_{The representation of Business Units taken part in the research, first nine of them were the target of the cross-functional research. Units 10-21 were available to choose in terms of 3 Sociometric questions: Communication of the employees, Help or information access, Participation in one project team. This representation was provided by the Head of the HR department of the Bank “A” according to their needs of conducting the research on the CF interaction within the company. The business departments were chosen as the target of the research according to fact that their interactions inside the organization are more covert and still these units are in high necessity of ongoing interaction.}

_{Table 2}

_{The representation of Business Units taken part in the research}

The distribution of other departments (10 - 21) on the organizational chart in terms of their CF capacity is understandable and thus could not be included in the SNA research (Yang & Tsai, 2019). It is still arguable whether to include all the employees into the SNA, referring the fact of full covering off possible interactions between each node, which could be either an employee, the stated group of employees by their personal characteristics or the whole departments, but with the different statistical instruments and mechanisms this lack of full coverage limitation could be leveled. With the survey validity ratio based on the proportions of the representatives taken part in the research and actual personnel headcount it is possible to find the optimal algorithm of conducting this particular SNA research.

_{Table 3}

_{The validity of the survey responses rates}

Data analysis strategy

_{The collected data was analyzed using the Structural Equation Modeling approach with the SmartPLS program (Borgatti, 2015), version 3 and Gephi, the program for the network visualization (Huisman, & van Duijn, 2014). The original table formatting is saved. The MS Excel program was used to calculate the Cross-Functional index for each business unit. The SEM approach was used because its high efficiency in analyzing the statistical background of the network interconnections with the focus on providing not only the existing calculations, but also the predictions for improving the models (Butts, 2007). Moreover, the SmartPLS contains all necessary functions to test the stated hypotheses (Leguina, 2015). The set of the variables was organized in the most suitable way. The sociometric indicators were compound by one to ten variables for each indicator (Communication, Help or Information, Project team). The Knowledge Sharing variables were compound as an average score from 6-point Likert scale for each of 6 indicators (Internal motivation - three questions, External motivation - four questions, Management practices aimed at motivating knowledge sharing - three questions, Management practices aimed at providing opportunities for knowledge sharing - seven questions, Management practices aimed at developing knowledge sharing abilities - three questions, Individual knowledge sharing activity - seven questions). In addition to the Cronbach alpha coefficient (б ? 0.7), which was used to identify the reliability of the survey, the composite reliability indices (CR ? 0.7) and the average explained variance (AVE ? 0.5) of the obtained scales were estimated (Schreiber, & Zylka, 2020).}

_{Apparently, the data analysis strategy implies several steps to be taken to achieve the goal of the study. First, we have used descriptive statistics (frequencies) to understand our sample. Another descriptive tool has demonstrated the average scores, median, standard deviation, minimum and maximum scores for such the Sociometric and Knowledge sharing variables. After this step, we used correlation analysis to determine the correlation between variables and understand their feasibility to be included in the further investigation of relationship between cross-functional interaction and knowledge sharing. The major limitation here is that the coefficients may be highly dependent on the size of sample and its representation can be subjective. In the matter of fact, correlation does not mean causation, thus there is a need to use the other tool that can provide us with such an information. In the SmartPLS, two consecutive algorithms were chosen. The first one is PLS algorithm based on the Partial Least Squares approach. The second is the Bootstrapping to determine the significant intervals and T- statistics. In the PLS algorithm the following settings were chosen: the Path weighting scheme, the maximum number of iterations is 300, the stop criterion (10^-X) is 7, the individual initial weights were not stated. In the Bootstrapping algorithm the following settings were chosen: the number of Subsamples is 500, the Parallel processing is enabled, the basic method is enabled, the confidence interval method is Bias- Corrected and Accelerated (BCa) Bootstrap, the test type is Two tailed, the Significance level is 0.05. Via the PLS and Bootstrapping algorithms the hypotheses H0-H4 were tested with adjusting the several indicators. During the research, the new indicator Cross-functional index (CFI) was developed using the MS Excel. The CFI is a number from 0 to 1, calculated by the sum of CF interactions of each actor of the survey, where 0 is the interaction within the mutual department and 1 the interaction within the other department divided by the number of possible interactions (10). The CFI is calculated for each actor separately by the three sociometric questions, calculated for each department and also general CFI is calculated for the whole sample. After that, the SEM method was employed for both department and the CFI control variables. Running these algorithms, the most explanatory effective model was found.}

_{To conduct the strong SNA research, two tools were chosen. SmartPLS tool is a statistical program which uses SEM method based on the Partial Least Squares (PLS) approach (Borgatti, 2015). The second tool is Gephi, the network visualization program.}

Description of the results

_{The analysis of the data with the abovementioned statistical and visualization tools have demonstrated several interesting and important points. First of all, it is necessary to mention that the Cronbach alpha coefficient and the composite reliability indices are for the sociometric and knowledge sharing scales are higher than 0.7. This indicator shows that the algorithm of the translation of the survey is reliable and the items included are appropriate. As the descriptive statistics states, the average score for CF interaction for average worker is 0,488 out of 1, which indicates that CF cooperation in the organization is quite balanced, the score near 0 would indicate the lack of cross-functionality within the departments, the score near 1 would indicate the overabundance of cross-functionality in this particular company, because the organization is not project-based and do not seek to extend the CF interaction on significantly higher level.}

