Document Type : Original Article

Authors

1 educational administration ,management,Kharazmi University, Tehran, Iran

2 educational administration,, Kharazmi University, Tehran, Iran

3 Department of Planning and Educational Development Kharazmi University, Tehran, Iran

4 گروه مدیریت آموزشی، دانشکده مدیریت، دانشگاه خوارزمی، تهران، ایران

Abstract

Background and Objectives: The researchers and their researches need to be evaluated to recognize each one's strengths and weaknesses. Then, it is possible to invest and make scientific policy on long term goals as well as short term goals in this field. So far, several indices have been presented for evaluating researchers, each of which merely emphasizes a particular aspect of evaluation, and each has its own deficiencies. Recently, a model has been proposed by Cuellar et al. (2016), titled "Scholarly capital model", which examines the various aspects of the scholarly activities of a researcher. They define the model of scholarly influence (scholarly capital model) as "the ability of a researcher to include his thoughts in the works of other researchers" or "the extent to which a researcher influences his own research field." They proposed three variables of social influence, intellectual influence and venue influence to evaluate the research. The purpose of this research is to evaluate the researches related to the field of distance education indexed on the Web of Science database using the scholarly capital model.
Methodology: The present study is an applied study that has been conducted by researchers in the field of distance education studies based on the model of scholarly capital model through using existing approaches in the field of scientometrics and social network analysis. The data needed for research is extracted from the Web of Science database. The research population consists all the documents published in the field of distance education. The results of search strategy retrieved 31607 records from 1985 to 2016. By Using the Bibexcel software the data was synchronized. The co-authorship symmetric matrices, was extracted. Subsequently, using the UCINET software, the centrality indices were calculated. After analyzing all the indices by using Amos and Lisrel software, we examined and tested the research hypotheses and fitted the model. Also, Kolmogorov-Smirnov test was used to check the normality of variables.
Findings: The results of the first research hypothesis test indicate the direct and significant effect of the social influence on the ideational influence, and the hypothesis confirmed according to the path coefficient, 0.95 and t-statistic 45.9 at the 0.05 level. The structural equation modeling indices were used to fit the research model. Accordingly, the indices were estimated as follows:
In the research model, K2 has degrees of freedom of 3.97 which is less than 5. Also, the root mean square error of the approximation is 0/032 and less than 0.08. Given that the incremental growth index, normed fit index, non-normed fit index and comparative fit index are higher than 0.90, then the model shows acceptable fit and also are confirmed. The results of the second hypothesis test show the direct and inevitable significant effect of the social influence on venue influence, and the hypothesis is confirmed with a path coefficient of 0.70 and a T-value of 27.12 at the 0.05 level. In order to fit the research model, the structural equation model is used to fit the indexes. Accordingly, the indices are estimated as follows:
In the research model, the K2 value has degrees of freedom of 92.4 and less than 5. Also, the root mean square error of the approximation was 0.016 and less than 0.08. Given that the incremental fitness index , normed fit index , non-normed fit index , and comparative fit index  are all higher than 0.90, so the model is acceptable and validated. The results of the third hypothesis test of research and structural relationships between the variables of the research model -using structural equation modeling- indicate the direct and inevitable significant effect of the venue influence on the ideational influence , with a path coefficient of 0.84 and a T-value of 5.93 at 0.05 level (because ((t)) is outside the range (1.96, -1.96). In order to fit the research model, the indices of the structural equation modeling were used. Accordingly, the indexes are estimated as follows: In the research model, the K2 value is 0.063 and less than 5. Also, the root mean square error of the approximation is 0.033 and less than 0.08. Given that the incremental growth index, normed fit index, non-normed fit index and comparative fit index are more than 0.90, then the model shows an acceptable fit and therefore is approved.
Discussion: Using the data from the field of distance education studies, the researchers tested the of scholarly capital model. The results of data analysis in this research confirm the scholarly capita model and shows that there is a significant relationship between the variables of this research. The existence of this relationship can be due to the fact that researchers with stronger social interactions can contribute better than other researchers and may increase the quality of the works. Also they are in a better position in terms of co-authorship and its indices. Furthermore, the direct and significant effect of social influence on intellectual influence was confirmed by using structural equation modeling: social influence has a positive and significant effect on the ideational influence. The existence of such a relationship can be explained by the fact that researchers who have stronger social interactions can contribute better with other researchers and consequently may increase the quality of the works; hence, they are in a better position in terms of co-authorship and its indices. The results of structural modeling test also showed that there is a significant relationship between social influence indices and venue influence indices. In other words, higher researchers with higher social influence have better venue influence. The confirmation of the hypothesis between the variables of social influence and the venue influence is also largely justifiable, since a significant portion of the validity of each journal comes from scholars who send their research papers to those magazines, and if the quality of these studies is rich and appropriate, it absorbs citations and thus increases the credibility of the magazine. In general, it can be stated that the relationship between researchers and journals is bilateral and reciprocal; each one may add each other's credibility.

