. Schmalenbach Business Review: ZFBF, 63 (4), 376-392. Retrieved from https://proxylibrary.hse.ru:2190/docview/900452830? accountid=45451
26. Franco, C., & Peter, F.W. (2018). On building partnership networks in an innovation context. Management Research, 16 (2), 179-196. doi:http://proxylibrary.hse.ru:2112/10.1108/MRJIAM-06-2016-0675
27. Frolov, Yu.N. (2013). Setevoye vzaimodeystviye obrazovatel'nykh organizatsiy: strategicheskiye vyzovy i novyye podkhody [Social partnership as a factor in the development of network interaction between universities and employers in managing the training of qualified personnel]. Tsarskoye Selo Readings, 2 (XVII). https://proxylibrary.hse.ru:2283/download/elibrary_21450255_98734872.pdf
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