_{Table 4}

The second step of the research was analyzing sociometric variables by the three indicators with the Department as the control variable. In this step, both algorithms of SmartPLS (PLS algorithm and Bootstrapping) were employed. The several models were done with the different distribution of the variables. In the first case, the path coefficients were connected by each possible interaction within the departments. In this way the path chart looks like path of three connections: Department with Communication sociometric question (1 - 10 possible choices), Department with Help or Information inquiries (1 - 10) and Department with the Project team participation (1 - 10). In this model were also additional variables such as Career start, Job position and Educational level. These variables were added to test the algorithms on the insight of possible influences that could lead to understanding the predictable outcomes. Each department was numbered according to the Serial numbers of Table 1. In this CF approach the model was estimated as the influence of the employee's department on the ties with other departments ( 21 possible connections for three sociometric ties each). The model was constructed by the abovementioned path algorithm to evaluate the bond strength via the statistical package of SmartPLS. After that, several interesting results were obtained. First of all, the p values for each path were significantly lower than 0.01 with minimum 11 zero digits after the delimiter. Moreover, the p values for Dept. - Communication and Dept. - Help/ Information were the same: 0.000000000000057. This result leads to the assumption that the model is wrong, despite the fact it is fully rejects the hypotheses 0-3, which could be the final step of the first of the SNA for this research.

_{Table 5}

_{Path coefficients}

Ringle, C. M., Wende, S., and Becker, J.-M. 2015. "SmartPLS 3." Boenningstedt: SmartPLS GmbH, http://www.smartpls.com.

_{Nevertheless, we decided to develop two new models for these sociometric ties according to the previously developed CFI. The essence of these model implies that each CFI for each node is evaluated in two ways. In the first method, the CFI is distinguished in two groups, where the score below 0,5 is marked as 0, and the score equal or higher than 0,5 is marked as 1. The same algorithms were employed for this method. And the results are much more reliable.}

_{Table 6}

Ringle,C. M., Wende, S., and Becker, J.-M. 2015. "SmartPLS 3." Boenningstedt: SmartPLS GmbH, http://www.smartpls.com.

_{In this case, the path coefficients depict more observable influence of the department distribution in the organization according to the sociometric components of the research. After obtaining these results, the hypothesis H0 could be rejected on the significance level of 0.05, which means that there is a significant difference between the selection structure of department employees on their communication during the last 6 months with other departments. The second indicator of “Help or Information inquiries” is also significantly valuable, which means the H1 could be also rejected and there is a significant difference between the selection structure of department's employees on their help or information inquiries with other departments. Despite of that, the significant difference between the departments and the Project team participation is not observed on the significant level of 0.05, so the H2 is confirmed on this level. The possible reason of this outcome results in the formulation of the survey, where the option of choosing the departments' representatives was the last one out of three sociometric questions. Moreover, this reason could be transferred as the number of the representatives with the moderated CFI equal to 1 for the Communication indicator is 98 out of 166, whereas the same number for the Project team indicator is 53 out of 166.}

_{According to these results, the Bootstrapping algorithm shows the strong correlation of the selection differences for both Communication and Help/ Information indicators, which means that the CF interaction for these organizational concepts is profoundly observable.}

_{According to these results, the smoother CFI was developed with three possible options, with the 0 when CFI is lower than 0,33, 1 - ( 0,33=<CFI,0,66) and 2 - (CFI>= 0,66). This plain distribution would also be structurally equated with the bootstrapping algorithm in the SmartPLS. The results of this statistical run are the following:}

Table 7

_{The path coefficients are estimated in the same way as in the previous CFI analysis. In this step, the same hypotheses (H0, H1) are rejected to the p values on the significance level of 0.05 with the values of 0.0095 and 0.0344 for Communication and Help/ Information respectively. Hypothesis H2 is confirmed as in the previous analysis with the p value = 0.3166, which means there is still no significant difference for the selection distribution for the Project team participation.}

_{The consequent step of analyzing CF interaction after the statistical analysis, is to present the visualization of these particular interactions. With the Gephi, version 0.9.2, the ties were marked as the nodes of the graph as each representative from the departments 1-9 from the Table 1, marked consistently for each employee for the particular departments; and the edges of the graph as the departments 1-21 from the Table 1.}

_{The visualization is made for the Communication during the last 6 months indicator, using the Fruchtelman Reingold algorithmic layout.}



Figure 1 The visualization of the departments' ties via the Communication indicator

_{Using this graph, we could observe the centrality of the sample and strength of the nodes, which are weighted according to the sample distribution. For example, if the representative from department 1 for the first sociometric question have chosen nine options of communication during the last 6 months only with employees from the same department 1, there would be not 9 edges from the node of this employee, but the edge would be weighted as 9. The centrality is balanced according to the sample, which represents the sample ratio. The visualizations on other sociometric connections are available in the Appendix 1 and 2.}

_{The final step of the research was the Knowledge sharing analysis among these departments.}

_{The average score for knowledge sharing at all is 4.39 out of 6, with the highest rate of 5.45 out of 6 for internal motivation for knowledge sharing and the lowest 3.09 out of 6 for Management practices aimed at motivating knowledge sharing. The full table with the descriptive statistics is presented in the Appendix. With the further analysis using SmartPLS, the test showed that sociometric and knowledge sharing variables have normal distribution. The correlation indicates the future possibility of the SEM analysis separately on the Sociometric and Knowledge sharing variables. The distribution is normal.}

_{Table 8}

Ringle,C. M., Wende, S., and Becker, J.-M. 2015. "SmartPLS 3." Boenningstedt: SmartPLS GmbH, http://www.smartpls.com.