Keywords

Abbasi, A., Altmann, J., & Hossain, L. (2011). Identifying the effects of co-authorship networks on the performance of scholars: A correlation and regression analysis of performance measures and social network analysis measures. Journal of Informetrics, 5(4), 594-607.
Bornmann, L., Mutz, R., Hug, S.E., & Daniel, H.D. (2011). A multilevel metaanalysis of studies reporting correlations between the h index and 37 different h index variants. Journal of Informetrics, 5(3), 346–359.
Chiang, J. K., Kuo, C. W., & Yang, Y. H. (2010). A bibliometric study of e-learning literature on SSCI database. In International conference on technologies for E-learning and digital entertainment (pp. 145-155). Springer, Berlin, Heidelberg.
 Chin, W.W. (2003). Issues and opinions on structural equation modeling. MIS Quarterly, 22(1), 7-16.
Cuellar, M. J., Vidgen, R., Takeda, H., & Truex, D. (2016). Ideational influence, connectedness, and venue representation: Making an assessment of scholarly capital. Journal of the Association for InformationSystems, 17(1), 1–28.
Davies, R. S., Howell, S. L., & Petrie, J. A. (2010). A review of trends in distance education scholarship at research universities in North America, 1998-2007. The International Review of Research in Open and Distributed Learning, 11(3), 42-56.
Egghe, L. (2005). Power laws in the information production process: Lotkaian informetrics. Oxford (UK): Elsevier.
Freeman, L.C. (1979). Centrality in social networks conceptual clarification. Social networks, 1(3), 215-239.
Hasanzadeh, p., Isfandyari-Moghaddam, A., soheili, F., Mousavi Chalak, A.  (2019).Co-authorship and the Relationship between Social Influence and the Extent of Effectiveness and Productivity of Researchers in Domain of Chronic Cardiovascular Failure. Journal of Scientometrics. (Forthcoming). (In Persian)
Hirsch, J.E. (2005). An index to quantify an individual's scientific research output. Proceedings of the National academy of Sciences of the United States of America, 102(46), 16569-16572.
 Khasseh, A. A. (2016). Knowledge Structure in Metric Studies: Analysis of Co-citations, Co-authorships, and Co-words of Records Using Network Analysis and Science Visualization. PhD Dissertation, Payame Noor University. (In Persian)
Leimu, R., &Koricheva, J. (2005). Does scientific collaboration increase the impact of ecological articles? AIBS Bulletin, 55(5), 438-443.
Li, E. Y., Liao, C. H., & Yen, H. R. (2013). Co-authorship networks and research impact: a social capital perspective. Research Policy, 42(9), 1515–1530.doi: 10. 1016/j.
Li, J., Wang, M.H., & Ho, Y.S. (2011). Trends in research on global climate change: A Science Citation Index Expanded-based analysis. Global and Planetary Change, 77(1), 13-20.
Mingers, J., Macri, F., & Petrovici, D. (2012). Using the h-index to measure the quality of journals in the field of Business and Management. Information Processing & Management, 48(2), 234‐241.
Morel, CM; Serruya, S. J.; Penna, G. O.; Guimares, R. (2009). Co-authorship network analysis: A powerful tool for strategic planning of research, development and capacity building programs on neglected diseases. PLoS Negl Trop Dis, 3:8, 1-7.
Mousavi Chalak, A., Sohieli, F., Khasseh, A. A. (2017).The relationship between social influence with productivity and performance in co-authorship social network of Quran and Hadith studies. Library and information science.    20(3), 50-74. (In Persian)