_{*. Correlation is significant at the 0.01 level (2- tailed)}

_{**. Correlation is significant at the 0.05 level (2 tailed)}

_{Table 9}

As expected, the management practices on the knowledge sharing motivation significantly positively correlate with the external motivation. The 0.581 coefficient is quite high at statistically significant at the 5% level which means a high confidence of the correlation and gives the variation of variables together. Linear positive relations between these two variables means that the more management practices developed on motivation for knowledge sharing, the more the employee would be externally motivated. Moreover, the IKSA positively correlates with the practices on the developing the knowledge sharing the correlation coefficient of 0.489. This coefficient is not to large, but still the future SEM analysis is possible on this path (Kogovљek, Coenders, & Hlebec, 2013). Table 3 shows the correlation between all indicators which were analyzed with the SEM algorithms. Nonetheless, the correlation between two groups of indicators - sociometric and Knowledge sharing is not significant, so the relevant cell are colored grey for the convenience of reading the Table 3. The Bootstrapping algorithm was employed to calculate the path coefficients of the knowledge sharing indicators, and the model was tested in several ways. The most optimal way is to connect the path coefficients consistently from the external and internal motivation indicators towards other indicators and the management practices indicators towards the IKSA indicator (Hassan, Dias, & Hamari, 2019). As a result of modeling and comparison of the obtained results with theory, the model with the T- statistics shown in Figure 2 showed the best fit to empirical data.

Figure 2 The impact of management practices and motivation on IKSA: model empirically established in the organization of the studied sample

_{Table 10}

Ringle, C. M., Wende, S., and Becker, J.-M. 2015. "SmartPLS 3." Boenningstedt: SmartPLS GmbH, http://www.smartpls.com.

_{With the SEM method, the most suitable path model was developed. The analysis of the existing model based on the predictions on both motivations ( internal and external) showed us the indirect influence on the management practices (Lin, Wang, & Kung, 2015). On this step, with testing the H3 and H4, we could confirm these hypotheses, which means that internal motivation and practices on developing the knowledge sharing do influence the IKSA indicator in terms of this research with p- values of 0.0066 and 0.014 consequently. With the path chart it is also predictable the possible margin opportunity of the knowledge sharing model, with the influence on IKSA with the other management practices. The interesting point about the SEM results is that the external motivation does not significantly influence the IKSA, which means there is an opportunity to develop the suitable and necessary conditions to the employees of providing the existing correlation within the working environment. With the T- statistics it is also observable that the strongest influence is between the external motivation and the motivation for knowledge sharing with the p- value aiming towards 0 point.}



Conclusion

Discussion of main results

_{The obtained results have demonstrated several important points. First of all, the cross-functional index differs within the structural divisions of the organization with the average score for the whole business units of 0,488 out of 1. Being a newly developed index, the future research of the CFI helped to indicate the phenomena of the interactions within the company. In this study, approximately 30% of IKSA might be explained by the internal motivation and practices aimed on developing KS implemented by the organization's management. Thus, the future aggregation of those practices with the stimulation of the employees' internal motivation could lead to CF interaction increase.}

_{Most bank workers in this research are determined as ready to employ CF mechanisms within the departments due to the existing CFI and presented connections on the Gephi graphs. In this particular way, the employees are said to be prepared for the CF workload with the project teams and tight interconnections within the departments to perform more efficient business environment. Considering the CF interaction for the organizations, it is crucial for organization to establish the continuous knowledge sharing flow and to indicate most vulnerable departments in terms of their cross-functionality.}

_{Considering each knowledge sharing dimension separately, the analysis showed that the external motivation significantly correlates with the IKSA indicator as well, as by Kianto et al. (2011) predicted. With the lack of implementation of external motivation mechanisms, the overall IKSA score could downgrade and the managerial practices would not be able to fix the situation.}

_{The p values above the 0.05 significance level were obtained for the two sociometric concepts of Communication and Help/ Information inquiries, whereas the Project participation variable was over this significance level, which shows the possibility of adjusting the model according to organizational specifics with the deeper understanding of the existing mechanisms on project formations in the organization.}

_{The understanding of these indicators gives an opportunity for the deep look inside the organization's real network, not based on the administrative organizational chart. The centrality, which is depicted on the Figure 1, indicates the ties of the 166 survey representatives with the very centre of the РОЗ/ККО department due to the approximately 50% of the whole sample, but the center is surrounded by the nodes of the departments, whose representatives did not take part in the research. This graph have represented the ties for the sociometric analysis of the Communication during the last six months connections. It is observable, that the nodes are arranged in the clustered way with the most connections within the one department, but the graph also showed the connections which are as not straight-forward as they could be interpreted at the first glance.}

_{The interesting results were also obtained for other sociometric analysis using Gephi. The Help/Information inquiries are also distributed mostly by the departments, but the project preferences are stated as the broad range of possible variations, which could be interesting concept to discuss in the organization.}

Contribution to research

_{Besides of all, our research contributes in the field of SNA research in terms of studying the cross-functionality in the organizations and its analysis with the statistical and visualization tools. Results have been considered as highly reliable with this instruments according to their application in the research practices. The tendency of implementation of CF mechanisms and knowledge sharing practices is described in this work, which reflects both CFI and IKSA indicators.}

_{Moreover, the paper provides new theoretical knowledge considering the CFI based on two slope calculations. As mentioned earlier, the prior researches have focused mostly on the separate parts of cross-functionality in the organizations, whether on the plain ties of the employees or the knowledge sharing without the inside organizational interconnections. Yet, the study concentrates on the possibility of using the SNA mixed methods to find out the CF paths in the organization and the CF as a multilevel construct varied by the three sociometric indicators.}

_{Finally, we proved that the SNA is suitable for understanding the cross-functionality within the organizations by adjusting several levels in the research and combining them into one significant group.}

Implications for practice

_{There are some implications for the managers and especially HR managers. Managers should take into consideration the knowledge sharing flow, the CFI of each department while preparing of drafting the project teams (Ghobadi, & D'Ambra, 2012). Being a project leader, it is vital to take into consideration the CFI of each employee, his/ her previous and profoundly established ties within the professional and social groups in the organization. A correctly organized CF interaction could lead to the preferably innovative performance without the lack of communication or even its absence during the project work.}

_{Additional crucial aspect is that the project preferences of the employees do not significantly differ within the departments. This implication could be interpreted in several ways, but this phenomena should be examined in each organization as a case. In communication with the staff, it is crucial for managers to follow the established communication strategy in order to find out the possible opportunities to improve these particular strategies.}

_{The practical solutions which could be taken from the research could be implemented in the examining the current communicational paths in the companies, especially within the business units in the innovation- oriented organizations (Ungureanu, Cochis, Bertolotti, Mattarelli, & Scapolan, 2020). These communication paths and interaction ties are highly important to study by the managers who seek to develop the CF mechanisms and are in search for the mechanisms which the project teams could be based on. The CFI and knowledge sharing indicators could be calculated in each organization by the algorithms that were provided in the research and the algorithms from the prior researches, that were included due to developing the data analysis approach.}

_{Furthermore, the opportunity of practical implementation of the abovementioned algorithms could lead to the efficiency increase, developing novel innovation product and services via the project passed solutions.}

_{To sum up, the CFI and IKSA are the strong indicators to understand the CF possible capacity in the organization.}

Limitations

_{Despite developing the reliable model for the CFI and IKSA indicators, it is still space for future understanding other relations that affect these indicators. One of the crucial points is that it is yet questionable which other factors influence the employees' perception of the cross-functionality and the motivation for knowledge sharing (Sami, & El Bedawy, 2019). Since it only 30% of IKSA explained in the SEM model, we still assume there are 70% of unstudied influential factors left.}

_{Secondly, the study has not taken into account other possible algorithms to conduct the SNA. SmartPlS and Gephi are still strong instruments to develop this kind of the research, but there is still space for employing other programs, which are not based on the SEM algorithm as SmartPLS do (Nurek, & Michalski, 2020). The choice of this programs was explained to the analysis of the prior researches, but the possibility of getting different results with other approaches exists yet.}

_{Moreover, the sample size is still debatable. As it was stated, the sample size is reliable according to Baruch and Holtom (2008), but in terms of conducting the SNA in the organization, the more ties are able to analyze, the more precise the results are. The 19 percent sample ratio could be acclaimed as reliable due to the preserving of the departments proportions for the business units population as the whole staff volume and the survey representatives.}

_{Furthermore, the possible limitation of this research is the geographical aspect. As it was stated, the research was conducted in the North-West branch of the major banks, so the possible transfer of this research results to other organizations, that are geographically, demographically or corporately differ from the studied organization is still questionable (Wu, Hsu, Yeh, 2007). The distribution of the results to the banking industry market is also uncertain, because the organizations differ by their strategies, working environment and corporate cultures.}

Future research

_{As our research has proved the possibility of employing the CFI, the pure impact of internal motivation and developing knowledge sharing practices on the IKSA, further research could be a great application of the existing study. Above all the measured factors, it is welcomed to add other indicators such as the leadership performance or participation in innovative project teams to investigate the possible ways of building new correlation models and path diagrams (Strese, Meuer, Flatten, & Brettel, 2016b).}

_{The adjusting of other indicators could improve the IKSA path model according to the new operational challenges which arise on the researching knowledge sharing step. The existing model does not cover every component of possible knowledge sharing aspects, so the opportunity to increase the validity of the model by fixing the lack of possible options.}

_{To exclude the limitation of CFI, the new algorithms of its calculation could be employed. Moreover, the distribution of the index by 0 - 1 or by 0 - 1 - 2, is also debatable in according to implementation of the developed path models. The path models' correlations on the existing significance level could be also improved to the side of 0.01 level, what would eliminate the possibility of the error of the first kind in the SEM appearance.}

_{Furthermore, the existing path model do not imply the introduction of the demographical characteristics of the respondents. The existing algorithm does not take into consideration the gender or age distribution via the departments, so the possible interconnections of these indicators could arise in the future research.}

_{As for control variables, the department and IKSA could be complemented by indexes for the sociometrical distribution in accordance with developing the path models, which include the united indicators of cross-functionality and knowledge sharing capacity.}