Podsakoff, P. M., MacKenzie, S. B., Podsakoff, N. P., & Bachrach, D. G. (2008). Scholarly influence in the field of management: A bibliometric analysis of the determinants of university and author impact in the management literature in the past quarter century. Journal of Management, 34(4), 641-720.
Price, D., & Gursey, S. (1976). Studies in scientometrics. 1. Transience and continuance in scientific authorship. In International Forum on Information and Documentation, 1(2). 17-24.
Rowe, F. (2014). What literature review is not: Diversity, boundaries and recommendations? European Journal of Information Systems, 23(3), 241-255.
Sadatmoosavi, A. (2015). Analyzing the structure of co-authorship social network of the researcher from the field of nuclear science and technology using egocentric and sociocentric approach. PhD dissertation. Islamic Azad University, science and research branch, Faculty of humanities. (In Persian)
Sasson, A. (2008). Exploring mediators: Effects of the composition of organizational affiliation on organization survival and mediator performance. Organization Science, 19(6), 891‐906.
Skinner, J. K. (2015). Bibliometric and social network analysis of doctoral research: Research trends in distance learning .Doctoral dissertation, The University of New Mexico.
Soheili, f (2012).The Analysis of Social Network Structure of Co-authorship in Scientific Output of Information Science Researchers for the Purpose of Recognition and Measurement of Co-authorship Relations, Interactions and Strategies in this Discipline. PhD Dissertation, Shahid Chamran university of Ahvaz. . (In Persian)
 Soheili, F., Hadi Sharif Moghaddam, H.,  Mousavi Chelak, A.,  Khasseh, A. A. (2016). An Evaluation of iMetric Studies through the Scholarly Influence Model . Iranian journal of information processing and management, 32(1).25-50. (In Persian)
Stewart, M., Smith, P., Barron, A. (2010). International Review of Research in Open and Distance Learning Volume 11, Number 1.
Stringer, Michael J. (2009). A Complex Systems Approach to Bibliometrics. Ph.D. dissertation, Northwestern University.
Truex III, D. P., Cuellar, M. J., & Takeda, H. (2009). Assessing Scholarly Influence: Using the Hirsch Indices to Reframe the Discourse. Journal of the Association of Information Systems, 10(7), 560--594.
 Truex, D.P., Cuellar, M.J., Takeda, H., & Vidgen, R. (2011). The Scholarly influence of Heinz Klein: Ideational and social measures of his impact on IS research and IS scholars. European Journal of Information Systems, 20(4), 422-439.
Tsai, C. W., Shen, P. D., & Chiang, Y. C. (2013). Research trends in meaningful learning research on e‐learning and online education environments: A review of studies published in SSCI‐indexed journals from 2003 to 2012. British Journal of Educational Technology, 44(6).68-79.
Wu, B., & Zhang, C. Y. (2013). Evaluation research in e-learning system. In Applied Mechanics and Materials (Vol. 333, pp. 2239-2242). Trans Tech Publications.
Ye, Q., Li, T., & Law, R. (2013). A co-authorship network analysis of tourism and hospitality research collaboration. Journal of Hospitality & Tourism Research, 37(1), 51-76.
Youtie, J., & Bozeman, B. (2016). Dueling co-authors: how collaborators create and sometimes solve contributorship conflicts. Minerva, 54(4), 375-397.
Zancanaro, A., Todesco, J. L., & Ramos, F. (2015). A Bibliometric Mapping of Open Educational Resources. International Review of Research in Open and Distance Learning, 16(1), 1-23.