_{Further research could also take into consideration the organization's corporate characteristics such as industry, geographical location, the business size or even the volume of the Internet business. The innovative companies nowadays could not be physically scaled, but their business appearance is still an issue for the prospective research.}

_{The coming possibilities of developing this research is the elimination of the demographic gap of the sample and the enlargement of the sample. As it mentioned in the Appendix 5, the gender ratio in the studied organization for women/ men is 78/22, so the research on the gender ration on this particular market should also be conducted and introduced in our research to develop more reliable path models also. The demographic issue could also be research in terms of the sociometric part of this research. The age indicator could be very important in understanding the possible network connections, the employees' communication paths and formatting the project teams. Appendix 5 also indicates, that the millennials are the vast majority of the staff with the 18% for the 18-24 age and 72% for the 25-34 age. The prospective research could go deeper in understanding the age aspect on the workspace to develop novel hypotheses about the existing paths of cross-functionality and knowledge sharing in terms of the SNA (Myers, & Sadaghiani, 2010).}

_{To conclude, nowadays the employees are surrounded with the implementation of the innovative practices. Managers are always searching for the efficiency improvement to achieve better business results. The chase of these practices have led to developing the implementation of the Social Network Analysis in terms of understanding some of the most crucial components of each organization: cross-functionality and knowledge sharing. Provided results have shown us that understanding them could lead to new areas of understanding the company not only as the formalized institute, but as the dynamically developing network. The obtained results gives us an opportunity to state that there is a significant difference between the selection structure of department employees on their communication with other departments and their help or information inquiries within other departments. Moreover, it is stated that the internal motivation of each employee influences}

Reference list

1. _{Aalbers, R. H. L., Dolfsma, W., & Leenders, R. Th. A. J. (2016). Vertical and Horizontal Cross-Ties: Benefits of Cross-Hierarchy and Cross-Unit Ties for Innovative Projects: Vertical and horizontal cross-ties. Journal of Product Innovation Management, 33(2), 141-153. (Carrington, Scott, & Wasserman, 2005)}

2. Aalbers, H. L., & Dolfsma, W. (2015). Innovation networks: Managing the networked organization. Innovation Networks: Managing the Networked Organization, 1-180. https://doi.org/10.4324/9781315757520

3. Ali, A., Bahadur, W., Wang, N., Luqman, A., & Khan, A. N. (2020). Improving team innovation performance: Role of social media and team knowledge management capabilities. Technology in Society, 61, 101259. https://doi.org/10.1016/j.techsoc.2020.101259

4. Appel, A. P., de Santana, V. F., de Paula, R. A., Pinhanez, C. S., & Cavalvante, V. F. (2013). Social network analyses in organizations: Challenges and approaches for studying work networks. IFAC Proceedings Volumes, 46(24), 443-448. https://doi.org/10.3182/20130911-3-BR-3021.00094

5. Ashenbaum, B., Blair, C. W., & Brewer, B. (2020). The influence of the competitive landscape on cross-functional interactions between procurement and engineering. Journal of Purchasing and Supply Management, 26(1), 100595. https://doi.org/10.1016/j.pursup.2019.100595

6. Ashok, M., Narula, R., & Martinez-Noya, A. (2016). How do collaboration and investments in knowledge management affect process innovation in services? Journal of Knowledge Management, 20(5), 1004-1024. https://doi.org/10.1108/JKM-11-2015-0429

7. Bai, W., Feng, Y., Yue, Y., & Feng, L. (2017). Organizational Structure, Cross-functional Integration and Performance of New Product Development Team. Procedia Engineering, 174, 621-629. https://doi.org/10.1016/j.proeng.2017.01.198

8. Baker, W. E. (2019). Emotional Energy, Relational Energy, and Organizational Energy: Toward a Multilevel Model. Annual Review of Organizational Psychology and Organizational Behavior, 6(1), 373-395. https://doi.org/10.1146/annurev-orgpsych-012218-015047

9. Banach, K., & Kasprzyk, R. (2017). Software environment for rapid prototyping of graph and network algorithms. COMPUTER SCIENCE AND MATHEMATICAL MODELLING, 7.

10. Barгo, A., de Vasconcelos, J. B., Rocha, Б., & Pereira, R. (2017). A knowledge management approach to capture organizational learning networks. International Journal of Information Management, 37(6), 735-740. https://doi.org/10.1016/j.ijinfomgt.2017.07.013

11. Baruch, Y., & Holtom, B. C. (2008). Survey response rate levels and trends in organizational research. Human Relations, 61(8), 1139-1160. https://doi.org/10.1177/0018726708094863

12. Behrendt, S., Richter, A., & Trier, M. (2014). Mixed methods analysis of enterprise social networks. Computer Networks, 75, 560-577. https://doi.org/10.1016/j.comnet.2014.08.025

13. Berasategi, L., Arana, J., & Castellano, E. (2011). A comprehensive framework for collaborative networked innovation. Production Planning & Control, 22(5-6), 581-593. https://doi.org/10.1080/09537287.2010.536628

14. Bertolotti, F., Mattarelli, E., Vignoli, M., & Macrм, D. M. (2015). Exploring the relationship between multiple team membership and team performance: The role of social networks and collaborative technology. Research Policy, 44(4), 911-924. https://doi.org/10.1016/j.respol.2015.01.019

15. Bogner, A., Littig, B., & Menz, W. (Eds.). (2009). Interviewing Experts. https://doi.org/10.1057/9780230244276

16. Borgatti, S. P. (2015). Software Review: FLAME (version 1.1). Field Methods, 27(2), 199-205. https://doi.org/10.1177/1525822X13506976

17. Borgatti, S. P., Brass, D. J., & Halgin, D. S. (2014). Social Network Research: Confusions, Criticisms, and Controversies. In D. J. Brass, G. (JOE) Labianca, A. Mehra, D. S. Halgin, & S. P. Borgatti (Eds.), Research in the Sociology of Organizations (Vol. 40, pp. 1-29). https://doi.org/10.1108/S0733-558X(2014)0000040001

18. Borgatti, S. P., & Molina, J. L. (2003). Ethical and Strategic Issues in Organizational Social Network Analysis. The Journal of Applied Behavioral Science, 39(3), 337-349. https://doi.org/10.1177/0021886303258111

19. Buchanan, E. A., & Hvizdak, E. E. (2009). Online Survey Tools: Ethical and Methodological Concerns of Human Research Ethics Committees. Journal of Empirical Research on Human Research Ethics, 4(2), 37-48. https://doi.org/10.1525/jer.2009.4.2.37

20. Butts, C. T. (2007). Social Networks, 29(4), 603-608. https://doi.org/10.1016/j.socnet.2007.02.001

21. Camacho, D. (2019). The Four Dimensions of Social a Analysis: An Overview of Research Methods, Applications, and Software Tools. 73.

22. Can, U., & Alatas, B. (2019). A new direction in social network analysis: Online social network analysis problems and applications. Physica A: Statistical Mechanics and Its Applications, 535, 122372. https://doi.org/10.1016/j.physa.2019.122372

23. Carnabuci, G., & Diуszegi, B. (2015). Social Networks, Cognitive Style, and Innovative Performance: A Contingency Perspective. Academy of Management Journal, 58(3), 881-905. https://doi.org/10.5465/amj.2013.1042

24. Chen, W., & McDonald, S. (2015). Do Networked Workers Have More Control? The Implications of Teamwork, Telework, ICTs, and Social Capital for Job Decision Latitude. American Behavioral Scientist, 59(4), 492-507. https://doi.org/10.1177/0002764214556808

25. Cross, R., Borgatti, S. P., & Parker, A. (2002). Making Invisible Work Visible: Using Social Network Analysis to Support Strategic Collaboration. 44(2), 23.

26. Cross, R., & Parker, A. (2004). The Hidden Power of Social Networks.

27. Dalkir, K. (2013). Knowledge Management in Theory and Practice (1st ed.). https://doi.org/10.4324/9780080547367

28. Davila, G., Varvakis, G., & North, K. (2019). Influence of Strategic Knowledge Management on Firm Innovativeness and Performance. Brazilian Business Review, 16(3), 239-254. https://doi.org/10.15728/bbr.2019.16.3.3

29. Di Vincenzo, F., & Mascia, D. (2012). Social capital in project-based organizations: Its role, structure, and impact on project performance. International Journal of Project Management, 30(1), 5-14. https://doi.org/10.1016/j.ijproman.2011.03.006

30. Edmondson, A. C., & Harvey, J.-F. (2018). Cross-boundary teaming for innovation: Integrating research on teams and knowledge in organizations. Human Resource Management Review, 28(4), 347-360. https://doi.org/10.1016/j.hrmr.2017.03.002

31. Eisenberg, E. M., Johnson, Z., & Pieterson, W. (2015). Leveraging Social Networks for Strategic Success. International Journal of Business Communication, 52(1), 143-154. https://doi.org/10.1177/2329488414560283

32. Ellison, N. B., Gibbs, J. L., & Weber, M. S. (2015a). The Use of Enterprise Social Network Sites for Knowledge Sharing in Distributed Organizations: The Role of Organizational Affordances. American Behavioral Scientist, 59(1), 103-123. https://doi.org/10.1177/0002764214540510

33. Freeman, L. C. (2004). The development of social network analysis: A study in the sociology of science. Vancouver, BC?: North Charleston, S.C: Empirical Press?; BookSurge.

34. Ghobadi, S., & D'Ambra, J. (2012). Knowledge sharing in cross?functional teams: A coopetitive model. Journal of Knowledge Management, 16(2), 285-301. https://doi.org/10.1108/13673271211218889

35. Hassan, L., Dias, A., & Hamari, J. (2019). How motivational feedback increases user's benefits and continued use: A study on gamification, quantified-self and social networking. International Journal of Information Management, 46, 151-162. https://doi.org/10.1016/j.ijinfomgt.2018.12.004

36. Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural Equation Modelling: Guidelines for Determining Model Fit. 6(1), 8.

37. Huisman, M., & van Duijn, M. A. J. (2014). A Reader's Guide to SNA Software. In J. Scott & P. Carrington, The SAGE Handbook of Social Network Analysis (pp. 578-600). https://doi.org/10.4135/9781446294413.n38

38. Jackson S. E., Chuang C.-H., Harden E. E., Jiang Y. (2006). Toward developing human resource management systems for knowledge intensive teamwork. Research in Personnel and Human Resources Management 25: 27-70.

39. Jassawalla, A. R., & Sashittal, H. C. (1999). Building collaborative cross-functional new product teams. Academy of Management Perspectives, 13(3), 50-63. https://doi.org/10.5465/ame.1999.2210314

40. Johnson, R., Kovбcs, B., & Vicsek, A. (2012). A comparison of email networks and off-line social networks: A study of a medium-sized bank. Social Networks, 34(4), 462-469. https://doi.org/10.1016/j.socnet.2012.02.004

41. Johnson, J., Borgatti, S., & Everett, M. (2013). Analyzing Social Networks.

42. Kadushin, C. (2005). Who benefits from network analysis: Ethics of social network research. Social Networks, 27(2), 139-153. https://doi.org/10.1016/j.socnet.2005.01.005

43. Ken-Opurum, J., Lynch, K., Vandergraff, D., Miller, D. K., & Savaiano, D. A. (2019). A mixed-methods evaluation using effectiveness perception surveys, social network analysis, and county-level health statistics: A pilot study of eight rural Indiana community health coalitions. Evaluation and Program Planning, 77, 101709. https://doi.org/10.1016/j.evalprogplan.2019.101709

44. Kianto A., Andreeva T., Shi X. 2011. Knowledge Management Across the Globe - An International Survey of KM Awareness, Spending, Practices and Performance. Paper read at 12th European Conference on Knowledge Management, at Passau, Germany, September 2-3.

45. Kleinbaum, A. M. (2012a). Organizational Misfits and the Origins of Brokerage in Intrafirm Networks. Administrative Science Quarterly, 57(3), 407-452. https://doi.org/10.1177/0001839212461141

46. Knein, E., Greven, A., Bendig, D., & Brettel, M. (2019). Culture and cross-functional coopetition: The interplay of organizational and national culture. Journal of International Management, 100731. https://doi.org/10.1016/j.intman.2019.100731

47. Kogovљek, T., Coenders, G., & Hlebec, V. (2013). Predictors and outcomes of social network compositions: A compositional structural equation modeling approach. Social Networks, 35(1), 1-10. https://doi.org/10.1016/j.socnet.2012.10.002

48. Kurt, Y., & Kurt, M. (2020). Social network analysis in international business research: An assessment of the current state of play and future research directions. International Business Review, 29(2), 101633. https://doi.org/10.1016/j.ibusrev.2019.101633

49. Leenders, R. T. A. J., & Dolfsma, W. A. (2016). Social Networks for Innovation and New Product Development: Social Networks for Innovation and New Product Development. Journal of Product Innovation Management, 33(2), 123-131. https://doi.org/10.1111/jpim.12292

50. Leguina, A. (2015). A primer on partial least squares structural equation modeling (PLS-SEM). International Journal of Research & Method in Education, 38(2), 220-221. https://doi.org/10.1080/1743727X.2015.1005806

51. Lin, Y., Wang, Y., & Kung, L. (2015). Influences of cross-functional collaboration and knowledge creation on technology commercialization: Evidence from high-tech industries. Industrial Marketing Management, 49, 128-138. https://doi.org/10.1016/j.indmarman.2015.04.002

52. Liu, J., & Moskvina, A. (2016). Hierarchies, ties and power in organizational networks: Model and analysis. Social Network Analysis and Mining, 6(1), 106. https://doi.org/10.1007/s13278-016-0413-2

53. Lombardi, S., Cavaliere, V., Giustiniano, L., & Cipollini, F. (2020). What Money Cannot Buy: The Detrimental Effect of Rewards on Knowledge Sharing. European Management Review, 17(1), 153-170. https://doi.org/10.1111/emre.12346

54. Marin, A., & Wellman, B. (2011). Social network analysis: An introduction. In J. Scott, & P. J. Carrington (Vol. Eds.), The SAGE handbook of social network analysis: Vol. 11. Thousand Oaks, CA: Sage.

55. Martin, J. A., & Eisenhardt, K. M. (2010). Rewiring: Cross-Business-Unit Collaborations in Multibusiness Organizations. Academy of Management Journal, 53(2), 265-301. https://doi.org/10.5465/amj.2010.49388795

56. Matthews, R. L., & Marzec, P. E. (2012). Social capital, a theory for operations management: A systematic review of the evidence. International Journal of Production Research, 50(24), 7081-7099. https://doi.org/10.1080/00207543.2011.617395

57. McLoughlin, S., & Stewart, I. (2017). Empirical advances in studying relational networks. Journal of Contextual Behavioral Science, 6(3), 329-342. https://doi.org/10.1016/j.jcbs.2016.11.009

58. Minnaar, L., & Heystek, J. (2016). Online Surveys as Data Collection Instruments in Education Research: A Feasible Option? South African Journal of Higher Education, 27(1). https://doi.org/10.20853/27-1-233

59. Myers, K. K., & Sadaghiani, K. (2010). Millennials in the Workplace: A Communication Perspective on Millennials' Organizational Relationships and Performance. Journal of Business and Psychology, 25(2), 225-238. https://doi.org/10.1007/s10869-010-9172-7

60. Nguyen, N. P., Ngo, L. V., Bucic, T., & Phong, N. D. (2018a). Cross-functional knowledge sharing, coordination and firm performance: The role of cross-functional competition. Industrial Marketing Management, 71, 123-134. https://doi.org/10.1016/j.indmarman.2017.12.014

61. Nonino, F. (2013). The network dimensions of intra-organizational social capital. Journal of Management & Organization, 19(4), 454-477. https://doi.org/10.1017/jmo.2013.20

62. Nurek, M., & Michalski, R. (2020). Combining Machine Learning and Social Network Analysis to Reveal the Organizational Structures. Applied Sciences, 10(5), 1699. https://doi.org/10.3390/app10051699

63. Olaisen, J., & Revang, O. (2017). Working smarter and greener: Collaborative knowledge sharing in virtual global project teams. International Journal of Information Management, 37(1), 1441-1448. https://doi.org/10.1016/j.ijinfomgt.2016.10.002

64. Paюaoрlu, D. (2015). Analysis of the Relationship Between Human Resources Management Practices and Organizational Commitment from a Strategic Perspective: Findings from the Banking Industry. Procedia - Social and Behavioral Sciences, 207, 315-324. https://doi.org/10.1016/j.sbspro.2015.10.101

65. Petersson, A. M., Lundberg, J., & Rantatalo, M. (2017). Ideation methods applied in a cross-functional inter-organizational group: An exploratory case study from the railway sector. Research in Engineering Design, 28(1), 71-97. https://doi.org/10.1007/s00163-016-0238

66. Popelier, L. (2018a). A scoping review on the current and potential use of social network analysis for evaluation purposes. Evaluation, 24(3), 325-352. https://doi.org/10.1177/1356389018782219

67. Pournader, M., Tabassi, A. A., & Baloh, P. (2015). A three-step design science approach to develop a novel human resource-planning framework in projects: The cases of construction projects in USA, Europe, and Iran. International Journal of Project Management, 33(2), 419-434. https://doi.org/10.1016/j.ijproman.2014.06.009

68. Priyono, A. (2016). Cross-Functional Collaboration in Sustainability Contexts: Exploratory Studies in Remanufacturing Companies. Procedia Economics and Finance, 35, 553-562. https://doi.org/10.1016/S2212-5671(16)00068-X

69. Ringle, C. M., Wende, S., and Becker, J.-M. 2015. "SmartPLS 3." Boenningstedt: SmartPLS GmbH, http://www.smartpls.com.

70. Roberts, L. D., & Allen, P. J. (2015). Exploring ethical issues associated with using online surveys in educational research. Educational Research and Evaluation, 21(2), 95-108. https://doi.org/10.1080/13803611.2015.1024421

71. Ruoslahti, H. (2020). Complexity in project co-creation of knowledge for innovation. Journal of Innovation & Knowledge, S2444569X19300630. https://doi.org/10.1016/j.jik.2019.12.004

72. Saade, R. G., & Nijher, H. (2016). Critical success factors in enterprise resource planning implementation: A review of case studies. Journal of Enterprise Information Management, 29(1), 72-96. https://doi.org/10.1108/JEIM-03-2014-0028

73. Sami, M., & El Bedawy, R. (2019). Assessing the impact of knowledge management on total factor productivity. African Journal of Economic and Management Studies, 11(1), 134-146. https://doi.org/10.1108/AJEMS-05-2019-0188

74. Sandhu, S., & Kulik, C. T. (2019). Shaping and Being Shaped: How Organizational Structure and Managerial Discretion Co-evolve in New Managerial Roles. Administrative Science Quarterly, 64(3), 619-658. https://doi.org/10.1177/0001839218778018

75. Schreiber, R. R., & Zylka, M. P. (2020). Social Network Analysis in Software Development Projects: A Systematic Literature Review. International Journal of Software Engineering and Knowledge Engineering, 30(03), 321-362. https://doi.org/10.1142/S021819402050014X

76. Sergeeva A. V., & Andreeva T. E. (2014). Kak upravlencheskie praktiki vliyaut na obmen znaniyami mezhdu sotrudnikami? Resultaty issledovaniya v srednih shkolah [ How do managerial practices influence the exchange of knowledge between employees? Research results in secondary schools]. Russian journal of Management, 12 (2), 67-98.

77. St.Petersburg School of Economics and Management, National Research University Higher School of Economics, Kudriavtseva, E. I., Volkova, N. V., & St.Petersburg School of Economics and Management, National Research University Higher School of Economics. (2019). The Role of Network Communication Activities to Establish the Project Team. Russian Management Journal, 17(1), 47-70. https://doi.org/10.21638/spbu18.2019.103

78. Strese, S., Meuer, M. W., Flatten, T. C., & Brettel, M. (2016a). Examining cross-functional coopetition as a driver of organizational ambidexterity. Industrial Marketing Management, 57, 40-52. https://doi.org/10.1016/j.indmarman.2016.05.008

79. Strese, S., Meuer, M. W., Flatten, T. C., & Brettel, M. (2016b). Organizational antecedents of cross-functional coopetition: The impact of leadership and organizational structure on cross-functional coopetition. Industrial Marketing Management, 53, 42-55. https://doi.org/10.1016/j.indmarman.2015.11.006

80. Tsai, K.-H., & Hsu, T. T. (2014). Cross-Functional collaboration, competitive intensity, knowledge integration mechanisms, and new product performance: A mediated moderation model. Industrial Marketing Management, 43(2), 293-303. https://doi.org/10.1016/j.indmarman.2013.08.012

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